Spandan Pal

The Ultimate Guide to Product-Led Growth: A Framework for Growth that Sticks

September 17, 2024

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0 min read

What is a product-led growth strategy?

Product-led growth (PLG) is a business strategy and technique that sets the product as the major engine of customer acquisition, activation, satisfaction, retention, and scalable expansion. Customers remain loyal to a brand that offers an innovative and personalized product experience. It most likely happens when all of a company's digital-facing teams—marketing, product, customer success, and more—unite around product-led growth.

Due to Product Led Growth’s pivotal position in some of the most successful SaaS start-ups in recent times (Notion, Dropbox, Canva, Figma, Slack and Dropbox), PLG is being increasingly considered by high-growth teams as a winning growth strategy.

A closer examination of PLG, reveals a fundamentally new approach to product development. One that makes the product the star of the show throughout the whole user experience. Salespeople, marketers, and engineers all play supporting roles in a successful assisted sale, but the product is the star of the show. User-driven virality, activation and engagement journeys, and the first steps toward an assisted sale are all examples of this.

Why is product-led growth the future of SaaS?

In contrast to sales-led organizations whose sole objective is to move a buyer from A to B in a sales cycle, product-led companies invert the typical sales paradigm. Product-driven businesses make this possible by providing the consumer with the "keys" to the product and assisting them in experiencing a meaningful consequence while using the product. At this point, upgrading to a premium subscription is an obvious choice.

On the surface, product-led development may appear to be a straightforward model for your buyer to test before purchasing. However, upon closer inspection, product-led growth is an entirely novel strategy for expanding a SaaS business.

Product-led growth entails that every team in your organization influences the product. Your marketing team will inquire, "How can we develop a demand flywheel for our product?" Your sales staff will inquire, "How can we use this product to qualify leads?" Your customer success team asks, "How can we design a product that facilitates customer success beyond our wildest dreams?" By focusing every team on the product, you establish a culture based on enduring customer value."

By focusing on the product throughout the organization, product-led businesses frequently gain:

By letting your prospects onboard themselves, you can drastically minimize the time-to-value and sales cycle of your prospects.
Reduce Customer Acquisition Costs (CAC) by allowing users to upgrade independently.
Higher Revenue Per Employee (RPE): Less hand-holding results in increased profit margins per customer.

Product-led growth isn't just about disrupting "how" SaaS businesses sell; it's your only means of survival.

Key product-led growth metrics

It's critical that everyone in your team understands and adheres to the same set of performance goals. While there are new measures to rally behind in a product-led growth model, many on this list may sound familiar — many SaaS organizations already do prefer to track product-led growth metrics.

1. Activation Rate: The percentage of users that have found meaningful value in a product.

2. Customer Lifetime Value (CLV): An estimate of how much income a single customer will bring in for your company over the course of your engagement.

3. Acquisition: The number of users who have signed up for your free trial/tier.

4. Product Qualified Lead (PQL): A product qualified lead (PQL) is a lead who has derived significant value from your product via a free trial or freemium model. These customers are primed for an upsell.

5. Net Revenue Churn: This metric, which is commonly represented as a percentage, reflects the amount of money lost after accounting for new and expansion revenue.

6. Average Revenue Per User (ARPU): This metric is excellent for gauging the general health of your company. To determine this, divide the monthly recurring revenue by the total number of clients.

7. TTV: The amount of time it takes new users to reach their Activation moment is known as the Time-to-Value (TTV). The more quickly you can activate users, the more likely it is that they will remain active. The key objective of a successful onboarding process is to minimize TTV.

8. Expansion Revenue: One of the most crucial levers for SaaS growth, expansion revenue serves as an anti-churn strategy. Revenue from existing customers that is created through upsells, add-ons, cross-sells, and other strategies is measured by expansion revenue.

9. Free-to-Paid Conversion Rate: The percentage of trial users that have upgraded to a paid membership.

Is your product fit for product led growth?

If your organization possesses the following traits, you might want to employ a product-led growth approach to increase product adoption: In fact, a PLG approach can influence your marketing, product optimization, pricing, and sales strategies.

The product market conditions are favorable for the proposed strategy to be implemented successfully.
The user views the product as a "highest-value-product" that they wish to use on a frequent basis, which increases its perceived value.
The user is able to quickly and easily achieve large ongoing benefit with little to no assistance from corporate staff.
Rather than setting the price, "paywalls" in a product, track how much value the user is receiving and adjust their pricing accordingly.
Market, sell, and onboard new consumers are all made possible by the product.
The goal of marketing is to get people to interact with the product rather than the sales team.
A network effect is already incorporated into the product.
Companies that use the product have a vocal proponent who works tirelessly to spread the word about how fantastic it is.

Modelling growth around a product-driven approach

You can escape the onslaught of increasing client acquisition expenses and declining customer willingness to pay for your product by focusing on product-led growth. You must do the following actions in order to create a successful product-led business:

Learn to articulate the benefits that working with you can offer to your clients.
Make sure you express this value to your customer in a way that is pertinent to them and the situation they are in.
Ensure that you deliver on that value.

How does the Product-led growth model fare in comparison to other models?

Growth models commonly used by companies include one or a combination of the following:

Sales-led
Marketing-led
Product-led

Each of the three models has a different internal alignment. Companies that are mostly sales-driven are designed to help the sales team succeed as the company's principal revenue generator. We ask, "Will this help the sales team win more deals?" while making decisions about anything from employee training to software purchases to marketing strategies.

