Customer Data Platform use cases: Auto

With interactions occurring across many devices and channels, travel companies can no longer rely on legacy systems to understand who their customer is, how and when to reach them, and how to measure the influence of marketing, product, and engineering initiatives on the bottom line. That's why many forward-thinking travel companies have started to consider using a customer data platform to unify and orchestrate their customer data.

To find the right CDP, marketers need to consider their current data maturity and how it can be used as well as what their future goals are and what data will be needed to achieve those goals. Data maturity levels can be broken down across four maturity levels, from least to most mature: Foundational, Insight and Experimentation, Omnichannel Engagement, and Continuous Optimization. This blog will take you through use cases at each level to help you determine which CDP features are relevant to your situation and find the CDP that will help you meet your current and future business needs.

Objective:

Establishing essential data processes and deploying standard marketing technologies

Organization focus:

Centralizing of clean customer data, connection to marketing, BI, and analytics tools.

Modern marketers want to innovate as quickly as their businesses and customers, but struggle to get the engineering resources needed to implement new tools. A well-instrumented CDP can democratize data access around a single source of truth, delivering and maintaining clean, complete data feeds to different business stakeholders’ systems of choice via pre-built connectors and/or API, without depending on engineering. A CDP should also be able to create, update, and send audiences to marketing and advertising platforms, without manual list pulls, enabling unparalleled speed and agility.

Use case: Decrease time to feature value

Auto companies’ have begun to use the in-dash infotainment feature in cars as a digital channel for further engagement with customers. To make the infotainment center a worthwhile feature for customers, auto brands need to use them to connect to services customers are already using in real time. To make the development and support of the infotainment system and its marketplace, auto brands can use customer data like location, purchase information, and preferences to help customers find and pay for road trip and commute essentials like restaurant orders, coffee orders, hotel reservations, and gas stations all from the comfort of the driver’s seat. Marketplace lets you order food, make restaurant and hotel reservations and find gas stations from behind the wheel. Using a CDP, auto brands can keep comprehensive profiles up to date in real time to suggest marketplace partners in the area.

The majority of active internet users still interact via browsers, making it the most important digital channel for consumers. As a result, web analytics are essential for marketing organizations; however, the shift towards mobile and connected device engagement has shown brands that they also need to be able to collect and analyze data from every touch point across the entire journey to understand how interactions influence customers. This can be tough because legacy systems are not built with mobile in mind, but a CDP can help; using mobile-specific SDKs to collect data from apps then delivering it to web analytics platforms creates a complete view of the customer’s journey and enables further analysis and attribution.

With a complete view of the customer journey, marketers don’t have to rely on “last click” attribution. Instead, they can test and integrate new tools to attribute weight to each interaction using a CDP. Marketers can use data from their CDP as input to these tools to test different algorithms and interfaces without instrumenting each attribution tool individually. A CDP can also track the long-term performance of customers acquired through advertising by associating campaign membership with full lifecycle events and attributes with acquisition source to inform strategic resource allocation decisions or direct systems that programmatically calculate bids to reflect the value of each opportunity.

Use case: Gather on-the-road customer insights

Legacy analytics platforms are not optimized to collect and process data from digital touch points like connected devices. As infotainment systems become more and more common in consumer cars, auto manufacturers need to be able to collect this data and use it to understand their customers better. Similar to airlines’ infotainment needs, auto brands can use customer engagement data from infotainment centers to guide content, product, and advertising strategies. Because many of the infotainment marketplace apps are reliant on strong relationships with service partners, having this customer data helps auto brands strengthen their propositions to partners by providing relevant customer data like time spent, feature usage, and demographics.

Brands want to create a superior experience for their users, which means they need a roadmap that delivers the best product to their customers consistently. For apps, that means minimizing reliance on third-party code that requires additional instrumentation and maintenance that may burden the user experience and divert engineering time.

