Factors Blog

We all have heard the saying: “Change is the only constant,” which translates to the fact that everything changes, every time. While that is too philosophical, and since marketers – more so data-driven B2B marketers – are not really fans of philosophy, there is a need to “measure” change. And not just that, a business needs to “attribute”, “rank”, “predict”, “explain”, and hopefully even “bring about" change using data analytics. Since statistics is the mother of all real-world measurements, and probability is the grandmother thereof, the science of measuring change too lies somewhere close by.

Since surface (i.e., not deeply thought-out) definitions are easy to make but difficult to follow with real-world data, the real challenges are solved by taking support from the well-researched areas of data science and probability. One extreme is to discuss the topic of change with its extreme roots, but that could easily fill-up a book (which is under way), but we stick to measuring change w.r.t. marketing analytics.

The questions

The main questions from a marketing perspective are: “What main factors changed from last week to this?”, and “By how much did these main factors change?”. But before that, let us understand something even more basic.

Why to measure?

Marketers take various targeting decisions on a daily basis – which audience to target, how frequently, and with what kind of marketing campaigns. They also have to keep track of various prospective customers, their journey, their overall statistics, and most importantly, reasons why a particular technique worked (or didn’t work) – be it a campaign or a strategy. And there are various goals when it comes to marketing. While some techniques could be motivated towards increasing reach (where it’s important to maximize the number of eyeballs a webpage could get), others increase conversions (at various points in a funnel – form-fills, email cold/warm calls, prospects, pre-sales, sales, etc.).

The foregoing marketing goals are achieved by measuring both static marketing performances (i.e., what happened today / this week), and also more dynamic, time-aware ones. While analyzing performance over time is a comprehensive view of marketing goals’ achievements / shortcomings and hence makes it cumbersome to digest multiple metrics of interest in a single frame, summaries of the same are preferred. And one of the summaries of a dynamic measurement of marketing performance comes in the form of change – that is, what changed, by how much, and why. In this article, we would focus on measuring overall changes, and would dig deeper into measuring the causes for the same in a follow-up blog.

What to measure?

Based on the business requirements, marketers focus on tracking and eventually improving relevant KPIs (key performance indicators). For the scope of this article, we would explain change analysis taking the example of one important metric marketers are concerned about – the number of leads they generate every week. They achieve this by driving relevant visitors to their website every week. Any change in the number of leads that they get as compared to what they expected calls for an investigation on the reasons for the difference between expected and actual metrics.

Since the first step into any such investigation is to measure and compare (with last week) some global performance indicators measuring the reasons for change, the same is the focus of the current article. Hence, keeping a webpage in mind, we take the example of measuring the number of visitors (those who reached the website), the number of leads (visitors who reached target), and the conversion rate therefrom (leads per visitor).

When we perform these measurements for two given periods of time (say consecutive weeks), we could compare them. Let V1, L1, and C1 represent the number of visitors, number of leads, and conversion rate of week 1 respectively, and let V2, L2, and C2 represent the same measurements from week 2. 

How to measure?

The four simple change measurements one could perform are the following:

1. Total change: This is the most basic measurement of interest that preserves both the “unit” and the “sign” of change. For example, if the number of website visitors who filled out a form changed from 50 last week to 75 this week, we get an absolute change value as +25 form-fillers. In short, it answers the question: “How much more/less?”.
   More formally though, one could measure absolute change in visitors (𝚫V = V2-V1), leads (𝚫L = L2-L1), and conversion rates (𝚫C = C2-C1).‍
2. Relative change: While absolute change remembers the unit of the entity one is measuring, it is – more often than not – more convenient to adopt a normalized change score. Taking the same example as before, change in the number of form fillers from 50 to 75 means a change value of +25, but the same is true if form-fillers had increased from 150 to 175 (viz., +25). What separates the two cases is relative change (i.e., how much did the unit change per unit original), which is +0.5 (=25/50) for the former (50→75), and +0.17 (=25/150) for the latter (150→175). The specialty of this score is that it remains a fraction between -1 and +1, and helps in comparing two “changes”. In a day-to-day language, a “percentage” variant of this metric is used by marketers.
   Again, a formal representation of relative change in visitors (𝚫rV = 𝚫V / V1), leads (𝚫rL = 𝚫L / L1), and conversion rates (𝚫rC = 𝚫C / C1) would also help engrave the idea.‍
3. Percentage change: As described earlier, it’s easier to understand when one says “the number of visitors saw a 50% increase” (as opposed to saying the relative change of visitors was +0.5). Therefore, as a human-friendly change metric, the percentage variant is more popular than its math-friendly counterpart (relative change).
   A notable caveat is as follows. “From X to zero” would mean a “100% decrease” (and vice-versa), but “from zero to X” turns out to be an “infinite% increase”, which is absurd. One workaround to rectify this is to call “from zero to X” as “from min to X”, where min could be set based on the metric of interest (e.g., min could be just 1). Another workaround is to call “from zero to X” as a “100% increase”. Another interesting point is that “no change”, it’s called a “0% change” – even if it is “from zero to zero”.
   From the perspective of our current example, percentage change in visitors (𝚫pV = 100 x 𝚫rV), leads (𝚫pL = 100 x 𝚫rL), and conversion rates (𝚫pC = 100 x 𝚫rC) could be computed by simply multiplying the relative change by 100.‍
4. Factor change: Some marketers (and sometimes marketers) like to express change in percentage differences, and sometimes in factor increments/decrements (and some do both), and this is purely a personal/company-wide choice. Picking the same example from above, whether it is convenient to say “the visitors increased by 50%” or “this week’s visitors are 1.5x last week’s” differs from use-case to use-case, but is only a different way of expressing relative change.
   Although one has to be cautious, however. 

1. “1x” means no change (a “0% change”). For example 100→100, 1→1, 0→0, etc.
2. “0x” means a “100% decrease”, from, say 100 to 0, 5 to 0, but not from 0 to 0 (since we choose to call it a “1x” change.
3. “2x” means a “100% increase”.
4. “1.5x” means a “50% increase”.
5. In general, “kx” means a “100*(k-1)% increase/decrease”, where it’s an increase when k > 1 and a decrease when k < 1.
6. When saying “kx”, k never goes negative.

Going by the earlier example of website visitors, we could be interested in the factor change in number of visitors, (𝚫fV = V2 / V1), leads (𝚫fL = L2 / L1), and conversion rates (𝚫fC = C2 / C1).

Measuring overall change

Depending upon the business and the audience, there are multiple combinations of possibilities. We only cover some of them, summarizing overall (global) change. And as mentioned earlier, our next article on this topic would dive deeper into digging up the “reasons that drive this overall change.”

No change

Let us start with a simple world, and slowly drift towards complex scenarios. Suppose our website had 1,000 unique visitors last week, and 1,000 new unique visitors this week (i.e., there was no change in the number of visitors). Of the 1,000 users last week, 20 signed-up for our newsletter, and the same trend continued this week as well. In other words, both last week’s and this week’s conversion rate was 2% (20*100/1,000). What does this tell us about this week’s performance over last? That it remained the same! In other words, there was a 0% increase/decrease in both reach and lead conversion rate.

Proportionate increase in visitors & leads

Now, if we had more visitors (say 1,500) this week as compared to the last, with a proportionate increase in leads so as to maintain the 2% conversion rate, it would amount to a 50% increase in visitors and leads, but a 0% change in conversion rate.

Increased leads, retained visitor count

On the other hand, if the total number of visitors had remained the same, and leads would have increased by 50%, this would increase the conversion rate by 50%.

Increased visitors, no change in leads

If, however, the number of leads would have remained the same despite an increase in visitors (by, say, 50%), we would see a 33% fall in conversion rate.

Disproportionate increase in visitors & leads

It is also possible that the number of leads increased by a disproportionate amount, which led to an increase in conversion rate.

Measuring change factors

We just discussed how measuring overall change is straightforward: simply report the signed/relative/percentage difference or factor change in visits, leads, and conversion ratio between the two weeks. But this is only half battle won. What is ideal is to measure the “causes” for the change. For example, we know that a 2x increase in visitors (V2 = 2V1) and 1.5x increase in leads (L2 = 1.5L1) – and hence a 25% drop in conversion rate (C2 = L2/V2 = 0.75L1/V1 = 0.75C1) – happened. But why it happened is one of the most important questions change science has to answer.

What causes change?

A seasoned marketer can quickly understand the main factors that led to a drop or a rise in scale (#visitors) or conversion (leads/visitor). But where data analysis comes in is in short-listing such factors from the rest, and hence help the marketer with her weekly (or periodic) decisions. In this article we only give an intuitive idea of what causes change (and how we measure it). In part 2 of this series on Change Science, we discuss the exact procedures and methods to measure factors that cause change periodically.

The more we know about our customers, the more our analysis benefits. While measuring change, as it was mentioned above, we usually track the number of visitors (V), the number of leads (L), and the respective conversion ratio (C). Along with mere counts, one ought to measure the profiles of such visitors and leads – in both weeks. For example, with a 2x and 1.5x raises in visitors and leads, to know what caused it, one has to track how the properties of visitors and leads have changed. The properties we are referring to are usual user/event based properties – from simple demographic ones such as Location (country, city, etc.), User Agent (browser, OS, etc.), etc. to marketing-oriented ones such as Referrer (domain, campaign, etc.) – along with their values (a “property=value” combination looks like “country=India”, for example). In summary, in addition to tracking overall statistics (visitor and lead counts), we also track counts “grouped-by” their properties.

Needless to mention, such properties are too many to count, and property=value combinations are even more. This is where Factors.AI comes in. We track-down each factor, measure the change attributed to the factor, and surface top insights. And we repeat the process for any event pair combination (in say, a funnel), for every property, and for every consecutive period of interest. We urge you to follow-up with us in the next article that describes how exactly we surface the main change factors via our very own Weekly Insights feature.

Stay tuned.

Martech has been around for what feels like forever. However, it’s in recent decades that the ‘tech’ half of martech has evolved to warrant an entirely separate category of industry. 

The rise of martech has been significant. Chief Marketing Technologist lists more than 7000 products in the martech landscape in 2022 and this number is steadily increasing. All of these products rely heavily on personal data collection. It’s fair to say that the industry is not a heavily regulated one, like finance and insurance. However, with the rise of data privacy laws, things are changing and marketing is at the forefront of its impact.

The primary categories of marketing technology:

1. Advertising

2. Content Marketing and Experience

3. Social Media

4. Commerce and Sales enablement (includes CRM tools)

5. Data and Analytics

6. Administration and Productivity

The tools from all these categories are dominantly data-driven and are aimed at giving more and more information and insights for marketers to work with. Teams all over are dependent on data to derive a 360 degree view of a customer profile. 

