In June, Netflix announced a price increase and the airwaves erupted in complaints of price gouging. But, this price increase may have been a brilliant, efficient and considerate
form of market withdrawal under certain circumstances.

Pricing is employed as a means of market penetration, but is seldom utilized for exiting a market. Yet, pricing provides a means of market withdrawal which allows customers to find alternative suppliers in an orderly manner. While it is unlikely that Netflix truly intends to exit the business, there utilization of pricing reminds us of several previous activities and it serves as a reminder of an important, underutilized method of market withdrawal.

We have worked with clients in considering pricing as one of a number of options for exiting a product or product segment. The objection most used is that the company withdrawing from the market does not want to be perceived as gouging their clients. Instead, the preference is to withdraw product from the market and leave the loyal customer in the position for an alternative sometimes with, but often without, a recommendation from the supplier. We have, nonetheless, worked successfully to implement a pricing approach to market withdrawal. We must also say that market reaction was quite positive.

Although long before the founding of Ozanne Analytics, one of the best examples of utilizing pricing to withdraw from a market comes from GE in the 1960′s. GE manufactured a key component of a large number of both commercial and military products. This component is virtually unknown to most people today. It was a vacuum tube (For information, go to http//en.wikipedia.org/vacuum tube). The tube business was being cannibalized by a somewhat more familiar product – the transistor or semiconductor. As demand declined for vacuum tubes, marginal costs increased substantially.

Many suppliers had withdrawn from this increasingly unprofitable market leaving a small number of players mulling alternatives from market exit. Customers were moving towards alternatives, but had to change their processes (and products) to meet the new technologies. GE decided it could not withdraw and leave these its customers in a lurch. Instead, GE decided to “encourage” faster movement away from the tubes to the new technology by increasing the price. In many cases, the price increased by hundreds of percentage points. This approach, they reasoned, provided cost incentives to change technologies (or suppliers) while providing support until that change could be effected. The decision was communicated and the strategy implemented.

Within months of the price increase, the other two major suppliers of tubes WITHDREW from the market leaving GE as the only provider. As late as the 1980′s when I was working for GE, the Owensboro, Kentucky plant met the worldwide demand for these tubes and was immensely profitable. Moreover, the customers were very pleased that GE had not left them without alternatives. In fact, in the short (and long) run, the strategy was extremely effective.

As you think about market withdrawal, think about pricing as a facilitator. Do it right and who knows.

We have had inquiries regarding behavioral segmentation – requests for more detailed guidance. The summer has been really busy and I did not want to give your questions short
shrift so I am a bit tardy in responding to your comments. Here are some thoughts regarding the methods that would apply to a hotel chain.

A hotel chain may wish to utilize the current in-house data on its customers. This may not be perfect, but it is a good, reliable and inexpensive place to start.
Let me begin by noting what you can expect to ascertain from the analysis.

1. You will be able to determine key segments and the percentage of your customer base in each segment. I can tell you that most of these segments will be familiar to you because of your knowledge of the business. Some segments which you may hypothesize exist will probably be subsets of others. It will provide targeting information and opportunities. There will be several segments of which three will be: (a) loyal frequent customers; (b) loyal less frequent customers; and (c) one time visitors who you will never see again
   and with whom you should not spend any time.
2. You will understand the characteristics of the segments for further use.
3. As you act on the segments, you will be able to measure movement across segments over time.
4. Don’t forget that since this is your data, no one else will be able to understand your customers as well as you can understand them.
5. You will be able to use any questionnaire research to hone your understanding given what you know from the customer behavior. That is, you can also target your marketing research given what you find from the analysis of your data.
6. You can also utilize the data to compare with other studies you have performed although this can be a bit tricky sometimes.

In addition, when you have finished the analysis, you are likely to be able to utilize the analytic output together with other information, such a census data, to target potential new customers immediately. You can always go back and refine your data collection and analyses later if desired.

