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  • Writer's pictureThe GoVi Team

How GoVi Looks at Marketing Attribution

This year is already shaping up to be a rough one in terms of marketing budgets, team cuts, and more pressure for results across the board. Marketers are used to their success being defined by a wide array of metrics, but ultimately what really matters is what moves the hard-dollar needle. We expect marketing attribution to be a hot topic in 2023 as brands get more stringent with their budgets and their ROIs. Brand and revenue teams must prepare to see their spend examined under a microscope and their success defined by what they report and how they report it (we’ll get into vanity metrics another time).

So what is Marketing Attribution?

Marketing attribution is the process of attributing a marketing-sourced touchpoint or interaction to a particular sale or customer action. It is an essential part of measuring the effectiveness of marketing campaigns and identifying which channels and tactics are most effective in driving conversions. According to studies by Epsilon and AdRoll, "90% of marketers agree that understanding the customer journey is important, but only 22% say they have a good understanding of it." 22% is a wild number to consider, when customer engagement is such an important part of the process of brand growth. Without intimately understanding the customer experience, it’s incredibly difficult to optimize the campaigns used to gain customers, keep them loyal, and gain that all important word of mouth marketing.

However, attribution can be a challenging task. With the proliferation of digital channels, there are often many touch points and interactions that occur before a conversion, making it hard to determine which ones are most important. Unfortunately, brands often fail to determine which metrics are relevant to their specific business and goals. Sometimes what is relevant to one brand’s strategy is simply a distraction to the next. A major part of creating a successful attribution model is determining how to read data and which points are informational and which are transformative.

One of the main challenges of attribution is the fact that customers often interact with a brand through multiple channels and touch points before converting. For example, a customer may see an ad on Facebook, visit the brand's website, and then make a purchase on their mobile phone. In this case, it can be difficult to determine which touchpoint or channel was most influential in driving the conversion. According to a study by the CMO Council, "52% of marketers struggle to attribute cross-channel marketing efforts to conversions." This illustrates the difficulty of attribution in a multi-channel, multi-touchpoint environment and doesn’t include the reach of a brand’s own customer’s advocacy, reviews, posts, or any other metric not directly driven and thoroughly tracked by the brand itself.

While there are a plethora of attribution models in the market, the following are some of the most commonly used models in use today to attribute marketing efforts to conversions:

  • Single touchpoint attribution: This model attributes the entire conversion to a single touchpoint, such as a specific ad or email. This model may be attractively simple, but it completely fails to take into account the other touch points that may have significantly contributed to the conversion.

  • Linear attribution: This model attributes equal value to all touch points in the customer journey. While this model is more comprehensive than single touchpoint attribution, it does not take into account the relative importance of each touchpoint.

  • Time decay attribution: This model attributes more value to touch points that occur closer in time to the conversion. While this model is more sophisticated than linear attribution, it does not take into account the relative importance of each touchpoint.

  • Last touchpoint attribution: This model attributes the entire conversion to the last touchpoint before the conversion. According to a study by Google, "last touchpoint attribution models can undervalue the impact of earlier touch points in the customer journey by as much as 50%." This model is useful for identifying the most effective channels for driving conversions, but it does not take into account the role that earlier touch points may have played in the customer journey.

  • Multi-touch attribution: This model attributes value to multiple touch points in the customer journey. There are several variations of multi-touch attribution, including data-driven models that use machine learning algorithms to determine the relative importance of each touchpoint. According to a study by the CMO Council, multi-touch attribution models can provide up to 200% more accurate insights into the customer journey compared to single touchpoint attribution models.

Is that all?

One thing that is often overlooked in attribution is the role of non-obvious sources of revenue. These are sources of revenue that may not be immediately apparent or factored into the analysis at all, such as referrals or word-of-mouth marketing as referenced above. These sources have historically been difficult to attribute because there hasn’t been a reliable method for a brand to drive impactful and consistent advocacy campaigns. While that has now changed with the public availability of GoVi, a majority of marketers still don’t understand the power of word-of-mouth marketing as it relates to their brand, often because they don’t understand how to create a campaign to drive such activity in their customer base. These (historically) non-marketing sources and touchpoints are an incredibly important part of the customer journey, driving well over six trillion dollars in spending annually.

The importance of looking at non-obvious sources of revenue cannot be overstated–not only because they can provide valuable insights into the customer journey and help identify opportunities for improvement, but also for the sheer impact they have on buying and loyalty decisions. For example, a customer who is referred by a personal connection (such as a friend, colleague, or family member) is far more likely to make a purchase than a customer who simply sees an ad. By understanding the role that these sources of revenue play in the customer journey, marketers can optimize their campaigns by actually engaging where the customers are, and where they seek out information, thereby greatly improving the return on investment of their marketing efforts.

What does this all mean?

While there are many options available to marketers to track their campaigns, and the subsequent attribution such as Google Analytics, Adobe Analytics, Mixpanel and more, all of which offer a dizzying array of features and capabilities, none have quite cracked the code of customer interactions outside of a brand’s walled garden. Website visits actually do come from somewhere, including the reason behind the searches that often lead to the website. App usage is driven by more than a simple download, and customer interactions don’t stop with the brand’s store or website–they happen with other customers, and with potential customers. Marketers truly looking to move the needle need to understand more than what a pixel can show.

It can be challenging to identify exactly what to track and how to track it due to the proliferation of digital channels and the myriad of touch points/interactions that happen as a result. Any of the models we’ve highlighted have the potential to offer at least some benefit, as could just about any standard platform used to gather and interpret data on marketing efforts and customer journeys. At the same time, however, failure to fully understand the shortcomings of a given model can actually lead to more harm than benefit, as decisions are made and strategies are formed based on entirely wrong information–or, more commonly, based on entirely wrong interpretation of available information. And while we can’t recommend any one particular platform since each brand’s needs are different, one thing that we at GoVi are sure of is that the most successful companies in the future will be those who have broken out of historical models, metrics, and attribution to include the actions of their own customer base that go beyond just purchasing as a significant source of revenue, data, and growth.


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