By serving as a centralized data hub, a customer data platform is able to capture first, second, and third-party data through a single endpoint, then share it with multiple systems without placing additional tech strain on the app. This centralized data layer ensures the end-user remains unaffected as additional tools are introduced or updated, or as data schemas are changed. Minimizing dependency on third-party code allows product and engineering to avoid unforeseen SDK implementation and maintenance projects from marketing and other business stakeholders, so they can focus on building the best, most differentiated product.

Use case: Focus on core product development

To offer customers the greatest flexibility and make ordering a ride an even easier choice, ride-hail services are constantly looking to add features to enhance customer experience. With a long list of product improvements, the product and engineering teams need to be able to move forward instead of getting stuck on maintenance  Allows them to link a ride to two customers’ profiles and purchasing information.

Objective:

Creating structured methodology for running test-and-learn processes and creating a culture of data-driven decision making. This may include leveraging non-marketing data.

Organization focus:

Measure ROI and customer lifetime value (CLV) impact of new marketing and customer experience initiatives.

Brands want to understand the mobile customer journey holistically and use this knowledge to prioritize future roadmap items based on customer needs, and demonstrate the business benefits of their product recommendations. Using a CDP enables them to combine mobile product, marketing, and purchase events through a single combined data set so that they can understand bottlenecks, identify key areas of improvement, and make better roadmap decisions.

Use case: Cater to different customers' priorities

When customers are considering booking a ride from a ride-hail service, there is always a question of what they prioritize more: speed to destination or lower price for the ride? Choosing a solo car ride ensures customers get to their destination as quickly as possible, but the rates are likely to be anywhere from 150-500% more expensive than riding in a pool. On the other hand, riding in a pool keeps costs low for the customer, but because pools pick up customers based on proximity, there could be many stops between a customer’s point of departure and destination. Both of these situations are exacerbated when this experience takes place in a city like New York, where there is a lot of traffic (leading to longer rides and higher costs) and there are a lot of users (which could further slow down a pool).

By using a CDP to understand how customers interacted with the app, the ride-hail product team was able to develop a plan to create a feature that provides the best of pools and solo rides. In New York, two main highways run up and down the island of Manhattan, so the ride-hail service introduced a highway express pool which feeds location data to the CDP for matching customers in the same general area going to the same general area; drivers then use the highway instead of going up one of the avenues in the hopes to pick up additional fares along the way. This feature proves to be a satisfactory compromise for customers that want to get to their destination quickly and cheaply, and encourages customers that wouldn’t book because of price or time to purchase a ride.

Brands want to be able to not only know what customers are doing, but how they can improve customers’ experience while they’re doing it. Improvement can only come from experimenting with new product features, content, and workflows and using a CDP allows you to do experiment with different parts of your business more easily and quickly. A CDP reduces the data wrangling required for each experiment, reducing the cost of set up failure by making it easy to revert, and making it easy to create experiment segments and holdout groups on which to experiment. Experiment variants can be created based on customer attributes and behaviors across systems, including entry channel, initial product purchased, content consumed, current sales funnel stage, etc. Variant behavior is then gathered from source systems and third-party enhancements for analysis. Whether a brand is running experiments on purpose-built software or by hand, a CDP makes experimentation easier and more scalable.

Use case: Match

Objective:

Optimize organization-wide initiatives using customer data and supporting marketing campaigns

Organization focus:

Optimizing marketing, digital advertising, and product-led retention/growth leveraging customer behavior, testing, and targeting across channels and touch points.

Modern marketers want to personalize messages by segment or persona to improve experiences and outcomes, but many legacy systems don’t collect and store the right customer data, at the right level, in the right way, at the right time. A CDP enables marketers to collect information about customer preferences and profile information to determine what information, content, or offers are most likely to appeal to them. By using rule-based segmentation, customers are automatically placed into audience segments comprised of customers with similar profiles which can be used to power marketing campaigns across channels, including search.

