The true test of your marketing campaigns isn’t their sleek appearance, but their ability to drive meaningful results. To maximize your organization’s potential, aligning your strategies with the right audiences is crucial. A well-tuned attribution model is essential for producing clean, actionable, and reliable data.
We’ll guide you through the complexities of attribution models, highlighting when B2B companies typically resort to customization. We’ll also explore steps to take if your model’s intricate components become unreliable, potentially delivering flawed insights.
What Drives Organizations to Choose Custom Attribution Models?
Most self-built, DIY attribution models are driven by 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.
Organizations need to customize attribution models to manage marketing efforts effectively to ensure seamless data flow across various platforms like CRM systems and marketing tools. A unified data ecosystem offers a comprehensive view of customer interactions, enhancing the understanding of how marketing impacts customer journeys and conversions.
Sophisticated attribution models recognize that buyer journeys are rarely linear, especially in B2B contexts where multiple campaign members engage at different stages. Customization is crucial to define buyer personas, sales funnel stages, and touchpoints that influence each.
A clean, standardized event timeline from multiple sources, including event tags, helps accommodate complex buyer journeys by attributing specific marketing activities to different customer journey stages. This granularity empowers businesses to make informed decisions and focus on effective strategies.
As organizations mature, they should seek more granular insights. For example, instead of just showing that marketing touched 70% of deals, aim to increase that percentage in the next quarter.
Building The Right Attribution Model
It can be daunting to build 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.
Highly sophisticated DIY attribution models answer specific questions, like comparing marketing channels or understanding campaign impacts on opportunities. They require deep data analysis and customized inputs. For example, analyzing two webinars that seem equal in responses may reveal which one to focus on through granular insights.
The key to a successful attribution model is understanding its purpose. This involves team members who can ask the right questions to the CMO and grasp the practical applications for the broader marketing team. The person who understands the usage of attribution data might not be the same person who:
- Figures out how to connect all of the necessary data sources
- Models the data appropriately
- Maintains the model any time a new tool is purchased
- Designs the reports in a visual layer
A full-scale solution needs a team with varied skills to connect data sources, normalize information, and create a comprehensive data map. This might require a data warehouse or data lake with ETL connections to key marketing and sales tools.
Finally, someone will refine the data into a visual layer, whether by using Excel for pivots and charts or connecting to BI tools like Looker, PowerBI, or Tableau.
The Toolset for “Close Enough” Attribution
If you’re reading this article, you’ve probably already hit the limits of your CRM’s attribution capabilities. CRMs can provide you with multi-touch attribution, but it’s important to understand that you’re limited to the data housed in your CRM and that it’s not practical to push everything you want to see in an attribution model into that CRM. CRMs are protective of storage and processing time – and there are a lot of limitations around joins. Trend analysis and cohorting data are nearly impossible.
Your CRM attribution is also likely restricted to campaign touches. We’ve seen mad scientist operations professionals build campaigns and replicate sales tasks and even web data as campaign members, but we don’t recommend this approach. Building virtual campaigns in a database to house this information saves you a lot of data stored in your CRM.
To understand how campaign data influences reports, you can use advanced logic in your CRM to automate opportunity contact role creation. This helps gauge if prospects engage with tradeshows or webinars but isn’t ideal for CMO presentations on departmental contributions to pipeline and bookings. It also complicates understanding website content or email marketing impact without skewing marketing credit. For campaign optimization, multi-touch attribution or influence reporting in CRM is useful, but it falls short of calculating marketing ROI or showing each team’s contribution to bookings.
The Toolset Options for Multi-Touch Attribution For ROI Calculations
A data warehouse is another option for building a custom attribution model, as it provides a central repository for all the data needed to perform attribution analysis. With connections to core data repositories, such as marketing automation platforms, CRM systems, and web analytics tools, a data warehouse can be a powerful tool for analyzing marketing performance across channels and touchpoints.
Another pro when using a data warehouse or data lake is the ability to consider cross-functional efforts leading to a booking. Whether we marketers like it or not, we’re ultimately judged by booking attainment. We can’t control much beyond lead creation, but a company’s health isn’t determined by lead volume. A company’s health is determined by a business’s ability to win deals. Because sales play a critical role in closing opportunities in B2B SaaS, we need to factor in their efforts before calculating ROI.
Still not bought into this approach?
To measure spending efficiency, use Customer Acquisition Cost (CAC), which totals marketing and sales expenses divided by the number of customers acquired. Consider both sales and marketing contributions when estimating ROI. Building and maintaining a data warehouse is complex and resource-intensive, requiring specialized skills. Carefully weigh the costs and complexities before proceeding.
There are tools, like CaliberMind, that are structured as a customer data platform (essentially a data warehouse with all of the logic necessary to connect disparate data sources) and can be implemented quickly. Our average implementation is about eight weeks, with reports delivered in the first two weeks and time for quality checks built in to ensure the logic is customized to your organization.
Some other benefits to purchasing a tool purpose-built for marketing analytics:
- The ability to normalize data and push it back into your source systems
- Deduplication logic that can also be used to cleanse your CRM
- Additional reporting capabilities like engagement scoring at the account level and funnel analysis
- The ability to trend and cohort data natively
Purchasing a specialized product means leveraging years of expertise in implementing attribution models across various companies. Experienced companies have refined their models by managing multi-year customers, offering a ready-to-go solution rather than starting from scratch.
Selecting the right technology for building an attribution model involves evaluating factors like the complexity of your marketing operations, data availability, and team skills. By weighing the pros and cons, you can choose the technology that best suits your needs and enhances your understanding of how your marketing efforts impact revenue growth.
Tools to Avoid
Some technologies can’t be used to build a multi-touch attribution model. One example is Google Analytics. While Google Analytics is a powerful tool for web analytics and reporting, it is not designed to track multi-touch attribution. It can track the first and last touch, but it does not provide visibility into the touches in between.
Another example is social media platforms like Facebook, LinkedIn, and Twitter (X). While these platforms provide powerful targeting and analytics capabilities, they are not designed to track multi-touch attribution. They can track clicks and conversions, but they do not provide visibility into the entire customer journey.
Avoid Turbocharging Collapse: Attribution Isn’t a Set It & Forget It Kind of Thing
Once you have built your custom attribution model, it’s important to test it and refine it over time. This will involve analyzing your results and making adjustments as needed. You may also want to consider A/B testing different attribution models to see which one works best for your business. As your business grows, your models will need to be revisited. Getting cross-functional buy-in is an ongoing process, so socializing your model and ensuring new business leaders are very familiar with the logic and process you use is as critical as maintaining new connections when tools are swapped out in your marketing tech stack.
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 manageable.
If Your DIY Custom Attribution Model Breaks Down, Get Back to Basics
If your DIY custom 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.
Building your own attribution model can be a challenging task, but it can also be a great way to gain deeper insights into your marketing campaigns. Whether you choose to hire an expert or build your own model, it’s important to keep in mind the pros and cons of each option. With the right tools and skills, you can create a powerful attribution model that will help you make data-driven decisions and improve your marketing ROI.
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.
