Customer Data Platform Retail use cases

With interactions occurring across many devices and channels, retailers 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. To maintain journey concurrency and reach customers in the right moment with personalized content, many brick-and-mortar and ecommerce brands 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: Launch new hubs

Many modern beauty retailers have realized that their customer base prefers to shop online but only if it feels like they are a part of a larger community where they can get advice, recommendations, and see other users’ reviews. Creating a community and purchasing hub via an app is the best way to deliver a great experience as customers go about their day, but launching an app requires significant engineering resources and the implementation of many third-party vendors.

Using a CDP helps retailers streamline the process of launching and maintaining a mobile app by providing integration with leading marketing, analytics, and BI tools. With a CDP as a mobile data layer, brands can create and launch applications that are stable and provide customers with the features they are looking for, while minimizing lag and downtime. Democratized data access allows cooperation between teams without slowing down the development cycle and allows each team to use customer data however they need to create the best customer experience.

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: Better attribution

Online retailers are often overwhelmed by the amount of data generated by a customer’s journey towards purchase. The amount of data from different channels and devices can make it difficult for marketers to understand which customer engagements were meaningful and led towards conversion. That’s where attribution providers can help, but only after a company finds the attribution vendor and methodology at the right level of sophistication. Often, engineering teams spend the time and energy integrating their customer data to what seems like the right attribution vendor, only to realize that that attribution vendor doesn’t have the right capabilities.

Using a CDP can alleviate this problem in two ways: streamlining data integration and connecting relevant data from other vendor systems. Pre-built connectors to leading attribution vendors make it easy to integrate customer data from your CDP and requires minimal engineering work. The ease and speed of integration with attribution systems mean that marketers are able to test different vendors and methods against each other without having to worry about increasing engineering debt. Secondly, because a CDP functions as the main hub of customer data for your entire stack, linking data from internal warehouses and CRM systems with the attribution vendors’ records allows marketers to identify the highest-density sources of high-value customers. With this information, marketers can invest further into the campaigns and channels they know will increase conversions and minimize wasted spend.

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: Improve end-user app experience

Retailers looking to expand their relationship with customers beyond the typical in-store purchase may look to apps as a way to continue their engagement. Creating an app that functions well and allows customers to browse, comment, and share is no small feat, however. To build an app, retailers want to use best-in-breed third-party tools to drive usage and adoption, but each tool typically requires instrumenting and maintaining its respective SDK. While the functionality is important to customer experience, adding multiple SDKs to the app could impact its stability. Instead of implementing each vendor’s standalone SDKs, retailers can implement a CDP to serve as its central mobile data layer.

Using a CDP to collect and distribute data to vendors in the app’s stack allows the app to benefit from the functionality of a best-in-breed stack while ensuring the app is stable and fast. Additionally, minimizing the number of third-party SDKs integrated also minimizes the need for SDK maintenance, freeing up engineering resources.

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: Fast-track checkout

Retailers looking to improve revenue margins know that the best way to do so is by giving customers the kind of experience they desire. For customers that routinely purchase an item, going through the whole checkout process can be a chore, especially if the item could be ordered through a delivery service or in-store at a similar price-point. This checkout process can become even more tiresome on mobile, where inputting payment and shipping information has to be done on a virtual keyboard with inefficient cursor control. With a CDP, retailers can gather data on purchase patterns and on-page behavior across web, app, and connected devices to see where customers fall off the checkout process and take steps to improve it. Improvements can be as simple as storing preferred shipping and payment information or they can be larger initiatives that bypass the original checkout process entirely, like creating a one-click checkout button that uses previous purchase information to complete the order in one fell swoop.

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: Optimize display

Retailers typically have a lot of different items grouped in categories to make it easy for customers to find, but there is a lot of variability in just how they decide to display. Having different item attributes available for customers to toggle on and off as they look through items can make a big difference, but the question of how many item attributes is too many is ever-present. Retailers using a CDP can create sample segments of randomly chosen customers to test different variants of views and search functionality to find out how customers want to be able to search for items easily and record the results to drive product development. A CDP allows brands to not only test different functionalities and displays, it also empowers brands to test content across channels and devices. Because A/B testing is made so simple, marketers are free to experiment as much as they want to find insights into their customers' desires without relying on engineering. Better A/B testing means better implementation of new ideas and more ways to delight and drive customers towards conversion.

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: Activate event-based customers

Segmenting can be used in a variety of ways in retail. In fact, some might argue that segmentation is the main driver of ecommerce revenue. Retailers can benefit from segmenting audiences to target by specific attributes or actions, like demographics, past purchases, browsing habits, product affinity, and lifetime value, among many others.