However, there are two prime marketing technologies that are heavily dependent on user data:

1. Identity resolution: Identity resolution is the process of aggregating and collecting data points on an individual user across platforms and devices. Identity resolution is a key marketing practice as teams use these ‘identifiers’ collected across martech platforms to create true customer journeys and profiles.

2. Customer Data Platforms: CDPs serve as the central depository of all marketing-related data. It is used for identity resolution and serves as a unified customer database.The data is collected from both internally and externally sources in real time across various touchpoints. This enables creating a database of customer profiles that are both centralised and updated. 

The Changing Landscape Of Marketing Data

There has been a rise of the personal data economy (PDE) where individuals want greater control over the use of their personal information. A recent survey has found that 97% of consumers are somewhat or very concerned about the protection of their personal data. 

However, though conversations surrounding consumer and personal data privacy have gained major ground since 2016, it is only recently that policy has made headway in this regard.

Legal initiatives like EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act  (CCPA) which is soon expected to be modelled in other US states, are aimed at regulating the business use of personal identifying information or PII.

These laws place limitations on the types of data that marketers and companies are allowed to collect and analyse. They also require explicit consent from consumers to use data for reasons beyond what was initially specified. 

To learn more about these policies and their effect on marketing, read on here. 

Apart from these laws, various companies like Firefox and Apple have also taken steps to limit the access of tools to consumer data. While Firefox now blocks tracking cookies by default, Apple has changed how marketers engage with user data and it doesn't allow them to see insights by device. There has also been a significant rise in the users of search engines like DuckDuckGo whose USP is ensuring customer privacy, with a 65% increase in its traffic. The largest benefactors and users of such data have been the marketing and advertising industries. 

The Impact of Data Privacy on Marketing

On one hand, a lot of martech is dependent on user data. On the other hand,data privacy laws and the limitations that they pose on the collection of user data are on the rise. Resultantly, the impact of these regulations and limitations will hit marketing teams harder than others as they become more ubiquitous. A recent study by Gartner found that 1 out of 5 marketers report privacy compliance as their main strategic concern. Furthermore, 73% of marketers are concerned that privacy regulations will have a negative impact on data and analytics that are used to collect and understand visitor behaviour. This can also make it harder to access consumer insights for personalised marketing. 

Another major impact of increased privacy concerns is the volume and quality of data that marketing has become used to. Marketers are used to leveraging a large quantity of consumer data to create campaigns that target audiences based on intent. However, as privacy concerns become larger and regulations and compliances come into effect, this will impact the volumes of good audience data. This, marketers fear, can lead to their current funnels becoming narrower. 

How do marketers move forward?

Even though data privacy laws make getting granular insights on volumes of data from certain channels difficult, it is definitely not going to be a thing of the past. Good attribution tools and analytics systems can use available data (even at less volumes) to give relevant insights. 

In coming times, quality over quantity will take up a whole new meaning in the martech landscape. Getting a granular understanding of consumer journeys will depend more on first party data and technology that makes use of less data to give more insights. Marketing teams will have to focus on finding the right tools and analytics solutions that are compliant with regulations and respectful of consumer data. 

It’s no secret that data privacy is a macro trend that’s here to stay, and with good reason. As social interactions and business operations increasingly take place in digital spaces, users are rightfully concerned about the safety of their sensitive information.

Accordingly, government bodies and security experts have established comprehensive privacy guidelines to ensure the protection of user data. Privacy laws such as GDPR, CCPA, and PECR limit the extent to which websites and businesses can track user activity without explicit consent. While there’s no doubt that this is a win for end users, it may seem like a cause for concern to data-driven marketing teams.

In fact, 73% of GTM teams believe that data privacy regulations will negatively affect their analytical approach to marketing. This article highlights why this is not necessarily true. Let’s explore how privacy-first solutions like Factors empower data-driven marketers to flourish in 2024 and beyond.

Marketers need data. Here’s why. 

Marketers need data to understand and improve the customer experience. This, in turn, results in better conversions and revenue. With data, analytics, and testing marketers can target the right audience with the right message and persuade prospects to become customers. Ideally, it's a win-win situation: marketers spend their budgets efficiently on campaigns that work, and buyers receive relevant promotions as opposed to spammy, spray & pray advertising. In truth, this is nothing new. 

Data has been leveraged by marketers and advertisers since the days of Ogilvy, and with sweeping digital transformation, data tracking has become all the more prevalent. For example, mobile phones today constantly transmit precise gro location as a common user identifier across consumer apps. In comparison, B2B tracking has remained relatively benign — yet effective. B2B marketers have the ability to identify companies visiting their website, track their page visits, scroll depth, and other noninvasive metrics to be able to understand and improve the customer experience. 

The dawn of privacy-first analytics

So far, this sounds great. However, while the intention with which marketers collect data is rarely malicious, the tools and techniques used in this process have been, until recently, without guardrails.

Fortunately, we’ve been seeing a dramatic improvement in data privacy and security in recent years. Today, privacy-first marketing intelligence and analytics tools (Like Factors 😉) honor privacy principles to ensure that data is used only for its intended purpose — to improve the customer experience. Even widely used tools like Google Analytics are having to rework their architecture to comply with regulations.

With tools like Factors, there’s no risk of data being collected without consent, shared with third-parties, or sold to advertisers. Even with this secure approach, marketers can continue to access everything they need to discover new prospects and optimize their performance without intruding on privacy. 

The most important aspect for marketers is to be able to draw the line between reasonable and intrusive tracking. Collection of PII without consent or the ability to identify individual users across websites is illegal and would fall under the latter. As an important practice, marketers should vet their technology vendors keeping this in mind. 

That being said, Factors and other privacy-compliant tools are secure by design. Customer information is protected without compromise on the quality of data, analytics, or insights derived. The following sections cover the basics of what you need to know about the most important marketing data privacy regulations — each of which should be considered when investing in marketing technologies.

1. First-party cookies

First-party and third-party cookies play important roles in the collection of user information. Here’s a quick overview of what cookies are and how first-party and third-party cookies differ from each other.

Cookies or HTTP cookies are tiny pieces of data that are sent to your browser from a web server. This data is stored locally on your device so that the next time you visit a website, it can identify you as the same user. So what’s the difference between first and third party cookies? 

‍First-party cookies: FPC are set directly by the website you are browsing. Their primary purpose is to collect analytics data such as page views, button clicks, and form submissions to improve website functionality and enhance user experience. Without first-party cookies, a user would have to sign in to an account every time they visit a new page on the website or app. Even the most basic preferences like language setting would have to be reconfigured on every page without first-party cookies. In short, they’re entirely harmless and collect basic website data to help marketers eliminate areas of friction and improve website usability.

Third-party cookies: Third-party cookies are tracker cookies which are set by third-party servers (or ad servers) independent of the website a user is browsing. Third-party cookies are accessible to any website that can load the server’s script. More often than not, these cookies are used for unsolicited advertising and are set by ad networks like Google’s AdSense program.

Websites that accommodate ad spaces from servers such as Google’s “DoubleClick” also allow them to place third-party cookies. These cookies can track your browser history and identify interests to facilitate retargeting. That way, when you visit a website that also hosts a similar ad server, it will display a targeted advertisement using the same third-party cookies.

Factors.ai only uses first-party cookies to enhance your user experience with zero intention in building an interest profile or a third-party context with first-party cookies. More information on the usage of cookies here. Third party cookies are  generally considered to be questionable and in some countries, illegal. This is because there’s no certainty as to what data these cookies are collecting and how that data is being used. Accordingly, it’s best to avoid tools that use third party cookies.‍

By design, Factors only uses first-party cookies to track visitor activity and enhance user experience. Tools like Factors have no ownership rights over your user data. They do not share or monetize first-party data collected from users in any way, shape or form.

2. GDPR Compliance

GDPR (General Data Protection Regulation)

General Data Protection Regulation is a privacy regulation standard that covers data protection andp privacy in the EU and European Economic Area. Under this regulation, businesses are required to receive voluntary or opt-in consent to collect personal information of customers, which needs to be clear and unambiguous. 

Personal information is defined by the GDPR as “any information which is related to an identified or identifiable natural person”. Information like IP addresses or any other data that can be traced back to a person is required for analytical purposes will require the user’s consent under the GDPR. This is why you may have noticed several privacy-compliant websites request consent on tracking personal information when you visit.

It is important to note that the consent of collecting personal information cannot be preordained or implied like in the form of pre-ticked boxes. Instead, the user must choose to opt-in to the collection of data and provide adequate detail on the information being tracked.

When complying with the GDPR, businesses must also comply with a set of rights with regards to personal information being collected. These include:

• The right to disclose and access the information collected
• The right to request for a correction of the information
• The right to request the erasure of personal information
• The right to register a complaint on the handling of personal information
• The right to request a restriction in the processing of personal information
• The right to object to the method in which your information is being processed
• The right to retrieve personal information and transfer it to another party, and
• The right not to be subject to a decision that is based on automated processing and has an adverse legal effect on the user  

‍Factors is aligned with GDPR rules and regulations. At present, Factors stores its data in US-based cloud-company servers. Note that the GDPR does not mandate the storage of data of EU citizens and residents within the EU. Additionally, while Factors collects IP addresses for high-level enrichment such as coarse geolocation (city, state-level) and account identification, this data is purged. We do not store IP or firmographic data in our database.

CCPA (California Consumer Privacy Act)

The California Consumer Privacy Act is a state-wide data privacy law that regulates how organizations handle personal information (PI) of California residents. Under the CCPA, the collection of personal information does not require opt-in consent for adults. That being said, just like the GDPR, users under the CCPA have the right to access personal information being collected and the right to opt out of the sale of personal data to third parties. 

‍Factors is CCPA compliant. In fact, by design, we do not have the ability to share, sell, or store personal data to third parties.

PECR (Privacy and Electronic Communications Regulations)

The Privacy and Electronic Communications Regulations (PECR) represents the UK's law on how businesses are allowed to market to UK consumers using electronic technology. ‍This regulation deals with unsolicited marketing, which includes things like cold calls, fax, text and emails, etc. PECR does not apply to solicited marketing — or marketing messages that are voluntarily requested. Even if a person has opted-in for marketing from your businesses, there are still instances that are defined as unsolicited, which would have to comply with PECR. As a marketer that relies on email marketing, detailed information on the consent must be provided to the person you are emailing. Consent must be received in the form of an action, whether it is written or ticked on a box. 

