Customer Data Platform use cases: QSR

Today's customers expect top-notch service in record time from every service in their lives, including quick service restaurants (QSRs). With customers accustomed to using their mobile devices for everything from grocery shopping to streaming to booking, neglecting the mobile opportunity could cost QSRs significant market share. Facing higher competition from other QSRs and from full-service restaurants means QSRs have to find a way to improve the customer experience if they want to grow their customer base and inspire loyalty in current customers. QSRs have always been top of mind for customers that value speed of service, but this won't be enough to help them win out over full-service locations prioritizing service speed improvements. Creating the kinds of experiences that will keep customers coming back for more requires QSRs to create better, more personalized experiences in and out of store only made possible by using a customer data layer able to collect and activate the right kind of 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: Democratize POS and business data

Quick service restaurants (QSRs) have a lot of moving pieces, a lot of customers, and a lot of data, as a result. As a result, each team within a QSR organization has vastly different data needs to find ways to improve product, experience, engagement, acquisition, and revenue. Typically, the data science team is tasked with pulling data and running models for each team on an ad hoc basis. Using a CDP allows data to be accessed in the way that each team needs by each team, so data scientists can focus on other work. A QSR’s marketing team, for example, may want to access point-of-sale data by location and by time of day to create a baseline before launching a video campaign. By using the CDP, marketers can pull this data themselves and throughout the campaign to measure success.  

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 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: Attribution partner data from kiosks

For most of their existence, QSRs have relied on partial customer data to understand the value of marketing and operational initiatives. This fractured view of the customer presents an issue for QSRs because of the highly-competitive nature of the industry; without complete information, QSRs are unable to differentiate themselves significantly and don’t have the data required to push further initiatives forward. QSRs need a data management system capable of cleansing and unifying data from all customer touchpoints, including digital properties like in-store kiosks, website, and apps. Legacy systems designed for web are unable to collect data from connected devices without a significant amount of extra work on the part of the engineering and product team, leading to strategy planning that is both incomplete and constantly out-of-date.  Customer data platforms are designed to collect data from all customer touchpoints, including POS, web, and app, for analysis and use in BI and marketing systems. With a CDP, QSRs are able to track what and when customers view and order to create personalized engagements; for example, if a customer frequently visits a QSR during lunchtime, the marketing team can use a push campaign to get the customer to order on the app ahead of time with the promise that it will shorten their wait time and make their day simpler. The promise of faster service makes it more likely customers will continue to purchase from the QSR, even on days when they have tight schedules. Attribution data from this kind of campaign can be collected by the CDP to help marketers evaluate the effectiveness of this strategy and improves efficiency.

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: Launch seasonal offering initiatives on time

Seasonal items are a big part of the financial year strategy planning for QSRs and puts them on tight timeframes for delivering supporting marketing materials and product features. To meet seasonal item launch deadlines, messaging and product features need to be updated as seamlessly as possible. For QSRs using an app as customer hub for delivering offers and allowing customers to purchase food remotely, product launches can be a concerning time.

Using a CDP as a mobile data layer provides the product team with the assurance that new tools can be integrated quickly, that the app is stable, and that lag and downtime will be minimized. This leaves the product and engineering team to figure out more pressing issues that could disrupt service, like maintaining infrastructure integrity when there is an influx of visitors excited to learn more about and order seasonal offerings.

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: Order tracker

Everyone wants to be customer-centric, but creating a customer-centric roadmap can be more complicated than it sounds for QSRs with limited insight into what matters to customers. To understand customers' experiences fully, QSRs need to be able to track the customers' entire journey across brick-and-mortar POS, marketing communications, and digital ordering channels, as well as gathering feedback data from as many experiences as possible in a consistent way. QSRs can use a CDP to collect and attribute data to specific customers across kiosks, in-store experiences, mobile ordering, social media, and other engagement channels for a 360 degree view of each customer and their pain points. Working with this clean data allows QSRs to analyze experience and feedback data to drive future initiatives. For example, one company found that they had a big speed problem. Typically, speed is a good thing for QSRs to have but according to their analysis, over time the speed guarantee offered by the QSR brand gave customers the impression that the food was of low quality and impacted ordering revenue. Faced with declining revenue, the QSR brand decided to do a product and branding overhaul based on customers' feedback, which included improving their recipes, as well as retiring their 30-minutes or less guarantee. Further feedback showed that while these initiatives were popular, customers still wanted to know when and where their food was; this lead to the brand developing a granular order tracker that allowed customers to follow along as their order was received, food was prepared, and food went out for delivery. This system created an illusion of control and security for customers concerned about the speed of their food delivery without impacting the overall preparation required to deliver high-quality goods. Using a CDP not only allows brands to collect significant data and standardize it for analysis to create customer-driven initiatives, it also allows brands to continuously collect data after the launch of initiatives to ensure their success and offer opportunities for further optimization.

