Q & A with Karen Majka, Project Manager, CustomerSat Professional Services
What are Positioning Charts?
KM: Positioning Charts in CustomerSat Enterprise 8 analytics are scatter plots that map your customers, sales regions, channels, served markets, products, or call centers based on mean satisfaction, loyalty, advocacy, and other customer or employee attitudes and behaviors. The charts enable:
- Sales execs to better manage accounts and territories
- Marketing execs to better manage channels and vertical markets
- Product managers to better manage product lines
- Service and support execs to better manage call centers
- HR execs to better manage workforce satisfaction and commitment by site.
We want to hear all about that. But first, how do you create the charts?
KM: Simply choose any rating question or variable for the X-axis – say, “overall satisfaction” or “number of years as a customer” – and any rating question or variable for the Y-axis – say, “likelihood of re-purchase” or “account tier.” Then select the type of data point you want to plot: strategic accounts, industries, products, regions, call centers, etc. (Figures 1 and 2). The X- and Y-coordinates of each datapoint are the mean scores of the survey responses or variable values that the datapoint comprises. You can also apply filters to the charts. It’s that easy.
Figure 1. Positioning Chart showing overall satisfaction and likelihood-of-re-purchase by strategic account. Rolling your mouse over each datapoint shows its co-ordinates and value of N (number of responses that the datapoint comprises).
Figure 2. Positioning Chart showing industries.
How do Positioning Charts enable sales executives to better manage accounts?
KM: Satisfaction-loyalty charts using customer accounts as datapoints provide one example. Accounts that are high in satisfaction and loyalty are called “Loyalists” – they advocate your company to others, creating positive word-of-mouth effects. A key corporate asset, “Loyalists” deserve to be delighted and preserved.
Accounts high in satisfaction but low in loyalty are “Mercenaries.” A company needs to make certain that every transaction with these often price-sensitive customers is profitable.
Accounts low in satisfaction and high in loyalty are “Hostages.” As soon as more attractive alternatives become available, these accounts are likely to switch. If possible, they should be satisfied.
Accounts low in both are called “Antagonists” (or “Defectors”), who create negative word-of-mouth by broadcasting their dissatisfaction to others. Neutralizing Antagonists may be as simple as apologizing or may require re-funding their money. If a significant number of accounts are Antagonists, there may be a mismatch between product or service capabilities and sales and marketing messages.
Similar insights are gained by using products (instead of accounts) as the datapoints. For example, if a product falls into the “Mercenary” quadrant – indicating that its customers are high in satisfaction but low in loyalty – introducing switching barriers into the product, if possible, would likely improve its profitability.
Can Positioning Charts be based on employee responses and variables as well?
KM: Absolutely. For example, use “employee satisfaction” on the X-axis and “years of service” on the Y-axis. Datapoints can be departments, business units, call centers, or sites. The labels “Loyalists”, “Hostages”, “Mercenaries”, and “Antagonists” may apply to groups of employees as well as customers.
A revealing analysis is to create two Positioning Charts using the same datapoints – such as call centers or sites – but using customer satisfaction and loyalty in one chart and employee satisfaction and service-longevity in the other. See if the call centers or sites don’t fall in approximately the same positions on the two charts.
Where did Positioning Charts come from?
KM: Two books, The Service Profit Chain (1997) and The Value Profit Chain (2003) by Harvard Business School Professors James Heskett, Earl Sasser, and Len Schlesinger, popularized charts of customers with satisfaction and loyalty on the axes. Positioning Charts, which let you select any rating questions as the axes and any type of datapoint, generalize HBS satisfaction-loyalty charts.
What’s a “service profit chain?”
KM: In a nutshell, the service-profit chain states that:
- Workplace climate and quality drive employee satisfaction
- Which drives employee loyalty and productivity
- Which drives service value
- Which drives customer satisfaction and loyalty
- Which drives profit and growth.
Joe Wheeler, Executive Director of The Service Profit Chain Institute, a Boston based consulting firm that works with organizations to help them implement Service Management strategies says, “Growing your percentage of ‘Loyalist’ customers can have an enormous impact on profitability. But it starts with having actionable data. We manage what we measure.”
I understand that Positioning Charts can also tell a lot about the relationships between variables.
KM: Yes. To gain insights about the relationship between the X-axis and Y-axis variables, choose either one of the axis variables in a Positioning Chart as the datapoints. If the datapoint variable uses a 10-point scale, the chart will have ten datapoints, one for each rating value. The shape of the curve formed by these datapoints tells us about the relationship between the two variables. Datapoints forming a straight line indicate a linear relationship (Figure 3). Datapoints forming concave upward or concave downward lines indicate non-linear relationships.
Figure 3. Positioning Chart in which the datapoints are the values of the X-axis variable, a 10-point rating question. Chart shows a highly linear relationship between X and Y variables.
In the examples of Figures 4 and 5, the X axis is an independent variable, the datapoints are the values of the X-axis variable, and the Y axis is a desired outcome, such as overall satisfaction, likelihood of re-purchase, or willingness to recommend. For clarity, in Figures 4 and 5 we label the datapoints “X=1”, “X=2”, etc., and show a best-fit curve on which the datapoints lie.
Figure 4. Concave upward curve. To improve the desired outcome, focus resources on providing outstanding performance and moving neutral customers to high satisfaction levels (maximize top-box percentages) for the independent (X-axis) variable.
In Figure 4, the curve formed by the datapoints is concave upward. This indicates that we make greater gains in the desired outcome by focusing resources on providing outstanding performance of the independent variable and moving neutral customers to high satisfaction levels (maximize top-box percentages), a common scenario. In Figure 5, the curve is concave downward. This indicates that we make greater gains by focusing resources on maintaining adequate performance of the independent variable and moving dissatisfied customers into mid-level satisfaction categories (minimize bottom-box percentages).
Figure 5. Concave downward curve. To improve the desired outcome, focus resources on maintaining adequate performance and moving dissatisfied customers into mid-level satisfaction categories (minimize bottom-box percentages) for the independent (X-axis) variable.
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