The rules of PECR slightly differ for B2B, where there is an exception to retrieving consent for emails and text messages. If you intend on the processing of personal information of corporate subscribers (B2B) or/and individual subscribers (B2C), the rules of UK GDPR apply.

Surprise, surprise — Factors is also aligned with PECR regulations.

SOC2 Compliance‍

While marketing under the aforementioned regulations would advocate a fair degree of privacy assurance to your users and necessitates consent. A Service Organization Controls 2 or SOC 2 compliance raises the stakes on the safety and confidentiality of customer data. SOC 2 is a set of criteria that define how a business should go about managing customer data and the examination of relevant controls in accordance with those criteria. While it is not legislation for data privacy, an SOC2 certification is the cherry on top of your data privacy practices and the forefront of establishing security standards as a part of being a privacy-first organization. It works on 5 trust principles:

1. Security: This involves the use of tools such as application firewalls and two-factor authentication for the protection against unauthorized access of systems.
2. Availability: This refers to the software, systems, or information that is available and is being maintained at a minimum acceptable performance level.
3. Processing integrity: This ensures that a system completes its objectives in a valid, timely and authorized manner with no errors in the system processing.
4. Confidentiality: Using encryption and limited access of data to ensure its disclosure is only restricted to a few people.
5. Privacy: This refers to the personal information of the system that is being collected, retained, used, disclosed and disposed of in compliance with the organization’s privacy notice and GAPP (Generally Accepted Privacy Principles).

‍Factors.ai is also SOC2 compliant.

Playing the long game — B2B Marketing Privacy In 2024 & Beyond

As more intent and uses of personal information by businesses get discovered, postmodern norms for regulation on the safe collection of data gets more rigid. Falling short on the compliance of these regulations will lead to the obstruction of marketing efforts. Here are some reasons as to why marketers should consider becoming privacy-first:

1. Data privacy and being privacy-first is bound to become an industry standard for marketing considering that web analytics is more of a necessity than a value adding requirement.
2. The legality of data privacy regulations will severely affect the operational efficiency, and even the going concern of the business. Data privacy under legislation is an obligation.
3. The conception of regulation for data collected and processed by artificial intelligence caused by an inevitable surge in automated workload is well underway.

Today, Google Analytics is illegal in Austria, Italy, Sweden, Denmark, and other European countries because the CLOUD Act allows US authorities to demand personal data from Google, Facebook, Amazon, and other US providers — even when they’re operating in external locations (like the EU). Regulation will only get more stringent — like the new revisions of the CCPA under the CPRA which goes into more detail on the sharing or disclosure of personal information. Being compliant early will help you stay ahead of the game.

More businesses will need to prioritize being privacy-first by building a decision framework around the management of personal information. This means making data privacy, its regulation, and the control of user data for the long haul the cornerstone of your business and marketing efforts.

In this article we’ll cover,

1. What is Revenue Intelligence?

2. Why are teams increasingly opting for Revenue Intelligence?

3. Revenue Intelligence to Optimize Conversions

• Breaking down silos between marketing and sales
• Solves for uncaptured data
• Solves for outdated and stale data
• Targeting entire accounts with ABM
• Give sales leaders total visibility/Access to the larger picture
• Accelerate sales cycles with more efficiency
• Forecasting

4. The Emergence of Revenue Operations and Intelligence (RO&I)

Revenue intelligence (RI) is a popular buzzword in today’s marketing landscape. This enthusiasm may be warranted. RI is revealing itself to be a powerful tool for marketing and sales teams to derive powerful data insights that were hitherto unforeseen. RI uses AI to gather data that would otherwise remain uncaptured.

Let’s start with an example. 

GrowNow is a marketing agency for start-ups. They focus on both digital and event services. Their content team has put out several articles on how marketers should approach scaling at various stages of growth. 

Akshat is the marketing head of Company X that has a fintech product. They’ve found their product-market fit and now they are looking to scale. He is searching online for ways to scale marketing and branding efforts. He comes across GrowNow’s website and finds the information that he is looking for.

He is not a lead yet but marketing has the information on how he came upon the website and what pages he’s engaged with. He finds his way back to the website a few days later whilst searching for more information on what tech stack his team would need. He downloads a free report on GrowNow’s website on the latest trends in martech. 

Finally, after a few weeks, Akshat comes back to GrowNow’s website, this time with a direct search and the intent to check out the services that GrowNow provides. He even fills a form for a preliminary call. 

Now that Akshat has been converted, he is pushed to Sales and GrowNow’s CRM has the information that he filled on the form: his name, email address, title and company. They might also have other information like the report downloaded by him. Marketing directs a few more adverts towards Akshat over the next few weeks. Soon sales gets on call with Akshat, they use this information to convert him and they are successful.

Later on, Deepti, the CEO of clothing brand Y which has several pop-up stores finds GrowNow in an article on up-and-coming marketing agencies and clicks on the link which redirects her to their website. She spends some time looking through the website and fills a form. On receiving a call from an SDR, she learns more about their services. Marketing continues to send the same adverts based on Deepti’s website activity. However, after a few calls, they quickly realise that Company Y and GrowNow do not have a good fit. Sales had the same basic information about Deepti as they did with Akshat. 

Both Akshat and Deepti’s customer journeys were a little different which sales were unable to access — like the data on their journeys pre-form fills. Similarly, marketing was unable to personalise websites based on Deepti and Akshat’s activities once they went down the funnel to SDRs. This in part, came about due to different locations of this data. Marketing has its data on first touch, web pages visited, time spent on webpages, adverts clicked on Google Analytics or other marketing platform while sales has its data on its CRM like Salesforce. Both departments were unable to access the other’s platform nor did they have an integration in place that allows for seamless flow of this information. 

This is where Revenue Intelligence comes in.

What is Revenue Intelligence?

In its simplest terms, revenue intelligence refers to the process of leveraging AI to collect, sync and analyse data across sales, marketing and customer success to produce critical insights and generate revenue. 

It is a powerful revenue operations tool that helps companies bring synergy between their customer-facing teams (marketing, sales and customer success) and make decisions that are powered by metrics. 

Why are teams increasingly opting for Revenue Intelligence?

More and more companies are increasingly realising the limitations of human intelligence in identifying important data points as well as the limitations on relying only on CRM data for insights on customer journeys.

The solution to this, has been to look at AI to collate and identify data that humans cannot. Furthermore, RI helps teams coordinate and capture data at the right time, before data decay diminishes value - 

1. Breaking down the silos between marketing, sales and customer success

Data silo is a problem when there is a lack of seamless coordination between teams, especially in terms of data collection and storage. A huge chunk of insights get lost when the data captured by these teams remains limited to their own teams. This is propelled by storing of data on different locations and difficulty in cross-departmental access of this data. All three of these departments are interacting with customers and have intelligence on customer trends and opportunities that get lost with interdepartmental misalignment with data getting siloed.

A revenue intelligence system captures and integrates the data from all these teams in real-time and creates a single, consolidated platform for the entire organisation. This ensures that everyone is on the same page and allows for seamless coordination between teams that helps create a unified strategy.

2. Solves for uncaptured data

Sales and customer success teams have to manually enter customer data like contacts, engagements, etc into their CRM. Two problems arise with this:

1. Manually entering data for each and every customer interaction is time consuming. 

2. This leads to negligence as many sales and customer success fail to enter all a lot of this data. Around 55% of salespeople admit that they do not enter all lead and customer data.

Resultantly, a lot of available data remains uncaptured and the company relies on this incomplete data for reporting, planning and forecasting.

RI solves for uncaptured data by automatically capturing contacts and engagements data from all customer facing teams, solving for both time and incomplete data, leading to more accurate and reliable sales reporting and forecasting.

3. Solves for outdated and stale data

Sales and marketing data is susceptible to becoming stale. 

Relying on manually entered contact details and the fact that people change jobs and positions and do not update their linkedin profiles leads to databases and CRMs being outdated and filled with errors. Good, high intent leads are very critical for both sales and marketing to reach their conversion goals. 

Then there is also the consideration for the hidden cost of redundant data. Bad or outdated data can muddle up research, competitiveness and accuracy of forecasts. Poor data leads to the wastage of sales’s time and IT’s time in syncing systems. It causes frustration when data-backed decisions fail to execute results.

RI solves for this by automatically tracking and updating changes to the leads in the CRM. This ensures more up-to-date and reliable prospect data.

Revenue Intelligence To Optimize Conversions

1. Capturing missing sales activity

We’ve spoken about the problems of unco-ordination and data silos between sales and marketing. When marketing is unable to access sales data, it prevents potential for improving marking activity and checking for inefficiencies in the existing process. As discussed earlier on the Factors Blog, getting multitudes of leads won't have a positive impact on revenue unless they are good, qualified leads. Infact, it may just lead to a waste of the sales efforts. In such a case, RI helps marketing access sales data that is pertinent for marketing’s processes and planning for more efficient campaigns.

Auto-creating of leads based on sales’ experiences, auto-removal of leads that sales has already dealt with or are low-intent based on previous experiences — both lead to coordination of data as well as a more seamless process of lead identification and capturing of contacts.

Furthermore, automated opportunity association of leads and tracking of interactions (emails, meetings, etc) helps get more insights from available data.

2. Attributing Marketing Touchpoints

Apart from sending better leads to sales, RI also helps paint a clearer picture of how marketing is helping sales acquire leads that lead to conversions. This helps in both having a better understanding of customer journeys and measuring the impact of marketing in the organisation’s overall functioning.

Revenue intelligence helps with marketing attribution reports that highlight marketings total impact, impact in each channel and the creation of first-touch, last-touch and multi-touch reports. RI also simplifies visualising the opportunity journey with easy spotting of marketing email and campaign touchpoints and deal updates as leads move through the funnel.

3. Enhances ABM 

Revenue Intelligence helps optimise ABM by improving the data quality of the contacts that are captured for the various accounts. With automation, more contacts can be captured. These contacts are also of better quality due to the improved tracking of customer engagements. 

RI also allows you to pursue better personalisation and target marketing efforts based on an account’s firmographic features and funnel position. So teams can get more meaningful insights from CRM and build improved target account audiences.

4. Giving sales leaders access to the larger picture

RI helps sales leaders have a better understanding of the customer journey and gain insights into the prospects that are coming in. Furthermore, having a real-time system of data relating to sales helps with insights into the sales process.

5. Improved sales pipeline

Better prospects, higher intent leads determined based on historical and real-time data improves the quality of leads entering the sales pipeline which in turn leads to higher conversions. Apart from higher output, RI also helps SDRs close deals faster and improve productivity. 

6. Forecasting

Revenue Intelligence helps sales forecasting by solving for outdated and uncaptured data to improve the reliability and accuracy of predictions. 

