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In 1908, Henry Ford introduced the Model-T to the world with a full-page advertisement in Life magazine. The print ad read like an article and was chock-full of technical jargon by design. Back then, a marketer’s function was straightforward — inform all potential customers of the existence and superiority of the product. Who you were marketing to wasn’t half as important as what you were marketing. As long as buyers in the market were aware of the Model-T’s vanadium steel chassis and four-cylinder engine, Ford’s marketing team could sleep well at night knowing they had done their jobs.
Of course, the role of the marketer has evolved *a little* since then. At the time, print ads were one of the few viable communication channels available to marketers. There was also a stubborn focus on the product itself — with little thought given to what worked for each customer. Owing to years of progress in marketing technology and a radical shift towards customer centricity, marketers today have a lot more to think about. Recent digital transformations have empowered marketers with dozens of channels: social media, email, blogs, videos, podcasts, websites, etc. In turn, they’re able to reach potential customers with content that’s specifically tailored to them.
On the other side of the equation, digital transformation has also provided customers with far more control. Relevant market information (product details, reviews, alternatives) is instantly accessible to potential buyers. And when your competitors are a single click away from you, there is no room for complacency. As a result, the modern marketer must go above and beyond traditional information distribution. Today, the four staple functions performed by marketers are:
- Delivering predictable pipeline and revenue
- Building the company’s brand
- Developing long-term growth initiatives
- And empowering the sales team
Still, as marketing has evolved in terms of technology and practice, analysing data and deriving insights have grown increasingly complex as well. While marketers are able to design sophisticated multi-channel campaigns, determining the basic metrics — what’s working, what’s not, which campaigns to invest in, etc. — can become tricky. Here’s an example to illustrate this:
Gendesk, a help desk software start-up, takes out advertisements on Youtube and Facebook. Deepti, a customer success VP, stumbles upon the YouTube ad while trying to watch a video of a sleep-talking cat. She takes notice of Gendesk and clicks through to their website. Though she likes what she sees, she forgets to sign up for a demo. Later that week, Deepti comes across the Facebook ad while scrolling through her feed. This time, she ensures to schedule a call and finds the product to be a great fit. After discussing with her team, Deepti decides to make the purchase.
As a marketer, this is great news. But when you’re looking to repeat this process in a scalable manner, a key question to ask yourself is “Which ad do I credit for the purchase decision?” Though there are cases to be made for each ad, the right answer is a subtle combination of both. Identifying this combination of credit, or in other words; determining the values to attribute to the various touch-points along the customer journey is now the holy grail of marketing analytics.
Enter: Marketing Attribution
The previous example was based on a highly simplified customer journey — one customer and two channels. In reality, marketers target several types of customers and employ several different channels to engage with their audience. What’s more is that the buyer’s journey is almost never a linear path. Deepti may well have stumbled upon the youtube ad, visited Gendesk’s website, interacted with their chatbot, reviewed the pricing page, read a blog about the product, and clicked back to the website before coming across the Facebook ad and making his purchase. Marketing attribution is a tremendously powerful system that determines these various touch-points along the customer journey and attributes a percentage value to each one of them.
Okay, but why’s marketing attribution so important anyway?
“The reality is that marketing has become THE most efficient way to accelerate growth in our digital economy. The imperative is to connect the dots, so each marketing expense dollar is aligned and reported against revenue growth.”
- Paul Albright of Captora.
A well-oiled marketing attribution system can result in efficiency gains of up to 30%. At its core, attribution modeling enables marketers to allocate resources in a strategic manner. Marketers can ensure that they’re actively driving conversions by optimizing their spending based on data-driven metrics. Zendesk’s marketing team, for example, can use a variety of attribution models to derive an understanding of what campaigns are working, and what campaigns aren’t. Accordingly, they can make evidence-based decisions on where to invest and what to alter. Ultimately, this results in a notable rise in ROI, a stronger grasp of SEO/SEM, and an improved alignment between marketing and sales. On average, marketers employ at least 6 communication channels to reach their customers today. As this number continues to rise, attribution will only become increasingly critical to the success of modern marketing initiatives.
