A multinational auto OEM headquartered in the US sought to benefit from the valuable perspectives of intenders actively shopping for a vehicle, but who have not yet purchased. It was important that the data was developed frequently enough to rapidly identify shifts in consumer sentiment, was comprehensive enough for broad applicability across many departments, and was structured enough for use in sophisticated analytic applications (as part of the company’s strategic initiative for increased use of data to support decisions).
Market Insight provided the OEM with “ShopprDNA” intender preference data, which the OEM put to use in multiple ways. In contrast to survey data from vehicle owners who had already made their buying decision, or data from paid clinic respondents, ShopprDNA data came from real shoppers who had no knowledge that they were being “surveyed.” The data was continuously sampled over time, and measured shopper preferences for over 100 vehicle attributes and features. Instead of being limited to a particular segment, the data spanned a large representative sample (thousands monthly) of the entire market of shoppers. The data was delivered in regular, ongoing, updates in a structured format optimized for use with databases and software tools, and was integrated into the OEM’s data warehouse. The OEM was then able to use its own data visualization tools to make high-level interactive visual dashboards available to analysts on demand. Separately, the OEM deployed data scientists to use open source software tools for exploratory data analytics to discover relationships, describe hidden shopper segments, and build predictive models.
The OEM has provided dozens of employees with the capability of accessing intender preferences through its internally accessible visual dashboards based on ShopprDNA, which continues to be updated on an ongoing subscription basis. It has become an invaluable tool for analysts and decision makers to quickly and interactively understand the perspective of intenders over time, within demographic groups, and within individual vehicle segments or across the full consumer market—influencing decisions ranging from vehicle contenting, demand forecasting, portfolio planning, and pricing. Furthermore, the richness and cleanliness of the data set proved ideal for data analytics efforts, which within the first week yielded numerous insights in identifying pockets of shoppers that could be targeted in marketing initiatives.