Customer Analytics Blog Series: Part 6 – Big Data Should Make ROI Easier to Measure

[BobL]

In my previous blog in the Customer Analytics series, I discussed the second of three challenges we uncovered in our survey about customer analytics usage. Now, let’s explore the final challenge, which has to do with users measuring a quantifiable improvement in their business to justify their investment in analytics.

Let’s face it – this is a challenge with most of the things we do in our business lives, and analytics is no exception. But when you have massive amounts of data at your fingertips, it really should be a lot easier to look back at past outcomes and measure the impact of a change you had made in the past.

But more importantly, the data should allow you to predict what the outcome should be if you do nothing, and then run a series of what-if scenarios to predict what the optimal outcome is. That way you can quantify the business impact for the people in the corner offices and help them justify the appropriate course corrections.

Let’s look at an example of how one company was able to quantify their analytics investment. Southern States Cooperative is one of our nation’s largest farmer-owned cooperatives, offering everything that today’s modern farmer requires through more than 1,200 retail locations across 23 states. They do a lot of business through catalogs they mail to customers and prospects, at a significant cost to their business. In an ideal world they want to send catalogs only to prospects, not to customers who would have purchased the promoted products anyway.

Check out their case study and video to learn how they were able to combine data from different departments and data warehouses to reduce the number of catalogs they mailed by 63%, while improving the response rate by 34%, and generate an estimated gross margin increase, less mailing cost, of about $200K annually.

Customer Analytics Blog Series: Perspectives from Industry Leaders

Part 1 – Customer Analytics Blog Series: Introduction

Part 2 – Customer Data and Insight Can Take Many Forms

Part 3 – Analytics Provides Input to Strategic Operations

Part 4 – Getting and Working with Data is a Problem for Many

Part 5 – Line-of-Business Users Want Access to Easier Tools

Part 6 – Big Data Should Make ROI Easier to Measure

Part 7 – Where are Companies Investing in Analytics?

Part 8 – Conclusion and Recommendations

Bob Laurent

Director of Industry Marketing