Customer Analysis: Overcoming the Imperfect Data Trap


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Have you ever felt like you had all the facts to make a decision? If you are like me — never. Whether for a new smartphone, a new car, someone to prune your trees — there is always more information available on the web, another friend to speak with, another competitor to consider.

Eventually, you just have to “suck it up” and make the best decision you can make with the information you have. You will NEVER have all the information — you just have to feel like you have enough to make an informed judgement.

We all make those sort of decisions in our lives outside of work – that is why I find it so surprising that marketers frequently get “stuck” and cannot make a decision without every possible piece of information.

That’s why I recommend applying the “good enough” strategy to data collection and analysis.

Note: This post is a bit more “down in the weeds” than others I write — if you want to learn about how to direct your team to overcome this thorny issue, pls read on…

In the early stages of customer data analysis, the data will not be very “clean” and the tools you will use will be very basic (think Excel). On the data side, often there are duplicate customers, bad or NULL data in certain fields and incomplete physical and email addresses, among other issues. On the technology and skilll side, your team may only have basic Excel skills so far (note the “so far”).

Yet Marketers can’t wait for perfect data. The pressure is on, NOW, and we need to find insights and take actions on them in the short-term for there to be a long-term at all!

I’m assuming you already have a marketing strategy with campaigns in place. You need to know which campaigns are the most effective to which customers and why. In order to get answers to those questions, you have to find a way to “duct tape” together a way to get that data.

What are the challenges and how do you overcome them? The most common data-usage barriers we see with our clients fall into three general categories: skill, technology and data quality.

Let’s look at each and explore some options for overcoming those obstacles.

1. Lack of Skill Set. If you don’t have the right person on your team to answer your questions, make your questions simpler. Try to stay more macro. Ask questions that can be answered with one analysis at a time. For example, ask “what are total sales from customers redeeming this coupon,” first. Then take the data on just those customers and look at average order value. Then look at how many dollars were spent on the promoted items, and so on. By setting up your questions as a series of basic analyses, you can make them easy to do for a team member with basic Excel skills.

2. Lack of Technology. Everyone has Excel, and most marketers can do the basic analyses and a couple of the “If-Then” and Lookup sorts of activities. The key is to make sure that you structure your IT request to ensure that the data that comes can fit into Excel (less than 1mm rows and not too many columns). If necessary, ask for a random 10% sample of customer data to make sure it fits in Excel.

3. Lack of Data Quality. By doing some basic Excel work, you can clean up your customer data to permit the basic analyses we discussed above. The approach is not to solve “the problems of the universe,” but to simply identify and exclude customers whose data is obviously incorrect. Eliminate addresses without common extensions: org., com., net. or edu. Eliminate customers with spending less than $1 or more than $10,000, customers with negative transaction counts or 100s of transactions. Doon’t try to fix them; just exclude them for now and worry about them later. As you do more and more of this sort of analysis, you will build a list of exclusions that you can repeat time and again, faster than before.

Just get rid of as much bad data as you can, and move on.

A 70% correct solution is 100% better than what you had before. Just about every marketing publication and blog talks about the critical importance of data to enhance effectiveness of marketing campaigns.

You know you need data. Get it any way you can.

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Republished with author's permission from original post.

Mark Price
Mark Price is the managing partner and founder of LiftPoint Consulting (, a consulting firm that specializes in customer analysis and relationship marketing. He is responsible for leading client engagements, e-commerce and database marketing, and talent acquisition. Mark is also a RetailWire Brain Trust Panelist, a blogger at and a monthly contributor to the blog of the Minnesota Chapter of the American Marketing Association.


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