Analyzing your customer data can produce all kinds of interesting, actionable information. However, in order to get a complete picture you need to be able to analyze and correlate all of your data – and this can be surprisingly difficult.
Here are the four most common reasons why:
- Incomplete or Inconsistent Data – If your customers are resistant to providing all of the desired information or you don’t have a good system in place to collect needed data, your files will be incomplete. Even if you are collecting data, it may be so inconsistent that your systems can’t tell when multiple files all refer to the same person. For example, Jenni Dinnen on 7 Union St, Jennifer Dinnen on 7 Union St, and Jennifer Dinnen on 7 Union Street may be showing as three customers rather than one.
- Data Not Shared Between Silos or Divisions – If your customer service, accounting, warranty and other systems do not use the same unique identifier for each customer or product, or are not capable of “talking” to each other, doing a complete customer analysis will not be possible. For example, the product codes used by one of our client’s sales team to place orders in their internal ordering system are different than the product codes used by their purchasing department to order those same products from the factory in China. This makes it very difficult to analyze the entire life cycle of each product.
- Decisions about How to Treat Households –Should two members of the same household be treated as one customer or two for data analysis purposes? This is not as straightforward a decision as you might think. Here’s an example: For a married couple, XYZ company would probably only want to mail one (1) holiday catalog (in this case both people should be counted as one (1) household and only one (1) catalog should be sent). It does seem silly and a waste of money that my husband and I both get the same Pottery Barn holiday catalog.
- Need for Manual Work to Clean Up the List – As mentioned above sometimes it takes an actual person to review the data, make intelligent decisions and clean it all up – before any analysis can begin. This takes time, patience and experience. Many companies leverage outside firms, such as MacKenzie Corp., to do this type of work.
In future blog posts I’ll delve more deeply into the problem of incomplete or inconsistent data, look at how to fix this problem and talk about how your business can benefit from doing so.