The primary goal of marketing-driven businesses is to persuade customers of the worth of a product and to meet their demands. A marketing team is supported by the firm in its attempts to attract customers and generate leads. After conducting customer research and advertising campaigns, the marketing staff has a key role in bringing new customers into the fold. Marketing initiatives can make all the difference in pipeline and growth in a highly competitive market where items are tough to distinguish.

Prospects must be told the value of a product explicitly under each of these approaches. Product-led businesses, on the other hand, place an emphasis on showcasing the true worth of their offerings. For example, product-driven organizations may encourage prospects to engage with the product immediately—in the form of a free trial or "freemium" subscription—instead of investing extensively in outbound sales. Allowing potential customers to test out a high-quality product before they buy it enables them to experience its advantages first-hand. It is unnecessary to persuade a buyer about the genuine value of a product created with this mindset.

Product-led businesses prioritize minimizing customer acquisition costs (CAC) to the greatest extent possible. Recruiting and training a large sales force is costly. Both traditional and digital marketing tactics that are of high-quality demand substantial and consistent financial commitments. However, becoming product-driven does not entail abandoning all traditional sales and marketing tactics. It is more important to be strategic with your investments and vision.

You still require a sales team to assist with selling and a marketing team to generate demand. But product-driven expansion increases the effectiveness of both sales-driven and marketing-driven activities. Ultimately, it is much simpler to sell and promote a product that customers adore than to sell and market an average product.

How does your business benefit from a Product-led growth model?

Are you unsure if product-led growth is the right path for your company? There are some definite advantages to product-led growth, even if it isn't ideal in every situation.

Accelerated growth - Rapid acceptance and expansion are the primary motivations for using product-led growth in business. To put it another way, you're getting to the point and decreasing the barriers to entry by allowing users to access the product for no charge.

Scalability - Slack and Shopify are two of the most successful startups in the world, but scaling up may be a challenge. If you're looking for rapid expansion, product-driven growth is often the best option. This is because product-driven companies can focus their resources on onboarding and serving customers while their competitors are focusing on developing their sales organizations.

Reduced Cost of Acquisition - Using a product-led growth approach significantly decreases a company's marketing expense because customer acquisition channels are already embedded into the product. Overall acquisition expenses are reduced as a result.

Better Rates of Retention - Product-led growth ensures that user expectations are aligned with the capabilities of your product, ensuring that your product is a success. A better fit for the user is created, which leads to increased user retention over time.

Customer Satisfaction - Growing a business with a product is mostly dependent on word-of-mouth advertising. It's not your company's job to persuade customers to buy your goods. It's your customers, not you, who drive adoption.

Customers are likely to have great things to say about this product because of its emphasis on providing real value. Other customers read these ratings and thoughts, which starts a virtuous circle of purchases. Product-led growth enterprises benefit from the fact that many consumers prefer to investigate things they've heard about online rather than in person.

Transition to product-led: What does it mean to be product led

Not only is the price model changing, but so are many other aspects of the firm as a whole.

Customers can test a product before purchasing it thanks to free trials offered by many companies.

As a result, product onboarding experiences are being improved so that clients may begin experiencing the product's value much sooner.

Product firms devote a lot of time and attention to actually distinguishing themselves from their competitors by uncovering fresh client problems and value to offer.

To deliver even greater value to a more narrowly focused group of clients, businesses are increasingly focusing on a smaller set of distinct demographics and the difficulties they face.

The distinctive value proposition is the focus of the messaging developed by the marketing and sales teams.

Optimizing pricing models around customer experience and providing more options, such as subscription models, is becoming more commonplace.

The term "value" appears in all of them. Customers churn and move on to the next product if they don't feel their demands are addressed and the product doesn't deliver value.

In order to become product-led, the first step is to discover what customers value the most about their products. What are the most common issues customers have with the product? What features and functionalities are absolutely essential to your clients in order to keep them engaged over the long term?

Companies can use this information to improve their product offering, onboarding experience, marketing and sales message, pricing structures, and trial choices so that customers can experience the product's unique value proposition at every stage of their customer journey.

Looking forward…

When brands correctly use product-led growth frameworks, they acquire an unfair competitive edge and experience substantially lower Customer Acquisition Costs (CAC). A good framework will also help reduce friction in the user onboarding process, which might impede the growth of your product.

But beyond the framework, product-led growth is really about knowing your customers' demands and identifying market gaps.

The Ultimate Guide to Advanced Marketing Analytics Techniques

September 17, 2024

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0 min read

This article on advanced marketing analytics covers…

What is advanced marketing analytics?
Limitations with marketing analytics
Normalization of data prior to marketing analytics
An overview of advanced marketing analytics techniques
How to implement the advanced marketing analytics techniques?
How Factors can help your business?
Frequently Asked Questions

The impact of advanced marketing analytics has consistently expanded in B2B marketing. Making sense of relevant marketing data using analytics has always been of interest to marketers, but the ability to connect large data-set across various channels and programs has taken data-driven decision making to the next level.

But how exactly can marketers go about making the most of their data? And once techniques and strategies for advanced marketing analytics have been identified, how can one ensure a smooth, frictionless implementation.

Most discussions of cutting-edge marketing and sales analytics tend to get bogged down in jargon. In this article, we will discuss the most effective methods of marketing analytics in simple terms. In order to provide a sense of how these methods operate in practice, we’ll also share a few concrete examples. Then, we'll highlight how advanced marketing analytics may be best used by data-driven marketers.

What is advanced marketing analytics?