The Emergence of Revenue Operations and Intelligence (RO&I)

RO&I is a tech category that leverages AI to perform the principal task of revenue operations: integrating sales, marketing and customer success. In other words, RO&I is technology that allows the integration of sales technology, marketing technology and customer success technology to provide an end-to-end solution from customer acquisition to retention and expansion.

Revenue Intelligence tools help teams get the best out of revenue intelligence and empower their Rev Ops efforts with better data and more improved efficiency in mapping customer journeys. Knowing when to reach out to potential customers with the right information at the right time is critical to improving experience and conversions. 

In This Article:

1. What is Search Marketing?

2. Why is Search Marketing Important For You?

3. Types of Search Marketing

     - 3.1 Organic Search Marketing

           - 3.1.1 SEO

           - 3.1.2 How to get started with SEO?

           - 3.1.3 Advantages and Disadvantages of SEO

    - 3.2 Paid Search Marketing

           - 3.2.1 Pay-per-click(PPC) or Paid Advertising on search engines

           - 3.2.2 How to get started with PPC?

           - 3.2.3 Advantages and Disadvantages of PPC‍

What is Search Marketing

‍As its name suggests, search marketing refers to that segment of digital marketing that focuses on marketing through Search. This involves improving your online presence on search engines like Google, Bing, Yahoo! and more. 

Search marketing improves a brand’s presence on the Search Engine Results Page (SERP) using techniques like brand building, SEO, and paid marketing. Simply put, the higher you rank on the SERP, more are the potential users who notice your content, and are thus likely to click on your content. This translates to increased traffic on your website, which in turn influences conversions positively.

Why is Search Marketing Important for you?

Around 80% of consumers conduct their product and service research online. Resultantly, search marketing should have a big role in your digital marketing strategy.

Search Marketing is intuitive in nature. The targets of your search marketing practises are individuals looking for your brand,  your competitors, your category, your use-case, the pain points you solve for, and so on. 

Ranking high on the SERP is easier said than done. As is expected, users click on the first few links on the search results page after making a search query. More than 90% of user traffic only visit websites that appear on the first page of Google’s search results page. However, as important as appearing on the first page is, it is even more important to show up high enough to catch the user's eye. Paid search marketing has better luck on this end. Further, considering that there are over 200 factors that impact your ranking on Google’s SERP, carefully planning your SEO and PPC marketing can help rank your website and content in the upper echelons of the SERP.

Search Marketing is of two types

1. Organic Search Marketing

As the name suggests, this comprises organically improving the website’s position on the SERP without any payments made to the search engine. Organic comprises Search Engine Optimisation or SEO.

2. Paid Search Marketing

This comprises PPC aka pay-per-click marketing. It involves placing ads on the SERP and paying each time the ads are clicked. 

Organic Search Marketing

Organic listings refer to links that appear on the search engine’s results page after a user has entered their query. These typically depend on the type of query: 

Navigational queries

where the user is searching for a specific website but does not enter the URL. Here, it is important to ensure that users are able to find your website with the simplest of google searches. 

Informational queries

where the user is trying to find more information about something like a term they haven’t encountered before, a problem that they are facing and want to understand more about it, a tool or category that they wish to learn more about, etc. This is also called the learning stage where the user might not be ready to buy a product but they are learning more about the use-case and the problem solved by that product. Exposure of the users to the brand at this stage of the customer journey through content marketing can steer the users towards the brand. 

Transactional queries‍

Where users are looking for specific products in a broad category. For example, if I feel like my company has scaled up and I need an ABM tool to improve my marketing and efficiency track all my accounts, I go on Google and search ‘ABM Tools for B2B SaaS 2022’. This would count as a transactional query. 

Search engines crawl the internet and review all the text, downloadable whitepaper, documents, media and all other content on each website. This data is used to rank websites on various quality signals like quality of content, how useful the content is for a user using their search engine, site speed, links to other websites, etc. The goal is to ensure that users are getting the best results for their search queries.

This is what determines the ranking of the websites on the search engine’s results page. SEO comes into the picture as the tool that helps you improve upon all the various factors that help Google and other search engines determine the quality and ranking of your website. SEO helps you optimise your website by including keywords and help you ascertain authority over the domain or area that your product or service caters to by creating high quality content. 

How to get started with SEO

SEO is a skill that takes time to learn. However, the fact that the learning curve is gradual does not mean that you cannot get started. SEO is a skill that gives returns at every phase of learning. 

As you improve, your ROI will increase but a good place to start with SEO is with these three important concepts:

Keyword research

This is the process of researching and analysing keywords and search terms that users enter into their search queries. Keyword research helps figure out what search terms your target audience is using while searching on Google and other search engines. It also helps figure what queries the target audience is using, popularity of search terms within queries, etc. This can help in ensuring that your content reaches the right audience and increases your traffic. The idea is to target the searches that your target audience is bound to make. Keyword research can help with getting your content in front of your intended audience.‍

Link Building

Link building or building links from external web pages to your webpage is another method of improving your website’s validity and authority in Google’s eyes. Backlinks, not only from other websites, but from sites with authority, improve rankings and visibility on the SERP. Since backlinks often connect websites with similar content, it is a way for Google to ensure that relevant content is being delivered to the users. 

Domain Page Authority 

Domain authority determines how your site compares to others in terms of how relevant it is within your category or industry. A site with a high domain authority ranks higher on the SERP as search engines identify these sites as having authority and therefore relevance over the information that the user is seeking. Domain authority is also scored by google based on those 200 factors that we mentioned earlier. So there is no set way to know how your website is being scored. However, there are ways to compare how strong your website is, compared to other websites that have similar content.

Advantages of SEO

1. SEO is free if you’re doing it yourself or internally with your marketing team.

2. The ROI is more long term with SEO, as compared to paid. If you have cracked your SEO and Google identifies your website as an authority, traffic increases and in turn, the increased traffic makes it easy to retain a higher ranking. 

3. Organic growth is a better judge of brand awareness. Increased organic traffic and ranking higher on organic queries is a sign of a strong brand. The brand gets more consolidated when people find you ranking high on their search results.

Disadvantages of SEO

1. As mentioned, learning SEO takes time. Even though any amount of SEO can improve the strength of your website, SERP rankings are highly competitive and it takes time to actually show up on the first page of the SERP. It is important to remember that many websites are creating the same content as you and these competitors are also continually improving their SEO.

2. SEO can be hard to scale, especially within small organisations where the team has to focus on improving the product, strategy, outreach and even on other aspects of marketing. Converting your entire website, existing content and incoming content to excellent SEO is a long task.

Paid Search Marketing

Pay-per-click or paid search marketing involves paid advertising where you pay for each click on the adverts placed on the search engine results. The amount spent per click is the cost per click or CPC. Search engines determine where to place the advertisement based on keywords and search terms that potential users are going to go to. The main platforms for PPC are Google Ads, Image Pack and featured snippets. 

But there are several companies that may be using the same keyword groups and search terms. And as with organic, the goal of the search engine is to show the most relevant content to the user, even for advertisements. Resultantly, search engines need to also rank ads that are placed on the first page of the SERP.

A Few Factors That Affect Your SERP  Rankings:

1. PPC bids

‍Also known as keyword bids, these are bids that online entities place on various keywords or keyword groups to secure ad space. This is commonly used by entities like Google AdWords where auctions are held on various keywords and search terms.

It is important to ensure that you are bidding on keywords that will bring in relevant users. Common keywords are usually more expensive since many people are bidding on them, plus they target a larger audience, a large chunk of which may not have the intent level for your product. On the other hand, niche keywords may be harder to research and figure out, the cost is lesser and the intent of the targeted users is higher.

2. Landing page experience

The landing page or the page that is linked to the ad plays an important role in how Google ranks your ad. Site speed, user experience, ease of navigation, etc are important factors that search engines take into consideration because a website that takes too long to load, is hard for the customer to navigate all spell bad customer experience and search engines won’t want to direct their users to such pages. 

Ad Quality: Apart from the landing page experience, search engines like Google also determine a quality score for your PPC ads. The other two components are 

CTR or the click-through-rate, which determines how likely it is that your ad is  going to be clicked when shown to a user.  

Ad Relevance, which determines how closely your ad matches the intent of the user’s search query.

Advantages of PPC 

1. PPC is easier to figure out than SEO. This is because the level of control that you have to ensure that your ad shows up on the SERP is higher than the control you have in SEO. The level of uncertainty regarding what works and what does not when determining factors that improve ranking on the SERP is also lower for PPC. The results are instantaneous and you can also A/B test your ads to see which ones perform better.

2. PPC is suitable for new companies. PPC is better for scaling up as you just have to pay more money to get more listings and more exposure. 

Disadvantages of PPC

1. Paying for each and every click can be expensive. Moreover, it is important to remember that each click simply re-directs the user to the website and does not guarantee conversions. Therefore, even if it is easier to scale with PPC than with SEO, there are budget constraints that may limit your ability to scale. 

2. The gains with PPC are more short term in nature. As compared with SEO, where the gains are more long term and the ROI increases as your organic reach becomes better, the gains for PPC are relatively short term. The ROI at each instance is dependent on how much you paid for the ads and how many conversions came from those ads. Once the ads are discontinued, they stop bringing in visitors to the website.

3. Issues with the steep learning curve. Although figuring out PPC and getting results is faster than SEO, issues can crop up with how steep the learning is. With SEO, even if the learning and growth is gradual, you get improved results at each level of learning. However, if you jump into PPC without learning the ropes behind keyword bidding, customer research, etc, you may lose a large chunk of your budget bidding high amounts of money on keywords that may not be bringing in conversions. This can lead to a wastage of money.

In conclusion, both PPC and SEO have their pros and cons. But rest assured they are really powerful tools that can scale up your marketing efforts and have a positive impact on website traffic, leads, sales and most importantly, revenue. If you want to learn more about this, you can check out Episode 7 of The Factors Podcast where we discuss both the organic first approach as well as the paid first approach for a company that has just found its product-market fit.

Outline:

• Introduction
• What is B2B Marketing Attribution and how is it different from B2C Marketing Attribution? 
• 7 Challenges with B2B marketing attribution

1. Tracking The Website Activity And Identifying Users Using Form Submissions, 
2. Identifying Accounts On The Website Even For Anonymous Users Using A Reverse IP Solution. 
3. Stitching Website Data With Map And Crm Data Using Email Ids (Specifically Unifying CRM Data Across Objects - Lead, Contact, Campaign Member, Activities Into A Single Timeline)
4.  Tracking And Defining Offline Touchpoints At The Same Level As Digital Marketing Touchpoints
5. Long Sales Cycles Implying Need To Track This Data Over Many Months And Years
6. Sales Marketing Alignment - Bringing In Sales Data
7.   Ability To Do All Of This At An Account Level                                                                         

• Takeaway

The B2B customer journey includes multiple people and touchpoints in the decision-making process.

