Demystifying predictive analytics: train and drive adoption

Saken Kulkarni, Slalom Consulting

Saken Kulkarni

By now your retail organization has analyzed the status quo, evaluated its infrastructure, designed its analysis, and visualized its data. Congratulations! You’ve gleaned rich insights using governed data that will truly drive your organization forward. But your insights will remain idling at the starting line unless you can spread the customer analytics gospel. Welcome to the final step in your predictive analytics journey toward customer centricity: train your users and drive adoption across the enterprise.

 

 

Demystifying predictive analytics_train and drive adoption

Whether it’s a modification to a company-wide healthcare plan or a divisional HR reorganization, change makes employees nervous, apprehensive, and resistant. Even in our personal lives, we often stick to what we know best, from exercise routines to lunch choices. This behavior is even more acute when it comes to technological changes. Luckily, there are ways to encourage and drive adoption without alienating your stakeholders. I have found that by following these four steps, adoption rates accelerate and your organization reaps major benefits.

1. Start small and start early.

The most common mistake that I see in retailers’ approaches to customer analytics is that they often rely on the “big bang” approach. For instance, all customer analytics methods and dashboards will be introduced at the same time while familiar reporting tools will be cut off immediately. Advanced customer analytics can be a daunting topic to understand—it’s much more than merely reporting your sales by product.

That said, start small by introducing a pilot program targeted at power users who will provide valuable feedback. Or only tackle one customer analytic measurement—like cross-sell propensity, for example—and obtain feedback before introducing more measurements. Additionally, begin introducing your plan for customer analytics early in the lifecycle. Update stakeholders during weekly meetings; demo your tools; and obtain feedback from your more apprehensive stakeholders to ensure that your solutions have the user in mind. I call it “ubiquitous training.”

2. Leverage technology for training material.

Most of us are inundated daily with emails and status documents, making it unlikely that we will read a fifty-page training booklet when we need to leverage our customer analytics platform. Moreover, as we learned in my post on data visualization, employees expect more from their work applications, devices, and platforms than they are currently receiving.

Rather than create page after page of screenshots, record videos demoing how to perform each calculation and drill into each visualization. A real-time video allows the user to follow along, pause, play, and fast forward to the section of their choice. Also leverage your social business prowess by sharing these videos on your company’s internal social network and allow for comments and voting. As a newly minted visualization fan, you don’t expect your reports to be one-sided, so why should your training materials?

3. Proofread your data.

You may have spent hundreds of hours preparing your data for analysis, identifying your input and target variables, and obsessing over the pixel width of the borders on your Tableau dashboards. But this hard work will be useless if your data is incorrect. Any good executive has internalized the important numbers of his or her business. Therefore, if your numbers are off, you instantly lose credibility and the training conversation shifts away from the applications themselves and toward the quality of your data. This isn’t a discussion that you want to have.

To avoid that conversation, conduct a thorough validation of your data at various points (e.g., before analysis, after analysis, and after visualization) before reporting the numbers. Your upfront work will pay off in the end.

4. Expect, tolerate, and encourage diverse opinions.

Even if you introduce your analytics step by step, record a wide variety of training videos, and proofread your data, you are not in the clear. You are bound to encounter stakeholders who are resistant to change regardless of how much care you put in your analysis.

Don’t be afraid of this dissent—encourage and embrace it! Diverse opinions on your customer analytics platform will ultimately make it better. Very likely, there are analyses or views that you had not thought about. Embrace the fact that your stakeholders are ready and willing to share their opinions and, when appropriate, incorporate them into your next release.

Introducing predictive customer analytics into your retail organization is a radical cultural change. It requires patience, perseverance, and a maniacal focus on details. But, if you analyze the status quo, evaluate your infrastructure, effectively design your analysis, visualize (not report) your data, and train and drive adoption, you are in great shape. Good luck!

About sakenkul
Saken is a Business Analytics consultant at Slalom Consulting. He focuses on the intersection of customer engagement, analytics, and data visualization

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