Advanced marketing analytics is an all-encompassing term. It refers to a variety of advanced techniques and tools that teams employ to extract additional value from their data. By utilizing advanced marketing analytics, you’ll be able to predict patterns and generate accurate behavioral forecasts of your target audience and customers.

In short, advanced marketing analytics increases the value of campaigns for marketers, optimize ROI, and scale growth and pipeline.

Various types of analytics constitute advanced marketing analytics:

Regression Analytics: Regression Analytics examines the links between a dependent variable and an independent variable. This is an excellent technique for identifying trends in data, as the associations discovered in your sample will also exist in the larger population.

Predictive Analytics: Predictive analytics is a crucial component of advanced analytics, as it enables the discovery of solutions to unanswered questions. This sort of analysis employs numerous techniques from other data processes (such as data mining, AI, machine learning, and modeling) to do a comprehensive study of available data in order to make a future forecast.

Prescriptive Analytics: Business analytics culminates in prescriptive analytics. This is the method of finding the greatest potential outcome by employing technology to examine raw data and make judgments based on existing descriptive and predictive analytics.

A few limitations with basic B2B marketing analytics

In some instances, analytics may misinterpret data, leading to ill-informed decisions. This section discusses the limitations that marketing analytics can present to an organization. A few limitations are listed below:

Misidentification of marketing needs: Basic marketing analytics methods can sometimes overestimate or underestimate the market’s needs and behaviors: what do customers want, how well are competitors performing, what messaging resonates most with the audience. This, in turn, may mislead your team into making suboptimal decisions around marketing strategy.

Evaluating marketing growth in the absence of a market share: Analyzing the market should provide an idea of your potential opportunities. However, this analysis may not always be comprehensive, leading to missed opportunities that may have otherwise seemed obvious. So, in order to be sure of your data, a comprehensive market share analysis is suggested to provide sufficient context.

Improper Data Interpretation: Collecting data from multiple sources aids in data analysis, but data interpretation is an entirely separate process. If anything, interpreting data requires significantly more effort — and failing to allocate adequate resources towards this step will likely result in inaccurate conclusions.

This is where advanced marketing analytics comes into play. By utilizing advanced methods, B2B teams can help themselves achieve improved operational efficiency, increased customer satisfaction, scalable revenue, and optimized ROI.

Normalizing data prior to analysis

Before engaging in advanced analytics, the wisest investment is to thoroughly prepare the foundations of your data. Ensure that fundamental reporting requirements are met through a robust automated data and reporting pipeline, which will liberate resources, eliminate human error, and enhance data quality.

The quantity & variety of data also have an important influence. The majority of advanced analytics approaches perform substantially better with bigger volumes of granular data acquired from several sources. Remember that the results of your deployments of advanced analytics will only be as good as the data you supply.

An overview of advanced marketing analytics techniques

#1 Customer Lifetime Value

It can be an expensive error to direct marketing efforts towards the incorrect audience. Using the aforementioned conversion prediction methods, marketers can generate a list of people that are likely to convert into customers, but how do you identify the most valuable leads out of this set?

Despite their initial satisfaction, a huge number of customers never come back to a business again. A possible churn awaits those who do so. Only a small percentage of customers will remain loyal over the long term, and even fewer will go on to become true brand advocates. This underrepresented group is the most important. Especially in B2B SaaS, the Pareto principle (80/20 rule) almost always holds true: 20% of consumers generate 80% of value.

Customer lifetime value (CLV) estimates a client's future earnings using a sample of their past purchases. With this information in hand, marketers may cut costs on clients who aren't lucrative, strengthen their focus on channels that bring in similar, paying consumers, and work to reawaken the interest of previously inactive customers.

How can Factors help with CLV?

Using Timelines, Factors can generate user and account level timelines of the entire customer journey from first touch to deal won. This is especially useful to B2B SaaS organizations wherein sales cycles involve several stakeholders from the same account. The ability to visualize every touch-point based on account, roles, etc offers granular insight into what’s working for whom and in which account.

#2 Marketing Attribution

How efficient are your methods for analyzing the efficacy of marketing campaigns? How much should you be investing in each advertisement? What channels should you cut to drive ROI? These are fundamental inquiries that any successful marketing strategy must address. Due to the increasingly complex, nonlinear nature of customer journeys, marketing attribution is increasingly gaining relevance.

There are a number of ways to implement a marketing attribution strategy, and some of them entail very basic business standards, like giving all of the credit for a conversion to the very first or very last click. There are other others, though, that demand more intricate mathematical and probabilistic methods. Multi-channel attribution makes sense for firms that operate in a digital environment and analyze engagement metrics like clicks, conversions, and click pathways. Marketing Mix Modeling is a supplementary method utilized by businesses that employ conventional marketing channels (MMM). It utilizes what-if scenarios and is based on regression analysis, a well-studied statistical method. What would happen, for instance, to income if spending on television were to rise by x percent? If you already know the answers to these questions, you can use them to better allocate resources for future initiatives.

Marketing Attribution on Factors

As previously mentioned, marketing attribution is the analytical science of determining which marketing tactics are contributing to sales or conversions. Attribution models give marketers insights into how marketing dollars are best spent by showing touch points that earn the most engagements. Factors delivers a robust marketing attribution suite with a wide range of multi-touch attribution models. Want to learn more? Find a good time here.

#3 Clustering

Clustering is a valuable tool for B2B marketers. The goal is help marketers divide their target audiences into manageable subsets. This allows for better targeted content, campaigns, and offers. It is possible to generate several types or clusters of customers using heuristic rules: "Show content of type A if the customer is a millennial; show content of type B if they are gen Z."

