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February 2003

Improving Healthcare Marketing Through Market Segmentation and Targeting

The benefit of accounting for individual differences in research and analysis

Jon Pinnell
MarketVision Research 

Background –Market Segmentation and Consumer Heterogeneity

Today's rampant product proliferation makes it hard to imagine that there once was a simpler time with few products and easier choices. However, the days of one product for all buyers has almost universally passed. In an effort to appeal to buyers and differentiate their products from competitors' products, manufacturers and service providers have developed more and more products, each different than existing products. This trend has existed for years in virtually every industry – including healthcare.

Markets consist of products and buyers. Products differ because buyers differ. Buyers potentially differ in many ways: their attitudes, their perceptions of brands, their buying patterns, the needs their products should meet, their price sensitivity, and many other ways.

Wendell Smith, who in 1956 wrote 'Product Differentiation and Market Segmentation as Alternative Marketing Strategies,' formally introduced the idea of segmentation. Before this, econometric models explained most market structure, with the key determination being product demand and substitution as a function of price. Items such as product benefits, buyers' needs, marketing communication, and branding were classified as non-price competition and frequently ignored as not material. An interesting early work is Robinson's The Economics of Imperfect Competition.

Before the idea of segmentation was formally introduced, manufacturers started to realize that buyers differ, but didn't have the structure or depth of understanding of the specific differences, or why these differences mattered. Knowing that buyers differ, manufacturers began launching new products based largely on either their internal beliefs or internal capabilities.

Both product differentiation and market segmentation produce markets that contain varied products. However, market segmentation aligns the varied products with the wants and needs of the buyers. Market segmentation reflects the evolution from a product-driven organization to a consumer-driven organization.

Over the past 50 years, market segmentation has been one of the most researched marketing topics, and a standard for most companies. Despite the prevalence of the 'idea' of segmentation, there is little agreement on the 'practice' of segmentation. There are an astonishingly large number of specific approaches and techniques used for market segmentation. Questions (and heated debates) continue about what dimension should be used to identify segments as well as what techniques should be used to distinguish segments.

In deciding how to form segments, several questions must be answered:

• What method should be used to segment the market?

• What basis should be used to segment the market?

• How many segments should be formed?

The method of segmentation refers to when the segments are 'defined'. There are basically two methods of segmentation: a priori and post hoc.

Segmentation requires that respondents be grouped based on some set of variables that are identified before data collection. In a priori segmentation not only are the variables identified, but also the segment definitions. For example, consider a study in which respondents are to be grouped based on age and number of children. In this scheme, each respondent could be placed in the proper category as soon as these pieces of information were known.

Post hoc segmentation, on the other hand, requires only that the variables be identified. The segments are then determined by using an analytical technique (often cluster analysis)[1] after all the data is collected. For example, if post hoc segments were to be formed on psychographics, the psychographic measures for all respondents would be cluster analyzed. Thus, the segments will not be known until after running cluster analysis. This allows groups to be formed based on natural breaks in the responses. This method is sometimes referred to as response based.

After determining what method of segmentation to use, a second critical question that must be answered is what characteristics should be used to identify the segments. These characteristics are referred to as the basis of segmentation. Common basis variables are discussed below:

Demographic

Historically, the most common methods of segmentation have been demographic in nature – women between 18 and 44 years, for example. These schemes have frequently been motivated from a perspective of shopping or media behavior and often do not distinguish brand behavior well between segments.

Geographic

Geographic segmentation calls for dividing the market into geographic regions. This allows the company to operate in only a few regions, because of limited demand or resources, or the company can tailor some element of the marketing mix by region. Food products are frequently varied by region. For example, because tastes differ, recipes are varied for different parts of the country. The differing products are sold in the same container with the same advertising and promotion in different geographic regions.

Attitudinal

Attitudinal variables are used based on the idea that opinions, traits, interests, and lifestyles influence buying behavior. Put another way, people with similar psychographic profiles should respond similarly to various marketing mixes. In particular, attitudinal segmentation can be useful to guide positioning and communication strategies.

