Enriching Customer Files With Shopping Behavioral Data While Remaining CCPA Compliant

Perspectives of a data enrichment company keeping privacy lawyers happy

by Rob McGovern, CEO of PreciseTarget

Brands now realize that data has become a critical success factor. Retail is going down the same path as the ad industry did when it became “programmatic”; data and algorithms are taking over, and nearly all customer interactions are determined by sophisticated algorithms, AI, and machine learning. The pandemic is serving as an accelerant in the retail transition given the increasing criticality of ecommerce. But how can a brand acquire the data it needs in a world of increasingly strict consumer privacy laws? 

Let’s examine the challenges facing retailers as they make the transition, the options for data enrichment, and a few tactics to earn an ‘A’ in the eyes of your privacy lawyers.

The Sparse Data Challenge

Most brands average only one or two transactions per year per customer — even fewer for the countless wholesale brands that recently pivoted to a DTC model. In many cases they have no historical customer data since most customers purchased in the retail channel.  While Amazon, Walmart, and Target have troves of data about their customers, the opposite is true for most  brands. Brands need to begin their journey by acquiring more data to solve their sparse customer data problem. 

As a result, data enrichment – or purchasing granular, off-the-shelf data to be “appended” to a customer file — has become top of mind for marketers in 2020.  Fortunately, it’s now possible to acquire and deploy that data easily and affordably.

Data Enrichment Beyond Low Fidelity Demographics

While it’s true most apparel brands start by acquiring demographic data, they quickly learn that not all 25-year-olds are alike. They come in different flavors, including young, middle aged, preppy, bohemian, tall, short, active, and fashionista. Even when they’re classified by life stage, there are huge variances – not all middle class, empty-nesters wear the same clothes, nor do all “soccer moms”.  We observe this in our daily lives, but data sets are only slowly evolving to offer more granular segmentations.

If you conducted an analysis of what drives apparel shopping decisions it would almost certainly reveal that consumer tastes, or preferences, are at the core. When we evaluate a sweater or pair of shoes our subconscious brain is asking whether the product is ‘me’.  A consumer’s taste lives at a deeper level than demographics.  What if you knew the tastes of everyone in your CRM, and could offer them the items in your assortment that best match their taste?  And how can you do that in the CCPA era?

A New Way to Enrich and Earn an A in Privacy

The CCPA introduced the concept of pseudonymization of consumer identities. One of the CCPA rules is that Personally Identifiable Information (PII) must be pseudonymized. This means it’s impossible to identify a consumer without additional information.

One method to accomplish the enrichment of pseudonymized identities is to use a third party as a trusted intermediary. The third party can act as the honest broker between the two parties, with neither party being able to see each other’s data. In the case of PreciseTarget , we use Neustar, a leading data company, as the honest broker. Neustar knows the identity of all US adults and can act as the honest broker between PreciseTarget and brands. Other data giants like LiveRamp also serve as neutral matchers in the data ecosystem.

Let’s say a brand wants enrichment data on a list of one million customers. The brand can provide the customer list to Neustar, who will translate the customer’s list into a list of pseudonymized identities. These synthetic identities will be transmitted to PreciseTarget, who will have no ability to identify the underlying consumers. PreciseTarget has a persistent pseudonymized identity on all US adults, which has been issued by Neustar. Neustar is the only keeper of the match key. This enables PreciseTarget to deliver enrichment data to its customers while remaining 100% compliant with CCPA pseudonymization requirements. 

Enriching with High Fidelity Taste Data

We recently announced the launch of a new solution that seamlessly enriches our brand clients with a dense taste file on each of their customers. The Customer Insights Profile (CIP) reveals a customer’s taste in nearly every apparel and soft goods category.  By applying machine learning algorithms to over 5 billion opt-in, SKU-level transactions provided by brands and retailers, we have successfully profiled the tastes of 99% of US adults. For example, a profile of a man would include a propensity score for every brand in every apparel and soft goods category. This score is the predicted purchase probability of each brand for this consumer. The insights profile also includes the shopper’s demographics, their category preferences, their typical category price tiers, and many other attributes. It’s a data set that presently covers over 1,000 brands and dozens of soft goods product categories.

For those brands and retailers who haven’t yet implemented sophisticated in-house data platforms, PreciseTarget’s data is available as an online service – we call it “Data Science in a Box”. The goal is to help our brand customers compete more effectively in the new data-centric retail environment. There may be a time when your brand implements sophisticated technology, but in the meantime, you can access taste data on your customers and prospects today.

It’s all pseudonymized and fully CCPA compliant.  So, you can get sophisticated insights and targeting data without fear of being out of privacy compliance and without the need for heavy technology investments. We call that a win-win.