With today’s abundence of and accessibility to large volumes of data, clustering has evolved into an effective technique to categorize a large number of clients based on any number of features or properties. These clusters emerge as a result of a statistical analysis of data using a measure of the mathematical distancing between various attributes. Customers with comparable ratings will be placed together.

Age, income, spend, duration of time since last purchase, etc. are all examples of features that can be used to segment customers. Similar success can be achieved when clustering keywords according to metrics such as organic ranking, competition level, and opportunity score. Product ads, marketing campaigns, advertisement groups, and so on can also be grouped together.

#4 Conversion prediction

Conversion prediction is not a straightforward operation because conversion rates are often in the single digits. It’s comparable to looking for a needle in a haystack. To increase your chances of success, you'll need a wealth of user behavior data from the past. Early behaviors connected to future conversion milestones can be determined based on this historical data.

Once you have identified people whose actions indicate a high possibility of conversion, you may prioritize and target them appropriately. In addition, this method is effective in identifying factors with the greatest impact on conversion. Depending on the site and its users, it may include a combination of industry, role, location, device kind, or any other combination of relevant dimensions.

How does Factors help with conversion prediction and optimization?

As the name suggests, Factors.ai is built upon an AI-powered analytics engine. Our proprietary ML algorithms empower markets to generate actionable insights from their data in a matter of seconds using AI-fueled Explain and Weekly insights. Curious to see our work in action? Find a good time here!

#5 Anomaly detection

The B2B SaaS marketing industry is rapidly evolving into a data-driven operation that involves near-real time iterations to campaigns and strategies. Naturally, this relies on a large, up-to-date volumes of data. Ad groups and keywords in display and search campaigns, for example, can be rather large and take part in hundreds of daily programmatic auctions; they also have their own unique conversion rates, budgets, returns on investment, and so on. That is,a vast variety of figures and metrics that constantly fluctuate.

Generally, the fluctuations stay within the range of naturally occurring variances, but exceptions may be found. As a marketer, one must stay vigilant so as to quickly respond with appropriate actions and reactions.

Anomaly detection makes use of statistical analysis and automated decision making to notify marketers when critical KPIs such as conversion rate, revenue, and traffic depart too greatly from the norm. This method treats data as a statistical time series, allowing it to automatically detect seasonal and weekly patterns while simultaneously avoiding false positives. This allows for quick identification of data outliers and under or over performing efforts.

How does Factors help with Anomaly detection?

Factors provides robust, customizable KPI reporting functionality. Set your parameters and sit back as Factors automatically alerts you through Slack and Email notifications when your KPIs extend past your preconfigured range. Furthermore, unlock insights into which marketing efforts are performing, and which ones aren't. Attribution and Explain on Factors may also be used to detect the anomalies across keywords, firmographics, channels, campaigns and more

#6 Forecasting

Forecasting is everywhere: financial markets, economic indices, corporate sales, etc. So, it's no surprise that this technique can also forecast online traffic, conversion, revenue, and other metrics marketers care about. Similar to anomaly detection, forecasting uses historical data to predict trends. This, however, is not always possible and, as a result, forecasts will likely be inaccurate — at least to a certain degree.

In order to make interpretation of predicted results more flexible, forecasting techniques provide bands within which forecasted data can range with given probabilities. If uncertainty is properly accounted for, forecasting can be used as a technique to help you better adjust your future campaigns and targets.

How to implement advanced marketing analytics techniques?

Marketing teams can benefit from advanced marketing analytics throughout the entire marketing process. Advanced marketing analytics helps firms automate and optimize their marketing efforts one step past conventional marketing analytics.

Gathering data from a larger variety of sources, not simply social media outlets, is an important part of deploying this type of analytics. More diverse data sets require more precise analysis if you want to get the most out of your data and make better decisions. There isn't a single business that has access to every piece of information it would need to make well-informed choices. Instead, firms should broaden their data collection to gain a deeper understanding of their field as a whole.

Try to supplement your data with that of larger external data providers. As a result, you can more precisely grow your business and build more insightful models. You should also see data with a more prospective eye. It's conceivable that marketing and analytics models used in the past won't be useful for planning future efforts. Finding new connections between online and offline market aspects and impacts is more important than using past data. An analytics model that aids in taking more consequential choices requires a more in-depth examination of client behavior.

Top-down data analysis is also crucial. By looking at the market as a whole, we may more easily identify a broader range of decision points from which to draw more predictive data. To get the most out of advanced analytics, granularity is essential.

The usefulness of data models can be increased by including the knowledge and experience of more people. The teams responsible for creating data models should include a wide variety of specialists. Data models that are both accurate and useful in practice benefit from the input of experts from a variety of disciplines.

Large amounts of different data are ideal for advanced marketing analytics strategies. This is why before implementing a new analytics model, a company should thoroughly cleanse its historical data and set up its infrastructure.

How Factors can help your business?

Automation of ordinary data processing and integration of contemporary software solutions into your workflow are always prerequisites for advanced marketing analytics. Manual data manipulation requires far too much time on mechanical activities rather than focusing on the analysis itself. Furthermore, manual processing exposes the granularity of your findings to human error.

