My CRM strategy includes a cross-sell process that classifies customers (leads). I understand how these classifications contribute to customer value, both when the process guesses right and wrong. When it gets it wrong, I suspect that some of the time this is due to insufficient quality in my information. I want to build a business case (with ROI for the different options) to fix this, measured by increase in total customer value. With so many vendors, tools, methods etc of "improving" the information quality (cleansing, augmenting, enhancing, validating, matching, mining, etc.), how do I compare the likely effects on my cross-sell process of such different approaches? How do I ensure I get the best bang for my buck? Should I trust a vendor to help me? Are there benchmarks?


Sid Adelman’s Answer: The first thing is to understand the quality of your data. It's likely that some columns are fine while others are terribly dirty. You can't do much before you know just how bad things are. You probably already know which data must be of high quality to satisfy your customer classification and, of course, those are the columns to focus on. If you don't know, talk to your business users. Give the vendors a sample of your data and ask them to do their magic and compare the results. As part of this bake-off you will want them to describe the process they went through to achieve their results to determine just how much effort you will have to expend.

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