Level: Intermediate Job Function: Analyst / Scientist / Engineer Robert Reul, Managing Director, Isometric Solutions
This work sheds light on the importance of latent drivers addressing both how consumer choices can inform business choices and how latent drivers can inform marketing strategy. In our study, a smart home security provider is competing in a crowded marketplace against both highly established competitors and deeply funded disrupters (Google, Amazon). Our task was to find the best value proposition to make a distinct marketing claim and thereby increase brand awareness and drive customer acquisition. The study tested a large set of alternatives. Here, typically the ranking or rating methods would be cognitively burdensome, resulting in inconsistent late-survey responses. Using the MaxDiff Design approach, we generated a balanced incomplete block design that efficiently tested 21 different value propositions in convenient subsets of five. The choice experiment was fielded to 1,524 respondents via sophisticated web interview. This yielded 30,480 comparisons analyzed with MaxDiff Choice to establish the relative marginal utilities, identifying value propositions with greatest appeal. To assure the ultimate marketing claim was as distinct as it was strong, Fit Model was used to analyze the relationship between marginal utilities and the sought outcome variable, likelihood to purchase. This revealed the unique and compelling “latent drivers” used as new advertising claims.
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Level: Intermediate Robert Reul, Founder, Isometric Solutions Eliminating discrimination in the workplace. Possibly the one goal shared by applicants and employers is the ability to seek and secure employment on a full set of non-discriminatory characteristics: knowledge, skill and experience. The unfortunate reality is that these are all too often trumped by other more easily discernable factors that have very little to do with job performance, and worse, embody unethical and illegal practices when hiring applicants for a job. Can data analytics be used to exclude discriminatory bias during the hiring decision? Using extensive academic literature on personality and performance, several frameworks emerge that serve as outcome vectors for predictive models, namely the “Big 5” personality traits and trustworthiness, likeability and confidence. But what to do about the predictors? Experimentation with facial image processing data from sources such as Google and Microsoft showed predictive promise. By using a series of analytic methods that screen for predictive potential and reduce dimensional complexity, predictive prowess emerges with a completely non-discriminatory set of latent variables that inform the “whom to interview”/ “whom to hire” recommendations based purely on cultural personality fit. This presentation will reveal the challenges and successes of this effort, proving it’s not only feasible – it may become the new normal.
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Bayesian Design and Analysis Delivers Profitable Market Share Gains
A major appliance repair provider sought to profitably increase market share by introducing the most competitive product they could consistently execute. The challenge was to assess from the vast spectrum of servicing feature combinations, just those that mattered most to the buying decisions - and to do so in just one study. When rendered, the experiment varied 43,200 choices with characteristics such as; service guarantee, service warranty, servicing urgency, and service price. Further, the study was fielded assuring specific subject variable considerations were given to differing customer populations and appliances needing repair.
Getting the service right. A utility neutral pilot study was first fielded to their employee population. Parameter estimates from the pilot study were then used to create locally-optimized nonlinear design. This choice experiment was then fielded in subsequent waves to both customer and prospect populations. Further, because preference characteristics were believed to vary by appliance, the survey was fielded such that respondents evaluated only choices based on specific appliance breakdowns they had experienced within the previous year.
Targeting the right service. A Hierarchical Bayes choice analysis was used to fit parameter estimates for each individual respondent surveyed. By modeling respondent-level parameter vectors, the company was able to identify prospective customers based on specific demographic, geographic, and psychographic profiles that showed who was most receptive to the new repair service offering.
Bringing home the service profit. The long-held belief that there was untapped revenue potential in the home repair business proved true. By using willingness to pay estimates, the study isolated the potential by monetizing high-utility service characteristics commanding maximum pricing. Premium service offers were then validated in a series of field tests revealing the market’s preference for the high-priced premium servicing offerings. At scale, when extrapolated to current and potential customers,
the profit contribution of the premium service offering take rate is astonishing.
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