Product Usage

Product usage variables are also used to segment markets based on category usage, product usage, or brand loyalty. It is a common practice to segment the market into users and non-users and also by level of use – though the practice of focusing on heavy users has been criticized (Twedt).

Decision Process

People decide which product to buy in different ways. The influence that people exercise over decisions, as well as their brand loyalty, variety seeking behavior, information search, distribution channel used and promotional sensitivities all impact the brand that they will purchase. While this focus is more prevalent in business-to-business settings, it is perfectly applicable to consumer buying as well.

Firmographic

The firmographic variables of industry, revenue, and employee size are standard bases for segmenting business-to-business markets. Firmographic segmentation is based on the idea that companies can be targeted based simply on what industry they are in or how large they are. The special needs of segmenting the business-to-business market are discussed in Moriarty and Reibstein.

Needs Based or Benefit Segmentation

Rather than relying on descriptive factors (like demographics, region, brand loyalty, or product usage), needs based or benefit based segments are derived based on causal factors relating to the purchase process. People buy products, and decide between different brands, because of the unique benefits that one product can provide over another and the relative importance of those benefits. Benefit segmentation partitions the market into segments based on benefits sought. The resulting segments include people that are seeking to realize some benefit(s) with a particular product based.

Another question that must be answered in any segmentation study is how many segments to use. The decision on how many segments has considerations from both research (statistical) and managerial viewpoints. The segments can always be made more homogeneous by increasing the number of segments. This could continue until the number of segments becomes unmanageable. One practical guideline, although not a mandate, is that unless the market can be broken into at least three groups, the advantages of segmenting are not cost effective over a general market approach; and attempting to market to more than seven segments is not practical for most companies.

There are obviously many possible ways to segment a market. There is no standard right answer and each situation can produce a different 'best' solution. What should be obvious is that although segmentation research involves substantial scientific processes, it also relies inescapably on judgment and the art of segmentation. While not wanting to further fuel this specific debate, two solutions that we have found particularly effective include using product needs or buyers' motivations as the bases of segmentation.

The idea of using buyers' needs as a basis of segmentation was introduced by Haley (1968). It suggests the most effective way to divide the market to guide product design is based on the unique needs that buyers are trying to meet with the product. For example, with a managed care product, the needs might include monthly premium price, co-pay price, access, network size, and availability of preventive services with different groups of buyers placing varying importance of the various needs. Similarly, for a surgical automation device, the needs might include device price, cost per use, ease of use, efficacy, level of automation, and breadth of capabilities. To the extent that potential buyers have differing needs, segmentation – and therefore multiple products – makes sense.

An alternative basis of segmentation is attitudinal or motivational segmentation, broadly referred to as psychometric. While less clearly useful for product design, this approach has found success guiding product positioning and marketing communications. Early discussions in this area of segmentation can be found in Lazarfeld and in Dichter. Psychographics are discussed in general in Gunter and Furnham as well as in Piirto.

By specifically mentioning these two bases of segmentation we are not suggesting that others are inappropriate. Rather, each segmentation exercise is unique, and various solutions can each be appropriate for each specific situation. These two approaches, though, seem to have the broadest appeal in a wide variety of instances.

Before determining if a firm should pursue a segmented marketing strategy, it has been suggested the segments must meet six key criteria: identifiability, substantiality, accessibility, stability, responsiveness, and actionability (Frank, Massy, and Wind). These requirements are each discussed below.

Identifiable

The individual segments must be different from each other. If all buyers in the market behave basically the same, there is no need to segment the market. Likewise, if segments are found in which the behaviors across groups are basically indistinguishable, the segmentation scheme is not accomplishing what it should be.

Markets for non-differentiated, or commodity products would not benefit from a segmented approach. A manufacturer of these products can more efficiently reach the entire market with one marketing strategy than with several. There are, however, few markets that are absolutely not segmentable. That is, almost every market has identifiable groups that behave differently and by focusing on these differences, companies can increase their marketing effectiveness.