The first step is to assemble the customer data you have on customer demographics (address, affiliations, and the like), visit information (dates, length of time, amount spent – which can be used to examine flow patterns), response to promotion programs and whatever else you might have.

From a statistical standpoint, there are several methods which could be used:

1. Cluster Analysis – K-means or hierarchical method which is generally preferred in the industry;
2. Q-Factor analysis which I prefer because of the reliability of reading the principal components and the rotated structures;
3. although with very large samples or 10,000 or more, R-Factor analysis gives quite satisfactory results.
4. Discriminant analysis which is appropriate with some data.

The key is to have someone perform the analysis who:

1. takes time to understand the data;
2. thoroughly understands the technique and nuances
   of interpreting the output from the analysis;
3. can help you apply the output to your needs.
4. Remember, another advantage of this approach is
   that it utilizes your proprietary data and is doing so gives you a strategic advantage over other approaches that tend to use information gathering templates and unreliable, ineffectual data gathering.

If your support cannot do all four of these, it is generally better to do something else. Using this approach correctly will provide insights with which you will certainly be pleased. However, you need to realize that the findings will not be rocket science as one of our European clients once said. Then, he added, “Of course, if it were rocket science, it would provide information which I wouldn’t believe”.

While we like to utilize three or four years of data, we find that even six months provides extremely useful insights allowing for actions when debate over actions has been the previous norm.

In terms of expectations, it is important to realize that there are likely to be few revolutionary outputs from the analysis. Rather, the analysis is more likely to define the six or eight critical issues for meeting customer needs as opposed to the 20 or more issues you currently feel you are facing.

You may have someone on staff or readily available who could help you with this. If you need outside assistance, it is our business to be able to help and we can so do as either project consultant providing guidance or as the principal consultant/analyst

As an aside, I think that you will find that the results are far more functional than results of surveys. Our experience (and the experience of most knowledgeable statisticians) with survey techniques is that the sampling frame can be perfectly designed and the output can be incredibly biased because of response rates. For an individual company, statistical techniques are usually less important because you possess information on a universe of your customers, not a sample.

I am confident that once you work with behavioral segments, you will see it provides more value and real feedback on any changing dynamics of your customer base than you have gained through other approaches.

Good luck if you pursue behavioral segmentation to understand your customers better. If we can be of assistance, that is what we love to do.

Over the past several months, numerous business people have asked about the salience of the segmentation approaches offered by a number of companies. These offerings are generally base upon demographic information available from a variety of sources especially the government census and its projections. The segments often come with cute names similar to those we have heard over the years like “soccer moms”.

Our response is that one must be careful in employing these pre-designed segmentation approaches because they are not based upon things that are related to the business at hand. Rather, they are generic. As with “soccer moms”, the capacity of the generic, pre-designed segmentation is limited. In 1996, “soccer moms” were defined as women with young children of pre-school or early school age. These women were often imagined to drive an SUV and cart their children to all sorts of events. Moreover, they were going to decide the 1996 election. As it turned out, women that fit the definition of “soccer moms” voted almost exactly as women writ large who voted for Bill Clinton 53% to 39%. So, while interesting for the media and the political junkies to talk about, there was no differentiation provided. Political party affiliation, family party affiliation, group membership and previous voting behavior determined present voting behavior. Behavioral segmentation would have been far more powerful in 1996 than the demographic definition. However, it was also far less interesting.

So it has proven in the business world as well. We have tested a number of segmentation approaches defined using demographic and attitudes. The objective has been determining the salience of the segmentation in predicting behavior. In each instance, other characteristics were far more salient in predicting behavior than these approaches. Moreover, in most cases, the segments overlapped enough so that they really did not exist as independent groupings. That is one reason why you see coupons dispensed at grocery and other stored based on behavior. In addition, segments differ substantially across product categories so a one size fits all does not work.

How individuals have behaved and been treated in the past is a far more accurate predictor of future behavior than demographic segments with attractive names. Individuals who purchase a particular product consistently continue to purchase that product unless something powerful intervenes. New customers need to be reinforced in their buying decision and previous success in certain activities by the seller is the most powerful determinant of future success with new customers. So, while demographic segmentation often offers catchy descriptions of segments, they seldom work properly. Moreover, these demographic segments often overlap with one another further limiting their power.

