A major U.S. restaurant chain derived substantial added value from its market segmentation study by commissioning Decision Analyst to re-analyze the segmentation data and conduct additional analyses.
A major U.S. restaurant chain conducted a large segmentation study of the U.S.’s “eating out” market, as a template for future strategy. A total of 2,000 U.S. residents aged 18 or older were randomly selected and invited to participate in the 40-minute online survey. Extra incentives were provided because of the survey’s length to help ensure that respondents stayed engaged and involved until the very end of the survey. A large multinational research agency conducted this segmentation study, and the final report was largely focused on the market segments derived through several iterations of SAS cluster analyses. The final report identified six segments, and then follow-up qualitative research was performed to help build the personas for the segments. The personas for the segments were distributed throughout the marketing and sales departments, and a typing tool was developed so that any subsequent research could be subdivided by the six segments, if desired. After the final presentation, the restaurant chain received boxes of cross-tabulations and a copy of the data file. The study’s cost was over $200,000.
A few weeks later, after no marketing actions or decisions resulted from the study, the senior management of the restaurant chain began to raise questions about the practical value of such a large expenditure. The Consumer Insights Department reached out to Decision Analyst to help formulate a response to senior management. Decision Analyst suggested that the data from the segmentation study be re-analyzed to enhance its value and to expand the amount of useful information derived from the study.
The primary objectives of the re-analysis were:
- To better define an optimal target market, if possible.
- To confirm that the segmentation was, indeed, optimal.
- To examine the competitive set of restaurants.
- To determine the perceptions and attitudes that were driving success.
- To explore the strategy implications of the survey results
Research Design and Methods
The restaurant chain provided the final report, the data file, and the final questionnaire to Decision Analyst. The Account Team and the Advanced Analytics Team poured over the data and designed an analytics plan, including:
- Universals. Perceptions and attitudes that are common across all respondents (i.e., universal) do not show up in segmentation analyses. Sometimes these universal attitudes are more important than the attitudes that differ across segments.
- Consumption Analysis. What share of the restaurant’s food and beverage sales are accounted for by different demographic and psychographic groups, by day of week? by time of day? by type of store?
- Competitive Analysis. What restaurant chains are most directly competitive to the client’s chain of stores? Which chains can be safely ignored?
- Key Driver Analysis. What perceptions and attitudes drive frequency of visits? What attitudes drive total spending at the chain and major competitors? What changes in perceptions or attitudes would drive growth in sales?
- Gap Analysis. What is the major difference between low-spend and high-spend customers in terms of other survey variables? And high/low category spenders?
- Media Analysis. What is the best media or media combination to reach the target audience?
- Geo Analysis. Can a mathematical model be constructed to marry the survey data to geo-demographic data so that the survey results can be mapped down to very small geographic areas (Census Block Groups) via Google Maps?
The re-analysis of the segmentation data revealed a segment of consumers who were far more important than senior management realized. The this new age group accounted for more than 60% of the chain’s sales, but it was only receiving 30% of the media spend. The key driver and gap analyses indicated that there were three “signature” items on the menu that drove frequency of visits and total sales volumes, and two corresponding attitudes reinforced these behaviors. The media analysis revealed that a combination of radio and outdoor advertising promised to deliver the highest rate of return on advertising investments. These findings led to major changes in marketing strategies, and the chain realized significant sales gains as the new strategies were implemented.
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