You might have heard the term predictive analytics and thought “that’s what I’ve always wanted”!!! For most marketers, this is the case and you blame the IT group for not being able to provide the dashboard and reports to show you what you know the data is trying to tell you. While true marketers have a highly attuned sense of intuition and insight, better outcomes will also result when we don’t skip steps. Here’s five ways to get started down the path of predictive analytics that will help you work better with your IT team and bring about long-term results:
1) Minimze What You Want to Measure
Whaaaaa???? I finally have all this data and you’re telling me to minimize what I want? (Please note that I did not say simplify, but minimize–big diff). Many marketers drive business analysts and IT personnel crazy by wanting to have everything on a dashboard or available to query because of FONHI (fear of not having it). This is inherent in marketers and makes many of us equivalent to data hoarders who are scared that we might not be able to undertake the segmentation that we want at the exact moment that we want it. While painful, listen to your IT partners and focus on what it is that you really need to measure that will drive the business forward.
2) Get the Right Data
You don’t need all the data, but you do need the key data as we’ve previously stated. The likelihood that all key data is in the same database is next to nil; moving to an enterprise architecture where it doesn’t matter where the physical location of the data lives is key. Organizations will increasingly need to move to modern data architecture models that focus on tying systems together making the movement and aggregation of data the primary concern and not the database architecture.
3) Prepare Data for a Predictive Analytics Model
Data can only be properly analyzed when it can be joined and segmented in multiple different ways. You need to ensure that your data is clean, proper data hygiene practices are followed and that it is structured in such a way that you can easily access it, move it and measure it.
4) Putting Processes in Place for Using a Predictive Analytics Model
Ensuring that staff understand the importance of capturing and cleaning data for use in analytics is key. Reviewing all entry points of data across the customer lifecycle from the viewpoint of analytics is a useful exercise to identify all of the areas where data is incomplete, erratic, overly restrictive and identifying processes that cause friction to the user can help showcase which processes are in need for an overhaul.
5) Use the Right Tools
Predictive analytics is typically a new endeavor for organizations and requires different technology products and methodologies to maximize success. Ensuring that your organization has a strong technology partner that has the expertise to consult on both the business and technology fronts will allow your organization to expedite its digital transformation. While the right tools are a necessity, the know how is the critical element in the world of predictive analytics.