Box: Getting a flavor of questions the latest marketing-data-science can answer*
Profiling the most promising customers
The internet age offers unparalleled opportunity for brands to target their advertising to consumers who are the most likely to respond. But what is the best way to do this targeting, or “profiling” of potential customers? Those who are able to read the tracks can resort to web surfing behavior. Web surfing can provide reliable clues of individual consumers’ propensity to purchase. In their article “Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting,” Michael Trusov, Liye Ma and Zainab Jamal develop and implement a targeting algorithm based on consumers’ online surfing data. Their approach is superior to existing methods, both in identifying the best consumers to target with digital advertising, and in avoiding wasted exposures to uninterested consumers.
Identifying relevant choice alternatives from a consumer´s perspective
Some high-technology product categories, for example television sets and digital cameras, offer a bewildering number of choice alternatives for consumers. What’s more, these offerings are subject to continuous technological innovation. How do manufacturers know which competitive products are perceived as similar – and therefore competitive - to theirs and how they should identify and target lucrative submarkets for their new offerings? In their article “Visualizing Asymmetric Competition among more than 1,000 Products Using Big Search Data,” Daniel Ringel and Bernd Skiera develop innovative mapping methods on search data at price comparison websites to obtain effective visualization of these complex market structures. Their approach offers a fast, easy to understand, yet comprehensive view of how new technological offerings compete with each other, as perceived by the buying public.
Filling individual shopping baskets through relevant product recommendations
In recommendation systems, in automated marketing and in customized targeting, practitioners would like to be able to use a consumer’s purchase history to predict the next product the consumer will buy. In their paper “Product Recommendations Based on Latent Purchase Motivations,” Bruno Jacobs, Bas Donkers and Dennis Fok apply a method that is often used in text processing to identify, from the consumer’s perspective, sets of products that tend to be purchased together. The authors’ analysis with latent Dirichlet allocation (LDA) performs better than typical collaborative filters and other model benchmarks. In doing so, it holds promise for a variety of new recommendation systems to build upon the improved predictive ability.
Knowing how consumers truly perceive your brand
Consumer perceptions of a brand are important for the management of the brand. Consumers readily express their opinions about brand attributes such as eco-friendliness, nutrition, and luxury via social media. The article “How #Green is Your Brand? Mining Cause-Related Brand Associations on Twitter,” by Arun Culotta and Jennifer Cutler provides a fully-automated method to monitor brand related messages in social media (Twitter). They track these perceptions by mining a brand’s social connections and demonstrate the method by monitoring 200 brands for these attributes. Their approach allows managers to react quickly and effectively to both opportunities and challenges in consumer perceptions of their brands.
*The details on methods and procedures are published in the original articles. They can all be found in Marketing Science, Vol. 35, 3 (May-June 2016).