How Predictive Marketing Offers Insight and Enhances the Customer Experience
Leveraging a data approach allows manufacturers and dealers to uncover hidden marketing opportunities, and predict future ones
We’re in an era where unique data is readily available, and sheds light on particular traits and likely-to-happen occurrences. In order to effectively manage large amounts of data, however, a predictive marketing platform is needed.
Essentially, this is a system that integrates data from disparate sources to inform decisions in real time. It enhances marketing experiences and reveals deep insights about the customer.
Predictive marketing can be used as an equalizer, enabling agencies to collaborate, while also allowing original equipment manufacturers (OEMs) to look across brands and manage entry, midmarket, and luxury-targeted customers across life stages.
Let’s take Volkswagen as an example. Volkswagen can start you off with a Beetle, move you up to an Audi Q5 as your income and need for capacity grows, and eventually deliver a beautiful Bentley or Bugatti to your driveway to enjoy in retirement.
This begs the question: How might a predictive marketing platform turn an OEM company into a more predictive organization?
Leveraging a data-driven solution that enables customer-centric business models can rapidly uncover several new opportunities for OEMs.
Dynamic customer-journey modeling
OEMs with visibility across fragmented silos, systems, channels, products, and audiences can increase their flexibility in their respective markets, creating more opportunities for innovation.
This is done through dynamic customer-journey modeling, which uses huge volumes of first-party transactional data to help OEMs, dealers, service providers, and other tech vendors deliver unique experiences at the touch-point level.
Both tier-one manufacturers and tier-two dealers can align messaging through a data-driven approach, and brand families can not only differentiate messaging, but can also spot cross-brand and cross-marketing opportunities.
For example, BMW enthusiasts are vastly different from Ford audiences, but even within BMW, a 3-series driver, an X-series driver, and an M driver are different animals.
Leveraging a data approach also allows manufacturers and dealers to uncover hidden marketing opportunities, and predict future ones.
For instance, companies can improve efficiency and ROI by connecting digital audiences across tiers. Earlier this year, Rocket Fuel Institute published a report noting how automakers realized an 18% percent improvement in tier-two dealer response when audiences were informed by activities taken by tier-one manufacturers.
Feature-set optimization and marketing
Brands need to discover new methods of identifying the right product or service for the right customer. Data-driven feature-set optimization unveils countless cross-sell and upsell opportunities to specific audiences at scale.
According to an Accenture report, automotive brands will evolve to a place where they can constantly improve the digital customer experience, focusing on enhancing the distinct stages of the journey: presales, sales, ownership, and repurchase.
It will be possible to create new segments of specialized vehicles designed for very specific needs.
A recent McKinsley & Company report noted that because of this shift to diverse mobility solutions, one out of 10 new cars sold in 2030 is likely be a shared vehicle, which could reduce private-use vehicle sales, an effect partially offset by a faster replacement rate for shared vehicles.
Based on this evolution, tier-one organizations must reevaluate their branding so drivers of the future are receptive to new utility standards. Drivers that own vehicles primarily for services will have different preferences than drivers who like to take cross-country road trips.
Either way, feature-set optimization will allow OEMs to better understand how driving styles and preferences map to actual utilization. Recursively, OEMs that understand utilization can predict styles and preferences for marketing.
The increasing percentage of electronic and software content in automobiles is a growing measure of value. A Global Industry Analysts Inc. report notes that in 1975, electronics represented only 10% of an automobile’s makeup, but by 2014, software and electronics comprised 60% of a vehicle’s makeup.
Because most cars will be digitally integrated, more data can be leveraged to improve maintenance through telematics communication, and brands will also be able to create more robust profiles and personalized messaging.
Parts and service automation
While consumer and brand interaction continues to move toward digital, more data is available to inform the supply chain, which can be mapped directly to customer preferences.
OEMs should capture customer intelligence as a continuous series of integrated moments. For example: a single interaction on a website, an instance in which someone builds a new car online, or a connected device that monitors driving behavior.
The data pulled from instances like these give OEM brands insights needed to improve customer service, learn from purchase cycles, and personalize experiences.
Data should also inform demand generation or alert service departments of available inventory, life expectancy of parts (such as tires), and up-to-the-moment supply levels. Examples range from dealer tire sale programs to recalls and service bulletin notifications to certified pre-owned inventory management.
Data closely aligned with the supply chain presents a tremendous opportunity for aftermarket services. In addition, if OEMs gather predictive intelligence about parts that need to be replaced more frequently as a result of driving behavior, this can inform product requirements for next year’s model.
Advanced tools such as machine learning and artificial intelligence are already being leveraged to simulate a wide variety of driving characteristics, helping OEMs and dealers to determine the optimal driving experience and products that emotionally satisfy valuable lifetime customers.
Nikos Acuña is director of innovation at Rocket Fuel Inc., a big data predictive marketing platform that leverages artificial intelligence to create meaningful brand experiences that drive results. He is the author of The Predictive OEM: Transforming Customer Experience through Data-Driven Innovation, and Mindshare (Motion Publishing, 2012), which received Foreword’s Bronze Award for Social Sciences and USABookNews.com’s Best Book Award in Psychology in 2012.