Substantial

The segments must be large enough to sustain the additional cost incurred by segmentation (like developing specialized communication messages). Segment size is a relative question based on the size of the company, the size of the market, and the buying power of each customer. Some firms can define each potential customer as a separate segment and tailor their products and marketing mix accordingly due to each buyer's individual buying power. A manufacturer of over the counter pharmaceuticals, however, must segment the market on a more general basis.

Accessible

The segments must also be reachable. To the extent that a segment exists, but cannot be educated about a product's potential benefits by advertising, point of purchase, or another method, it is of very little use to the company and of no more value than the unsegmented market.

Stable

Stability over time is also a reasonable concern for segmentation studies. The question here is not so much that one person or one company stays in the same segment all the time – people and companies change, but that over time, the same basic segments exist, in about the same size, with the same basic profiles.

The two key criteria in our work are identifiability and responsiveness.

Identifiability requires that each segment be unique from other segments. That is, that there is heterogeneity between segments and homogeneity within segments. Without meeting this requirement there is no need to or benefit of segmenting a market.

Responsiveness requires that the heterogeneity in buyers be related to the buyers' product choice processes.

Additional segmentation background is discussed from a strategic perspective in Myers, and from a more theoretical and technical perspective in Wedel and Kamakura .

Much of the early work in segmentation was based on clustering or grouping consumers based on similar traits. Many different techniques have been used to form market segments, but cluster is the most common. Most clustering algorithms can be classified as one of two standard types: hierarchical or partitioning.

Hierarchical methods often start with each observation as a unique segment and joins segments together until all observations are joined into the same cluster. The segments are joined by joining 'similar' elements or by joining elements such that the resulting clusters have the smallest possible within-group sums of squares. With these kinds of clustering, once two units are joined together, they cannot be pulled apart. That is, a true hierarchical relationship is assumed. (Some hierarchical methods start with one large cluster and split out clusters until each observation is its own cluster.)

An alternative to the hierarchical structure is the family of partitioning or divisive methods. The partitioning clustering methods are more varied, but they work on similar principals. These clustering methods are iterative methods. The typical process is to search the data set to find seed points. The method used to find the original seeds is one of the major differences among these methods, and generally rather critical (Milligan). After the original seeds are found, another pass is made through the data set assigning each case to one of the seeds based on a calculated distance between each observation and all seeds. The observation is assigned to the nearest seed. After this pass the group means are re-calculated and these become the new seeds. Several passes through the data set are made to reclassify cases based on the updated seeds. A convergence criterion is specified to stop the iterations when the cluster solution becomes stable.

Although these partitioning methods were originally designed to be efficient clustering algorithms for very large data sets, they have been widely accepted in marketing research applications, especially for segmentation. Punj and Stewart report that 'Ward's Minimum Variance, average linkage, and several variants of the iterative partitioning method tend to outperform all other methods. K-means (a standard partitioning method) appears to outperform both Wards and average linkage if a non-random starting point is specified.' They go on to say that the 'K-means procedure appears to be more robust than any of the hierarchical methods with respect to the presence of outliers... '.

Rather than clustering data for each person, much recent segmentation has been focused on disaggregating a total market into segments based on consumer responses or behaviors. Recent examples include Allenby, Aurora and Ginter; Lenk, Desarbo, Green and White; Desarbo et al; and Meyer et al. This interest in disaggregation is appropriate for a wide variety of marketing applications, including market opportunity assessment, product design, portfolio optimization, positioning, advertising copy development, media placement, direct marketing, satisfaction assessment, pricing, and channel management. The balance of this paper explores unique ways to disaggregate buyers in three specific applications for entities in healthcare industries: satisfaction research, product design research, and prospect targeting.

While the need to recognize the differences between buyers is real and growing in nearly every industry, several trends heighten the need to understand the variety of buyers in the healthcare industries specifically. These include:

• mergers and acquisitions;

• direct to consumer marketing – including CRM;

• deregulation;

• information available to the user, buyer, patient – especially via the Internet;

• increasing cost pressure and price awareness/sensitivity.

