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A Research Brief is a brief summary of research
Research Brief findings.
Context Matters When Segmenting THE PROBLEM
Market segmentation is a process that can help managers identify and target those con-sumers with whom they can achieve a competitive advantage. This process can be based on consumer variables but may not always effectively account for different situations (i.e. contexts). Segmentation that incorporates context-dependent preferences promises to deliver richer insights, but can be quite chal enging. For example, if asked in the middle of the afternoon, "What drink would you prefer right now?" some may reply Coke, others iced tea. Assistant Professor of Marketing and Contributing Researcher at the George- Asked the same question in the morning, some may reply Starbucks coffee, others juice. At town Institute for Consumer Research night, some may reply wine, others Perrier sparkling water. Unraveling the complexity of such context-dependent preferences can enable managers to more clearly identify and target consumer segments. Blanchard's research interests include the development of empirical Simon Blanchard and colleagues developed a unique modeling approach to empirically and statistical models to understand identify shared contextual preferences among consumers, segment them accordingly, and the large amount of heterogeneity characterize their composition. Described as "clusterwise multiple-ideal-point spatial meth- observed in consumers' deci- odology that estimates multiple ideal points at the market segment level," the model utilizes individual consumer preference data gathered from a single survey. It improves on previous methods that required long purchase histories or inadequately captured market complexity.
in partnership with
Wayne DeSarbo
To il ustrate their method, Blanchard and col eagues studied consumer preferences across A. Selin Atalay
different situations for over-the-counter (OTC) pain medications, a multi-bil ion dol ar industry with products used by over 80% of the population. While these products are often cate-gorized as general pain relief medication, they are differentiated by chemical composition (acetaminophen, aspirin, ibuprofen, naproxen sodium) and by marketing that highlights par-ticular usage situations (headaches, muscle pain, cold/flu symptoms, back pain).
FINDINGS
Using the model, three segments of consumers were identified when context was consid-ered in their preferences for OTC analgesics (see the table). In some instances a segment preferred a specific brand for a given malady, but in others multiple brands were equal y pre-ferred. Consumers in Segment 1 perceived Tylenol and Advil as the most effective overal . Across specific contexts, Excedrin was preferred for headaches, ibuprofen and naproxen brands were preferred for muscle/joint pain as well as cold/flu symptoms, and aspirin brands were preferred for backaches. Segment 1 consumers were characterized as price sensitive and they closely followed market information (e.g., advertising) on brand performance for different symptoms.
Consumers in Segment 2 perceived aspirin-based brands as most effective overal . Aspirin- Key Points
based brands were also perceived most effective for muscle/joint pain, but ibuprofen and • Consumers maintain brand naproxen based brands were preferred for headaches, and acetaminophen brands were pre- and product preferences that ferred for cold/flu symptoms. Segment 2 consumers were characterized as germ conscious are contextual y specific.
• Blanchard developed a Consumers in Segment 3 perceived non-aspirin brands as most effective overal . Ibuprofen model to identify segments and naproxen brands were seen as effective for headaches; acetaminophen brands were which share contextual preferred for muscle/joint pain, aspirin brands for cold/flu symptoms, and Tylenol was pre- ferred for backaches. Consumers in this segment were younger, anti-medication, and unlikely • Once a segment is to use OTCs when il .
identified, marketers can Segmentation Based on Shared Contextual Preferences for OTC Meds use characterizing traits to Segment 1
Segment 2
Segment 3
optimally target marketing Vanquish, Bayer, Bufferin, Anacin, St. Advil, Motrin, Aleve, Tylenol Advil, Aleve, Motrin, Nuprin Advil, Motrin, Aleve (ibuprofen/naproxen) Advil, Motrin, Aleve Vanquish, Bayer, Bufferin, Anacin, St. Tylenol, Excedrin Advil, Motrin, Aleve Tylenol, Excedrin Vanquish, Bayer, Bufferin, Anacin, St. Vanquish, Bayer, Bufferin, Anacin, St. St. Joseph, Bufferin, Bayer, Anacin IMPLICATIONS & CONCLUSIONS
Normal y, a marketing manager would study consumer preferences for their brand versus com-petitors' brands for specific consumer needs. However, when the consumer's preferences are considered across a variety of contexts, brand preferences become less clear. As the table shows, each segment has a portfolio of brands that vary according to the type of pain expe-rienced. A marketing manager for Excedrin may communicate an offer to Segment 1 with an emphasis on headaches, reinforcing its perceived effectiveness. Excedrin does not emerge as a specifical y preferred brand in other segments, despite the fact that its composition includes both acetaminophen and aspirin, ingredients identified by consumers in Segments 2 and 3 as relevant for muscle/joint pain and cold/flu symptoms. Discovering this, a manager for Excedrin may rethink how to position its product and how to communicate it to these two segments.
While the model was applied to OTC analgesics, it can be utilized for other categories of prod-ucts and services as wel . Managers should start by asking: Are we in a category for which consumers may vary what they purchase according to different situations? If so, studying seg-ments with respect to context will be very important for their marketing strategies and actions. Whether or not sophisticated modeling is used, managers must think about the types of con- The Georgetown Institute for
sumer research data to col ect to begin to add clarity to the complexity of consumer behavior.
Consumer Research, Sponsored
by KPMG, develops innovative,
Source: DeSarbo, W S., Atalay, A. S., LeBaron, D., and Blanchard, S.J. (2008). Estimating Multiple Segment-Level Ideal Points
ground-breaking research to il uminate from Context Dependent Survey Data, Journal of Consumer Research, 35, 142-153.
the challenges and opportunities This Brief, based on the work of Simon Blanchard et al., was composed by Chris Hydock in collaboration with Simon Blanchard.
of understanding and marketing to consumers. For more information, visit
http:/ consumerresearch.
georgetown.edu

Source: http://msb.georgetown.edu/sites/default/files/Issue%201%20-%20GICR-Research%20Brief-Blanchard-Contextual%20Segmenting.pdf