On average, 6 to 10 people are involved in the B2B buying process. And for 33% of B2B organizations, the sales cycle is extended beyond six months. 

Overwhelming, isn't it?

In a B2B business, there are multiple stakeholders at different stages in the buying journey. And it is essential to have content that appeals to them. Hence it becomes hard to build content pieces that provide educational value. 

However, it is not an excuse that hinders your growth. In this blog, we will discuss the seven main challenges with B2B attribution and how factors can help overcome them. 

How Is B2B Marketing Attribution Different From B2C Marketing Attribution? 

71% of Marketers believe optimizing the customer journey across multiple channels and interactions is crucial. This optimization can improve customer satisfaction and drive business growth. 

However, 50% of B2B marketers report limitations with their current analytics solutions. These reports are not providing them with adequate visibility into what channels or campaigns work best. 

The following are two reasons why traditional marketing analytics solutions fail to achieve this. 

1. Multiple stakeholders are involved in decision-making, and the buying journey is non-linear. It makes it difficult to predict the impact of marketing-driven interactions.
2. Sales cycles are longer and involve multiple online & offline touchpoints for educating and influencing the buyer's decision. 

Let's understand this with an example. 

A customer journey for a B2C brand that is selling chocolates will look like this: 

Clicks on an Instagram ad → go to the website→ to make a purchase. (Yes, that's it!) 

On the other hand, a B2B customer's journey will look something like this.

Visit website→Read product reviews→Attend a webinar→Engage with a sales representative→Make a purchase decision. [For example's purpose only]

Now, from the customer journey, it is clear that it has both online and offline touchpoints. A more detailed depiction of a customer journey in the B2b business is added below for your reference. 

Furthermore, users now tend to browse anonymously, making it harder to piece together the accurate buying journey. Website Visitor identification capabilities can help throw light on these otherwise untrackable touchpoints. 

Challenges With B2B Attribution

Here are the seven challenges faced by the marketing teams with B2B attribution and how to overcome them. 

1. Tracking Website Activity And Identifying Users 

• How many people visit my website, and who are they? 
• Which page are they landing on?
• Which content is driving maximum engagement? 
• Which traffic sources - campaigns, referrals are driving high-quality traffic to the website? 

These are some of the questions that cross the mind of a B2B marketer. Websites are the sales epicenters for B2B marketers. Why? Because all the lead generation and conversions happen via the website. 

At every stage of the buying journey, your prospects are consuming your content and comparing it with your competitors. They want to understand whether you can solve their problems faster and better. 

So, it is vital for you to track and identify the website visitors to prepare customer-centric marketing strategies. However, tracking a user's journey from the first interaction to conversion across months is a technically complex task. It includes

• Managing cookies,
• Tracking traffic sources via utm parameters, referral parameters, or click ids,
• And stitching that with the respective ad platforms.

How Can Factors.ai Help?

Factors.ai is an analytics solution purpose-built for B2B marketers. It has an inbuilt capability to track a user's journey from the first interaction to conversion and beyond. 

The solution is configurable, wherein marketers can set up their utm definitions and channel configurations. It also comes with the following

• Ability to track utm parameters and click ids.
• Native integrations with the main ad platforms, providing a cost-to-revenue view seamlessly.

2. Website Visitor Identification

The key to driving effective marketing is targeting the right audience with the right message at the right time.

And data is what you need to convert the hot lead! The more you know about your prospect, the more you can personalize their experience.

However, collecting user data is challenging for the B2B segment. According to a report by 6sense, only 3% of B2B website visitors will fill out any form. And the rest, 97% of them, will be labeled as anonymous traffic. 

But it would be misleading to say that 97% of anonymous users did not influence the decision-making process of the known 3% of users. 

Let's unpack this with an example now. 

For instance, six people from the same company visited your website, but only 1 filled out the demo form. Therefore, attributing all the marketing efforts to that single identified person and his touchpoints will be wrong.

All the users from that account and the campaigns/content they interacted with should be considered when building an attribution model.

How Can Factors.ai Help?

Collecting user data is crucial. But you can do that only with their consent, which means your anonymous visitors stay hidden. Therefore, you need a solution that tracks the data on the website, even for anonymous users. 

Factors.ai has an OEM partnership with 6sense to provide the best-in-class visitor identification to its customers. Thus, stitching together the entire account journey across all users. 

They use a reverse IP solution and get data on an account level rather than at an individual level. It further enables you to understand the companies the users are from and know more about your anonymous users.

3. Putting The User Data In One Place 

B2B Marketers today leverage multiple channels to promote content downloads, webinar registrations, and demo requests. It helps them engage buyers as per their preferences. 

However, with many campaigns, ads, and other marketing activities happening simultaneously, it becomes challenging for marketers to measure the influence of each of these efforts on pipeline and revenue. In many cases, the customer journey is siloed across multiple tools. For example, the Marketing Automation Platform captures the website activity, while CRM captures the post-sales hand-off events.

Most Marketing Automation Platforms also are not sophisticated to capture traffic sources accurately. Furthermore, CRMs keep the user data fragmented across multiple objects such as Leads, Contacts, Campaign Members, and Activities.

Hence, it isn't feasible to stitch together the user journey across all these tools at an account level. Therefore, to make result-oriented marketing strategies, you need to unify this data - both at a user level and then at an account level. 

How Can Factors.ai Help?

Factors.ai has out-of-the-box integrations with Marketing Automation and CRM platforms. And it can stitch all data with the website activity based on the user's email ID.

Also, Factors pulls in all the engagement data across both Hubspot and Salesforce across individual objects. 

For example, in Hubspot, Factors can pull in the Contact, Engagement, Form Submission, and Add to List activities. Within Salesforce, Factors unifies data across Lead, Contact, Campaign Member, and Activity objects.

It makes it easy for the decision-makers to get a 360-degree unified view of customer activities and behavior in one platform.

4. Tracking And Defining Offline Touchpoints At The Same Level As Digital Marketing Touchpoints

Both online and offline touchpoints are equally involved in the lead acquisition process. Hence, B2B marketers need to track them in a single timeline. 

Online touchpoints are easier to track through the well-established digital marketing ecosystem. However, offline touchpoints like events, workshops, meetings, and direct mail are difficult to keep track of. 

Therefore you need a solution that allows you to keep track of both touchpoints simultaneously and build an exhaustive account timeline. 

How Can Factors.ai Help?

Factors automatically track offline touchpoints, which are recorded in the MAP or the CRM. 

Further, Factors allows you to configure and define your offline touchpoints with a simple UI. It enables Marketers to map all their touchpoints at a user and account level for making data-driven decisions.

5. Long Sales Cycles Implying the Need To Track This Data Over Many Months And Years

Longer sales cycles are one of the unfortunate realities of the B2B buying journey. Due to the multiple stakeholders involved and shifting priorities, most buyers take much longer to make a purchase decision. On average, a customer conducts nearly twelve searches before interacting with a brand. 

With this and the complexity involved in the decision-making process, it becomes challenging to accelerate the sales cycle. As a result, the customers could take weeks, months, or even years to close the deal size. 

Therefore B2B organizations would need a solution that can manage voluminous data running into many years of interactions with their prospects. 

How Can Factors.ai Help?

Factors.ai allows you to keep a record of all the interactions across all the platforms, like websites and campaigns, within one platform. In addition, you can seamlessly store data for an extended period (no limits) and reflect back on it at any point to decide what really helped.  

6. Sales Marketing Alignment - Bringing In Sales Data

An alignment between marketing and sales can maximize the ROI of a business. But this alignment between the teams is often absent in B2B businesses. Each team believes their efforts were the reason for closing a deal, which could be one reason for this. 

Emphasizing that each team is part of a larger go-to-market function is one way to make them work together.

Once you form a synchronization between them, it will allow the marketing heads to get a unified overview of the data across both marketing and sales touchpoints. 

Furthermore, each team can review and analyze the attribution data to see which of their strategies are working and which are not. 

How Can Factors.ai Help? 

Factors.ai pulls in all your sales interactions from the CRM and treats them at par with marketing touchpoints. And it also provides a clear and consistent view of the customer journey. On top of the unified data foundation, both teams can get answers to questions such as; 

• How many touchpoints did it take to convert a deal? 
• How many of these were sales vs. marketing touchpoints?
• Were marketing efforts able to drive engagement with the right stakeholders in these accounts?
• When is the right time for sales teams to intervene to convert an account?

7. Ability To Do All Of This At An Account (company) Level

The most significant pain point of B2B marketers is the involvement of multiple stakeholders in decision-making. 

The person who made the purchase is not usually the one who initiated the process of buying the product. Instead, multiple people across different departments (technical support, finance, marketing) must have come across the different stages of the buying journey. 

The traditional methodology would want you to attribute all the credits to the person who bought the product. It makes sense because he is bringing in the revenue. 

However, tracking customer journeys at an account (company) level rather than at an individual-level is what your attribution strategy requires. 

How Can Factors.ai Help?

Factors.ai will give insights at a granular level by breaking down the customer journey at the account level. It will simplify and visualize the customer journey by giving you an optimized overview of every touchpoint that drives the velocity of conversions & pipeline. 

Do B2B Marketing Attribution The Right Way! 

To keep up with the competitive marketplace, you need a differentiated analytics tool that helps you connect the dots from initial interaction to conversion. 

While B2B Attribution is technically and organizationally a complex problem, overcoming these challenges is critical to ensure your efforts are well directed. Hence tools like Factors.ai can tremendously simplify the B2B attribution process and elevate your ROI. To get your B2B marketing attribution game on point and cost-effective, sign up now for a free demo today. 

Strike Three, You're Out!

Austrian data regulator, Datenschutzbehörde, recently found Google Analytics to be in violation of EU’s General Data Protection Regulation (GDPR) laws. It was revealed that data collected through GA from NetDoktor, a European medical news website, maintained inadequate protection against American intelligence agencies. Following the infamous Privacy Shield ruling in 2020, and a breach in European Parliament's Covid-19 Website in 2021, this is the third instance of GA operating an illegal mechanism to transfer data across borders in recent years. 

“This transfer was found to be unlawful because there was no adequate level of protection for the personal data transferred. Website operators cannot use Google Analytics while simultaneously being in line with GDPR”
— Matthias Schmidli, Deputy Head, Austrian Data Regulator

What’s especially worrying is that there was nothing uncommon about the way NetDoktor had been using Google Analytics. Like millions of other GA users around the world, NetDokter places third-party cookies on visitors so as to be able to capture user behaviour. The problem is inherently with Google Analytics, as all this data then travel’s back unchecked to the tech giant’s servers in the US.

