6 Steps to Actionable Analytics
Numbers don’t lie, but they don’t tell the whole truth. This reality is all too familiar to marketers, who pull seemingly endless reports from multiple sources, only to have the data say different, and sometimes conflicting, things.
You have a whole bunch of reports — some pulled by the same person, some pulled by different folks. They seem like they are reporting the same concept, but the data doesn’t agree. Let’s take something as “simple” as daily revenues.
At first glance of two similar reports, it’s clear that the reports don’t look the same. They just LOOK different when you put them side by side. This might not be a deal breaker, but it certainly slows you down.
Worse yet, they don’t match. In this case, the total daily revenues in two different reports might show two different numbers. This can happen for a number of reasons:
- One report is for new customers only, and the other is for all customers (I literally just came across this yesterday, and it was only because of a random question that I realized it.)
- One report is gross sales and the other includes refunds that were processed yesterday.
- One is from an outside vendor and one is from an internal resource, but their definitions for what they are pulling are different (sometimes, this is intentional to make their numbers look as good as possible.)
- One report is based on cash billings, the other is based on GAAP rules.
- Yesterday’s numbers change based on when you pull them — those familiar with GA (Google Analytics) and Adwords know what I’m talking about here.
The list could go on for pages. Regardless of how data-centric your role or organization is, it’s frustrating when two reports don’t match. And typically, if there are two reports that don’t match, there is a good chance there are numerous others that don’t, as well.
You want to be a more quantitative marketer. But these at-odds reports seem like more harm than good. Don’t despair. It’s possible to have an analytics program that provides real value, in a reasonable amount of time, with minimal back-and-forth. Here are six ways to get started.
Step 1: Define Your Reports Clearly
Start by gaining clarity on what a report is supposed to mean. This is easier said than done. Ask yourself: If the data doesn’t clearly roll up to a defined KPI, who cares? Once you’ve defined a report’s purpose, label it prominently as such, whether in the header, footer or somewhere else. You don’t need 37 footnotes in each report, but you shouldn’t assume that “people just know what this report is, because they’ve been looking at it for a while” either. Find a healthy balance between precise definition and annotation overkill.
Step 2: Validate Across Departments
Once your reports have been defined, share them across departments to ensure that they meet everyone’s definition of “truth.” This requires collaboration between marketing, IT and analytics. In some companies that’s three different people, in others it might be one (but hopefully it’s not zero!). When all involved parties have signed off, validate with your stakeholders. Make sure that the people running the reports are on the same page, and the people reading the reports know, and value, what they’re looking at.
Step 3: Audit Your Reporting Tools
Businesses often have too many reporting systems in place. I know of some marketers using GA, Omniture, Looker, VWO and Salesforce. Each department likes to pull “their” numbers from “their” system, and even then, different people in the same department sometimes pull from a different system because that’s what they are used to. The reports are then kept in Excel, Google Docs, on a network drive or on someone’s laptop. Determine which reports you actually need and which tools provide them. Standardize on solutions across the organization, and streamline your stack to only the systems that you really require.
Step 4: Designate an Owner
Now that everyone is on board, decide who owns your metrics. This is often the trickiest step, fraught with organizational, technological and political challenges. But while it’s not easy, it’s critical: Successful analytics programs are almost always centralized.
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