Factors assists businesses in automating marketing data processing and gathering advanced marketing analytics insights without the need for repetitive activities. The tool unifies data from a huge number of marketing data sources, standardizes insights, and loads them into a single warehouse. Marketers can then quickly transfer data to Factors’ dedicated dashboards to create sophisticated charts and graphs. With numerous pre-designed and customizable dashboard templates at your disposal, you will gain a fresh perspective on your marketing activities and efficiently optimize your marketing ROI.

FAQs

What is advanced marketing analytics?

Advanced marketing analytics refers to a detailed examination of various marketing data utilized to show new customer behavior patterns, market trends, campaign performance concerns, and other significant insights.

Why is advanced marketing analytics important?

A company can find new markets for their products, expand their customer base, and increase revenue with the help of advanced sales and marketing analytics. Monitoring the success of marketing campaigns is essential for their continued success. Using advanced marketing analytics methods will help you fine-tune your campaigns and spend where it counts.

How does data analytics help in marketing?

With the help of data analytics, marketers can glean actionable insights into their data's performance. This data is useful for determining the channels used by customers and prospects, as well as the most profitable campaigns.

How Does Advanced Marketing Analytics Help CMOs?

The use of advanced marketing analytics allows businesses to make more informed predictions about the future and to spot emerging trends earlier. More reliable insights can be obtained from a larger pool of high-quality data with the help of this type of analytics. A CMO will have access to more actionable data, which should lead to more effective campaigns.

Measure Your Campaign Success with These 9 ABM Metrics

September 17, 2024

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0 min read

From aligning the sales and marketing team to providing personalized campaigns to increasing the likelihood of converting a potential customer, ABM has become a key marketing strategy for B2B marketers. In fact, B2B companies now invest about a third of their marketing budget in ABM.

There is no doubt that ABM has proven to be effective in increasing conversion rates and ROI.

But how do you measure the effectiveness of an ABM campaign? Which metric should be considered for the purpose?

Don’t worry, we are here to help you. Let’s dive into the 9 ABM metrics you should measure to understand the campaign’s performance.

9 ABM Metrics to Measure Campaign Performance

1. Total Addressable Market

TAM (Total Addressable Market) refers to the total revenue opportunity available for a product or service within a specific market.

A common approach for calculating TAM is as follows.

TAM = (Total no. of potential customers) * (Annual contract value )

How to calculate total addressable market?

If a company offers a product that costs $9600 annually and its target customers are all SMBs in the US, which is 10,000, then the TAM will be 96 million dollars per annum.

TAM= 10,000 9600

TAM= 96,000,000

While TAM isn’t directly an ABM metric to track, it provides key insights into the following:

The size of their market opportunity
The potential revenue estimate

2. Pipeline Generated

This refers to the total amount of potential revenue that is currently in the sales pipeline.

By tracking the pipeline generated, teams can learn the following.

How many new opportunities have been created?
How are these opportunities progressing through the pipeline?
How much potential revenue can the business generate?

If you consistently generate more pipelines, it means the ABM campaigns are resonating with your target accounts and driving meaningful businesses.

Keep in mind that this metric may vary over time as opportunities progress through the pipeline. So, it’s important to track it regularly and adjust your ABM campaigns accordingly.

3. Close Rate (Conversion Rate)

Close rate refers to the percentage of target accounts that have moved through the sales funnel and converted into paying customers.

The general formula to calculate a close rate is given below.

Close Rate=[(Total no. of accounts converted)/ (Total no. of target accounts engaged)] *100

So, if a company engages with 100 target accounts in an ABM campaign and only converts 20 of them, then the close rate will be 20%.

Close Rate= 20 100 100

Close Rate= 20%

By tracking the close rate over time, one can identify which aspects of the ABM campaigns are working and which are not. Furthermore, businesses can calculate the close rate at each stage of the sales funnel and identify inefficiencies in the sales process. This can help businesses refine their ABM strategy and maximize results.

Hence, use close rate as a critical ABM metric to measure and improve the ABM campaign’s success. The following are some best practices to optimize close rates and yield better results.

Select accounts that align with your ICP criteria.
Personalize the marketing and sales strategies to provide the target account’s needs and address their pain points.
Align your marketing and Sales team to ensure that the messaging and offers are consistent through the sales funnel.
Develop a multi-channel engagement strategy to maximize the chance of conversions.
Regularly track and analyze the metrics and optimize the ABM campaigns as needed.

4. Pipeline Velocity

Pipeline Velocity refers to the speed with which a lead moves down the sales pipeline.

A lower pipeline velocity would indicate that there is friction in the pipeline. This friction needs to be addressed to avoid the loss of potential customers. You can calculate the pipeline velocity of your business using the following formula.

PV= (S *W *D)/ L

PV - pipeline velocity,
S - number of SQLs in the pipeline

W - win rate (%)

D - average deal size

L - length of the sales cycle.

So, if a company has 60 SQLs in their pipeline, with a win rate of 20% and an average deal size of $10,000, and the length of the sales cycle is 90 days. Then the Pipeline velocity will be $1333 per day.

Pipeline Velocity = 60 20% 10,000 90

Pipeline Velocity = 120,000 90

Pipeline Velocity = $1333.33 per day

To increase the pipeline velocity, focus on the following.

Increase your lead quality and ensure that the visitors fall in your ICP criteria by tracking qualified traffic.
If you are losing customers from the pipeline, determine what prompted it. Accordingly, make necessary changes to ensure they stay put and increase the win rate.
Align the marketing and sales team to make the messaging consistent and relevant for the prospects. Also, make the sales process more streamlined and remove any unnecessary steps. Both these can help improve sales efficiency and subsequently shorten the sales cycle.