Europe is increasingly agitated with the manner in which this exported data is being transported and stored. US surveillance laws* protect foreign data far less rigorously than they do domestic data. The uncomfortable implication of this is that, in theory, US surveillance agencies have the authority to harvest massive amounts of personal data sourced from big tech companies like Google, Facebook, and Microsoft. 

“What they do right now would be in violation of the fourth amendment if it’s for US citizens. Just because people are foreigners it’s not a violation of the US constitution”
— Max Schrems, Hon. Chair, NOYB

*Refer Section 702, Foreign Intelligence Surveillance Act & Executive Order 12333

What’s Next for Google Analytics in Europe?

After the episode in Austria, 30 other European countries are currently investigating the prevalence and extent of Google Analytics compliance violations.  While any firm decision is yet to be made, the law is explicit in its stance. At least as it stands, it is impossible to conform to GDPR while actively using Google Analytics. The Dutch (Autoriteit Persoonsgegevens) and German Data Protection Authorities are strongly considering banning Google Analytics in the form that it currently exists. It seems only a matter of time before the rest of Europe follows suit. 

What’s Next for Your European, Google-Analytics Running Website?

If there’s one thing to learn from NetDoktor’s complacency, it’s this — don’t be complacent like NetDoktor. Google Analytics is illegal in Europe. Google Analytics is not GDPR compliant. Ignoring privacy rules and regulations may result in expensive fines and damaged brand reputations. If your website is Austria-based — or even serves Austrian citizens — you should ditch Google Analytics immediately. For other EU-based websites, it is highly encouraged to replace Google Analytics with a 100% GDPR compliant tool before local authorities inevitably tighten enforcement.

"Instead of actually adapting services to be GDPR compliant, US companies have tried to simply add some text to their privacy policies and ignore the Court of Justice. Many EU companies have followed the lead instead of switching to legal options."
— Max Schrems, Hon. Chair, NOYB

Factors.ai is the #1 privacy-first Google Analytics alternative for your consideration. We provide end-to-end marketing analytics and revenue attribution using absolutely no third-party cookies. We’re also 100% GDPR, CCPA, and PECR compliant. Additionally, we recently secured SOC2 compliance — satisfying the Trust Services Criteria based on Security, Availability, Processing integrity, Confidentiality, and Privacy. Book a Demo with us to learn more about Factors.ai.

In an age where the functionality of the B2B marketing landscape becomes increasingly volatile, account-based marketing (ABM) and Revenue Attribution rise to the occasion. The adoption of ABM as an alternative to traditional demand generation is becoming progressively prevalent in the B2B space. Despite its increased use in recent times, the conception of several new and complex channels is promoting the need for ABM practitioners to be able to appraise their investments and optimise their ABM strategies. The incorporation of Revenue attribution in account-based marketing deciphers this challenge.

Understanding Account Based Marketing

What is ABM?

Account-based marketing or ABM is a strategic marketing approach wherein marketing resources and campaign efforts are directed towards targeted/key customer accounts. More specifically, ABM earmarks Ideal Client Profiles (ICPs) that would generate the most revenue.

ABM is known to be collaborative in nature, as most functional ABM efforts work in conjunction with other teams such as sales, operations, customer success, etc. This collaborative work is done during the earlier and final stages of ABM, the former of which involves scrutinizing your target accounts by soliciting the data  (i.e. profitability, ACV, retention rates of customers, technographic characteristics etc) in order to build your ideal customer profile. With this data, one can identify target accounts as well as target contacts within those accounts. 

While businesses *could* work with this list of prospects, most marketers further compartmentalize these accounts and contacts into tiers that rank prospects based on ratings assigned for revenue potential. This, ultimately, would help distinguish your marketing approaches — one-to-one, one-to-few, and one-to-many etc. 

The final stages of ABM involve engaging with your preferred accounts. What’s important here is that you integrate other prominent teams like sales, customer success, and operations to ensure an aligned execution of efforts.

When is ABM Necessary?

Given the sheer magnitude of money, time, research, and personal campaigns invested into ABM, generating an ROI for your ABM strategy necessitates its investment. The problem is that the efficacy of your marketing efforts will not be the same for all key accounts, but that’s obvious. What’s noteworthy here is that your marketing efforts on key accounts should have the lowest risk and the highest viability. This however only becomes feasible depending on the quantity and mostly the quality of the target market. The higher the number of key accounts available to target, and the better the revenue potential of each key account, the more suitable ABM will become for your targeted accounts. There are a couple of ways in which you can measure this:

• Measuring the annual contract value of your key accounts will help gauge the potential ROI if you were to use ABM, the higher the better your ROI. 

• For account quantity, a larger number of key accounts accumulated is preferable — if ABM is your main/exclusive marketing strategy — as they increase the probability of lead generation per account. 

• The TAM or total addressable market will help you gauge if your target market is too broad or narrow for a manageable audience for personalized marketing efforts, the smaller the size of the TAM the more serviceable the personalized engagement becomes. 

The Relevance of ABM

While account based-marketing is not a novel strategy, its emergence over the last couple of years has been excellent thanks to its adaptation to technology, automation, and the utilisation of tools by an increasing number of businesses. Enabling better synergy for its collaborative prospects as discussed earlier.

As of 2021, over 70% of marketers reported the use of ABM, 15% of whom grew from the previous year alone.This is owing to an overhaul of your standard marketing approaches partly as a consequence of the global pandemic causing a loss in value for traditional lead generation and volume-oriented targeting. What made ABM stand out is its versatility and its adaptability to its customer needs. This is because ABM focuses more on quality than the quantity of your broader customer base. Prioritising retention and marketing efforts on their targeted accounts instead of a broader miscellaneous customer base that would have a higher chance of disqualification. The businesses that utilised ABM before and during the COVID-19 outbreak, adapted to the changes — relating to industries like tourism and food service that took a hit based on PD — by reconstructing their key accounts and ideal customer profiles based on new factors, showcasing its versatility and popularity in choice in a changing economic climate.

Attributing ABM

How does Revenue Attribution enhance ABM?

The following are ways in which revenue attribution can help overcome some of the shortcomings of ABM and maximise its utility in practice: 

Measuring ABM Activities and Tracking ROI:

One of the core principles of ABM is that it prioritises and invests in appeasing your best revenue-generating key accounts through personalised engagement programs, this warrants the need to measure the engagement and campaign’s success. A common challenge in ABM and legacy ABM tools is that they fail to provide these insights. That being said, the utilisation of revenue attribution and attribution models accommodates this need as it provides insights into what channels drive revenue and can highlight poor performing channels and campaigns throughout all your key accounts’ pipelines. Tracking your account-based campaign’s ROI, and optimising your customer acquisition cost through those insights are all part of its preliminary functions. Not to mention, identifying a reliable cost per lead (CPL), allowing ABM practitioners to set a more practical CPL limit on their channels for their key accounts.

Key Account Mapping:

The steps involved in an ABM strategy are complex, yet straightforward. Your plan of action is to identify your ideal customer profile (ICP) and use that as a blueprint to locate your key accounts. But what about the people or stakeholders within an account? — 75% of ABM practitioners can’t find the right contacts at companies matching target profiles. And along comes the next challenge. How do we identify the stakeholders involved in the buying process? The solution to this problem involves rigorous research into key accounts and organisational structure. Revenue attribution embellishes this process thanks to its sheer detail in the compartmentalisation of the customer journey by analysing several touchpoints mapping out a multi-stakeholder journey. Highlighting all the stakeholders involved in the buying process, which will facilitate better planning by engaging with the right stakeholders and the optimisation of campaigns based on these insights.

Incorporating Data Attribution in ABM:

The incorporation of data attribution facilitates the ability to measure the impact of account-based activity over the lifecycle of your key accounts or customers and help increase the productivity of these activities. Identifying the right data using a few metrics will make it possible to understand if you have targeted the right accounts. For example, the progression rate and pipeline velocity will illustrate the rate or speed at which your MQL or marketing qualified leads among your key accounts move through the pipeline in their life cycles. But before doing so, it is imperative to associate the right data with your attribution. A lot of the data solicited through various touchpoints are unstructured, identifying intent and buyer interest using metrics such as bounce rate, click-through rate, lead conversion rate, etc., are all essential in data attribution.

Aligning Sales and Marketing:

The functionality of ABM is highly dependent on the collaborative efforts of various teams involved in the approach, especially the sales team. 42% cannot effectively run their ABM program as they find it difficult to align their sales and marketing teams. Meanwhile 86.7% of marketers that utilize multi-touch attribution state that they have a good relationship with their sales team. Why is this? This is because of the lack of shared data and leads. A majority of MQL or marketing qualified leads that pass-through sales teams get disqualified. Only a small percentage (27%) of those leads turn to SQL or sales qualified leads due to not getting a hold of the right stakeholder or decision-maker in the purchase decision. As mentioned earlier, r attribution streamlines this problem through multi-stakeholder tracking aligning MQLs with SQLs. Revenue attribution also enables better communication between the teams through reporting. Through revenue attribution, marketers can report on revenue numbers instead of other marketing vanity metrics.

Implementation‍

The problem with implementing attribution in ABM is starting out. Laying the groundwork for attribution is usually a trial-and-error process if you want to find the most efficient way to utilize attribution. Deriving an attribution strategy, deciding on what models to implement, testing other models, etc., are all common problems faced when implementing attribution into anything. These are inevitable and will cost money and time. In order to stay one step ahead of the game there is a way in which a marketer can anticipate preferred campaigns by targeted accounts and stakeholders. It is through the use of intent data. Regardless of the manner through which it is obtained, it can be very insightful for understanding the channels your targeted account stakeholder is deriving their buyer intent from. This data will prove to be useful in the formation of your attribution models as will be able to premeditate your own channel activity due to the information obtained through the intent data.

Once you have laid the groundwork. It is time to start tracking your engagement. Using multi-channel or multi-touch attribution makes a big difference. Considering the proportion of the investment and the degree of personalisation being used in your account-based engagement, single-touch models will not do an effective job attributing all of your activities — keep in mind that this is dependent on several factors like the number of channels, opportunity cost of channels, the channel intent, etc. In fact, a lot of marketers focus on bottom-of-the-funnel attribution investing in sales enablement to convert customers, while not realising that there are so many other factors to consider. The goal here is to organise your customers into accounts and map out the complete customer journey through the pipeline of said accounts. Pairing this with data obtained from your tech stack will enable you to identify the stakeholders involved in buying decisions within each account.