Segmentation’s real power often comes from utilizing a company’s own data. This data provides behavioral insights into the customer providing critical strategic and tactical information often unused or underused. Powerful segmentation approaches meet three criteria. Segments must be:

1. relevant;
2. independent;
3. predictive.

It is our view that if the segmentation approach fails these criteria, it should be discarded.

Have you ever been standing in a line at a “Convenience store” waiting for the clerk to serve a person in front of you wanting to buy lottery tickets or cigarettes or propane? If so, you have not been shopping at a convenience store owned by a customer of Ozanne Analytics. Customer service with rapid entrance and egress is never more important than at a convenience store. Customers are in a hurry whether it be to get their morning coffee or pick up that missing ingredient required at the party that is about to start. Service delay causes dissatisfaction and a search for a satisfying experience some place else. Working with a chain of regional convenience stores in an analysis of their store level data and data about the environment in which each store operates, our analysis showed that there were clear store characteristics related to increased revenue and, even more so, increased profitability. There were the easy answers that most convenience stores discovered years ago that are directly related to ease of entrance and egress:

1. Sufficient parking;
2. Stop light allowing for a guaranteed maximum waiting time;
3. No divider strips limiting access from one direction; and
4. A corner location.

Perhaps, since most convenience stores incorporate two or more of these in there site location, these were not the large drivers of profitability. A relatively straightforward statistical analysis of the data revealed one characteristics of store service related to a 6% elevation in revenue and a double digit profit differential. These stores were few in number, but each had an innovative manager and one common characteristic – they had established a second check-out to handle “special event” purchases, usually the sale of lottery tickets. A potential revenue/profit enhancement solution seemed at hand. From here, the only challenge left was to decide what other slow moving services should be offered at a separate check-out point and two other services emerged from observation and discussion – cigarette and propane sales. Second check-out points were established to handle these slower moving types of transactions including:

1. Lottery;
2. Propane;
3. Cigarettes usually; and
4. Sometimes gasoline.

This approach allowed the quick entrance and exit customers to avoid the interference of slower moving customers. Customer satisfaction increased and profitability rose substantially and even more than predicted.

Have you ever thought that you were overpaying for a service that you wanted? If so, you may have been purchasing from a supplier who ignored all of the data they possessed on the issue. Here is the story of one business with a lesson.

The Ozannes were asked to help better understand the impact of price on retention rates for an important business to business supplier of information. The business was convinced that it possessed a large market share (it turned out to be 25%) and that its competition was extremely limited. Since it believed it was unable to meet its revenue targets through business growth, senior management was determined to increase prices to meet the growth targets.

Of course, there are many elements that drive retention and price is but only one portion of the value/price relationship. With that in mind, we set about to attempt as best we could to isolate the impact of price with the client understanding we would error in our final assessment.

The assessment showed that their was relatively unitary elasticity in the relationship between customer retention and price at a price increase of between 4% and 6.7%; with greater than unity retention at rates below 4%; and a dramatic falloff of about 1.3/1 at rates between 6.7% and 10%. Above that point, there was no useful data. Utilizing this analysis, marketing recommended that the price increase be held to less than 6.5%. Upper level management overruled this recommendation and price increase were established at 8% to 10% over the next 4 years.

While changes in other dimensions of the market had influence, market share declined to something under 14% and loyal customer retention dropped from the mid 90% to the low 80%. First year customer retention dropped more dramatically from the low 70% to below 50% while revenue dropped considerably as measured in constant terms. Marketing management has since departed and major changes elsewhere are coming. Here, the Ozannes failed in their obligation to convincingly established the validity of their argument. Now, the challenge is help find the keys to save the business.

While pricing was not the only issue, and it never is, other problems were exacerbated as a result of a pricing decision based upon internal business drivers rather than market conditions.