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Category: Creating Customer Value

Health Care Reform and Customer Loyalty Analysis, Part 2

In my last blog, I reviewed some data from the World Health Organization on the amount spent on healthcare in the U.S., Canada, Sweden and Japan. The results of the data were counterintuitive – the higher the spending on healthcare, the lower the life expectancy.

As good analysts, we should scrutinize any data to ensure that it is not only appropriately used (and that any limitations to its use are noted and considered when making recommendations), but also that it adequately addresses the core business question. Of course, for this to occur, we must have a well-defined, properly framed business question.
I set out a challenge for our readers to consider what limitations existed in the data and how this might constrain what conclusions we could make about the linkage between healthcare spending and life expectancy. In this blog, I’ll review five factors that could impact how we interpret the data.

1.    What is the underlying question we are trying to answer?
The first issue that creates confusion (and, potentially, misinterpretation of the data) relates to the underlying question at hand – is the goal in the healthcare debate to increase life expectancy overall, to minimize the expense of healthcare, to get coverage to more people, or something else? Of course, like the business questions we face daily, there is not a single answer; I would suggest, however, that pundits are trying to answer too many questions with a single data set.

2.    Is the outcome metric the right metric?
The second issue relates to the core outcome metric being employed – mean life expectancy. While longer life expectancy is good, it does not tell us the whole story; to know that, we must understand the underlying distribution of the data. Consider the following data distributions:

                  Graph 1                                                 Graph 2
 

                Graph 3                                                    Graph 4

 The shapes of these distributions clearly suggest differences at play in the data; they do, however, have one thing in common – their mean value (78 in this case).

Let’s explore what these might mean from the perspective of life expectancy – how would we interpret each of these?

Graph 1: The variation around the mean is fairly constrained; this suggests that most people will live within a fairly tight range around 78 years.

Graph 2: The data skew toward a lower life expectancy (roughly 58 years). What this means is while the average life expectancy is 78 years, many people (about one-third) will not live past 63 years; on the other hand, there are a great volume of people that live much longer lives, which effectively increases the average.

Graph 3: This is a normally-distributed set of data with wide variation around the mean; this tells us that while life expectancy is 78 years, there is a high likelihood that one could die much earlier or much later.

Graph 4: This is a bimodal distribution – it tells us that we have higher mortality at earlier years (approximately 58 years) and later years (approximately 98 years). While the mean of this distribution is 78 years, most people, in raw numbers do not perish at 78 – they either die much earlier or much later.

For us to effectively compare means, we have to make an assumption that the underlying distributions are normally distributed – i.e., graphs 1 and 3 above. Moreover, we also must know the standard deviation of the distribution to make effective comparisons.

Incidentally, if we suspect the impact of getting healthcare to more people is a healthier population, I would suggest that simply looking at the distributions would be sufficient – in this case, we would hypothesize that countries with uniform, available healthcare might look like Graph 1, while countries with less available healthcare options might have distributions like those in graphs 2-4. In this case, the variation is the more important metric, not the mean.

3.    Exchange rate and geographic pricing difference
The data shown have been converted to U.S. dollars for the sake of comparability. This could say more about the valuation of the U.S. dollar than the state of healthcare; moreover, it is not uncommon for pricing practices among healthcare organizations to differ geographically based on market pressures, governmental controls, and other factors.

4.    Selection bias
The World Health Organization has data on many countries throughout the world; the example I have shown focuses on four countries. The extent to which these are not representative could have a dramatic impact on the confidence we place not only in the analysis, but the strength of our recommendations moving forward. Worse, if the examples have been hand-picked to reinforce our preferred argument, then we cannot make a scientific conclusion from the data provided.

5.    Overly simplistic models
Even if we assume that the core business question is clearly articulated, the model shown is too simple to capture the nuances of such a complex issue. For example, the data do not allow us to control for any variables that may have an adverse impact on per capita spend levels.  Several have been articulated in the debate – for example, the extent to which limits are placed on lawsuits, which impacts the cost of business as it relates to necessary tests and physician compensation (for malpractice insurance premiums) as well as the nature of the competitive environment (which places downward pressure on prices).

Simple models, while attractive, usually have limited utility; in addition, the simpler the model, the more defined and specific the underlying business question needs to be.

These are just five things to consider (I’m sure there are more – please let me know if you have other ideas). In my next blog (the last in this series), we will explore what we can take away from this example as we focus on the topic of customer loyalty management analysis.

Mark Ratekin
Sr. Vice President, Consulting Services & Resource Management

How do you build customer loyalty in tough economic times?