As mentioned earlier the functionality of ABM is heavily reliant on the collaborative work of other departments, and the same holds true with the use of revenue attribution. While the use of revenue attribution itself facilitates this alignment, that alone should not give you a reason to disregard it. Ensuring that both the marketing and sales teams are working with the same metrics and also the same stakeholders play a vital role in your ABM’s campaign success. Revenue attribution tools also benefit from data across teams, as mentioned earlier, the utilisation of your tech stack which would include things like your sales data and CRM data, etc., are essential in the functionality of your revenue attribution in ABM.

Challenges with ABM and Attribution

A lot of the challenges that arise from attributing ABM have to do with problems and mistakes marketers face when using attribution. Finding the most efficient model that is applicable for your ideal customer profile is not an easy task and has several hurdles. Identifying stakeholders will also only get more difficult considering the constant increase of the number of stakeholders involved in a B2B buyer decision due to sales cycles becoming increasingly bigger in size. Multi-touch attribution, in general, is a complicated and tedious process with more complex channels arising convoluting the entire journey. To overcome this, advancements in marketing technology have enabled us to accompany the right attribution tool that consolidates complex information into useful insights that will save time and effort in practice. Better yet, an AI-powered attribution tool that will eliminate the skill gap required to effectively utilize an attribution tool. With all the necessary tools and know-how available, you should be well equipped in attributing your account-based marketing.

Marketing’s transformation from a cost-centre to a revenue powerhouse — coupled with a boom in digital channels — means that marketers, now more than ever, require a granular account of their influence on pipeline and revenue.

Enter: Revenue Attribution.

B2B companies are prioritizing revenue attribution to measure their marketing performance and ROI, and track customer journeys. In fact, 76% of all marketers find that they currently have or will have in the next 12 months, the capability to employ a robust revenue attribution platform (Think with Google). Conceptually, the function of attribution is straightforward, but there are several mistakes that could easily skew your results and limit your progress when it comes to accurate, actionable revenue attribution analysis.

With that in mind, here are 8 common mistakes to avoid for your revenue attribution regime:

1. A lack of an attribution strategy

Despite the automation solutions that are embedded in most attribution tools today, it becomes easy to forget that your input plays a huge part in producing relevant results. Formulating a strategy is essential in being able to derive actionable insights from your attribution. At the end of the day, the relevance of tracking different channels and campaigns in a customer’s conversion journey is incumbent upon you.

Get organised! Start by cataloguing relevant channels to track as per your conversion goals. Label your channels and campaigns and assign budgets so that all your data across all your tools is coherent. Tracking irrelevant channels (or not tracking relevant ones) is a part of trial and error, but reliance on such incomplete data is a big red flag. One common example of this is: tracking only the performance of ad campaigns without testing its performance relative to other channels.  

Communicating with the appropriate personnel and others involved in the strategy to gain better insight on what to track and what not to is a good start.

2. Excessive reliance on preliminary revenue attribution models

The tendency to rely on preliminary attribution models — single-touch models like first and last touch or the popular last-click model — may produce quick and simple results to measure your ROI. This, however, can be an expensive mistake. Don’t get it twisted, single-touch models have their use cases — attributing PPC and short sales cycles to name a couple. But relying solely on preliminary models for all your marketing decisions will likely do more harm than good. Single-touch models are linear in nature, which is not conducive to most customer behaviour. Attribution is more effective when you strive to get as close as possible to analysing a customer’s journey across several touch-points. And having one touchpoint attributed to a customer’s conversion gives a vague, and often inaccurate, image of their journey.

3. Not testing multiple attribution models

This mistake is likely to be a consequence of the previous point — excessive reliance on preliminary models. But why is it important to test other models? When it comes to rule-based attribution and multi-touch attribution models, the general reasoning behind adopting a model is the nature of the product, the number of marketing channels, the length of the sales cycle, etc. While there’s nothing explicitly wrong with this, we cannot only rely on those factors.

There are several omitted variables around the intent of your attribution — measuring the functionality of different campaigns in conjunction with other channels, the relative probability of channel interaction, opportunity cost of campaigns, or just simply mapping out the most influential channel and ROI. Even the type of campaigns and the medium through which the customer interaction occurs could affect your decision in choosing a model. Some models are more applicable than others in producing reliable results, and the only way we’ll identify this is by testing out what works and what doesn’t.

4. Not understanding the limits of rule-based modelling

In practice, administering a combination of rule-based attribution and data-driven attribution is an effective way of producing reliable results. That being said, if you’re for the most part dependent on rule-based modelling, you’re unlikely to have transparent results. Rule-based modelling is limited, as the weights in the models would need to perfectly represent the influence of each channel in a customer’s conversion journey. This is highly unlikely as no two customers are the same. For example, a time decay attribution model will assign credits in ascending order regardless of the type of campaign or prospect’s actual behaviour. So, to help identify your most influential channels on average, data-driven attribution can be used to give credibility to different channels by assessing their KPI’s. This in turn will help you draft a custom model that makes sense to your attributing pattern.

5. Misaligning attribution data and customers/lead quality

In the pursuit of using your attribution data to aid your marketing decision making, sometimes you forget to categorize our data considering the customers involved or their lead quality. To make better sense out of your attribution data, we need to pair the interactions with customer IDs to avoid duplication of leads and accurate credit distribution across marketing channels.

Tracking our customers even helps assess the quality of their leads. What this means is some customers are likely to be more interactive and engaged with your brand than others. This even dictates if some of them become recurring customers or only ever interact with your business once. Tracking customer interactions helps you distinguish the quality of their leads. These values also contribute to calculating the LTV (Lifetime Value) of your customers.

6. Ignoring the bias

These mistakes have to do with certain biases that might compromise your decision-making pertaining to attribution. The most common ones are:

Correlation Bias

When attributing credit to different channels along your customer journey, there is a possibility for certain interactions to conceive other interactions (or at least a level of other interactions). One could over/underestimate the influence of channels with other channels simply because of the natural conversion of targeted customers. A conscious consideration of correlation vs causation must always be kept in mind.

Confirmation Bias

‍A confirmation bias is the proclivity to seek out information, and the interpretation of said information, to favour your results and personal beliefs. This type of bias is prevalent in attribution as it involves having to attribute your channels in accordance with the result that favour you. This would eliminate the organic element of attribution to favour your marketing ideals, ultimately leading to inaccurate findings and conclusions.

7. Failure to understand the channel intent

When you fail to recognize your channel’s intent, you fall short in gauging how much it facilitated a customer’s conversion. This could lead to poor decision making as a consequence. Some channels facilitate interactions with other channels more than they do sales — like a blog versus a targeted email campaign. Hence, it would be unfair to discredit the channels that did not directly contribute to sales — or other predominant goals — but probably contributed significantly to a customer’s decision to convert.

8. Attribution is not the Be-All End-All of your marketing analytics journey

As convenient and resourceful as attribution is, they will never provide a holistic, extensive picture. While attribution is valuable in showcasing a blueprint of your campaigns, channels, and marketing performance. You still require other analytics tools — Funnel analysis, Anomaly detection, SEO optimization, CRM, and other web analytics tools that help assess channels using premeditated metrics. These tools will ultimately compliment your data-driven attribution for a far more comprehensive analysis of your campaign and channel performance. In order to do this effectively, you will have to use these tools cooperatively and in real-time.

Acknowledging these limitations and making a conscious effort to mitigate them will help equip and optimize your marketing attribution journey. Don’t let the idea that there is so much that could go wrong make you apprehensive about trying out marketing attribution to begin with. Undoubtedly, it’s a steep learning curve, but the rewards far outweigh the risk involved.

And there you have it! If you’re interested in understanding how some of the most popular single-touch and multi-touch attribution models work, you might enjoy this blog piece. 

‍Finding the Relevant KPIs for your Business

Identifying KPIs that are relevant to your marketing team depends on your particular type of business. For D2C businesses that sell directly to customers, website traffic and cart abandonment rate are two essential KPIs. The former helps guage how successfully a given marketing campaign is able to encourage customers to click on desired CTAs and advertisements, while the latter helps figure out possible pain points for customers that may be hindering their completion of purchases. If your cart abandonment rate is high, retargeting ads on customers’ social media feeds with their in-cart products can serve as useful reminders to complete a purchase. Alternatively, it can help identify customers’ pain points like contentions with shipping or exchange policies, pricing, etc. Such insights are useful in determining next steps. Similarly, for B2C companies, customer retention rate is an important KPI. Unlike B2B businesses, B2C deals seldom involve long term contracts and a continual inflow of revenue from paying customers. Finally, for B2B companies, a KPI like Customer Acquisition Cost (CAC) is a useful measure of the overall cost involved in onboarding a customer. 

In this article however, we deal with a primary KPI(s) that impacts all businesses: Conversion Rates.

Conversion Rates

Conversion rates may refer to different concepts. It can mean conversions per activity; which measures how many customers perform the desired activity  (clicking on an ad, signing up for a webinar, downloading a free booklet, etc) — all of which can be a part of an overarching campaign or strategy. Conversion per Activity is an important metric in it's own right when it comes to determining what works in your overall strategy. 

While these activity conversions contribute to the ultimate success of the marketing campaign, the actual success is measured by sales conversions — How many people actually converted to paying customers?

Hence, conversion numbers usually fall into two categories:

Category 1: Lead Generation

These include conversions per activity, website traffic, social engagement, etc. Sometimes these indicators receive a bad rap for being some what superficial. However, they have their own value to marketers in understanding the overall efficacy of a strategy. 

For example, Website traffic may not directly measure the impact of a strategy in acquiring new customers, but it can help determine impact of a strategy on brand awareness. This can be particularly useful when there is a strong correlation between awareness and sales. If 20% of your website traffic has converted to paying customers, improving the website traffic may have a positive impact on the final conversion numbers. Alternatively, if boosting website traffic does not seem to have any positive impact on sales, it can be a sign of potential customer pain points or inefficiencies in the overall marketing strategy. 

Category 2: Sales Conversions

These are conversion metrics that measure for concrete, direct impacts on revenue. Here are three influential metrics to keep an eye out for:

I. Campaign Conversions or Conversions per Campaign:

‍This determines what percentage of traffic to a certain campaign landing page/webinar/new subscribers to a newsletter — turned into a customer. 

How to measure: To find the campaign conversion rate, divide the traffic by the customers attributed to that traffic. For example, out of a 100 attendees to a webinar, 7 convert to paying customers, the conversion rate is 7%. Or if your ad had 200 interactions that can be tracked to 15 conversions, then you divide 15/200 to find the conversion rate of 7.5%.

