Advanced Personalization: Unlocking the Secret to Shopping Experiences That Create Value
Consumers have quickly adapted to data-driven retail: They routinely receive e-mails and view ads based on their purchase and search histories. They take rewards for repeat visits to a retailer in stride. They know downloading a retailer’s app is a big deal that will provide incentives in exchange for their data.
That’s why retailers can’t stop at simply personalizing the various channels and media they use to reach customers. Retailers seeking to stay ahead of the masses need to move to advanced personalization. They need to expand beyond conventional data to blend in things like weather, events, location and in-store shopping behaviors to deliver a highly relevant, hyper-personalized shopping experience that addresses a customer’s immediate needs via the touchpoints that are the most meaningful for them.
What does advanced personalization look like? Consider two shoppers: they’re the same gender, the same economic status. They are both married, both have kids, both live in the same general area, and have shopped in your store. Most every retail segmentation engine would recognize them as the same cohort, and deliver the same promotions and messaging via the same media.
But one of those shoppers passes your store every weekday on his commute. The other comes in only on weekends. One prefers alerts in the app, and the other likes text messages. When it comes time to promote a refreshing drink to those two shoppers, what will work better: a generalized e-mail promoting a two-for-one deal at the start of the week whose forecast includes three-digit temperatures? Or tailored messages, delivered to shopper A just as he’s nearing your store on the day the heatwave is actually starting, via the app alert he prefers, while Shopper B gets his via text a day later when he’s in town for the weekend?
Savvy retailers know the richer the data, the better the personalization, and are investing accordingly. CRM/personalization is among the top five hottest technologies for retailers, with 28% planning to invest this year, according to the RIS’ “29th Annual Retail Technology Study.”
The road to advanced personalization. The fact is, retailers already own a lot of the solutions and data they need to advance the maturity of their personalization. What’s missing is an efficient infrastructure that allows them to unify, enrich and analyze that data with cutting-edge AI and machine learning so those solutions are working with more refined results. To get there, retailers should follow the five steps below:
#1. Establish a Platform to Collect, Centralize and Prepare Data
When the loyalty solution doesn’t talk to the marketing suite and it’s separate from the e-mail app, messages to customers are disjointed and are limited to what that individual app knows. These apps are also typically not set up to collect, analyze and act on “new” data points like sentiment. Each of those solutions gets better by sharing a single repository for customer data.
A single data platform unifies that data, applies advanced analytics and sends it back to point solutions to get better results. Important characteristics of a unified data platform include:
- A flexible, API-based infrastructure
- Easy integration with legacy systems that lack APIs
- Automated extract/transform/load (ETL) capabilities
- ID resolution and segmentation capabilities
- Robust security, including automation to avoid human error that leads to breaches
- Ease of use, so users can create and execute data functions without the need for IT
- Seamless flow of data back into point solutions so that those users don’t have to change any processes to benefit
#2. Enrich Data with Internal and Third-Party Sources
Retailers who know more can do more. By pursuing a broader array of data types, retailers can add vital information that allows them to leverage what they know to become more relevant. When a retailer is clearly keeping a customer’s needs top of mind and steps in at that moment of truth with a solution, that’s satisfying. That same offer at the wrong time and place is just noise. Below are some unique data feeds innovative retailers are leveraging:
Weather: Red Wing Shoes uses current weather data to send promotions to customers only at those times when that type of weather historically drives increased boot sales.
In-Store Behavior: Retailers can convert real-time data into insights to better engage their customers on the spot. When Fabletics customers share their e-mail with a store associate, for example, the associate can add the items they tried on and liked to the customer’s online shopping cart, data that not only helps the customer with future purchases, but guides merchandising and other retailer decisions.
Events: From the Gilroy Garlic Festival in California to the Indy 500, every local area has events that shape local tastes and purchasing for a specific time period. Factoring in affinity groups alongside everything else you know about a customer can be key to becoming their go-to retailer before, during and after that event.
Location: American Eagle Outfitters can identify and verify customers via sensors at front doors and dressing room entryways. The sensors trigger American Eagle’s mobile app to open on the customer’s smartphone, which can then offer incentives, customized options, and a personalized experience.
Online and Social Behavior: Diane von Furstenberg is leveraging machine-learning technology to create in-the-moment experiences by leveraging data such as other shoppers’ online behavior and shopping bag reminders. Bag reminders alone have driven substantial revenue, while offers have scored a 20% conversion rate among those targeted.
#3 Apply AI and Machine Learning
The key to leveraging rich and varied data in real time to produce an advanced personalization experience is to automate the application of predictive analytics processes. This a great opportunity to apply artificial intelligence and machine learning; algorithms built into the data layer can recognize trends such as the impact of 80-degree weather on sales and apply that data in future situations where it’s relevant. With rich, varied data, these algorithms can generate even more precise insights to drive personalization execution.
The more real-time this process can be, the more relevant the personalization. For example, a retailer collecting data about a shopper’s encounters with in-store touchpoints like digital displays and items she tried on in the dressing room can help an approaching associate guide her to additional styles similar to those she’s already encountered.
Global “no-brand quality goods” retailer Muji combined online browsing data and in-store purchase history via a customer data platform, which allowed the retailer to acquire and aggregate fast-moving data streams in a dynamic, scalable way. By applying analytics to the aggregated data and feeding the results to their marketing tools to personalize their messaging, Muji saw a 100% increase in coupon redemptions across all store locations, increased customer lifetime value, increased in-store foot traffic and a 46% increase in revenue over a two-year period.
When unified data has been enriched by AI and machine learning, the output it feeds back into point solutions is superior. So when a marketer opens up an e-mail tool and selects and audience and a message, they are bringing all of the retailer’s data about that customer to bear to boost the success of that campaign.
#4. Fill in Missing Point Solutions
With the right infrastructure in place, retailers can add missing point solutions to round out their ability to understand and serve customers. By integrating these solutions from the start with rich data processed with artificial intelligence and machine learning, those applications can deliver better results from day one.
Sharing data across touchpoints and applications also means customers get a consistently rich experience with each channel: mobile, social, messaging, e-mail, call center, chat, e-commerce and in-store. Advertising is also enriched when it is based on enriched data.
#5. Comply with Privacy Regulations and Customer Expectations
Any form of personalization requires sensitivity for how data is collected and used. As personalization matures to leverage more data with greater accuracy, retailers must be particularly careful to ensure its use is both welcome and compliant with applicable regulations.
Some tips to avoid the creep factor:
- Understand who your customers are and their comfort with trading personal data for benefits; gain permissions where needed and offer transparency with customers about how they will use the data. Stitch Fix, for example, clearly communicates at multiple steps in its order process that the detailed data it collects will be used to curate a personalized collection.
- Communicate with customers via the media they choose versus all media. That is key to being helpful, and not creepy.
- Comply with the privacy and security laws in all jurisdictions where you operate, including e-commerce. Most North American retailers will need to adopt California Consumer Privacy Act of 2018 (CCPA) best practices, for example, and GDPR if they want to sell into Europe.
Staying Ahead of Consumer Expectations
Every time any type of business moves toward more contextually relevant, personalized customer experiences, it raises the bar for all businesses, including retail. That means retailers must find ways to constantly push the bar on how well they leverage everything they know about individual customers to present increasingly real-time, meaningful shopping encounters.
To do that, they must create a customer data infrastructure, one that allows them to gather all relevant data together in one place, apply advanced, AI-fueled analytics, continue to add new data types like live in-store data, and deliver it quickly to the touchpoints that shape those shopping experiences. When retailers can be helpful and relevant, customers reward them with bigger spending and repeat visits.