Listening to the customer becomes even more crucial in economic downturns. It is the company that continues to meet customer needs by creating customer value, even if it means cutting into profits, that will come back quickly once economic conditions improve. 

Some of this is seen by watching current tv advertisements. Take for example, LensCrafters, an eyeglass company, who is currently advertising free lenses for children 17 years and under – www.lenscrafters.com. They have reached out to parents at a time before the new school year starts, recognizing that when there is a diminished household budget, children will be a high priority. By drawing the family into the store, they are also building household loyalty, not just that of a single customer. In many households there are multiple members who need corrective vision.

Another example is Sears’ protection plan when purchasing appliances, should the consumer lose their job – www.sears.com. Again this program is designed to attract and retain customers.

In both of these examples, the companies are striving to create customer value, which in turn has been shown to result in increased customer loyalty.

Pamela Toft
Vice President
Walker Information  

The More Things Change…

Over the past American Idol season, my colleague, Brad Linville, wrote two blogs about plotting each Idol contestants’ probability of winning the season 8 crown. The corollary to customer management is the benefit of accurately determining the future value of customers by using something like Walker’s Value Mapping process. You can read Brad’s first blog describing the Idol Map and the value of strategically mapping your key accounts here, it’s a good read.

But it’s his second blog on the topic, that inspired my current post. In that post, after the season finale of Idol, Brad shows how the Idol Map changed over the course of the season and discusses why businesses also need to periodically update their customer value segmentation.

The idea of changing your customer value segmentation got me thinking about a number of things, but I want to focus on one of them here. The underlying tenet of segmenting customers by their value is that higher value customers should get more resources/services than lower value customers.

But what happens when a customer’s value decreases to a point that you have to take away those resources or services?

The common sense answer is they will not be happy. But how unhappy will they be? What happens to their future behavior with your company? Can anything help offset the "demotion"?

Luckily a recent article in the Journal of Marketing has taken a step toward empirically answering these questions. The article, titled "Does Customer Demotion Jeopardize Loyalty?", provides these conclusions:

  • Demoted customers have significantly lower loyalty intentions, lower revenue, and fewer transactions after the demotion. All the empirical evidence points to these outcomes being affected by the demotion, not the reason why they were demoted.
  • The negative effects of demotion far outweighs the positive effects of being promoted to a higher value segment in the first place. Demoted customers end up with significantly lower performance than similar customers who were never promoted.
  • Clearly outlining the criteria for promotions/demotions reduces the negative impact of a demotion. This is achieved by shifting the locus of control to the customer ("you are being demoted because your actions did not meet the criteria, not because we wanted to"), but it does not fully offset the negative effect of the demotion.
  • Treating the demotion with sensitivity and sympathy also helps reduce the negative impact but does not fully offset it.

We are now left with two somewhat conflicting facts: 1) It is important, even necessary, to segment your customers based on their future value and to update that segmentation at regular intervals; and 2) The process of demoting a customer to a lower value segment can have a significant negative impact on their attitudes and behaviors toward your company.

Is there a way to structure an account valuation system in a business-to-business environment that minimizes the downside – the negative impact of demotion?

There are probably a host of ways to do this, but here are a few I’ve been thinking of:

  1. Develop a valuation method that accurately predicts a customer’s long-term value. Value segments based on things like revenue or the number of product lines purchased will be inherently unstable over time and result in a constant flow of promotions and demotions. A more stable segmentation scheme will include things like customer potential and the level of partnership between your company and the customer.
  2. Determine if it’s even necessary to tell the customer what segment they are in. Another colleague of mine has a post addressing this point that you can read here.
  3. Create a value proposition for each customer segment that is perceived as a win-win scenario. If you segment your customers correctly, and fully understand the segments and their unique needs, then you can create a service package that meets your customers’ needs and ensures that you are maximizing your profitability on those accounts. For instance, a large account that is already giving you a large portion of their share of wallet will not mind that you do not assign an extra resource to expand your penetration of the account like you have with a similar large account and a low share of wallet.

Like I mentioned above, I’m sure there are many more ways to minimize the prevalence of customer demotions but these are good ones to start with. Whatever strategies you employ make sure you’re taking a long-term view of your customers’ value so the more things change, the more you’re able to provide value to, and get value from, your customers.

Troy Powell, Ph.D.
Vice President, Statistical Solutions
Walker Information

More Minds Are Better Than One

Collaboration is difficult for large companies. Getting a cross-functional, global team together to participate in the development of a customer solution is difficult. Just navigating time zones is a real pain. As Strategic Account Managers we deal with this regularly. Thankfully, Web 2.0 is making participant involvement easier. Not only is participation easier, but the inclusion of more of the right minds often leads to better solutions.