Given these new forces and changes, the need to understand customers and their unique needs and characteristics is stronger than ever, allowing healthcare marketers to:

• retain current customers;

• develop new products and services;

• acquire new customers.

Each is discussed below.

Application to Satisfaction Research – Customer Retention

In the healthcare arena, satisfaction research is conducted among a wide variety of audiences (patients, users, providers) for a wide variety of purposes. The purposes might include accreditation, compensation, regulatory information, or process improvement. It is the last item that is of relevance here – when the research is conducted to guide process improvements with the intent of increasing outcomes such as overall satisfaction, brand loyalty or financial performance. Some of the interactions between these items are illustrated in figure 1.

Researchers frequently conduct statistical analysis on satisfaction data to help prioritize areas for process improvement. For example, satisfaction with specific process characteristics, such as ease of scheduling an appointment, length of time waiting to see a doctor, accuracy of insurance filings, and access to a specialist physician, might be analyzed to determine which one has the strongest relationship with overall satisfaction. It is not uncommon for researchers to use linear regression, logistic regression, structural equation modeling, or simple correlations to explain these relationships. Sometimes referred to as key driver analysis, this approach develops a 'weight' that describes either the strength (as with correlation analysis) or the magnitude (as with regression analysis) of the relationship between each process area and overall satisfaction.

The weights developed through any of the techniques just mentioned are identical for all respondents. This implies that each consumer has the same motivations with the same relative influence assigned to each motivation. This implicit assumption of homogeneous motivations is clearly a false over-simplification in nearly every instance.

Latent Class Analysis is a variation on regression analysis[2] that simultaneously finds segments based on respondents' answers and conducts regression for each identified segment. In this application, the segments are derived based on the similarity of each segment's weights, or relative motivations in explaining overall satisfaction or other outcome measures. The procedure of conducting a Latent Class Analysis is discussed more completely in Pinnell (2001).

LCA Synthetical Data

Consider the following example with synthetic data (into which we added error to each respondents' responses). Suppose there are two populations in the data with known — and different — importance structures (weights) as shown in table 1.

After conducting a simple linear regression by regressing X1, X2, and X3 onto the overall rating, we observe the parameter estimates (ß1, ß2, and ß3) shown in table 2.

The model appears to fit well as the Adjusted R2 is a respectable .75 and all three parameter estimates are significant (p < 0.05). From a modeling standpoint, such regression results would please many analysts.

However, we see that the model suggests that all attributes are, for all practical purposes, of equal importance, and suggests that the third attribute is the most important. A researcher conducting this aggregate level analysis would be unaware that two distinct populations exist and might suggest that the third attribute be an area of focus, even though it is the least important to the largest percentage of the population.

After fitting a two-class latent class regression model to these data, the researcher obtains the parameter estimates shown in table 3.

This researcher has located two distinct and identifiable groups in the data. The first group places the highest degree of importance on X1, while the second group places the most importance on X3. The researcher would now correctly conclude that, for a large percentage of the population, efforts to improve the organization's performance on X1 would yield the most influence on overall satisfaction. Similarly, efforts to improve organizational performance on X3 would impact overall satisfaction for a smaller, but readily identifiable, percentage of the customer population.

LCA - Empirical Data with Satisfaction Research

The following demonstrates Latent Class Analysis with actual survey data. The data are from approximately 1,500 interviews conducted among members of a large health plan. Please note that the data have been disguised.

Table 4 shows the relative importance weights predicting overall satisfaction. The items are broken into three tiers of importance with five items in each tier, from most important to least important. (+++ = most important, + = least important). The most important attributes dealt broadly with medical advice, coverage information, and convenience of seeing your PCP.

Next, we conduct a latent class analysis to explore if there are segments of plan members with differences in their key drivers. Table 5 shows the results of the LCA with the five most important key drivers for each segment indicated (with a +) - see table 5.