Beyond your standard segmentation, an interesting use case used by some retailers is event-motivated purchase segmentation. Certain times of year, like Black Friday or Cyber Monday, have subsets of customers primed to spend money on that day thinking they will be able to get an item they have been wanting at a great price or find a deal for an item they may not have even considered purchasing previously. Taking advantage of event-motivated customers is an effective way to re-engage with customers already in the database, and can extend to personal annual event purchase dates like anniversaries and birthdays. Online retailers can create an audience of customers that purchased products during one or more holiday season or personal annual event celebration the previous year, then send a special promotion or anniversary/birthday reminder to encourage return purchases.

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: Get that first purchase

Customers have more choices than ever when it comes to purchasing goods and services, making it important that retailers use every opportunity to push customers toward conversion. Frequently, retailers find that the most difficult step in the customer journey is getting a customer complete an initial purchase.

To address this issue, retailers can use a CDP to devise an automatic messaging strategy to encourage customers to complete their first purchase. By tracking visitors to the site across all devices, a retailer can determine if a customer has purchased from the site previously. If a customer had not purchased anything but had either spent a predetermined amount of time viewing an item or they had viewed the item on more than one occasion, their visit event triggered an onsite message to appear offering a 10% off coupon in exchange for their email. This discount is often enough to push a customer on the fence about purchasing an item to complete the purchase. If they don’t complete the purchase, entering their email allows the retailer to add them to future promotional email campaigns and gets those new visitors closer to completing their first purchase and becoming customers.

A CDP can capture visitor behavior on one company site or app and deliver related messages when the same visitor appears on any other company-owned site or app—even if that person was anonymous when they abandoned. A CDP can also read behavior history to flag inactive customers. In addition to triggering an email or push message, it can also trigger special messages when they appear on a different app/site or the same app/site. This is especially useful because the email addresses of inactive customers may no longer be valid.

Use case: Abandoned cart re-engagement

Ecommerce retailers face a lot of competition and a lot of data from a lot of different people. Making sense of this data to understand what leads one customer to complete a purchase while another customer drops off completely is of the utmost importance for these brands. Using a CDP, ecommerce companies can determine which events correlate with purchase intent and trigger a message when a high-intent action is taken by the customer to push th c em towards conversion.

An online retailer seeing a decrease in revenue despite an increase in inbound traffic and no changes to their demand generation programming might take a look at their customers’ in-cart data. Looking at their customer data showed that many visitors came to the site and a healthy number of those visitors added items to cart, but they did not complete their purchases.

Retailers can target visitors with items in their carts by making a rule to trigger an “in your cart” reminder email when a customer added an item to their cart and did not purchase within 12 hours, 36 hours, and 72 hours, for example. For customers who simply forgot or were perhaps on the fence about completing their purchase, a message as simple and direct as this can encourage them to complete their purchase without additional retailer investment.

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: Find more high LTV customers

Finding the right customers to market to is one of the biggest problems faced by retailers. Retailers can analyze their existing customer database to determine which of your customers have high lifetime values (LTV) and use this information to find lookalikes likely to become high LTV customers.

A department store retailer analyzing their database may find that households with school-age children between the ages of 5-12 make large purchases at the beginning of every school year, and once again in the Spring as children wear out or grow out of clothing. Further analysis can lead to additional data about the family’s demographics and income that are highly correlated with these purchases. Taking this information and forwarding it to a paid media platform like Facebook can help the retailer find other families within the service radius that fit the profile attribute parameters of existing high-LTV customers and market offers heavily towards the parent in charge of these purchases ahead of these two purchase periods.

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 customers that have purchased

Retailers using outdated systems often find themselves serving irrelevant ads to customers. One of the biggest issues retailers face is ad spend being used to serve ads to customers for products that they have already purchased. For the customer, receiving these ads is a nuisance and can make future ads less effective. To ensure ads aren’t served for products already purchased, brands can use a customer data platform to create suppression audiences, which excludes customers based on history, purchases, demographics, and other attributes. Unlike other customer data management solutions, CDPs are able to collect purchase data in real time, rather than in batches, so customers can automatically be added to suppression audiences automatically and immediately. That means less wasted ad spend, and a better customer experience.

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: Control how many times ads are displayed easily

Retailers often struggle with the same ads being shown to the same person, making them both annoying to the consumer and ineffective. Managing communication across channels is the only way to ensure that customers are shown the right ads the right amount of times, but with many channels and marketing tools it's often an unwieldy process. A CDP provides cross-channel frequency capping allows marketers to precisely define the number of times an ad is scheduled to be shown to the same customer and the channel it is shown on, giving marketers better control over customer engagements and paid ad spend.

