In the fast-paced world of B2B marketing, your campaigns may appear sleek and enticing, but the ultimate question remains: Will they drive meaningful results? To unlock your organization’s full potential, it’s essential to align your strategies with the right audiences.
To power your strategy, your attribution model needs to be a fine-tuned engine that delivers clean, actionable, and reliable data. As CaliberMind’s Head of Insights, I’m here to steer you through the diverse terrain of attribution models and shed light on when B2B companies typically venture into customization. Moreover, we’ll delve into the steps to take if the intricate components of your model threaten to unravel, potentially leaving you with a heap of unreliable insights.
From factory standards to souped-up models
It can be daunting to choose an attribution model for your organization, but your choice should be based on the questions your business needs to answer and the depth of insights required to drive success.
At the simpler end of the attribution model spectrum are ‘out-of-the-box’ options that use data within your CRM system, including leads, contacts, campaign members, and opportunities. These models, like the one offered within Sales Cloud that operates like a scorekeeping system, typically employ a single-touch attribution approach, attributing all the “credit” for an opportunity to a specific touchpoint, be it a campaign or an event. While these models are a step up from relying on intuition and hunches, they fall short when dealing with multiple data sources.
The next step up is a customized attribution model built to address specific business questions. These models often employ a multi-touch approach and can be reported on regularly to assess marketing and sales performance. They offer a more refined understanding of the customer journey and provide insights into which touchpoints contribute most to conversions.
At the other end of the spectrum are the highly sophisticated DIY attribution models. These models are designed to answer intricate questions, such as comparing the effectiveness of different marketing channels or understanding the impact of specific campaigns on opportunity creation and advancement. They delve deep into data analysis, often requiring customized inputs and advanced methodologies. For example, your team may want to take a deep dive into the data on two webinars that, on the surface, appear to have garnered the same number of responses. However, more granular insights may show your teams which one to double down on. Overall, this deep dive will help answer more high-end questions around specific opportunities or opportunity stages like distinguishing between which campaigns helped you create opportunities versus those that allowed you to advance opportunities.
What drives organizations to customize their models?
Most self-built, DIY attribution models are driven by very specific questions that rely on complex application integrations and unpacking complex customer journeys. When your existing model becomes predictable and can’t take these deep dives, it’s time to upgrade.
To leverage a diverse set of software applications and platforms to manage marketing efforts, organizations often need to customize their attribution models to ensure that data flows seamlessly across these varied sources. This ecosystem might include CRM systems, marketing automation tools, web trackers, and more. At the heart of these advanced models is the need for a unified data ecosystem that offers you a comprehensive view of customer interactions. This integration allows you to get a more precise understanding of how marketing efforts impact customer journeys and conversions.
Sophisticated attribution models acknowledge that buyer journeys are rarely linear. In the B2B world, your customer buyer groups have multiple campaign members who enter at different points in the journey and each navigates through a series of touchpoints, interactions, and decision-making stages before making a purchase. Customization is necessary to accurately dissect these intricate journeys. It involves defining different buyer personas, recognizing various stages within the sales funnel, and understanding the specific touchpoints that influence each persona and stage.
The real engine, as always, is a clean, normalized, and standardized event timeline that comes from multiple sources and includes other fields by which you could slice the data such as channels, or event tags for that specific campaign member. Event tags help to accommodate complex buyer journeys by attributing the impact of specific marketing activities, like campaigns or events, to different stages of the customer journey. This level of granularity can empower your businesses to make more informed decisions and help you lean more into whom you should focus on rather than just what in your strategy.
As your organization matures and seeks to be more tactical, your teams should ask more specific, granular questions. This depth demands a model that can harness finer-grained data and insights. Your insights can lack actionable nuance if you’re not pushing enough on business questions. For example, is it sufficient to show that marketing touched 70% of deals this month? Or can you take control of that and move it up to 71 or 73% for next quarter?
Seeking a middle ground
There’s a vast landscape of middle-ground DIY models to bridge the gap between the standard and intuitive models and the highly sophisticated ones. In the middle ground, we will encounter a blend of technology and strategy that enables businesses to gain valuable insights without diving headfirst into the complexities of advanced customization. These models are typically characterized by a combination of essential components, such as CRM systems, marketing automation platforms, web trackers, and more. In essence, they provide a solid foundation for data-driven decision-making.
A prime example of this middle-ground DIY model is often built around core components like a central data repository, a business intelligence layer, a CRM, a marketing automation platform, and a web tracking or segmentation tool. The advantage of a middle-ground model is that it caters to organizations seeking a balance between simplicity and sophistication. It allows you to measure critical metrics, such as the origin of leads or requests while providing insights into which campaigns or events contribute significantly to attribution.
At the heart of this middle ground lies a unified event timeline—a backbone that aligns activities with the customer journey. Integrating campaign data, channel information, and other event tags into this unified timeline, you gain a comprehensive view of your customer interactions. This unified approach ensures that marketing efforts are aligned with the evolving needs and preferences of different buyer personas and stages within the sales cycle.
As we navigate the landscape of attribution models, it’s crucial to recognize that the middle ground offers viable and effective solutions for many organizations, empowering them to answer more specific questions while mitigating scope creep into an unwieldy model that can lead to its collapse.
Avoid turbocharging collapse
When you turn down the road of customization, it can be tempting to build a model that can not only integrate all input sources and answer all of your current questions but also anticipate inquiries down the road. Proceed with caution at these junctures because it’s easy to overshoot your goals and miss valuable insights on your current priorities.
One common pitfall is overengineering your model, resulting in complexity that’s challenging to maintain and troubleshoot. Over-customization can create convoluted data pipelines, making it harder for you to extract meaningful insights from intricate data structures. This complexity can divert your teams’ focus from decision-making to data deciphering.
Another risk lies in excessive micro-segmentation. While customization allows for specialized buyer personas and journey stages, too much fragmentation can obscure the overall view of the customer journey, hindering actionable insights.
Model instability is another concern. When attribution models become overly complex, they may become fragile and prone to errors and you’ll end up with inconsistent reporting that undermines your model’s credibility.
To prevent DIY model collapse due to customizations, your organization needs to strike a balance and focus on critical business questions. Regular maintenance, documentation, and transparency in customizations are essential to ensure your model remains comprehensible and manageable.
If your DIY model breaks down, get back to basics
If your DIY attribution model is teetering on the brink of collapse, it’s crucial to take a step back and get back to basics. Reach out to your CMO and ask them to prioritize their top three or four inquiries. It’s possible to significantly simplify your model by aligning it with their core questions. If the primary focus is on opportunity acceleration, narrow down the model to touches that matter most in that context. Or, if your emphasis shifts to web interactions, adjust your model for that focus.
Remember that trust is key. To regain confidence in your model, sense-check it with key stakeholders to ensure it aligns with the current questions and your business needs. It would be terrible for your colleagues to dismiss your model – and all your efforts – because it’s indecipherable or irrelevant.
DIY attribution models can be powerful tools and, of course, you want an attribution model that can evolve with your organization. It’s less important which model you select and more vital to give it ongoing attention and check that it aligns with business objectives. By revisiting your model’s core purpose, seeking input, and staying agile in adapting to changing priorities, you can prevent the collapse of your DIY model and ensure its continued success.
Have more questions? Check out the next installments of our attribution series or reach out to us at CaliberMind for guidance more tailored to your needs.
Misha is Head of Insights at CaliberMind. His experience with marketing and machine learning fuels his passion for driving actionable business objectives through data analytics.