By Andy Schumeister
Enterprise Product Marketing Manager @ Segment
With data protection regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), it’s no surprise that customers expect companies they engage with to respect and protect their personal data.
According to a study from Accenture, “87% of consumers believe it is important for companies to safeguard the privacy of their information.” At the same time, “58% of consumers would switch half or more of their spending to a provider that excels at personalizing experiences without compromising trust.”
So what does it take to deliver respectful and personalized customer experiences?
The short answer is first-party customer data. Activating this data requires the technical capability to collect every first-party interaction and integrate that data into the marketing and analytics tools your teams use to provide customer-first experiences.
In this chapter, we’ll help you construct a data strategy to deliver private, respectful, and personalized experiences. We’ll explain:
When it comes to customer data, not all data is created equal.
There are two distinct classes of data that marketers and businesses rely on to engage with their customers: first-party data and third-party data. These types of data are collected from different sources, are used for different purposes, and are even subject to different requirements under regulations like the General Data Protection Regulation (GDPR).
First-party data is data you collect directly from your customers based on how your customers use your products or services. This includes information on which products a customer views or purchases from you, how often they visit your website or mobile app, and even data that’s stored in your CRM.. For the most part, your customers understand that you are collecting this data—for instance, providing it via a form completion—and expect that you use it to provide an intuitive user experience as they continue to engage with you going forward.
First-Party Data Example Here’s an example: Let’s say I go to Bonobos.com to buy a new pair of pants. Before I complete my purchase, Bonobos asks me if I want to create an account. I fill out a form and tell them my name, share a bit about my clothing style, and let them know that my preferred store location is in San Francisco. When I complete the purchase, the pair of pants also becomes part of my profile. This is all first-party data, or information, that I have knowingly shared with Bonobos.
Third-party data, on the other hand, is user or behavioral information that companies purchase or acquire from 3rd parties. It’s often aggregated from multiple websites and segmented based on user interests, demographics, shopping behaviors, and more. This data is often collected with questionable consent and shared across companies without explicit consumer permission.
Third-Party Data Example Here’s an example: I apply for a credit card and provide details about my job, income, and address. If the credit card company were to sell that information (along with information from other applicants) to a real estate company, that company would be purchasing third-party data. While I directly provided the information to the credit card company, I did not choose to share my information with the real estate company.
At Segment, we often refer to the act of companies sharing third-party data with each other as “data gossip.” If you’ve ever received an email promotion from a company you never shared your email address with, you’ve experienced data gossip. Your customers wouldn’t tolerate their grocer telling their banker what they just purchased. And data gossip is no different. Moving away from third-party data will improve customer trust, which in turn will boost your brand’s reputation.
First-party data is valuable for showing customers that you’re attentive to their needs, showcasing products that fit their interests, or removing irrelevant content. It also has many advantages over third-party data. First-party data is not shared with other businesses, which is beneficial for both your customers and your business. It’s typically more accurate than third-party data as well, because it reflects actual customer behavior from your own channels (web, mobile, in-store, etc.).
Many benefits of first-party data are due to the fact that the data is collected from customers you have a direct relationship with.
Here are a few reasons why having a direct relationship matters:
Accuracy Having a direct relationship with your customer means the data you collect from your customers is likely more accurate than third-party data. That’s because the information is either provided directly from the customer or is based on their actual use of your website, app, or service. When third-party data is purchased, this data reflects a single point in time and degrades in quality over time.
Respect Unlike third-party data, first-party data is collected with consent from your customers. This means that your customers are aware of the type of information you’re collecting as well as how it’s being used.
To take action on your data, you first need to understand what it’s telling you. And before that, the teams who use that data need to have confidence that it’s accurate and reliable. To help you get to that state, we’ve outlined four core requirements for data activation below:
Alignment Before making rash business decisions or building a personalized user experience, it’s important to get alignment between data stakeholders within your organization. This means coming to an agreement about what first-party data you will be collecting and gaining a general consensus for how it will be used.
Standardization After getting everyone on the same page about first-party data collection, you’ll want to be sure all of the data you collect is standardized across the various touch points from where it’s collected. This means establishing a source of truth that clearly defines what data you’re collecting, provides a consistent naming convention and schema, and provides context as to how to interpret the data. Much of this was already covered in the previous chapter, in the section on developing your data dictionary.
Validation Next, you’ll want to be sure your data is collected in the expected format defined in the previous steps. Even with rigorous naming conventions and instrumentation instructions, data that does not match your spec will inevitably make its way to your marketing and analytics tools. That’s why it’s important to have quality assurance (QA) checks in place to catch dirty data before it reaches the tools where you want to use it. This is a tedious problem to solve, and it’s why we’ve incorporated a data validation feature into our Protocols product which automatically catches every incorrect property or data type found.
The last thing you’ll want to confirm before acting on your first-party data is that it’s consistent across all of the tools where it will be used. This means that when a data value changes in one place that it’s also reflected in other tools to prevent a disjointed customer experience. For example, let’s say that a user on a free plan upgrades to a pro plan, becoming a paid customer. You’d want that new event (
Plan Upgraded) to be consistent across any tool you use to reach customers so that your future communications will reflect their new status: a pro-plan customer.
Now that you’ve got a grasp on what first-party data is, the benefits of using it, and requirements for taking action on it, you can start to formulate a strategy to uncover insights and put it to use. Both of which will be covered in the last two chapters to come.