Situation:
Apple CarPlay and Android Auto enable drivers to use a phone’s app functionality on their vehicle’s built-in display for things like getting directions, controlling music, making calls, and receiving messages. More than 90% of new vehicles produced for the U.S. market had Apple CarPlay and Android Auto installed in 2022.
However, GM will not offer these apps on new EV models. Instead, GM wants to transition drivers into its own digital ecosystem. GM claims its software offers more built-in functionality than what drivers get through their smartphone apps. For example, GM’s software allows drivers to reach more deeply into a vehicle’s controls with smart suggestions in map mode that monitors the vehicle’s speed and performance and then suggests the best time & place to re-charge along a planned route. New vehicles will receive new digital features and services similar to how phone users can download apps. This allows vehicle manufacturers like GM to generate revenue long after selling the vehicle. The payoff to GM is predicted in the billions. GM believes an add-on feature for hands-free lane-change could generate $20 billion to $25 billion in revenue annually by 2030*.
Research Objective:
At this early development stage, let’s assume that GM’s primary business issue is determining the types of digital features it should develop and market to maximize interest and minimize its potential to alienate current Apple CarPlay and Android Auto users. Let’s also assume that other business decisions like pricing and UX are determined later in separate workstreams. Given this, let’s set the research objectives as: 1) prioritize long lists of digital features to develop based on driver interest, 2) understand the combination of digital features that maximizes reach to the broadest population, and 3) identify & profile distinct groupings of drivers based on their digital feature preferences.
First, sampling. We want to include current EV drivers who do not reject purchasing another EV vehicle and those who do not currently have an EV vehicle but demonstrate at least some interest in purchasing an EV vehicle. We also want to include Apple CarPlay and Android Auto users and non-users. There will also be other controls for age, gender, geography, and income or other proxy for EV vehicle purchase price. Bottom-line, we want the consumers in the research to represent a broad mix of consumers who would be in GM’s target market for an EV vehicle purchase.
Next, research design. For the objectives described, Socratic recommends using a multi-response MaxDiff analysis and then applying segmentation modeling on the MaxDiff results to identify distinct sub-groups based on their digital feature preferences. This approach allows GM to make development decisions based on the broader population but also allows GM to investigate distinct sub-groups according to their relative sizes, digital feature preferences, demographics, vehicle buying preferences, and attitudes.
Multi-response MaxDiff analysis is a choice-based exercise within the survey that ultimately prioritizes features based on two metrics within the same exercise – in this case, interest in digital features and uniqueness. On each MaxDiff screen, survey respondents review a list of digital features and select the one they are most interested in and the one they think is most unique. This choice exercise repeats across several survey screens. From this exercise, modeling shows the ranking of digital features and the amount of relative preference between features. (As an aside, we can also anchor the MaxDiff analysis to determine the absolute appeal of each digital feature, which may be important to assess whether current Apple CarPlay and Android Auto users accept these GM features.) This output also lends itself to quadrant mapping so that GM can further prioritize digital features according to the desired and unique ones.
TURF Analysis on the MaxDiff modeling reveals the top combination of digital features that maximizes interest across the broadest population. This modeling approach is useful for GM in further prioritizing the list if it wishes to launch with only a handful of features and wants to know the top combination that optimizes reach.
Segmentation can also be applied to MaxDiff results to identify distinct groupings of drivers based on their digital feature preferences. For example, there may be a group of consumers who are highly attracted to mapping and navigation features over all other features, while another group has much higher interest in digital features that control ambiance and cabin comfort. The segmentation modeling identifies these sub-groups and their relative sizes within the broader population. The analysis can then fully profile each of these segments on things like age, amount of time spent in vehicle, stated driving needs, purchase price willing to spend on a new EV vehicle, etc. Further, the MaxDiff modeling and TURF can be replicated within each segment to help GM maximize interest in its targeted marketing efforts –e.g., which digital features to market in targeted advertising to drivers who highly prefer ambiance and cabin comfort.
Outcome:
This one piece of research can inform many business decisions for GM on an opportunity predicted to be worth billions. As examples, GM can guide its app developers on the digital features to prioritize in its pipeline. GM’s strategy & product teams can also use these insights to make decisions on different vehicle models – e.g., vehicle models focused on ambiance and cabin comfort that upgrade seats and sensory materials, or vehicle models focused on driving performance that upgrade wheels & tires and road lighting. GM’s marketing team & agency partner can use these insights to develop personas around specific drivers groups based on their digital feature preferences.
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*(2023, April 16). GM Ditches Apple CarPlay on EVs as Fight for Your Car’s Screen Intensifies. Wall Street Journal. https://www.wsj.com/articles/gm-ditches-apple-carplay-on-evs-as-fight-for-your-cars-screen-intensifies-29b51de8