5. Churn rate

From a B2B perspective, it is the rate at which a company loses its clients or customers.

It is a crucial ABM metric as it helps businesses understand the health of their customer base and their ability to retain them. A business can calculate the churn rate by dividing the number of customers a company lost during a specific period of time by the total number of customers the business had at the beginning of that period.

So, if a company starts the quarter with 100 customers and loses 20 customers by the end of that quarter, then the churn rate will be 20%.

Churn Rate= 20 100 100

Churn Rate= 20%

A high churn rate is detrimental to a B2B company. It will result in revenue loss and increased expenditure to acquire new customers to replace lost ones. Following are a few ways to reduce the churn rate.

Build strong relationships with the customers
Provide excellent customer service
Offer personalized solutions
And deliver on the promise you advertise

6. Customer Lifetime Value

CLV refers to the total net profit a company can generate from a customer over the entirety of their relationship.

It is an important metric to measure as it helps determine the value of a customer and helps businesses decide on how much they should spend on acquiring new customers or retaining existing ones. The larger the customer lifetime value, the less you need to spend on acquisition costs.

When it comes to Customer Lifetime Value, there is no specific formula to calculate it. But if you consider the definition, “CLV is how much a customer is paying to a company over a period of time", then you can calculate CLV with the following equation.

CLV= Avg. Monthly Recurring Revenue * Avg. time duration a customer stays with a business

How to calculate customer lifetime value?

So if a company’s average MRR (Monthly Recurring Revenue) is $1000 and the average time period a customer chooses to stay with the company is 8 months, then the CLV will be $8000.

CLV= $1000 8

CLV= $8000

Average Customer Lifetime Value per Industry

Source: firstpagesage

7. Customer Acquisition Cost

CAC, or Customer Acquisition Cost, refers to the total cost spent by a company to attract new customers.

The metric is calculated in a set period of time, and the formula for calculating it is as follows.

CAC= (Cost of sales and marketing/ New customers acquired)

How to calculate Customer Acquisition Cost?

So, if a company spends $400K on sales and $300K on marketing and generates about 700 customers by the end of the fiscal year, then CAC will $1K per customer.

CAC= $400K +$300K 700

CAC=$700K 700

CAC= $1K

Compare the Acquisition cost with the Customer Lifetime Value (CLV) to understand the business’s profitability. If the cost of acquiring a customer is higher than the revenue generated from that customer over their lifetime, then the business is likely to lose money. In this case, it’s time to reevaluate the marketing strategies or consider investing in alternative approaches to lower the acquisition cost.

Average Customer Acquisition Cost per Industry

Source: firstpagesage

8. Average Deal Size (ADS)

This is a metric used to measure the average value of each sale made by a company.

By tracking the average deal size, a business can understand how much the customers are willing to pay/invest in their products/services.

ADS is often calculated monthly or on a quarterly basis and can be calculated by dividing the total value of all deals closed by the total number of deals closed during a given period.

ADS= (Total value of the deals won /Total no. of deals won)

So, if a company closes 10 deals in a given month, and the total value of the deals is $200,000, then Average Deal Size is $20,000.

ADS= $200,000 10

ADS= $20,000

9. Length of Sales Cycle

Sales cycle length is the total time a company takes to complete a sale, from the customer’s initial contact with the company to the final closing of the deal.

The sales cycle length differs from industry to industry. For example, according to Klipfolio, the average B2B SaaS sales cycle length is 83 days, whereas, for a B2C company, it will be a week or less.

It is an important metric for businesses as it can impact the revenue, profitability, and overall success of the company. For example, if the length of a sales cycle is higher for a company than its competitors, it indicates that there are inefficiencies in the sales process that need to be addressed.

If you want to calculate the sales cycle length, simply divide the total number of days taken to close each deal by the total deals won.

Sales Cycle Length= (Total no.of days taken to close each deal / Total no. of deals won)

So, if a company closed three deals, each taking 35, 55, and 90 days, then the average sales cycle length will be 60 days.

Sales Cycle Length= 35 +55 +90 3

Sales Cycle Length= 180 3

Sales Cycle Length= 60 days

Measure the Success of Your ABM Campaigns with Factors

Factors, with its ABM analytics feature, enables users to access a range of different tools and techniques for analyzing and presenting the data in a way that is easy to understand and use.

Factors’ deanonymization feature can provide a complete view of your visitors at an account-level and track entire customer journeys.
Its robust CRM integration can empower your marketing team to segment accounts and contacts based on criteria like firmographic, behavior, and engagement. This allows marketing teams to identify high-value accounts and target them with personalized campaigns.
Its customizable dashboard provides visualization of all critical data, data-driven insights, and more - all within a single dashboard. The feature provides a comprehensive view of all accounts, helping marketers to get all the information to make an informed decision on marketing strategies.

An example of how Factors’ dashboard looks

Ready to take your ABM campaigns to the next level? Look no further than Factors, and ensure your efforts pays off. Book a demo to understand how factors can take your ABM campaigns to the next level. Or sign up here to try Factors for free!

What is Last Click Attribution and How Can SaaS Companies Use It?

September 17, 2024

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0 min read

Attribution helps SaaS companies identify which sales and marketing efforts result in a conversion. Doing so allows marketing, sales, lead gen, and other teams to identify the actions that drive conversion and revenue.

Additionally, with the help of attribution, SaaS teams can optimize budget allocation for various channels and campaigns to improve the conversion rate.