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: Test checkout methods for speed

Customers want QSRs to be exactly that: quick. Among one of the most important factors contributing to a customer’s experience at a QSR is how quickly they were able to place an order and how quickly their order was prepared. But, QSRs are often the victims of their own success; many QSRs working with small retail spaces are unable to keep up with demand and end up with long wait times for ordering. To keep the line short and serve all of their customers, QSRs have turned to experimenting with how customers can place their orders. After seeing moderate success from the launch of a mobile app for ordering ahead of time, innovative QSRs wanted to find a way to reach the customers that were reluctant to download the app: order kiosks.

Before investing wholesale on kiosks, the QSR company needs to perform a test run and see if the idea is viable. Using their CDP, the QSR is able to collect kiosk data from every transaction, visit duration data from their beacon provider, customer feedback, and kiosk vital function logs and compare them to both traditional POS and mobile app transactions. These findings can then be used to either move the program forward and further tweak the process as additional data is learned, or halt the program before too many resources are spent.

Leading QSR Panera followed this experimentation process and installed kiosks at one busy location near Fenway Park. According to their data, 60 percent of lunchtime transactions are completed on the touch-screen kiosks, which is comparable to the transaction percentage of streamlined drive-thru windows. Following this success, Panera’s 2.0 platform has been expanded to 400 restaurants, with plans to install kiosks in virtually all 2,000 stores in the near future.

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: Match regional tastes

Different regions of the United States have distinctly different cultures and food preferences, making it important for QSRs to understand and align their offerings to local tastes. Segmentation by location allows QSRs to align themselves with smaller local audiences, or serve regional or national audiences more efficiently. This takes into account both regional preferences and distribution concerns. QSRs may find certain products do better in specific areas and adjust their product distribution and special offerings accordingly.  For example, states in the Southwest and nearby areas, like Nevada, California, Colorado, and New Mexico, have a taste for roasted green chiles commonly grown in the area. A QSR can take this into account and offer a burger, for example, with a roasted green chile topping during growing season that is sure to strike a chord with customers in that region.

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: Increase app downloads

People are creatures of habit and can be hesitant to try new things, so even if a customer is a regular at a QSR, it can be very difficult to convince them of the value of an app. Unregistered customers can be provided offers to encourage them to register for the app, like a free side or drink. This messaging will target only unregistered customers, rather than existing customers, to encourage sign ups. Once customers are known within the app, they can receive additional offers throughout the day or week and updates on their order status.

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 with Point-of-Sale redemption

QSRs have highly limited time decision-making periods during which to target customers. When a customer is hungry, they are more likely to go to the QSR that is fastest, closest, or most convenient. One way to get ahead of this is to re-engage with customers that have already purchased items at that QSR recently by sending them promotional offers that can be redeemed at point of sale in-store, in-app, or on the website. Data points like customer behaviors, purchase recency, purchase frequency, and previously purchased item history can provide marketers with the right contextual information to tailor offers. An example of this would be a coffee shop sending a customer that normally stops by in the morning for a pre-work coffee an offer for a discounted beverage in the afternoon. By mid-afternoon, the customer may have hit that midday slump and a discounted cup of coffee or other beverage may be enticing enough to bring them back to the store during a lower sales traffic period.

Further, this additional visit data can be fed into predictive models to tailor future offers and refine offerings and segmentation.

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: Non-profitable exclusion from win-back campaigns

Quick service restaurants often use offers to drive customers to download and use their app, but sometimes this tactic just doesn’t stick in the long-term. Some customers will download the app, use it once for the offer or discount, then delete it or never open it again. These customers’ actions show that this offer doesn’t lead to profitability for the QSR, but tracking these customers can be tricky. Using their CDP, the QSR can use the details for the initial app download and put rules in place to determine their specific window of profitability. In this case, if a customer downloads the app and uses the offer, but does not make another purchase for 3 months or uninstalls the app completely, they won’t be served win-back offers to entice them to use the app. This doesn’t mean that the customer won’t be served ads at all, it just will allow the QSR to spend that budget on channels that have proven effective.

