Marketing attribution is the process of identifying and assigning credit to marketing touchpoints that influence a customer’s journey toward conversion. It helps businesses understand which channels, campaigns, or interactions drive value, help them optimize the strategies and investment, and make data-driven decisions to maximize return on marketing investment.

Most organizations rely on traditional last-touch attribution, a simple model that assigns 100% credit to the last engagement or touchpoint in a customer’s journey with an understanding that the most recent touchpoint is playing the critical part in driving the conversion. While popular for its clarity and simplicity, last-touch routinely undervalues awareness-building channels and biases the investment toward end-of-funnel tactics/touchpoints. In practice, this can distort strategy and reduce overall pipeline growth.

Drawing on my experience of implementing attribution systems at scale, in this post I examine the shortcomings of last-touch and explain how transitioning to multi-touch attribution (MTA) can take care of these shortcomings.

When we first built attribution systems, at a leading tech enterprise, we started off with the last-touch attribution. It was quick, simple, and gave executives a clean story, and credit went to the channel that closed the deal, which was mostly the retargeting and email campaigns. However, as our marketing strategies evolved and campaigns grew more complex, we discovered its flaws. Channels that nurtured awareness and interest were downplayed, while touchpoints closer to the conversion appeared to drive most of the success.

Here are a couple of examples of how last-touch attribution works in a retail and B2B SaaS environment

Retail / E-Commerce

A customer’s path to purchase begins when they notice a Facebook carousel ad for sneakers. Later, they come across an Instagram ad from the same brand and eventually visit the company’s website, where they browse several products. Although they receive an abandoned cart reminder email, the actual conversion only happens when the customer clicks on a Google Shopping ad and completes the purchase for $300. In a last-touch attribution model, the Google Shopping ad receives 100% of the credit for the purchase, since it was the final interaction immediately before the sale, ignoring all other touchpoints.

B2B SaaS

A prospect exploring security solutions for their business first engages with a LinkedIn ad that highlights an industry challenge. They then attend a thought-leadership webinar hosted by the vendor, download a whitepaper on best practices, and spend time reviewing the product comparison page on the company’s site. Ultimately, the prospect responds to a personalized email campaign, books a demo, and subscribes to the software for $200K. Under last-touch attribution, the entire credit of $200K is given to the email campaign, because it directly preceded the conversion.

In both these examples, 100% credit was given to the last engagement, ignoring all the other interactions customers had. If used to drive the marketing strategies, this might lead to the undervaluation of all the other interactions and lead to an imbalance in the way investments are prioritized. In the rest of this post, I would focus on our transition from last-touch to multi-touch attribution, and what we learned along the way.

Methodology

Multi-Touch Attribution Explained

In contrast to last touch, the multi-touch attribution method assigns proportional credit to all customer touchpoints that influence a buying journey and conversion. Unlike single-touch models (first-touch, or last-touch), which only credit one interaction, MTA evaluates the entire buyer journey, ads, emails, social posts, website visits, revealing how each channel contributes to the outcomes and guiding sharper marketing strategy and budget allocation. I used the MTA model when I was working for a top tech company and their SaaS business.

As we worked towards adopting MTA, our goal was to develop a sound data pipeline that could both capture and examine how the array of customer interactions impacts decision making. We assigned weightings to every engagement, based on its relative contribution to the journey using machine learning algorithms. After establishing these values, we then used them across the customer journey to understand what impact various touchpoints had on conversion and business results.

Here is an illustration of an MTA for the B2B SaaS fictional bookings used as an example in last-touch attribution.

The illustration shows that the MTA model was allocated $200K in bookings across the buying journey according to the historical contributions of each engagement. This strategy shows the full customer journey–from awareness campaigns on LinkedIn, to email campaigns, down to that last conversion. Using these insights, business owners can more wisely allocate budget across channels in a way that enables them to achieve optimal return, as opposed to investing heavily in one activity.

Practical Guidance for Practitioners

Transition From Last-Touch to Multi-Touch Attribution as an Evolutionary Process: Transitioning from one model to the next is best thought of as a gradual journey rather than an overnight change. By serving as a baseline, last-touch or first-touch can give teams the start they need to wrap their heads around attribution and data flows with an executive team. Linear attribution is frequently the following step, where credit is divided equally among touchpoints and organizations are motivated to see the big picture of the customer journey rather than just that last click.

For businesses with longer sales cycles, time-decay attribution provides additional nuance by giving more weight to recent interactions while still acknowledging the influence of earlier ones.2 As data matures and the infrastructure improves, algorithmic methods such as Shapley values and Markov chains may be applied to account for channel synergy and sequence effect, which use more computation and need caution in interpretation.1,3 Crucially, attributions is not a technical but an organizational challenge. Those numbers must be married with a story that explains why early-stage activity matters and not just closing tactics so that attribution insights can inform realistic budget allocations and better strategic decisions.

Don’t Settle

In the end, last-touch attribution tells an easy but often misleading story, whereas MTA is harder to build and explain, but gives a relatively accurate picture of what is driving conversions. By phasing our transition (simple models first, advanced ones later), we can achieve better budget allocation, stronger marketing strategies, and more credibility with leadership. The lesson for practitioners: don’t settle for the last click. Move toward multi-touch step by step, with evidence.

Disclosure: I used ChatGPT to paraphrase a couple of sections, but updated those sections.

References 1. Shao, X., and Li, L. (2011). Data-driven multi-touch attribution models. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). https://doi.org/10.1145/2020408.2020467

2. Anderl, E., Becker, I., Wangenheim, F. V., and Schumann, J. H. (2016). Mapping the Customer Journey: Lessons Learned from Graph-based Online Attribution Modeling. International Journal of Research in Marketing, 33(3), 457–474. https://doi.org/10.1016/j.ijresmar.2016.03.001

3. Dalessandro, B., Perlich, C., Stitelman, O., and Provost, F. (2012). Causally motivated attribution for online advertising. Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy. https://doi.org/10.1145/2351356.2351363

***Kapil Kumar Sharma **is a Data Architect at Cisco Systems with 18 years of experience in Information Technology, specializing in data engineering, AI-driven architectures, cloud computing, and business intelligence. A Fellow of the British Computer Society and Senior Member of IEEE, Sharma is the author of *AI Sales Architect: Transforming Leads with Advanced Data Strategies.

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A Practical Guide to Last-Touch and Multi-Touch Attribution

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