In this article, we’ll discuss the following

What is Last Click Attribution
How can SaaS companies use this model?
The difference between Last Click and First Click Attribution models.
The limitations of Last Click Attribution.
How Last Click Attribution works in Factors

What Is Last Click Attribution

Last Click Attribution (LCA) model credits 100% of a conversion to the last touchpoint a buyer interacts with for a conversion event in the buyer’s journey.

Image showing how Last Click Attribution (LCA) works. LCA gives credit to the last touchpoint a customer interacted with before making a sale

Here’s an example to help you understand how LCA works.

Let’s say a CMO is looking for attribution software to help them tie their marketing team's efforts to conversions and revenue generated.

During market research, they come across a LinkedIn advert for Factors.ai and land on the features page and get insights into how the application can provide a solution to their needs.

Later that week the CMO comes across a blog talking about why CMOs should care about B2B Marketing Attribution. Upon reading this article, they finally decide to sign up for a demo.Last Click Attribution

Here the last touchpoint the CMO interacted with is the blog. So the LCA model will give full credit to the blog for the CMO’s conversion (demo sign up).

Last Click vs First Click Attribution: What’s the Difference?

First Click is another simple attribution model that is similar to Last Click. Both of these are single-click attribution models, they attribute conversion to a single touchpoint.

The key difference between the two models is that Last-Click attributes a conversion to the final touchpoint. Whereas First-Click gives credit to the first touchpoint that led to a conversion.

LCA gives credit to the last touchpoint a customer interacted with before making a sale, whereas FCA gives credit to the last touchpoint a customer interacted with.

We will explain how First Click Attribution (FCA) works by using the same CMO’s demo sign-up example.

The differences between the two models are that

LCA attributes the conversion to the last touchpoint before the sale whereas FCA Attributes the conversion to the first touchpoint in the customer's journey.
LCA emphasizes the impact of the last touchpoint before the conversion while FCA measures impact of the first touchpoint in the customer's journey.
Last Click Attribution is easier to implement compared to FCA as it needs sophisticated software for comprehensive tracking and data collection.
Last Click is commonly used in B2C businesses, B2B companies can also use it jointly with other attribution models. First Click on the other hand is commonly used in B2B businesses where the sales cycle is longer and consideration for purchase is high.

LCA and FCA attribute conversions to different touchpoints. LCA is easier to implement compared to FCA. While FCA is commonly used in B2B businesses, LCA is used in B2C and jointly with other models in case of B2B.

How Can SaaS Companies Use This Model?

Last Click Attribution is a cost-effective model that SaaS companies can use to identify and optimize various campaigns and channels driving conversions. LCA is available for free on Google Analytics.

LCA provides an intuitive framework to make sense of the nonlinear and long SaaS sales cycle with quick insight into the final touch-points before conversions.

Last Click Attribution can be used flexibly with any conversion event in the sales funnel, like

Demo form signup
Marketing Qualified Lead (Newsletter sign up)
Sales Qualified Lead
Deals won, and more.

Limitations of Last Click Attribution

The simplicity of the model is what makes Last Click Attribution so attractive, but this simplicity comes at a cost.

The model has some limitations that can impact its accuracy and functionality in certain situations. Here are some of its limitations:

Values few channels highly: LCA will highlight channels such as retargeting ads, direct website visit, etc where the conversion is usually high. Due to this marketing teams usually end up allocating more budget on these channels.
Disregards contribution of other touchpoints: Last Click Attribution doesn’t account for the possible influence that the other touchpoints could have played in the purchase process.
Inaccurate measurement of long-term impact: Not all customers make impulsive buying decisions, at least not in the B2B space. Last Click Attribution does not factor in the long nurturing period in the B2B sales cycle and the various touchpoints that help nurture a prospect.

As the average timeline of the B2B customer journey is increasing, it’s key for marketers nowadays to understand the various factors influencing a prospect's decision.

B2B SaaS companies with long business cycles need to ensure that their efforts are aligned and are contributing to the end goal of converting prospects into paying customers. In this case, the LCA model will give you a skewed perception of the effectiveness of the marketing strategy.

However, these limitations . In the above case, LCA should be used along with other types of attribution models to cover for its shortcomings.

How Last Click Attribution Works in Factors

Last Click Attribution is still used in many B2B SaaS companies where the sales cycle is shorter, and the decision-making process is less complex.

In Factors, you can easily create intuitive Last Click Attribution reports. Additionally the tool presents key metrics such as spend and CPC to help marketers improve budget allocation towards campaigns that work.

Factors.ai Last Click Attribution report showing a break-up of various marketing channels with key metrics such as Clicks, CTR, conversion and Cost per Conversion.

The Cost Per Conversion metric when used along with LCA gives insights into the cost-efficiency of the employed strategy. Marketing teams can use this information to optimize budget allocation for their channels and campaigns to further improve conversions and ROI.

Graphical view of Last Click Attribution report along with Cost per Conversion data, revealing the campaigns that are driving conversions cost-efficiently.

FAQs

1. Are there other attribution models apart from LCA?

While there are several types of attribution models, the six most common ones apart from LCA are:

First-touch Attribution
Last Non-Direct Touch Attribution
Linear Attribution
U-Shaped Attribution
Time Decay Attribution
W-shaped attribution

2. Should I use Last Click Attribution for my business?

The decision depends on the specific needs and goals of your business. Last Click Attribution is a simple model, but it is not the best fit for every company. Consider the limitations of LCA and explore other attribution models and choose the one that aligns with your needs.

