Pricing innovations and incorporating them into existing product lines are the final stages of the product development cycle. Shelf Test is a popular survey-based approach to solve these kinds of business problems in many different categories. This powerful Choice-Based Conjoint technique creates the ability to closely replicate consumer purchasing behavior in a realistic environment and accurately estimate preferences in multiple scenarios.
Survey-Based Research for Innovations
Choice-Based Conjoint (CBC) Analysis or Discrete Choice Modeling (DCM) is widely used in survey-based research for innovations. Applying this methodology, one can test new products and offerings in competitive context and compare them with existing products and offerings in different what-if scenarios. CBC takes into account heterogeneity of consumers’ preferences providing more accurate estimations compared to other statistical models for optimization and pricing.
In a standard CBC, each product or offering is viewed as a combination of attribute (feature) levels. For example, a chocolate bar might have attributes of brand, size, flavor, price, and so on. The levels for flavor may be original, orange, almonds, coffee, etc. Respondents are exposed to a series of tasks (screens) with a few products created from a combination of levels from all or some of the constituent attributes and asked to choose from the options they are shown. In many CBCs, a “None of these” option is presented as one of the alternatives on each screen. The tasks for respondents are generated using principals of experimental design to ensure high-quality results.
The information about tradeoffs and choices is used to estimate utilities for attribute levels individually for each respondent. This allows understanding respondents’ preferences in any hypothetical scenario with products consisting of any tested attribute levels, including new products that have never been introduced to consumers yet.
Shelf Test Research for Pricing
One of the CBC variations used for pricing research is a Shelf Test. It is focused on only two attributes – product and price. Typically, the number of products and price points evaluated in this kind of study is relatively high and number of products (alternatives) shown on each screen in the conjoint exercise is higher than in a standard CBC. When needed, in a Shelf Test, products could be presented as images, and each task could mimic a real planogram or a realistic “shelf”. Respondent are asked to “pick” one or more products from the “shelf”.
What makes a Shelf Test such an attractive option for pricing research? First, it inherits the main advantages of a standard CBC. Respondents are reacting to realistic tradeoffs, price sensitivity is evaluated in competitive context, preferences for new products can be accurately estimated in hypothetical scenarios. In addition to price optimization, problems of line optimization and line extension can be successfully solved based on a model built with a Shelf Test.
The approach is especially beneficial for large CPG categories where new products can be tested with a current planogram for a particular retailer. In this case, the Shelf Test tasks are as close as possible to what consumers see in the store, and respondents’ reactions and choices are natural.
Shelf Testing for Pricing Across Multiple Retailers or Channels
If an innovation is priced across multiple retailers or channels, a Shelf Test could still be the best choice for a study. For some categories and products, it is difficult to identify and describe all the attributes that are important, and importance of different attributes is different for different consumers. Buyers in these categories have an idea of a product as a whole and sometimes don’t even consider a full list of features to make a purchasing decision. Often, they form their perceptions of a product and pricing based on some external factors not included in the test, such as reviews and ratings on the Internet. Therefore, the most appropriate layout for a pricing CBC in this case would be Shelf Test, where all relevant products, including innovations, are presented to respondents with short meaningful descriptions and in realistic price ranges.
Shelf Testing in Practice
For example, let us consider pricing of a new vacuum cleaner and washing combo. This innovation is competing with many house cleaning devices in a wide price range. If we try to present each product in the corresponding categories as a set of attributes, the list of these attributes will be extremely long, many of the attributes will be represented in only one or a few products. Also, showing too many attributes for each product would make the tasks very lengthy and tedious for respondents. Moreover, respondents are buying vacuum cleaners based only on a subset of features, mostly relying on descriptions, so evaluating a long list of attributes for each product in particular order does not accurately simulate consumers’ decision-making process in this category. Our solution for this pricing problem would be a Shelf Test.
First, we list the innovations and all relevant competitors in the category. We would expect the list to include about 20 to 30 products. Then, working together with the client, we compose descriptions for each product reflecting the actual marketing language, similar to what is expected on packaging, online, in reviews, etc. The next step is to identify the price intervals we want to test for each product. Shelf tests provide great flexibility with prices to test. Price interval can be specific for each product and the same test can include products with very different price ranges, from the most basic to the most luxury in the category.
The Limitations of Shelf Testing in Innovation
A Shelf Test is suitable for pricing in many cases, but it has its limitations. In a Shelf Test, utilities are estimated for each product and each price point in the exercise, but there is no way to separate utilities for individual product features. That means the modeling is limited by tested products only. In general, this kind of CBC does not allow estimation of preference for additional products composed from different features of products included in the test. Also, there is no way to estimate importance of particular features for respondents’ choices. If the focus of the study is testing of innovations for a product line and pricing, these limitations are acceptable.
To summarize, Shelf Test is one of the most efficient methods of price and line optimization for innovations. It allows accurate estimation of price elasticity for new products and offering in presence of competitive products in various scenarios. The choice tasks presented to respondents in a Shelf Test study realistically mimic the actual purchase situation and respondents demonstrate their preferences in the most natural way.
Written by Faina Shmulyian, Vice President of Data Science, at Big Village Insights.
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