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Signs You’ve Over-engineered Your DIY Attribution Model

Posted November 6, 2023

Attribution was once marketed as a silver bullet, capable of providing definitive answers to all your marketing ROI questions. The range of attribution complexity is vast; at one extreme are the organizations that believe a website form can be a proxy for attribution (it can’t), and at the other end are the ones that attempt to over-engineer their DIY attribution model into a crystal ball (they can’t). 

In reality, attribution is more of an estimate than an absolute truth. Though it is a valuable tool and you can tailor a DIY model for your needs, be cautious about which data you collect and how you position and communicate it. Before you become ensnared by the pitfalls – or if you already have – as a B2B Insights Leader, I’m here to shed light on the benefits and hazards linked to DIY attribution models. I’ll delve into the driving forces behind attribution model customization and how marketing analytical skill gaps and misaligned perspectives can interfere with a model’s effectiveness. I’ll also highlight the importance of asking the right questions to avoid over-engineering your model and offer insights into how to simplify your model for more effective decision-making.

What are DIY attribution models and how do they evolve?

DIY attribution models are customized to attribute activities to specific sales and marketing objectives, such as conversions, revenue, or pipeline growth. These models rely on predefined rules to assess touchpoints and understand their contributions to desired outcomes. Initially, businesses just tweaked standard models to incrementally optimize campaigns and resource allocation. But these days, the attribution field is more elaborate than ever, with increasingly multifaceted demands on these models.

As a result of C-Suite expectations, companies often seek to gauge marketing’s impact beyond merely measuring campaign effectiveness. They see business success as a three-legged stool; with operations at the top, and sales, marketing, and customer success as the legs. Because one wobbly leg can destabilize the whole enterprise, executives seek a broader view of operations. 

For example, digital marketing can effectively build awareness and reach, but not necessarily conversions. Bridging the gap between traditional metrics and business objectives fuels model customization to give executives a wider lens. But when you create more sophisticated attribution models, you can also create issues with data complexities, mistrust in the metrics, and analytic skill gaps. 

How data drives complexity

Many factors lead to complexity, but let’s debunk the myth that excessively iterating your model is one of them. In the world of DIY models, ongoing tweaks and adjustments are standard practice. The key isn’t to halt these iterations but to ensure you can drill down on the most pertinent questions. 

When customizing your attribution model, be cognizant of the complete picture. If you find yourself constantly tweaking your model from logarithmic to weighted to time decay, it’s worth examining whether you’re asking the right questions or solving the right problems. If you’re harnessing data that does not fit organizational needs, you’re likely to breed doubt in your model. Teams can be equally wary that the person designing and delivering the insights is tailoring the model to fit their agenda.

Complexity also arises when the motivation behind iterations is solely to justify decisions, be it spending, credit allocation, or any other aspect. If your goal is merely to convince stakeholders rather than make better decisions, you’re likely making things unnecessarily complex.

How a lack of marketing analytical skills drives customization

Another hidden challenge you can face is the limitation of your ability to interpret your numbers. In today’s data-rich environment, you find yourself in a sea of data, drowning in siloed information from various sources. Navigating through these silos, extracting meaningful insights, and comprehensively presenting them to your entire organization is a formidable task. 

One critical misstep is assuming the right data sets are in place before committing to key performance indicators (KPIs). Many organizations plunge headfirst into setting ambitious KPIs without fully understanding the existing metrics. This often causes a mismatch between expectations and reality, where marketers struggle to prove their initiatives’ impact due to incomplete or poorly aligned data. It’s up to you to harness this imperfect data and hone your ability to share its value with the executive team in a way that resonates with them.

A matter of perspective - how the misuse of attribution models drives customization

In the realm of marketing attribution models, misusing these models often drives unnecessary customization. One core factor is misaligned expectations. Attribution models are designed to provide insights but are often misappropriated as tools for assigning absolute credit or blame to a department for a lack of conversions. This narrow view fails to account for the intricate interplay of marketing and sales efforts. When there are concerns about bias, people tend to fine-tune models to fit their notions of fairness.

Although it’s key for you to incorporate various departments, be mindful that complexity crops up when you accommodate multiple perspectives and objectives. Before you let the chorus of voices lead you to over-engineer your model, have cross-team discussions on what success means for your whole organization. Set clear goals, agree on which metrics matter most, and align everyone’s expectations.

Drivers of customization and how to avoid them

Marketing and sales efforts are often intertwined in complex ways, impacting the pipeline and deal closures. Presenting attribution solely as a marketing-centric tool can spark conflicts when sales leaders feel their contributions are overlooked. As departments vie for recognition and credit, things can devolve into a contentious food fight. To build trust in your figures, collaborate and ensure implementation isn’t biased by the individuals setting the rules and scoring criteria.

In essence, misusing attribution models often leads to over-customization due to unrealistic expectations, lack of trust, and failure to involve key stakeholders. Be proactive – educate teams to find insight in your attribution model instead of seeing it as a tool for assigning arbitrary credit. By fostering a deeper understanding of attribution and its limitations, all teams can make better decisions and avoid unnecessary complications.

Reboot: Curbing over-engineering by asking the right questions

When complexity increases, there comes a point of diminishing returns. At this juncture, consider shifting towards standardized, out-of-the-box models to avoid a slippery slope. It’s essential to recognize complexity won’t always give you better results, and sometimes, the simplest models are the most effective. On the other hand, don’t sacrifice your ability to answer necessary, but complex questions for the sake of simplicity. 

Once you reach a cross-department consensus, boil your attribution model down to the essentials. Using the simplest model possible after agreeing on the underlying data points and their usage helps ensure you have a globally accessible attribution model. Instead of designing complex mathematical models, it’s more important for you to guide your teams to find value in attribution. 

In your quest for an effective DIY attribution model, strike a balance between simplicity and complexity. As we’ve discussed, straying too far in either direction can lead to challenges. By engaging in cross-functional discussions and aligning on what success means for your organization, you can avoid falling into the traps of over-simplification or over-engineering, ensuring that your model answers your organization’s key questions. 

Need further insights? Explore our attribution series and upcoming articles or connect with us at CaliberMind for guidance specifically tailored to your needs.

Camela Thompson is a Go-To-Market Leader and a B2B Insights Leader. She runs her own consultancy, specializing in creating customer-centric brand narratives and cross-functional go-to-market strategies. 

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