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: Order update concurrency

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: Create custom menus

Food preferences are highly unique to each person, offering a perfect opportunity for QSRs to provide recommendations based on previous customer interactions. A CDP can help QSR apps display products similar to previously ordered items by tracking and aggregating their browsing and event data and cross referencing it with the customers’ local time. That way, a customer browsing in the morning can receive recommendations for breakfast items while a customer browsing in the evening can receive recommendations for dinner items. Personalized menus can be created according to dietary preferences or restrictions as well, like offering low-calorie items for health conscious customers or vegetarian options for customers that don’t consume meat. By creating these personalized menu and reordering experiences, QSRs make it easier for customers to purchase items they already love or find others they may enjoy.  

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: Offer products at the right time and place

QSRs rely on their convenience to customers to drive sales and loyalty. That’s why proximity marketing is so important for QSRs; using beacons as trigger points for push campaigns can make all the difference when a customer is nearby and making up their mind about where to pick up lunch. Location marketing using beacons can take many different shapes; for example, if a customer hasn’t made a purchase recently but enters your geofence, a push notification with an offer or a reminder of new product availability can provide the catalyst for them to enter and make a purchase. QSRs can also use proximity marketing to inform marketing by providing them with customer routine information. A QSR using beacons, for example, can surmise that a customer when a customer is commuting and the likely radius of their job in relation to the QSR. That information can be appended to their customer profiles to further inform offers, strategy, and aid in reporting.

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: Streamline checkout process

Many QSRs offer apps where customers can place orders for food delivery, but can face small issues during the purchase process that ultimately lead to customers not placing their order. By analyzing customer actions on the app, QSRs can identify these points and address them to help customers have an easier path to purchase. Often customers look to ordering in when they are short on time and need food now, so some QSRs have taken to displaying previous orders with an easy reorder button and saved payment information. This takes out the deliberation and time spent browsing through the menu and entering their credit card information. On the other hand, QSRs may find that customers are hesitant to create accounts and provide personal information to yet another app on their phone, so QSRs may find that adding a “Checkout as guest” feature can help these customers feel safer ordering food. Because a well-instrumented CDP can track anonymous user data and append it to customer profiles when they become known, QSRs can offer this guest checkout feature without worrying about duplicate data in their systems should a customer decide to create an account in the future.

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: Update the get-one-free punchcard

The concept of loyalty programs has been around for QSR brands for a long time in the form of punch cards. Taking this program to the next level can be done most effectively by leveraging an app. By connecting customer profiles, app usage, and point of sale data, QSRs can use a CDP to set up and maintain an app feature that allows customers to accrue points every time they purchase based on dollar amount or purchase size towards rewards. Loyal customers can work towards special offers, free future items, and use their app to order and pay or redeem rewards in real time. Introducing a tiered loyalty system that provides increased perks encourages customers to purchase towards achieving the next level of loyalty. This kind of loyalty program offers customers an easy way to feel pampered by the brand and ensures they keep coming back, leading to increased sales and better customer experiences.

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: Deliver the freshest product

Quick service restaurants that want to deliver the best customer experience need to be able to deliver the freshest food possible. To do so, they need to be able to track customer interactions across the customer journey to receive feedback on their purchases and time orders so that customer’s food is both ready at the right time and is fresh. Connecting app purchase information with location data through a CDP allows QSR brands to receive the order when a customer is at work or at home, then start to prepare it only when they cross into a geofence that places them 10 minutes away. Order updates can be sent to the customer via push notification, in-app message, text, or email so they know when their orders are ready. This process makes it easy for QSRs to deliver the best food at the right price to customers every time.

QSRs are able to provide levels of service and convenience previously thought impossible thanks to the development of new channels and customer-driven initiatives. As customers view, purchase, and consume products, their experiences are largely improved by the technological advancements made by QSR brands. From each channel and each experience, QSRs have access to unprecedented amounts of customer data and a way to truly understand what their customers want. This data represents an opportunity any brand would be remiss to not jump on—the chance 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; instead, marketers are dealing with legacy systems unable to keep up with the ever-increasing number of SaaS applications that house and action data. QSR brands 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!