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: Help customers find the right fit

One of the most difficult parts of online shopping for customers looking to purchase new clothing or shoes is finding the right fit. Retailers can help address this issue by providing recommendations based on previous purchases. But, that doesn’t just mean offering customers items similar to those they have previously purchased; innovative retailers are using CDPs to collect and aggregate customer fit data based on previous purchases to recommend which size a customer should purchase. For example, if a customer has purchased a pair of designer jeans in a size 4, the retailer can use a CDP to gather fit feedback and compare that pair of jeans’ attributes to the ones the customer is currently looking to purchase and recommend a size 2 because these jeans run large. With fit recommendations available, customers feel more confident making the purchase and with each purchase, their fit recommendation can be added to their customer profile and become more tailored.

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: Turn in-store visits into purchases even after customers leave

Customers often look at items online before going to the store to compare prices and features, creating an opportunity for retailers to leverage location for proximity-based marketing. When a customer enters the store, a beacon collects their customer data. This data can be connected to purchase history, browsing history, and the retailer’s app to send in-app and push messages with offers that are relevant to the customer. If a customer makes a purchase in-store, that information can be added to their customer profile. If a customer doesn’t make an in-store purchase but uses the app in-store, this could also trigger a message 12 hours later for a follow up email marketing campaign. Understanding when customers are in-store and what the do in-store can help create highly personalized engagements that are contextually relevant.

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: Re-introduce urgency to online shopping

Online retailers benefit greatly from customers’ ability to browse for products from the comfort of their homes or connected devices, but it can also be detrimental because it eliminates the urgency felt to purchase in-store. Instead of purchasing, they may look at an item several times or place it in their cart before exiting the site. Tracking customer visit events can provide retailers with the information to create personalized engagements to guide customers through sticky points in the customer journey. For customers that don’t quite find the exact item they desire, say in one color versus their preferred color, retailers can create an email campaign that informs them of new stock that fits the item parameters, for example. Customers can also be driven back to the site towards conversion by creating personalized email or ad campaigns for sale events that display items they have had in their carts recently or that they have viewed or favorited recently. The prospect of additional savings can provide the impetus to purchase items they have been considering but have not purchased.

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: Offer even greater shopping convenience

Retail shoppers love being able to find and purchase items on the go, but don’t necessarily love the shipping process. Addressing this pain point through loyalty programs is not only a way to improve the shopping experience, it also makes the retailer front of mind when a customer decides to purchase something. While Amazon’s Prime program was the pioneer, many retailers are now offering expedited shipping for customers in their loyalty programs. Once a customer reaches a certain purchase threshold over a predetermined period of time or outright purchases membership to the loyalty shipping program for the year, future orders are automatically upgraded to expedited shipping at no extra cost. As long as the customers feel that the benefits outweigh the initial financial investment, they are more than happy to continue purchasing from that retailer. Moreover, because customers know they have this expedited shipping available to them they are more likely to make impulse or short-notice purchases as well as planned purchases leading to increased revenue with relatively little investment on the part of the retailer. While this seems relatively simple, retailers need to be able to create and update complete profiles of customers tracking their purchases, preferences, and coordinate this data with their operational systems to make the process as fast and streamlined as possible. Connecting this data requires the use of a CDP capable of integrating data from across the retailer’s infrastructure and can also help increase loyalty program conversion by providing potential rewards members data for email, social, ad, or push campaigns for better targeting.

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: Improve customer satisfaction before and after purchase

Customer satisfaction is as dependent of the post-purchase experience as pre-purchase for retailers. Understanding how to better serve customers both before and after they purchase an item can make a huge difference in an online retailer’s revenue and brand reputation, especially when it comes to delivery and returns. Using a CDP to link purchase, logistics, and customer service data can help retailers get ahead of customer service tickets related to delivery delays or issues. By providing customer services with real-time shipping information, agents can proactively reach out to customers via their preferred communication channel to alert them about their order being delayed before the customer ever realizes there is an issue. This leads to a hugely more positive experience for the customer because they don’t have to spend the time tracking down the issue and worrying about when and if their package will be delivered. Returns, on the other hand, can also become a more positive experience for customers by connecting return shipment data to messaging channels to let the know when their return is received and their accounts are credited. Feedback can also be incorporated to their customer profile at this point to make future purchase more successful. This higher level of customer service shows customers that the retailer is highly competent and customer-centric leading to greater satisfaction.

Retailers now have access to unprecedented amounts of customer data thanks to customers' engagement across devices and channels. This data represents an opportunity that brands 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; 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 media 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 ROI, explore our platform demo here!