Over my last two blogs, I have taken a decidedly apolitical view of some of the data used in the debate around healthcare reform in the U.S. and provided some commentary on why the data used may be insufficient to thoroughly explain the current situation in a comparative context. Interestingly, these same five shortcomings can also have a detrimental effect on our ability to motivate customer-focused change in our organizations. In this final entry, I will recap my criticisms of the data on healthcare reform and talk about what we should take away in our daily roles of analyzing and interpreting data in order to help our organizations become more customer-centric.
The five criticisms, and what we can learn, are as follows:
1) What is the underlying question we are trying to answer?
We often hear from clients that their primary goal is to measure loyalty; I respectfully disagree. Simple measurement is not enough – we need to consider the key business issue our company is dealing with and how customer strategies impact that issue. This, of course differs by customer (or, at a minimum, by industry).
In a generic context, our goal should be to threefold – first, we need to understand how customer loyalty can help us to maximize the financial health of our company. Second, we need to understand what customer experience gaps exist in our environment that prevent us from realizing these financial gains, and third, we need to establish processes and procedures that enable us to systematically close these gaps. In undertaking these three steps, we will enjoy some collateral benefits:
- We will understand where we have financial security (and risk) in our customer base.
- We will understand who our most valuable customers are.
- We will understand what potential revenue gains exist among our current customers.
In short, measurement is only the first (and perhaps easiest) part of the process. To gain the full ROI on a customer loyalty initiative, we need to address all three questions. This requires that we carefully consider the core business need and frame our research around addressing this need.
2) Is the outcome metric the right metric?
There is a lot of debate about the metrics – loyalty, NPS, satisfaction, value, quality; the list goes on and on. Here’s the harsh reality – there is no one right answer! All of the metrics just mentioned can have some utility in a customer loyalty initiative; the key is to align the right metric to the business question so that the results are reliable, actionable, and resonate in our firms.
3) The role of exogenous variables in our analysis
In the healthcare reform example, I talked about how exchange rates can impact how we view the per-capita spend on healthcare. These types of factors that are outside our environment (and, by extension, outside our control), can have a meaningful and material influence on how we interpret our results and make change over time. While we cannot control these factors, we can often control for them in our modeling. It is wise, therefore, to be looking ahead to determine what factors we might be facing in the near future. Examples include:
- Changing economic conditions;
- Regulatory changes in your market;
- Competitive forces – new entrants, exiting competitors, supplier influences, resource constraints, etc.
- Changing consumer tastes; We recommend taking a fresh look at your program every couple of years to see how things are changing in your environment (and we need to revamp in the program to keep it fresh, relevant, and impactful).
This can also relate to how we assimilate non-survey data into our analysis – while this strengthens our analysis and recommendations, it is important to identify the limitations of such data. For example, if we are incorporating financial data into our analysis (and we should to ensure we are tying back to tangible business outcomes), we may need to deal with the same exchange rate issues (when dealing with international entities), which can impact how we interpret and use the information.
4) Selection bias
The cornerstone to any analysis is the sample that is used to build the data set. It is imperative that we have well thought-out sampling plans. For example, some items to consider include:
- How do we define an account – one person or many people? One customer organization or entities within that organization?
- Should our sample mirror our revenue composition? If so, do we weight results, control the sample, or both?
- Do we have access to (and can identify) all relevant customer contacts? Should we differentiate by decision role, for example?
Taking the time necessary to build a sound sample frame will help to ensure your confidence in the results.
5) Overly simplistic models
Albert Einstein said “Everything should be made as simple as possible, but not simpler.” The same is true in terms of our customer loyalty efforts – we need to align the rigor of the data with the gravity of the business question at hand. Sometimes this will be as simple as asking a handful of questions and reporting top-two box scores; other times, it will require a highly complex sample and survey design with hyper-complex statistical analyses.
What won’t generally work is an overly simple, one question survey with an open end attached. You will get data (sometimes a lot of it), but you will spend more time trying to find the story in the data than if you had a more rigorous tool.
The idea of starting with the end in mind often works – if we can identify what information we want to convey, it helps us to properly design the program in total.
So, to tie it all up – the prescription for a sound analysis of customer loyalty is alignment of loyalty to corporate objectives, careful planning to make sure that the metrics sync with the business objectives and use of proper techniques.
I hope you found some value in this analysis – if you have questions or comments, please let me know.
Sr. Vice President, Consulting Services & Resource Management