The Broken Nature of B2B Marketing Attribution: Why Change Is Needed

“How much return on investment did we get from this marketing campaign?"

"Is our marketing budget being allocated correctly? “

“What channels are delivering the most sales for us?”

Many marketing teams today can relate to questions like these—especially in a climate where we are asked to do more with less. The work we do as marketers starts well before the point of purchase or conversion, with a lot of things that can go right or wrong in between.

For many B2B marketing teams, multi-touch attribution (MTA) models are a starting point in measuring the effectiveness of their efforts in driving desired business outcomes like sales. In summary, these models look at the channels or touchpoints that make up a typical buyer journey and the contribution of each in driving a conversion or sale. By adding a weighted value to each touchpoint, you can calculate which channels have the most impact on conversions and sales. However given the increasingly complex and fragmented nature of the B2B buyer journey, MTA models are coming under increased scrutiny.

For MTA models to work effectively, they need to be able to track user interactions across all channels that make up the B2B buyer journey. Increased data privacy laws across different jurisdictions are limiting the amount of data companies can capture without a user’s consent, significantly reducing the amount of data available for attribution purposes and hampering the overall effectiveness of the model. This no doubt will become an even bigger issue as time goes on.

Furthermore, MTA models often struggle with capturing offline and anonymous activities that increasingly make up today’s typical B2B sales. Take for example, where an existing client recommends a company’s product to an industry peer in a casual conversation at a networking event—these types of introductions can be a significant factor in driving B2B sales today but remain untracked under multi-touch attribution. Other activities including anonymous web site visits from a prospect would also fall outside the attribution model.

Probably one of the biggest limitations of multi-touch attribution lies in its failure to properly measure how different marketing channels work together in a typical B2B customer journey, It often overlooks how all channels cumulatively work together in creating synergy making the false assumption that individual channels work independently of each other. For example, if a prospect views a LinkedIn ad but does not engage with it, and then clicks on a display ad just before the point of converting or making a sale, the display ad will receive credit while the LinkedIn ad will not. However, in reality for many B2B sales today, the LinkedIn ad can play a crucial role in building initial awareness and influencing the prospect's decision to engage further. This synergistic effect of multiple channels is particularly common in B2B marketing, where channels collectively work together in building cumulative awareness across a target market.

It can also work that individual channel overestimate their influence under MTA. Taking the same example above of a prospect clicking the display ad before the point of conversion or sale. It is automatically assumed the ad influenced the prospect just before they converted but it may be the case the prospect would have converted anyway in the absence of seeing the ad.

While MTA has value for marketing teams, its limitations mean that it is unable to fully capture the complexities of today’s complex and changing B2B buyer journey. There is now a need to complement MTA with other models like Marketing Mix Modeling (MMM) which offer a broader understanding of how marketing drives sales.

MMM is a data-driven methodology that combines machine learning and statistical analysis, enabling B2B marketers measure the incremental impact of their efforts in achieving desired business outcomes including conversions and sales. With its ability to integrate vast amounts of sales and marketing data, MMM moves beyond mere attribution to predict the incremental impact of a company’s marketing efforts providing the confidence of knowing where and how to spend their marketing budget.

Compared to multi-touch attribution MMM considers the impact of other factors in addition to campaign channels in driving sales including product/pricing changes, macro/industry-related events and seasonality. A good analogy in explaining the differences between both models is to think of MTA as focusing on the micro and short-term nature of marketing, enabling marketing teams to optimize campaigns in real time. In contrast, MMM takes a more macro and long-term view, providing marketing teams with insights on how their marketing efforts influences sales over time.

While MMM comes with challenges—including integrating different sources of data and allowing time for the model to mature, the long-term benefits make it worthwhile. B2B companies that invest in MMM today are in a much better position to adapt their marketing efforts going forward, ensuring that marketing spend and resources are allocated wisely.

At PredictiveB2B, we partner with companies to remove the mystery from their marketing and sales data, helping them to create insights that they act on. We focus on the KPIs that matter most and that align with our clients’ desired business outcomes. We enable businesses to predict the impact of their marketing efforts providing clarity and confidence on where to spend their marketing budget.