Notice that 14 of the 20 key drivers for the four segments are not among the top five drivers in the aggregate model. Unique items of importance for each segment include medical information via phone, access to specialists, availability of preventative care, panel size, and choice of doctors – none of which were among the five most important overall.

For the purpose of identifying areas for improvement, focusing on key drivers, which can become key differentiators, the segment level prioritization should be considered first.

Application to Product Design Research – Develop New Products and Services

To guide product design efforts, researchers have used a number of traditional techniques and methods. Researchers frequently use direct methods of questioning such as scalar importance questions for product design research. A researcher who wants to understand the importance of product features when designing a managed care service plan, for example, might ask benefits managers:

Using a scale from 1 to 9 where 1 means 'not at all important' and 9 means 'extremely important,' how important is:

• the number of PCPs in the plan?

•  the number of Specialists in the plan?

• the office visit co-pay?

• the pharmacy coverage?

• the out of network coverage?

•  the monthly premium?

This approach is able to include a large number of attributes and can be applied in a large variety of settings. These ratings can be used (via cluster analysis) to identify market segments. These resulting segments should relate to behavior, as these are the characteristics on which buyers will choose between products. However, if we were to learn that there is a segment for which the monthly premium is the most important plan characteristic, we don't learn what price they would find acceptable, just that price is the most important. Similarly, another segment might be most concerned about network size, but we don't know if they require 100 or 400 PCPs. Also, researchers using this approach often find that everything is important. There is nothing forcing respondents to trade-off one feature for another. When shopping, consumers rarely have the opportunity to have more of every feature that is desirable and less of every feature that is undesirable. Rather, they must make trade-offs between product features or characteristics. To illustrate, the ideal product might be 100% safe and 100% efficacious, but that ideal might not be possible. Rather, a trade-off (realizing that this is not the way the topics would typically be presented) might be required between the following alternatives.

• Alternative 1: Safe in 99.9% and Efficacious in 70% of patients, and

• Alternative 2: Safe in 99.1% and Efficacious in 85% of patients.

A physician could be presented with these two alternatives and asked which one he would be most likely to prescribe. This question represents a discrete choice modeling task. Discrete choice is a consumer research methodology[3] that is related to conjoint analysis. It allows researchers to identify individual's preferences for unique elements of a product or service.

In discrete choice studies, respondents are presented with several configured products (combinations of product features) and asked which one they would purchase. This exercise, or choice task, is repeated several times, each time carefully varying the products shown to the respondent. The following are data from a discrete choice study conducted among benefit managers to guide managed care product design. The attributes in the study are shown in figure 2a and 2b.

Using a discrete choice methodology we can learn how important specific product features or service characteristics are to respondents. These preferences are expressed through utilities. Table 6a and 6b shows the derived average utilities from the respondents in the study.

Improving from a $10 prescription co-pay to a $3.50 prescription co-pay produces a relatively small gain in utility (24 utiles), but improving from a $20 office co-pay to a $5 office co-pay produces nearly twice the change in utility (46 utiles). So in total, there are 70 utiles of improvement that can be made from lowering transaction co-pays from the highest studied level to the lowest studied level. However, that 70 utile difference is less than the 93 utile difference between a $75 monthly premium and $200 monthly premium, suggesting that this firm would benefit from lowering monthly premiums, even if co-pays are increased.

For the purposes of our discussion related to segmentation, the utilities are interesting but the differences in utilities between respondents are even more interesting. Historically, discrete choice studies have been conducted by developing utilities only at the aggregate, or maybe subgroup, level. Recent advances in statistical computing have made another set of techniques available for discrete choice modeling. Hierarchical Bayesian (HB) methods[4] now allow the estimation of individual level parameters from choice data. Effectively, Bayesian methods allow respondents to share data amongst themselves so that the researcher is able to produce utilities for each individual. The effectiveness of HB with discrete choice studies is discussed in Pinnell (2000).