Use case: Showcase the features that matter

Cars are both an object of convenience and luxury for many customers, leading to purchase paths guided by very different motivations. Auto brands need to be able to tap into the motivation for each customer to be able to create marketing content that resonates. Using their CDP to collect attribute data like age, marriage status, number of children, income, last vehicle purchase, and location from second- and third-party sources to help determine which model of car they are most likely to be interested in. If a customer is in their mid-40s, living in upstate New York, with three children, and their previous purchase was a sedan 5+ years ago, they are likely ready to purchase a larger car to accommodate their family size and four-wheel drive for the icy conditions often present in winter. This kind of segmentation is important because customers tend to become desensitized and so you only get a few opportunities to capture their attention before they stop engaging with campaigns. In the case of this customer, for example, Jaguar Range Rover’s marketing would want to emphasize the space, fuel economy, and safety of their Range Rover SUV rather than its status as a luxury symbol.

Brands need to know who their audience is to market efficiently. That means being able to convert a customer from unknown to known, and tying their unknown data back  can identify customers who have not yet registered and trigger messaging campaigns to encourage registration to help take customers from unknown to known. This same messaging feature enables a CDP to trigger any type of messaging to customers based on app/web behavior or other non-email behavior loaded into a CDP. Identifying known and unknown product users allows for distinct messaging based on the brand’s end-goal for the customer.

Use case: In-app offers and registration perks

Brands need to be able to use paid media efficiently to push customers stuck in their journey toward conversion. A CDP can address specific bottlenecks in the customer journey by syncing customer lists to paid media platforms. This is effectively the same as list selection for traditional marketing channels, with similar requirements for complex selections, access to full customer data, and extract creation. Customer lists can be selected based on user profile attributes and historical data available within a CDP. Unlike conventional (manual) methods, lists are updated in near-real-time to maximize relevancy and effectiveness.

Use case: Target smarter

Brands know that engaging existing and identifying additional customers that fit your ideal customer profile (ICP) is a solid growth strategy, but putting this into practice can prove difficult. Using a CDP enables you to select and deliver targeted messages to different cohorts of customers based on value-based scores by passing numeric attributes and customer IDs to paid media platforms, like Facebook. You can also create lookalike audiences using the value-based scores to find more customers like your current highest value customers with higher granularity than is available in the media systems’ limited data store.

Use case: Re-engage through new leasing programs

Car purchases tend to have longer sales cycles because of the size of the investment, the financing typically required, and the life of vehicles. This long sales cycle results in long periods between customers purchasing a second vehicle, especially if they are looking to replace their initial vehicle purchase, which means they have to either trade in or sell their car before purchasing another. While many companies have just accepted this long stagnant period as business as usual, innovative automotive companies have launched new app-based leasing programs to re-engage existing customers between vehicle purchases.

Instead of using segmentation to try to re-engage customers through ad and email campaigns, automotive brands are using their customer data platforms to identify customers that would be interested in a monthly subscription program that allows them to switch cars in and out throughout the course of the month. Auto brands are able to skip the dealership entirely and engage the customer directly through their app to deliver the cars their customers need now; whether that’s switching to an SUV when family is in town, or switching to a compact for commuting, automotive brands have a direct pipeline to understanding what their customers need while reaping the benefits of the subscription model.

Just as you can target exactly who sees an ad, a CDP can create and sync suppression lists to paid media platforms to ensure campaign dollars are not spent targeting the wrong people. Using custom rules set by marketers, the CDP can move individual customers in and out of suppression lists as their attributes and actions qualify or disqualify them from receiving certain ad content.

Use case: Suppress ads to non-profitable customers

Despite their best efforts to expand, ride-hail services don’t always have the demand necessary to make certain offers or features profitable in low-demand areas, like rural communities. Rather than targeting these customers along with urban customers with offers for commute savings, ride-hail services can create a suppression segment to exclude customers in low-demand areas based on second- and third-party location and demographic data. These segments can be created automatically by the marketing team using attribute-based rules for audience targeting. Rule-based segmentation is a key benefit of a CDP because it allows marketers to skip the process of manually pulling data for engineering to query and maintain; instead, segments are automatically updated according to the most up-to-date information on individual customers. If a customer moves from a high-demand area to a low-demand area, the ride-hail’s CDP automatically moves them from the targeted audience segment for a particular offer and moves the into the suppression audience segment to ensure. Marketers can then create a different offer that is more tailored towards customers in low-demand areas that is more likely to produce a profit.

