One unexpected result of the demise of the third-party cookie, which we covered in an earlier blog, is a seemingly greater appreciation for the power of first-party customer data. After searching far and wide for a magic bullet to power a personalized customer experience, marketers have found that a solution has been close at hand all along. In recognizing first-party customer data as powerful currency to create personalized customer experiences, all that’s left is for marketers to understand how to optimize its use.
First, what is first-party customer data and how to acquire it? There are a few types. There is first-party, cookie-based customer data, which in contrast to third-party data is data collected from online session on a company’s own website – pages and content viewed, time on page, clicks, items placed in a shopping cart, etc. This can be known (a person has logged in or otherwise identified themselves) or anonymous (affiliated with the device). Either way, this data help marketers personalize a website experience by populating forms, saving credit card information and search history, etc.
Detailed, behavioral first-party cookie-based data tells marketers a great deal about what a customer is doing and what they’re thinking about, which are important clues for capturing customer intent in the context of a greater customer journey.
Those clues solve more of the puzzle when session data is correlated with other forms of first-party data. Every point-of-sale system, e-commerce site, social site, CRM system or mobile app also collects first-party customer data, as do low-tech sources such as forms, letters and physical mail. Every interaction with a customer from a product evaluation phase through a buying phase is rife with potential sources of first-party data.
Finally, first-party customer data includes data that a customer volunteers, otherwise known as “zero-party” data, which includes filling out and returning a product registration form, completing a survey, commenting on Facebook or another social site, participating in a loyalty program, etc.
First-Party Data and Contextual Interactions
Before using first-party customer data to optimize a personalized customer experience, it’s important to understand that real insight only comes from aggregating all the various sources of information to create a unified customer profile. A unified customer profile, also known as a golden record, will include a customer’s likes, dislikes, preferences and behaviors – on multiple devices and for any ID associated with the customer – over time. A golden record is the key for marketers to use the information to present a relevant, personalized experience every time the customer interacts with a brand.
Exactly how personalization is achieved through first-party customer data depends on possessing a keen understanding of the intent and current engagement of the customer. If a customer is operating anonymously on your website, for instance, first-party customer data will be used in a contextual manner – responding to the signals a customer (or the device associated with a customer) is providing in the moment to turn around and provide the customer with a better experience. An example of this is responding appropriately to a search, or otherwise recognizing affinities and choices a customer makes throughout an online session.
Conversely, a marketer may have a deeper and more detailed picture of who the customer is – they may be in the mobile app, they’re logged in, they’re a repeat customer on the e-commerce site, etc. In that case, the context shifts to proactively enhancing or shaping the experience rather than reacting to real-time signals, showing products the customer has expressed an interest in, or letting the customer know how many loyalty points they’ve accrued and displaying items in that pricing tier.
Why Balance Matters with First-Party Data
Striking the proper balance is an important consideration for using first-party customer data. An appropriate engagement that delights a customer does not necessarily mean bombarding the customer with personalized offers just because first-party customer data reveals a customer’s preferences. Perhaps the customer is not in the buying phase of a journey. Or maybe you’ve sent two offers in the past five days and are reluctant to push your luck with a third.
First-party customer data must also be mined to gauge customer intent, which is where rules for frequency, recency and customer fatigue come into play. Automated machine learning is important to help marketers define and follow certain rules for delivering personalized customer experiences at scale rather than rely on educated guesses or intuition for how a customer – or thousands of customers – may respond to an offer, content or any action derived from use of a golden record.
First-Party Customer Data: Not Just for Marketing
A final consideration for how to use first-party customer data is its use beyond traditional marketing channels. A customer may appear on a support site, for example, looking for help setting up a product. Or perhaps they’re looking to join a community discussion about your company’s products or services.
Customers consider every interaction with a brand as part of one cohesive experience; they do not care about the distinction between, for example, shopping online or talking to a call center agent. To meet this expectation of consistent experiences after a sale, marketers must think of first-party data beyond its importance in customer acquisition and other “pure marketing” uses.
Rather, first-party data or zero-party data is often part of a value exchange with a customer; a customer who provides first-party data generally does so more than simply to receive offers, they’re sharing information about themselves in return for a better experience with the brand – on any channel and for any interaction. By treating first-party data as the basis for a relationship with a customer that’s built on trust, marketers reinforce the value of a personalized experience.
First-Party Customer Data and a Value Exchange
From the customer’s perspective, the value extends to a deepening trust that the brand will safeguard their personal information, protect their privacy, and honor their preferences. Marketers must be transparent about how their organization uses and shares customer data. In this sense, a personalized customer experience that reduces friction throughout the customer journey is just one part of the bargain.
With a deepening trust, of course, customers are then willing to share even more first-party data. Used right, a wealth of first-party customer data allows a brand to provide a relevant experience on any channel, in real time, that is always in the cadence of the customer journey. Every interaction with a customer is an opportunity for a brand to demonstrate that it values the relationship, as demonstrated by the experience it delivers in the moment of interaction.
The value extends beyond the personalization itself. It represents the brand, its products and services and the brand’s own values in terms of how it thinks about its customers. A personalized customer experience shows that a brand cares about managing a customer’s expectations through the collection of first-party customer data.
The brand’s bargain, in return, is improved customer lifetime value (CLV) from customers who remain loyal to a known, trusted partner who seems to know exactly what they want, when they want it, and exhibit respect for them as an individual with unique traits.