Exploring use cases for Data Driven Customer Experience (CX) within Automotive sales and service

Leveraging DDCX techniques to solve business problems:

• Personalization – Personalize customer engagement and drive lifetime value. Deliver personalization across self-serve portals, customer service agents, and marketing communications.
• Single customer view – Bring all customer data together to get a single view of your customer.
• Analytics and AI driven intelligence – use AI and machine learning models to drive intelligent recommendations for customers.
• Constant measurement and optimization – Measure the effectiveness of any marketing or self-serve initiatives using KPIs.

Use cases:

When solutioning, it is critical that the objectives, current ways of working, and potential to optimize are kept in focus. DDCX groups use cases into categories by primary persona for the interaction: marketing and sales, after sales and driver experience.

Marketing and Sales

The automotive industry is undergoing a major transformation. OEMs are under pressure to adapt to changing customer expectations and new business models such as electric vehicles, direct-to-consumer commerce and subscription models.

Data analytics and AI are transforming marketing and sales, enabling personalized campaigns and optimized lead generation. Connected vehicles are opening up new ‘in-life’ revenue opportunities, such as in-vehicle offers presented to the customer when relevant.

By leveraging the vast pool of data from the multitude of touchpoints, marketeers can make sense of the new routes to market and provide engaging customer experiences.

Aftersales

Aftersales data is extremely siloed, but by consolidating this data into a single view, OEMs and dealers can gain a deeper understanding of their customers and their vehicles. This information can then be used to:

Provide personalized service experiences: For example, it can be used to personalize service recommendations, send targeted marketing messages, and offer proactive customer support.

Create new revenue opportunities: the development of new aftersales products and services, such as predictive maintenance plans or usage-based insurance programs.

Enhance customer support: such as the identification and resolution of customer issues quickly and efficiently.

Leveraging the ‘in-life’ ecosystem we can enhance aftersales by:

Predictive maintenance: By using real-time vehicle data and AI analytics, we can predict when maintenance is needed before a problem occurs. This allows us to proactively schedule maintenance appointments and avoid costly breakdowns.

Usage-based insurance: OEMs and dealers can partner with insurance companies to offer usage-based insurance programs. These programs track how customers use their vehicles and reward them for safe driving habits.

Smart roadside assistance: Vehicle data can provide smarter roadside assistance services. For example, if a vehicle breaks down, this data can be used to diagnose the problem remotely and send a service technician with the right parts to fix it quickly.

Personalized service recommendations: OEMs and dealers can use customer data to personalize service recommendations by recommending services that are specific to the customer’s vehicle model and mileage.

Targeted marketing messages: Customer data can be used to send targeted marketing messages, such as coupons for service discounts or offers for new products and services.

Connected car

Connected car data is data generated by vehicles through onboard sensors and internet connectivity. It can be used to:

Improve vehicle performance: Connected car data can be used to monitor vehicle performance and identify potential problems before they cause a breakdown. This can help to reduce maintenance costs and extend the life of the vehicle.

Enhance vehicle safety: Connected car data can be used to develop new safety features, such as collision avoidance systems and lane departure warnings. It can also be used to provide real-time traffic information and alerts, helping drivers to avoid accidents.

Increase convenience: Connected car features such as remote start, door lock/unlock, and vehicle location can make life easier for drivers. Connected car data can also be used to develop new features, such as personalized navigation and recommendations.

Develop new products and services: Connected car data can be used to develop new products and services, such as usage-based insurance, predictive maintenance plans, and personalized marketing campaigns.

Connected car data can be used to improve driver safety by tracking the vehicle’s location and providing real-time traffic information and alerts. It can also be used to provide roadside assistance services, such as sending a tow truck to the vehicle’s location in the event of a breakdown.

Predictive maintenance can be used to identify potential problems with the vehicle before they cause a breakdown. Remote diagnostics can be used to diagnose problems with the vehicle remotely, without the need to take it to a workshop.

Driver behavior tracking can be used to track the driver’s driving habits and provide feedback on how to improve fuel efficiency and safety.

Location mapping can be used to provide personalized offers to drivers based on their common routes. For example, a driver who frequently drives past a coffee shop could receive an offer for a free coffee.

Customer Journeys

Each customer has specific concerns, and the way they travel through the journey is personalized to their own preferences and needs. The related use cases from the previous section, (e.g., MS1, DA1 etc.) are annotated within the journey.

Figure 2 – Example customer journeys with reference to use cases (see previous section)

The reference architecture shows an example of the technologies needed to implement the above use cases whilst also showcasing the implementation phases (crawl, walk, run). While this can be different for each company, the example below is designed to show that DDCX should not be considered a big-bang change but is a progressive introduction of new systems to enhance the user journey at multiple points.

Figure 3 – Example Salesforce Reference Architecture diagram

Figure 4 – Crawl, Walk & Run Chart

Summary

In conclusion, we can see that there are many use cases where DDCX can be applied to enrich customer engagement. Salesforce Data Cloud helps unify company data across multiple sources to enable users to have a single source of truth when engaging with customers. It’s important to understand that the reference architecture provided are examples. Each client use case should be reviewed carefully to ensure the appropriate systems are used to meet the solution. In our final article, we will cover the end-to-end architecture landscape and explore key fundamental components to deliver DDCX. Find out more about our Salesforce partnership.