Objective:

Engaging and improving customer experience in real time

Organization focus:

• Incorporating algorithms for continuous optimization, managing channel-neutral customer preferences, and omnichannel attribution.
• Leveraging consistent data, technology, and processes across all channels to develop contextual customer engagement strategies that drive corporate objectives.
• Calibrating marketing technology capabilities for continuous adjustment based on customer needs.

At the highest stage of maturity, organizations need to focus on creating better experiences for their customers on an ongoing basis. Using a CDP allows companies to make adjustments based on data and improves how brands track attribution. A CDP’s view of product, marketing, and service interactions—and customer purchases—provides the data needed to inform bottom-up multi-touch attribution models that measure the impact of touch points on business results at the user level. Many businesses consider these models to be more reliable than top-down mix models that rely on statistical methods to discern contributions but struggle to assemble the granular data needed to make them work.

A CDP can be used an orchestration layer that provides an overview of all activity across all sources, and sets rules to direct messages based on complete information about the customer. Messages can then be personalized and delivered across all channels while maintaining a consistent customer experience. CDPs can also help customers set up cross-channel frequency capping, which limits the number of times an ad is scheduled to be displayed to a customer. Frequency capping reduces ad fatigue and ensures customers won’t grow tired of seeing your brand’s communication efforts.

Use case:

A CDP may ingest user scores from predictive models, whether they are homegrown or machine-generated, to provide the app or site CMS with real-time support for recommendations. These recommendations can include first product, cross-sell, and upsell. Because the CDP was also used to inform the models, these recommendations are based on data captured across every channel, not just the one the user happens to be visiting.

Use case: Location recommendations

Ride-hail services want to make the booking and riding process as easy as possible for their customers. One way to to do that is to auto-populate a list of location recommendations for rides based on where they have traveled frequently when customers begin entering their travel information. This list can include frequent destinations like home, office, and daycare, but it can also include recommendations to cafes, restaurants, or attractions that they have also visited recently. For the more everyday recommendations of daily commutes, these recommendations make it a little easier for customers to book their ride while the other recommendations can encourage spontaneous ride booking to favorite, out-of-the-way spots. In both cases, these recommendations are highly tailored to the individual customers, which makes it more likely customers will book the ride.

Brands need to reach customers not only when their messaging is relevant, but also where it’s relevant. A CDP can ingest location information from web and apps, append it with signal from location data services, apply rules to uncover opportunities, and then trigger relevant messages. This extends far beyond conventional push notification systems and helps customers receive messaging relevant to their geographic context.

Use case: Match ride demand with availability

Location is critical to the success of ride-hail companies; drivers need to be near where there is demand for rides to make the service worth it for both drivers and riders. One way to tap into demand is to use beacons to deliver ride discount messaging containing a special promo code to customers within a certain radius of events like trade conferences, concerts, festivals, and sports matches. Coordinating drivers to be near the event starting 20 minutes before the end of the event makes choosing that ride-hail service convenient and affordable for riders, leading to increased rides with low investment.

A CDP can assemble a complete set of interactions between the company and each customer to create maps of customer touch points over time, with separate maps built for different segments, products, tasks, or locations. With this information, a CDP can identify the most productive paths and find the points where customers are falling out of the process.

Use case: Lower the bar to purchase

Using a CDP, airline sites can track customer interactions across devices as they open email promotions, search for flight options, compare dates and departure times, as well as fare classes to get a better understanding of what drives customers to book. For some airlines, that may be offering a 24-hour free cancellation period for indecisive customers or sending a small discount via email after a customer has viewed flights with the same departure, destination, and date range. If a customer has downloaded and uses the airline’s app, they can also be sent in-app or push messages when price decreases or when the flight is close to filling up.

