The amount of data generated by marketing and sales platforms today is truly fantastic. However, when it comes to generating reporting and insights from that data, marketing automation and CRM platforms generally fall flat.
That’s because their out-of-the-box reports focus on tactical reporting, rather than deep-dive analysis and insights. That is why I often find clients putting all of their data into data warehouses in order to connect it to other systems and do the deeper analyses.
But it takes more than just setting up the API connections and sucking all of the data into a “data lake” (my latest favorite term I’ve heard).
First, the ability to generate relevant insights depends on the quality of the data generated. My colleagues and I have previously discussed the benefits of implementing a campaign reporting framework.
Second, the ability to use those insights to drive better results depends on getting the right data into the right systems.
Before you do any of this, though, you must first identify which questions you are trying to answer. This will serve as the underlying foundation of your reporting framework.
What do you want to know? Everything, of course.
When we are helping a client with their demand generation, we always dive into what they will want to know about the success of their nurtures, lead scoring, lead management, and so forth. The answer, inevitably, is everything.
But rarely is “everything” important. And it takes substantial resources to organize huge volumes of data in different systems into the right reports and analysis.
I group the different types of questions that you can have into two main buckets:
1. Operational questions are the ones that are regularly asked to run the day to day (or quarter to quarter). These include questions like:
- How many leads are in each stage of the funnel?
- How many responses did we get to the last campaigns?
- What campaigns are driving the most revenue?
DemandGen CEO David Lewis goes over these types of questions in detail in the Analytics chapter is his book, Manufacturing Demand.
These types of questions are so important to running marketing and sales that it makes sense to put in the effort to connect all the data together, perform the necessary data hygiene, and create dashboards and regular reports to answer them.
2. Strategic questions, on the other hand, go beyond the operational questions to help determine the direction of activities and finance. These can include everything from technology investment and ROI-type questions to evaluating a new type of campaign or direction.
Finding the answers to strategic questions most often requires pulling together data that is not regularly joined. It may also require data cleansing or other processes that combine different data sources or views of the data. This is often a time-consuming approach, but the strategic nature of the question typically makes it worth the time. And if the question becomes a regular one, it could move into the operational bucket.
When asking strategic questions, it’s important to identify the reason for the question and the possible courses of action once you’ve answered it to make sure you’re not just asking for the sake of asking.
Several years ago, one of our clients asked how different lead sources impacted opportunities. At the time, this was not a common question and it took quite a bit of data mapping, data cleansing, and system integration to come up with an answer. However, they didn’t have any way to apply more money towards the higher performing lead sources, so answering the question ended up taking a lot of resources without much gain.
It’s not just about the customer….
Orienting your marketing around the customer is vital to success. Too often, I see clients trying to frame their marketing in terms of the product and/or internal organization rather than the pain points and problems the customer is trying to solve.
But when it comes to pulling out actionable insights from your data, both the customer interactions and the internal processes have to be taken into account.
For example, we helped a client with a client segmentation analysis to drive cross-sell and up-sell campaigns. We pulled data from multiple systems across different parts of the organization: marketing, sales, finance, client success, etc. These systems were not integrated, so despite the insights we gleaned the client could only define actionable segments based on data from their CRM.
Although the analysis was insightful, the actionable result was only marginally better than manually segmenting by industry. We were not able to use the rich insights because we couldn’t get that data into the marketing system in order to take action on it.
Understanding the data chains between systems, along with how the data is used within each system, is very important when deciding whether to put resources and effort into answering a specific question.
But when it all works right…
Several of my clients have reached the point where their questions have been defined, dashboards have been put in place, and they are using the data operationally. This doesn’t mean their work is finished. There will always be new initiatives, directions, and strategic questions to answer. Since the operational concerns have been addressed and the data chains, systems, and processes have been put in place to support them, the group is free to look at how to grow into new areas.
And more importantly, their teams are turning the reporting into actionable insights. After all, that’s the point of gathering all that data, isn’t it?
Ryan Johnson develops and implements marketing automation strategies for DemandGen clients. As a DemandGen Consultant, he has helped clients across a wide range of industries to streamline and optimize their marketing and sales processes to drive measurable success and ROI.