Personalization has emerged as a top priority in retail. Brands and retailers are investing in data and technology to deliver more tailored experiences across their digital and in-store experiences. By 2022, personalization will push a revenue shift of $800 billion to the 15% of companies that get it right. However, despite this opportunity, personalization is rarely meeting expectations. In our recent survey of 1,000 consumers, over half said a personalized home page would be useful, yet less than 20% said the recommendations they saw were relevant.
Consumers want personalized experiences, and brands and retailers are trying to deliver. So, what is keeping them from success? Ultimately, it boils down to data. In today’s digital world, brands can collect consumer data in a variety of ways, but the problem is having visibility into all data that’s available, organizing it and putting it to work in the right way.
Below are three levels of consumer data brands must collect to provide the highest quality personalization, listed from foundational to best practice.
1. Transaction and Website Interaction Data
The most foundational type of data is about existing customers and their interactions with websites or stores, including transactions. Downloading a mobile app, viewing multiple product pages or interacting with product reviews all indicate interest and purchase intent. This is particularly useful for retargeting campaigns or product recommendations on and off websites.
Although this data is useful for re-engaging consumers and encouraging conversion, it does not enable brands to learn more about who their customers are or anticipate their needs. To do that, additional demographic and off-site data need to be layered on.
2. Demographic Data
Once brands see how shoppers are interacting with them, it is critical to learn a little more about them. Basic information like name, age and location can personalize e-mails, offers, etc. According to Accenture, 75% of consumers are more likely to buy from a retailer that recognizes them by name or recommends options based on past purchases.
On its own, however, this data has limitations. It doesn’t provide insight into the shopper journey. Even worse, with only demographic data, brands can make incorrect assumptions about consumers’ behavior. For example, a company might serve a 44-year-old man from Alaska a recommendation for a winter coat, when really he’s shopping for an inflatable pool as a gift for his niece in Texas. This is a huge miss — 41% of consumers said that bad personalization is enough to drive them to a competitor.
3. Holistic Buying Journey Data
Brands should be able to serve personalized content, regardless whether consumers had shopped with them before.
This requires high quality data about shopping behavior beyond their own websites. Information like where visitors came from, what they were looking at and whether they made a purchase all map the online journey. With this, interests and behaviors are identified in advance, making it easier to suggest solutions that reflect actual needs.
The right consumer data is essential for personalization. Unfortunately, many struggle with how to collect, organize and analyze this effectively. By integrating these types of consumer data together, brands and retailers can provide the most tailored shopping experience.