Legacy loyalty programs focused on serving promotions to repeat customers rather than driving loyalty. The next-generation of loyalty programs uses web/app usage data, in-store purchases, loyalty status, points balances, redemption, and in-store inventory to make the optimal offer for each customer. A CDP makes these data points available to systems that use predictive modeling and optimization to find the best offers while balancing customer goals, business goals, and business constraints, and can help these systems deliver relevant messages across channels.

Use case: Create integrated loyalty programs

Infotainment systems built into cars’ dashes are proving to be a highly fruitful channel for customer engagement long after the car’s initial purchase. Automotive companies can take advantage of this new channel for engagement to deepen customer loyalty by linking preferred vendor loyalty programs to the car itself.  

For example, a car manufacturer can integrate their infotainment marketplace with a gas station company’s fuel rewards program to allow car owners to pay for gas from the comfort of their dash while earning and redeeming rewards in the process. Facilitating fuel purchases helps to increase loyalty to both the car manufacturer and to the gas station chain, as customers become accustomed to the ease of this transactional experience.

A fully-enabled CDP can ingest customer transactions on web, apps, call center, retail kiosks, and other channels in real time, as they happen. Using Identity resolution features, the customer can be identified and the information about the engagement can be used to inform channel systems of the customer’s specific preferences and history to guide current and future interactions.

Use case: In-app service requests

There’s nothing worse than going about your morning only to realize your car has a serious issue that needs to be addressed immediately. Auto companies looking to deliver a next-level customer experience are now offering standard diagnostic testing via customers’ apps. Customers can choose to schedule automatic or manually run diagnostic tests to know when their vehicle needs to be serviced. This diagnostic data can be connected to location, profile, search, and messaging services to automatically find nearby certified service stations, customers can then schedule a service appointment and provide a full report of the issues found in-app, so drivers can get back on the road in no time. While regular servicing is still recommended, this extended level of diagnostics and servicing helps customers keep their car, and their lives, running smoothly.

For the first time in recent memory, auto companies have direct access to their customer base. Instead of dealing with middlemen across several auto industry functions, from leasing and purchasing to rental and ride hailing. Auto companies' unprecedented access to customers and their data presents an opportunity for brands to understand how different facets of engagements influence customers' journeys towards conversion and act on those learnings. But marketers are no longer dealing with a finite number of systems; rather, marketers are dealing with legacy systems unable to keep up with the ever-increasing number of SaaS applications that house and action data. Companies need a CDP that can not only help them overcome the customer data silos and unify their data from across their stack, they also need a CDP that is able to take their insight, orchestration, and activation to the next level by providing a way to create and maintain persistent customer profiles, execute experiments, improve targeting, and power acquisition and retention.

Finding the right CDP requires that marketers consider their current and future data goals and what kind of data they need to achieve them. Using defined use cases as the basis of their search for a CDP will ensure that marketers choose and implement the right customer data platform for their business' needs, making it a safe investment.

This guide has provided some of the most common use cases for travel companies at different data maturity stages, but there are still many more advanced applications for companies looking to improve their marketing and analytics ROI. mParticle is not only able to meet all of the use cases described in this guide, it is capable of becoming the customer data hub and agility layer that brands need to succeed in the digital era thanks to its ability to:

• Natively collect data from all sources
• Cleanse and transform customer data
• Resolve customer identities
• Create and maintain persistent customer profiles
• Enrich customer profiles with data from first-, second-, and third-party tools
• Support consent and privacy management
• Segment audiences on the fly
• Orchestrate data to the marketing and BI tools needed
• Do it all in real time

As the customer journey continues to fragment, finding the right CDP will only become more important. If you’d like to learn how mParticle can help you unify your customer data, boost engagement, and increase advertising and marketing ROI, get in touch!