incorporating preference construction into the choice modeling process.
Within the framework of utility theory and conjoint analysis, R provides both an introduction (Stated Preference Methods using R) and access to advanced algorithms (hierarchical Bayes choice modeling). However, generalization remains a problem. The experimental procedures that elicit stated preference are not the same as those in the marketplace where purchases are made for differing occasions, purposes and participants. Preferences are not well-formed and stable, but constructed on the fly within the choice context. Even price sensitivity depends on framing, which is why we see such robust and resistant order-effects when costs are increasing versus decreasing (e.g., a 10% price increase seems less objectionable when it comes after a proposed 15% increment than when it comes after a 5% raise).
Such context dependence is the reason why so many of us who use choice modeling in marketing research seek to limit the number of attributes and their associated levels and demand that the experimental arrangements mimic as closely as possible the actual purchase process. But even with such restrictions, the repetition from presenting several choice sets leads consumers to focus on what is varied and induces sensitivities that would not be found in the marketplace where these attributes levels would be constant or difficult to find. Moreover, our designs attempt to keep attributes independent so that we can estimate separate effects for every attribute. Yet, customers enter with conceptual structures that link these attributes (e.g., larger quantities are discounted and premium brands cost more). Do we disrupt the purchase process when we ignore such shared conceptual spaces?
Consumers learn quite a lot about a product category before they decide to purchase anything. The SmartWatch will serve as a good example because it is relatively new and still evolving. The name “SmartWatch” invites us to transfer what we know about SmartPhones and their relationship to cell phones. There has to be brands (where’s Apple?) and alternative versions running from basic to premium (good-better-best). Considerers will be talking to others and reading reviews telling them what is the best device for their individual usage and needs. This is the product representation that one learns in order to decide to enter a product category. You answer the question “Do I really need or want a SmartWatch?” by learning what is available and deciding what you are willing to spend to obtain it. When we enter the marketplace, we enter with this shared representation and we tradeoff specific features or pricing offers within this understanding. Those of you with machine learning backgrounds might wish to think of this as a form of unsupervised feature learning.
R provides the interface for representation learning about brands and products categories. Although one has a number of alternatives, I will keep it simple and discuss only one approach, nonnegative matrix factorization (NMF). I am thinking of feature or representation learning as a form of data reduction or manifold learning as outlined in Section 8 of the Yoshua Bengio et al. review paper. Consumers populate the rows of the data matrix, and the columns might span brand and feature familiarity, benefits and features sought, or expected usage. It is easy to generate a long list of columns just for features alone. Moreover, features are linked to benefits, and both features and benefits sought flow from usage. Obviously, the consumer requires a simpler representation and NMF supplies the building blocks.
Diving into the details, a potential customer wanting to use the SmartWatch in their fitness program would attend to and know about features related to their intended usage. Would they be likely to remember a bunch of specific features, or would they learn what features were standard on the basic version and what features were extras on the more premium models? Brand affordance organizes perceptions along a continuum with different features at the lower and higher ends of the scale. Simultaneously, consumers are differentiated along with the features, for example, some SmartWatch prospects will be interested only in convenience and discretion. The co-clustering produced by matrix factorization provides the underlying representation of both consumers grouped by the benefits and features they seek and those same benefits and features clustered because they are sought by the same consumers.
The R package NMF supplies the interface and several ways to display the results, as I have shown in previous posts:
- Identifying Pathways in the Consumer Decision Journey
- How Much Can We Learn from Top Rankings
- Taking Inventory
- Uncovering the Preferences Shaping Consumer Data
- Customer Segmentation Using Purchase History
- Continuous or Discrete Latent Structure?
- Attention is Preference
- Jointly Segmenting Consumers and their Preferences