Take for example a story about Best Buy that was relayed in a McKinsey Quarterly article titled Six ways to make Web 2.0 work. "When Best Buy experimented with internal information markets, the goal was to ensure that participation helped to create value. In these markets, employees place bets on business outcomes, such as sales forecasts. To improve the chances of success, Best Buy cast its net widely, going beyond in-house forecasting experts; it also sought out participants with a more diverse base of operational knowledge who could apply independent judgment to the predication markets. The resulting forecasts were more accurate than those produced by the company’s experts."

The Best Buy example is an effective demonstration of tapping into the cognitive surplus that often exists in organizations to develop a more effective solution. Just think about the possibilities if we were to tap into the same surplus that exists external to the organization. Companies are developing customer communities as a means to co-create new products and obtain input on existing products, services and support. It might make the job of solution develop easier and more effective. If you’re not already, you should be thinking about ways your business can put more minds inside and outside the organization to work on your most pressing business challenges.

 

Noah Grayson
Senior Vice President
Walker Information

Leslie Pagel

What’s in your tackle box?

Meet Pam. Pam is the Customer Experience Officer at a mid-size (and fictitious) software company, ExactTech. Over the past several years, ExactTech has experienced continuous double digit growth. While the growth is celebrated, it does create an ongoing challenge for Pam. Specifically, Pam is focused on creating and maintaining an exceptional customer experience during this period of rapid growth. 

When faced with critical business challenges, Pam typically heads outdoors because it helps her clear her mind. For this challenge, Pam decides to go fishing. After all, she is searching for answers.

Pam heads to her favorite lake. As she gets settled, her mind starts wondering. "ExactTech needs a scalable solution to serve and engage its customers," she thinks to herself.

She takes out her tackle box and thinks, "There have to be others who have faced a similar issue. What did they do? What were some of the lessons they learned? Were they successful?"

Pam looks into her tackle box and contemplates which combination of bobber, hook, bait, and location will yield the most fish. At the same time realizing she must leverage the right combination of resources from her network to generate a comprehensive list of ideas for solving her business challenge.

As she searches through her tackle box she selects a bobber and hook and thinks to herself, "which of my family and friends can help me with this challenge?" She decides to use this network as a sounding board, since they are ExactTech consumers.

Then she picks the bait. She wants a reliable bait, with proven experience. "Who has experience in adjusting to the challenges brought on by rapid growth," she questions. She thinks about her business community, the people at ExactTech, and other professional connections she has made. She identifies a handful of people that she wants to involve in her collaboration.

As Pam uses her GPS system to navigate the lake she wonders, "what tools can I leverage to reach beyond my network? I shouldn’t limit my brainstorm to people I know. I should engage others." She considers tools like LinkedIn, Twitter, and Google searches to extend her reach.

After a successful day on the lake, Pam heads home, but she isn’t leaving empty handed. She now has a plan for how she will use her network and tools to support this initial problem solving stage. She values the experiences and opinions of the people within her network and welcomes this opportunity to expand her professional connections to others.

What is in your tackle box and how are you using them to solve your business problems?

Credits: Comic Adaptation – Steve Miller; Design – Dan McCormick

Note: This blog was originally posted in Customer Connection on 4/14/2009.

Phil Bounsall

Let Your Customers Help You Forecast

Given today’s economic and business environment, there is much discussion about improving the accuracy of forecasts. I just read an article in CFO Magazine (http://www.cfo.com/article.cfm/12668080?f=search) about improving forecasts by challenging the inputs used. The article suggested various inputs of information and ways to make the forecasts more accurate. However, one critical source was suspiciously absent – the perspective of customers.

Sadly, this information is rarely used effectively in sales forecasting. Customer centric companies frequently put time, effort and money into gathering customer insights from a variety of listening posts that often reveal future purchasing intents and other intentions important to growth plans. Far too often the voice of the customer never makes it beyond the marketing and customer service departments.

Companies often tend to expect growth from all customers, yet each relationship is unique. Understanding these relationships—from our customers’ perspectives —can pinpoint where growth will come from and where it won’t. Or, in these tough times, we can identify where to expect high levels of customer retention and where to expect higher levels of attrition.

Finance executives are trained to focus on empirical models and analyses. Strategic account managers and sales professionals generally rely on more anecdotal models or gut feel about the relationship and creating value for customers. When it comes to forecasting, both methods have generally proven inaccurate enough to create large and sometimes unmanageable forecasting errors.

The introduction of insights from customers into those models can tighten up our forecasts significantly.