Conducting the analysis using HB and cluster analyzing those individual level parameters, we find four clear segments of the population, each approximately equal in size. Each segment has different key buying factors. The utilities of the key segments are summarized in the figure 3.

Segment 2 is most driven by transaction costs (office and prescription co-pays) but is much less sensitive to monthly premium. Segment 4, on the other hand, is very sensitive to monthly premium and is less concerned with transaction costs. Segment 3 is driven by network coverage, and the Segment 1 is most concerned with brand and access.

Recall from our aggregate model, it would appear that lowering monthly premiums would make the product more appealing even if co-pays are increased. It appears that this is truly the case in Segment 4, however with each change that lowers premiums and increases co-pays the product becomes less appealing to Segment 2. The finding from this study is that both Segments 2 and 4 should be targeted with specific products – identical except for their fee structure – provided that offering both is economically feasible.

With this approach, the company can also determine the costs and benefits of increasing network coverage and further investments in the brand name and image. Only by considering segments of buyers could these realizations be made.

Data Mining – Target Customer Acquisition

Finally, we present a technique to help target customers. These targets can be identified from data mining conducted on an existing database, such as sales data or transactions, or from survey data. We will consider another example using discrete choice, this time for a medical device. The purpose is to identify those doctors who are likely to be early adopters of this device. The results have been disguised. While this example is presented as identifying early adopters, the technique is broadly appropriate for targeting and classification, such as identifying likely to churn customers, those with specific medical outcomes, those with unusually high utilization, those likely to respond to a specific offer, or those physicians with more satisfied patients.

Using a family of techniques broadly referred to as classification and regression trees (CART), or sometimes as classification trees or decision trees (which we will use), we can identify the characteristics of early adopters in our example. The benefit of decision trees is that they can be easily communicated in words (rather than formulas) and graphically (through a tree structure). Decision trees produce a series of splits (often dichotomous) of a database, with each split identifying the single split that best discriminates the segments so that the two resulting groups are most different on the dependent variable. Decision Trees are discussed[5] in Breiman and in Loh and Vanichsetakul.

Figure 4 shows the results of the decision tree conducted on the adoption likelihood of a specific medical device - see figure 4. Here we see that the overall adoption likelihood is 38% for the total population. We also see that by simply dividing the database into two segments, we identify two strong segments of doctors with different adoption propensity (as shown in the first branch of the tree). And this first cut is based on only one variable. As we can continue to 'grow' the decision tree we find six unique and easily identifiable segments that range in their adoption propensity from 12% to 62%, over a 5 X improvement from highest to lowest. Targeting these early adopters will provide the largest benefit relative to cost for launching this product.

Conclusion and Discussion

Marketers frequently follow a development plan, as illustrated in figure 5, that shows a progression from 'Understanding the Market and Market Opportunities,' to 'Developing Products for the Market,' and 'Tracking the Performance of the Products in the Market.'

For each of the three phases in this cycle, we have identified a common type of marketing research or analysis and shown that segmenting and targeting the market can lead to a deeper understanding and more efficient product development and marketing effort.

The three techniques highlighted: latent class analysis, discrete choice with HB analysis, and decision trees, were each illustrated with a specific application. Each could easily be used for other applications. To summarize, latent class analysis will simultaneously conduct a regression analysis and identify segments of people that have different regression results; hierarchical Bayesian methods will estimate regression results for each individual even when there are not enough observations to do so with traditional methods; and decision trees can identify the characteristics that best distinguish between specific groups of individuals.

The methods are again summarized in the table 7 and 7b.

The discussion is motivated by an understanding that buyers differ, and that many potentially conflicting targets are involved with healthcare product development and marketing. However, by systematically understanding the differences between end users, influencers, and buyers, product and service providers are able to better design products for specific market segments and more effectively target those segments.

This paper presents three techniques that we have found particularly useful for specific applications. We anticipate that the debates that rage about the 'how' to segment will continue. We have presented three techniques for three applications that we have found useful and have hopefully illustrated the 'why' to segment and the resulting benefits.

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