Having a proper attribution model or platform in place is key to finding accuracy in such conversion numbers.

II. Website Conversion Rates:

It is safe to say that almost all B2B or D2C companies have websites which are their primary point of contact with potential and returning customers. So, the conversions from the website becomes an ultra important KPI. Although this indicator is calculated pretty much the same way as the campaign conversion ratio, it can get tricky as the customer journey gets complicated. There might be other touch points that impact the customer’s conversion decision even before they visit the website. Again, having a good attribution system is key to understanding the true impact of website traffic on conversions. It can help understand customer journeys and isolate the impact of the website on conversions. More importantly, it can help identify what works for the website and what doesn't. Insights like what pages converted users visited, how long they spent on those pages, what CTAs they acted on, etc can help figure out possible pain points and improve website conversions.

One thing to remember is that regardless of how customers make their way to the website and when they made the decision to buy, a website has important consequences for the conversion. In the digital age, a business’ website is essentially its storefront. It influences the customer’s perceptions and opinions of the business. In other words, it plays an important role in the customer journey. As such, the website conversion numbers are all too important to ignore for online businesses. 

How to measure: The most common and direct way of measuring the website conversion rate is to divide the number of conversions in a given timeframe by the total number of people who visited the website in that timeframe. For example, if in the past week, a site had 100 visitors, and 10 visitors converted to customers, the website conversion rate is 10%.

III. Lead-to-Close Conversion Ratio:

The Lead-to-Close Conversion Ratio, more popularly known as CVR, measures the number of sales that were made in comparison to the total number of leads the marketing team started with. This indicator helps marketers focus not only on creating leads but also on actually closing them. In other words, it helps create quality leads who will actually make the purchase. The effectiveness of the various components of the marketing strategy can be measured with the CVR. It gives the all important insight of which campaigns convert leads to customers and which do not.

How to measure: Similar to the aforementioned, the CVR is calculated by dividing the number of sales by the number of leads generated. For example, if you started out with 1000 leads from webinar attendees or newsletter sign ups or holiday ad campaigns and 170 of them convert to paying customers then you have a CVR of 17%.p

What’s New?

Over the course of the last month, Google has announced new updates with regard to Google Ads and its API. While there are several new feature announcements, this piece will focus on a few of the overarching updates and changes. These include:

1)     API support for Performance Max and Keyword Planner

2)     Demand forecasts on the insights page

3)     Performance Planner new features

4)     Spend fluctuations with tROAS and maximize conversion value

What Are The Implications? 

API support for Performance Max and Keyword Planner:

Google Ads API as described by Google is the “next generation of AdWords API”. This holds true as Google makes it clear that AdWords API will no longer be available as of April 2022. And as users make the shift to Google Ads API, they’ll find that the keyword planner can now be integrated with API support, which enables more flexibility with data and aids in keyword research. The API features the option to generate keyword ideas, historical metrics — such as average monthly searches over the past 12 months, an approximate monthly search volume. Also, marketers will now be able to forecast metrics for existing campaigns — such as impressions, click-through rate, cost, etc. API support for Performance Max campaigns will be available as well. This will expedite the management of your campaign along with making adjustments easier for developers.

Demand Forecasts on the insights page:

Google Ads now feature demand forecasts on the insights page. The demand forecasts will predict upcoming trends of search interests for products and services with the help of historical data. The forecast will generate data for up to the next 180 days.

The search trend insight is of two types — an “Account” insight which relates to demand forecasts generated through your existing ads, and a “Suggested” insight, whose demand forecasts inform you on other relevant opportunities. These forecasts will enable you to better understand the factors that promote the demand behind major trends.

Performance Planner new features:

The performance planner tool has a few notable updates mainly around efficiency optimization. Starting with a “Suggested Changes” column which offers bid suggestions and budget recommendations. Along with the option to add columns to a performance plan which will display secondary metrics which help reveal insights outside your key metrics. Users now can choose a specific time range for their historical conversion rate with the aim of using a historical conversion rate that has a similar time or date range that’s in line with their plan. Ineligible campaigns — which are campaigns that were deleted, drafted or had been changed to become eligible but have not been running for more than ten days — can now be added to your plan by using its past performance or manually adding in its forecasts so as to plan with your entire account.

Spend fluctuations with T-ROAS and maximize conversion value:

On the 2nd of December 2021, Google Ads users that were using the tROAS — target return on ad spend — and the maximize conversion value bid strategies experienced “spending fluctuations”, which were reported to Google Ads and later confirmed by a Google spokesperson. Google’s support agent reported that “the issue has been identified by the team and is resolved.” Google Ads liaison Ginny Marvin confirmed that affected users will receive credits for the spending fluctuations.

If you find ROI measurement of your content marketing efforts a challenge, you’re not alone. Only 8% of B2B marketers believe they are successful at gauging their content's ROI and influence on revenue.  With the content marketing industry constantly growing, making up between 25%-40% of B2B marketing budgets, it only seems fair to understand its metrics and incorporate ROI measurement into your content marketing strategy.

Ends That Justify Your Means — Why Do You Need To Measure Your Content Performance?

If It Won’t Convert, It Won’t Matter:

Content marketing has contributed substantially to the B2B marketing sphere. Blog posts, podcasts, infographics, etc. all play a major role in a business’s marketing efforts.  But there’s a fine line between good content and content that promotes lead generation. The end goal of content marketing is generating traffic and influencing the conversion of said traffic. So, a conscious effort to measure your content helps lay the groundwork for a content marketing strategy that prioritises the goal and justifies the cost of doing so.

If It Does Convert, By How Much?

‍When it comes to B2B marketing, your prime audience is pretty specific. Hence, your content is likely to have a larger impact on pipeline and revenue. 71% of B2B customers consume a blog before making a purchase. Quantifying information like this is effective in distinguishing your leads from your sales. The difference and variety of metrics available for your content provide valuable insights. Understanding the extent to which each metric attributed your leads is an essential aspect of painting a clear picture of your ROI. A classic example of this is to resort to vanity metrics such as organic search traffic to evaluate your content’s success rather than its bounce rate or impressions made which are more conducive in assessing an MQL — marketing qualified lead.

What You Could Expect for The Future:

‍Trial and error is an expected component of your content marketing track record. The data you amass by monitoring your metrics will prove to be insightful in the formation of your content marketing strategy and budget — including the provision of answers to common questions like “what type of content generates the most traffic?” “Which content influences the most revenue and pipeline?” and “Which content had the most effective link building and/or SEO rankings?”

Understanding The Metrics 

Historical Data and Monitoring:

‍A common barrier to entry for content marketing ROI is your access to customer historical data. To elaborate, your access to said data also includes the cost of acquiring it, the risk associated with losing it, and the availability of precise data when needed — relating to interactions with content. Most software available to track customer metrics like the touch attribution of content, the number of contacts from email, the revenue generated per customer, etc., are fragmented across different software with limited storage of customer data and are behind a paywall. There is even the risk of losing this data because of these stipulations. For this, it is recommended that businesses house their customer data using a data warehouse to retain the historical data of their customers and to use a customer data platform that will organise customer data and behaviour across various software in real-time into a comprehensive format suited for content ROI.

Lead Conversion:

‍The first step in measuring your content’s ROI is to establish what your lead conversion is. Or in other words, identify what customer action is considered a worthy result of your content’s purpose. This would vary depending on the product and what business it is being targeted to — so organising your leads or conversion goals in conjunction with your products is crucial. Some examples of conversion goals would be — signing into your website, downloading a demo, subscribing to a newsletter, or even a sale, etc.

Lead conversion rate: The number of leads relative to the number of visitors on a webpage. Divide the total number of leads by the total number of visitors.

Landing Page:

‍Your landing page is the first page of your website which is visited by a prospective customer. There are certain metrics that can be used to assess the attribution of your landing page to your conversion goal. Your landing page’s page views indicate the number of visits that have occurred on your landing page. The number of unique visitors helps you identify the number of people visiting your landing page, this is different from page views as it only counts the number of visitors and not the number of their visits.

Other useful metrics for evaluating attribution in your landing page include your bounce rate — which is the number of visitors that navigated out of your page after viewing only one page. Your average session duration is the average time lapsed during a session — a session being a user’s regular interactions — on your landing page. These metrics illustrate the authenticity of your content’s applicability for conversions.

Email Traffic:

81% of B2B marketers utilize email newsletters as a part of their content marketing strategy — making it the third most popular form of B2B content. If your business sends out newsletters, these metrics are important to track: An email’s open rate measures the percentage of emails opened, and if you link your content webpage in your email, a click-through rate distinguishes the number of users who’ve clicked on the aforementioned link and those who did not.

Social Media Traffic:

The most popular form of content (95%!) implemented in a content marketing strategy by B2B marketers is organic social media posts. On channels such as Twitter, LinkedIn, Instagram, and YouTube, Audience engagement on your posts in the form of Likes, Shares, Comments, and even Follows are useful metrics to assess the influence and engagement of your posts. Of course, click-through rate may be tracked as well.

The Nitty-Gritty — Measuring Your Content’s Influence and ROI:

Once we have gathered all the relevant data, we can now measure our content’s ROI. But before doing so, we need to assign a monetary value to your MQLs. If your conversion goal is a sale, then it is the revenue generated from that customer’s sale. If it is a campaign goal like demo scheduled, it is the forecasted revenue from prospective customers that’s most relevant.

Once this is established, organise this data in a coherent manner to measure its ROI. Start by isolating landing pages or content pages to measure them individually. Then we will allocate their respective data to them. For the sake of comparison and future content marketing strategy, it is imperative to distinguish your MQLs from your visitors. The last step is to assign your revenue to your MQLs, whether it be the revenue generated from sales or the forecasted revenue of a particular lead or conversion goal. And finally, we can calculate the ROI with our MQL revenue — the ROI calculation here would be the revenue generated from the MQL divided by the cost of production of the landing page’s content.

To illustrate — let’s say that you were measuring the ROI of one of your landing pages at the end of the month. Perhaps a blog in your payment gateway service company. Organically your blog has amassed 500 unique visitors, and around 300 through social media posts and email campaigns. Out of the 800 visitors, 60 of them signed up for a demo, whose forecasted revenue amounted to around $5000. Using the formula mentioned above and dividing the $5000 with the cost of the production of the content, you will measure your B2B content’s ROI.

Evidently, measuring the ROI of your B2B content is a tough nut to crack, and as I mentioned earlier, trial and error is an expected component of your content marketing track record. While quantifying your means will expedite your strategy, functional results take time and mistakes, and if you’re patient enough, they’ll yield.