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All that being said, marketing attribution isn’t without its challenges. In fact, even after the emergence of highly effective multi-touch models, several organizations continue to report attribution manually through spreadsheets.
There are many considerations that go into choosing the right attribution model which can present several challenges for the marketer:
The Sales Cycle:
Attribution is a lagging indicator. It takes time and patience to see if models are working. Based on the length of the sales cycle, the effects of a new campaign or changes made into existing ones will reflect much later into the future.
Ease of Set-up and Implementation:
30% of companies in the UK say that they have chosen their current attribution model based on ease of use. If put in a position to choose between a model that is easy to implement and a complex model that would be tedious for the team to implement, marketing heads would prefer the simpler model. Similarly, technological limitations may also hinder the execution and implementation of attribution models.
A Culture of Data and Measurement:
To be able to value the insights provided by attribution models, there needs to be a culture of measurement and accuracy within marketing teams.
Communication of Insights:
Communicating the insights from the model is significant for communicating cost justification as well as for taking action based on the insights from attribution. To get funds and approvals for software costs, and implementation costs in terms of time, effort, and training, the team needs to be able to communicate the insights well and accurately.
Attribution to Improve, Not Prove:
Marketers often use attribution to prove that campaigns are working. As mentioned in the earlier section, this is important to be able to justify costs. However, limiting attribution to this purpose can lead to lost insights and higher costs. Attribution, at its core, is directional in nature. Attribution models can be used to see what is working well and also to check what is not working and needs to be abandoned. Marketing and Sales teams are often working on several kinds of campaigns and this is a useful tool to see which campaigns are performing better and can be emulated in future projects.
Volume bias:
Most often, an organisation’s highest volume campaign can show up as its most successful campaign if marketers do not track other metrics like conversion rate and win rate. To understand, let’s consider the example of an organisation that sells CRM software to businesses. Say in the last six months, they saw a total of 500 downloads, out of which 400 were attributed to Campaign A which was implemented in the form of in-person promotional events like webinars while the remaining 100 were attributed to Campaign B which was implemented in the form of ads on YouTube and Instagram. By themselves, these numbers make it seem like Campaign A was the more successful campaign. But what if we find that the 400 downloads were made by customers from a total of 10,000 attendees in those in-person events while the remaining 100 from the second campaign were made by customers out of a total of 500 users who were presented with the ads. So if we look at the conversion rates for Campaigns A and B, we see that they were 4% and 20% respectively. This comparison could possibly give us the insight that if Campaign B was promoted further, with more funds and effort directed towards it, the organisation might’ve seen more downloads of its software with the it’s higher conversion rate relative to Campaign A.
Absence of predetermined hypotheses:
To get effective insights from an attribution model, marketers need to be specific about what they’re trying to measure. For example, say the conversion rate for leads from campaign X within the period of the last 30 days since it went live for geographic location Y- can be used to understand if a campaign was successful within the target audience from that location. If marketers do not know what exactly they are looking for, they will end up giving an overall attribution report and miss out on gainful insights.
Invisible touchpoints:
Several attribution models being used by organisations do not account for certain important touchpoints. Models that do not track the relationship between online activity and offline sales may lead to digital signal bias. For eg. one might have seen the ad for a clothing app on Instagram but they decide to go to the store and purchase the item. Models that do not include sales touches may not include the impact of sales actions. On one hand, it may hamper the accuracy of the outcome metrics and on the other, it may cause disarray with the sales teams instead of aiding collaboration between the two teams.
In order to choose the right attribution model for your team and reap the benefits that attribution brings to modern marketing, marketers need to be wary of these challenges and address them.
In further blog posts, we will be exploring the various challenges of attribution that we have outlined here in greater detail.
