June 22, 2023
Data is critical to running a marketing function. Whether it is building the quarterly plan for delivering pipeline targets, improving ROI for a paid channel, driving deeper understanding of the ideal customer profile, or optimizing conversions through the funnel, marketing teams need good data.
But actually getting marketing data into good shape is easier said than done.
Getting the right data in place to run analyses, answer questions, and lead the marketing organization requires an investment in the underlying infrastructure. Too often, teams are wanting to analyze the data now and have not made the necessary investments in the infrastructure. So we end up stuck in cycles of exporting reports to CSV, attempting to match together data, and building pivot tables… never quite resulting in the view of the funnel that we would like to have.
There is a better path. Check out the thoughts below for tips on getting your marketing data foundations in order.
When it comes to building out a data-driven marketing function and then underlying data to support it, there are a few principles that I always like to follow:
This is a valuable lens that you can view your marketing data through regardless of company size, industry, or sophistication. With these principles in place, it is simply a matter of finding the tooling and processes to fit your particular business. Each principle is covered in further detail below.
The data about our leads, target accounts, and customers is spread out across a variety of SaaS tools. Trying to answer simple questions can often become a game of attempting to translate the information from one tool’s report to another.
It is critical that you centralize your data.
Have a source of truth. One place to look when you want answers. Ideally, you would also store this data outside of the SaaS tool as well.
Without this, it is impossible to truly know what is going on with your marketing performance. One thing to remember is that every SaaS tool is building reports with a lens of justifying their own ROI as a tool or platform. If you add up the number of new leads from each ad platform or 3rd party tool you are using, they usually far exceed the actual number of leads you see coming into your CRM or marketing automation tool. Everyone wants to claim credit for the conversion.
Centralizing your underlying data is the base of a solid marketing data foundation.
With data centralized, it is necessary to resolve key entities and events across the various data sources.
As an example, you want to be able to say that this lead in Salesforce is the same as this contact in Hubspot, this User ID in your own product, and this cookie ID in your web analytics tracking.
While there may be some nuance for each individual business, in general, you want to be able to have a deduplicated view of the following entities, as well as their relationships to each other:
With these mappings in place, you ensure that you get a full, accurate view of the individual and the account, further establishing the foundation for solid analysis and understanding of the customer journey.
We’ve all been in the situation where the definition of a key metric is changed, and everything breaks down stream. Maybe you are restricting MQLs to only those individuals from a certain industry, or you are changing the employee count definition of a market sector.
After the change, it can feel like nothing is reliable! You have an ops team scrambling to update the 53 Salesforce reports that now need new filters, logic definitions across every object (leads, contacts, accounts, opportunities), the lead routing tool so SDRs get the right leads, and the marketing automation tools to ensure individuals are on the right nurture tracks. We’ve all been there, and it is painful.
As a principle, all logic should be centrally defined. This includes the definitions of things such as what makes an MQL, any attribution reporting, classification of demographics and firmographics, etc. When an update to logic is required, all downstream reports, tooling, and processes will just work.
Once you get automated testing in place to validate data accuracy, then you really can be confident!
With data centralized, entity relationships mapped, and logic defined, the final step is simply ensuring that the data is consistently pushed back to each end user tool. This ensures that each individual and team in the company can confidently trust and use the data in the tools they need to get their job done. Whether it is pulling performance reports on one particular activity to inform what to do next or building automation based on certain fields in the CRM, the data is there and can be trusted.
Once those foundational elements are in place, making use of your data becomes easy. Reports are easy to build (the data is there!) and are more reliable (no more trying to compare against the CSV you exported last month). Individuals on the marketing team, as well as those on other teams in the company, are all working from the same set of data. Among other things, this makes alignment of sales and marketing quite a bit simpler.
Marketing data can be simple with the right foundation in place.
So how do you actually get there? I’ll cover two approaches (using native integrations vs. the Modern Data Stack) in my next post. If you haven’t already, subscribe to follow along!
