Pick the right customer with your data?
In the March 2014 issue of the Harvard Business Review (HBR), Robert Simons, the Charles M. Williams Professor of Business Administration at Harvard Business School, writes that organizations need to identify their primary customer type, and focus their business delivery there. With the right subscription you can read the article here. The author’s thesis is that organizations won’t dominate their markets by trying to be all things to all classes of customer. Winners know who they are targeting, and why.
However, if organizations rely on their own data to classify customers and determine, for example, their profitability, I question the value of their results. Our “Poor Data Quality – Negative Business Outcomes” survey shows customer data quality results that should make you weep. Please keep the following two charts in mind as you read on. Note that in both cases, more than 80% of respondents cited poor customer data as a cause of difficulty or a limit on success. Would you make smart decisions and pick the right customer with data in this condition? Click either graph to enlarge it.
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Four Steps to Pick the Right Customer
Robert Simons discusses four critical steps to pick the right customer for your organization. Let’s take a quick look at how poor customer data quality will shoot down Step 1 before you ever get started.
Step 1: Identify Your Primary Customer – Robert Simons identifies three decision elements to help you pick the right customer as your primary target. These elements are perspective, capabilities, and profit potential.
Perspective refers to your business vision, culture, and the folklore of your organization.
Your reputation in social media and survey results will give you insight into whether your various customers and prospects share your concept of perspective. Unfortunately, your poor customer data is not likely to help you get a clear picture of customer buying habits, returns, use of incentives, and other insights that might confirm your concept of perspective, or cause you to vary it.
Capabilities, according to Simon, “refers to the embedded resources of the firm” and determines your organization’s critical differentiation for various classes of customer. You are probably positioned to serve certain types of customer better than others, but with poor quality customer data, you will have a hard time proving it. The same limitations you will have in validating your assumptions about perspective will challenge you in customer analysis.
Profit Potential segments your customers by their current and likely future profitability. Customers who can easily switch suppliers, buy only with incentives, have high rates of return, and need high levels of customer service are probably not your big profit generators. Does your poor quality customer data give you the profitability insights you need? Are you cross-selling and up-selling based on revenue instead of profitability? Customer data that is inaccurate, ambiguously defined, disorganized, or stale, is more likely to do your decision-making process harm.
Here are the other steps Robert Simons addresses.
- Step 2: Understand What Your Primary Customer Values
- Step 3: Allocate Resources to Win
- Step 4: Make the Control Process Interactive
I will look at the impacts of poor customer data on decision-making in steps 2-4 with future posts. However, I think you get the idea. You won’t have confidence to make good decisions that are informed by bad data. Chances are, you won’t pick the right customer. Poor customer data quality will lead you to a negative business outcome when attempting to segment your customer base and pick the right customer for your organization.
The Bottom Line
As Cal Braunstein of the Robert Frances Group and I speak with survey respondents we hear that many organizations are in denial about their poor data quality. One good way to send the wake-up call is to have us conduct a poor data quality – negative business outcomes survey especially for your firm. Your executives will receive eye-opening insights that will inspire the person in deepest denial to data quality improvement action.
One thing we hear again and again is that good things begin in earnest when high-level executives understand the data quality issue and get behind a data quality initiative. Why not get started down the road to data quality success today? Contact us, and let’s get your data quality improvement initiative underway.