7 Best HockeyStack Alternatives: Features, Reviews, and More

September 17, 2024

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0 min read

Investing in marketing campaigns and search engine optimization (SEO) efforts help improve website traffic — but is hardly enough to drive website conversions.

Marketing teams need to analyze campaign performance and direct spending towards touchpoints that drive results in order to optimize ROI. Here's where marketing attribution tools can help. One of the popular attribution tools available in the market is Hockeystack.

HockeyStack is a marketing attribution and analytics tool for B2B businesses. It provides no-code integration with third-party data sources and easy implementation. Some of its key features include funnel analysis, cookie-less tracking, and customer journey mapping.

However, certain limitations of HockeyStack have prompted users to look for alternatives. These include data inaccuracy, the learning curve for first-time users, and the limited number of integrations.

This blog discusses these limitations and lists 7 suitable alternatives to it. We will evaluate each alternative in detail, including the features, customer reviews, integration, and pricing.

Why are marketers looking for HockeyStack alternatives?

No tool is without its limitations. Here are 4 reasons why HockeyStack users consider looking for alternative solutions:

1. HockeyStack’s AI insights lack in-depth information

A lack of in-depth information can limit marketers' understanding of customer behavior. This results in less targeted campaigns leading to lower engagement and reduced conversion.

Users reviews show that HockeyStack’s AI insights are not informative

2. HockeyStack’s integrations are limited

Limited integrations can result in restricting the scope of data collection and analysis. This may lead to incomplete or inaccurate insights into customer behavior, affecting the campaign's effectiveness. Though HockeyStack allows custom-building of these missing integrations, it could take a few days.

HockeyStack has limited integrations, although they build custom integrations for users, it could take quite some time

3. HockeyStack’s issue with data inaccuracies

Data inaccuracies can skew performance analysis, leading to misguided decision-making. It may also result in misallocating resources and ineffective optimization, negatively affecting the overall marketing strategy.

Users reveal that there are data inaccuracies in HockeyStack that result in skewed results leading to misleading information

A few other cons of using HockeyStack are as follows.

Limited functionality.
Lacks some features compared to Google Analytics.
Its Survey feature is an add-on, and you have to pay extra.

All these limitations mentioned above help us understand why marketers are looking for an alternative. Read on to learn about the 7 HockeyStack alternatives.

Top 7 HockeyStack alternatives

1. Factors.ai

The features page of the tool Factors - the best HockeyStack alternatives

Factors is a tool built for B2B marketers to help with revenue attribution and marketing analytics. B2B teams of all sizes can use this tool to accurately attribute conversions to different marketing efforts throughout the buyer's journey.

It enables easy implementation and no-code integration with ad platforms, websites, CRM, and more. The tool also provides a customizable dashboard allowing users to centralize all crucial customer data in one place. This centralized data helps align marketing and sales teams and helps them optimize their efforts for conversion.

It allows marketing ops to monitor and analyze all key performance indicators in one location while ensuring marketing strategies align with established business goals.

Key features

1. Revenue Attribution

With Factors’ multi-touch attribution, marketers can understand each marketing activity's impact on the pipeline. Marketers can compare and choose the ideal attribution model to track and attribute revenue to high-performing channels.

2. Account Identification

Factors.ai's account identification reveals up to 64% of anonymous traffic — including company names, firmographics and website activity. As a result, it helps identify a pool of high-intent accounts and how they engage with your brand. This helps GTM teams target sales-ready leads and iterate website content to improve conversions.

3. ABM Analytics

This feature has a complete suite of intuitive analytics and reporting techniques to help run efficient ABM.

Campaign analytics - Centralizes all data in one place, allowing marketers to track and measure success easily.
Web analytics - Monitor conversions and user behavior and help optimize the website for better engagement.
Funnel analytics - Connects data from campaigns, websites, and CRM and offers a complete view of the customer journey. This also helps identify valuable accounts, allocate resources, and enhance marketing strategies.

4. Journey Analytics

The feature lets marketers gain deeper insight into the customer journey. It helps identify the paths and touchpoints that lead to a conversion. The feature also offers insights into what does and doesn’t help reach a defined goal.

5. Account Timelines

This offers a complete view of the timeline for account and user interactions. The feature also helps identify engaged accounts and helps your go-to-market team gain an advantage.

Integrations

Factors provide no-code integration with the following tools.

Hubspot
Facebook Ads
LinkedIn Ads
Google Ads
Salesforce
Segment
Bing Ads
Rudderstack
Marketo
6Sense
Clearbit
Leadsquared
Drift
Google Search Console
Slack
Google Spreadsheet

Customer Reviews

Pricing

On top of marketing attribution, Factors provide visitor identification and consulting service. The pricing info for each plan is as follows.

Analytics & Attribution

Starter – $399 per month
Growth – $799 per month
Custom and Agency – Contact for a quote

Website Visitor Identification

Starter – $99/month
Professional – $149 per month
Growth – $499 per month
Enterprise – Contact for a quote

Professional Services

Tier 1 – $500 for 10 hrs per month
Tier 2 – $900 for 20 hrs per month
Tier 3 – $1200 for 30hrs per month

2. Dreamdata

Dreamdata is a web analytics and attribution platform

Dreamdata is another web analytics and attribution tool for B2B companies. It analyzes the content and ad spending with respect to revenue and helps optimize them.

The tool automatically collects and connects customer data from different sources, simplifying the data for building efficient GTM strategies.

It can identify anonymous website visitors and provides insights into interactions at each stage of the customer journey.