Make The Most Of Your Company's First Party Data: 3 Use Cases To Implement Today

Several years ago, I saw an article in The Economist proclaiming that data had become the new oil. If data is so valuable, then proprietary data must be particularly valuable.

Marketers that use first party data well to create a competitive advantage will have an edge over marketers who either have very little first party data or marketers who do not use first party data to their advantage. 

Before diving into use cases, let’s define the term “first party data.” First party data is the information a company has about its customers. Examples of first party data include:

• Basic contact information
• Order and transaction history
• Mobile and web behavior
• Demographics
• Preferences and interests
• And more!

The key distinction between first party data and second and third party data is the ownership and access to the data. Second party data (another company’s first party data) and third party data are costly to acquire and not exclusive. Your biggest competitors could theoretically purchase the same data as you and wipe out any competitive advantage you hoped to gain. 

First party data is truly one of the only ways to hold any competitive advantage over your competitors. Companies that know their customers and consistently meet customer needs will do well over time. 

The above statements have been true for years, but recent changes to the marketing landscape, namely Google’s 3P cookie announcement and Apple’s recent IOS update, have further accentuated the value of using first party data to your advantage.

I want to share three high value use cases that can be used for either B2B or B2C use cases. I will explain the high level rationale behind each use case and offer some commentary on how to execute these use cases.

Customer Suppression

The customer suppression use case is the highest ROI use case that I can think of. It takes relatively little time and effort to create customer audiences to suppress, but it creates incredible gains in your media efficiency. The old adage “a penny saved is a penny earned” rings true here. Through customer suppression, marketers are able to reallocate dollars to new prospects. 

I wish I could give you a cost savings estimate, but it will entirely depend on your business and current campaign plans. If I had to throw a range out there, I would expect efficiencies in the range of 5%-25% from this use case alone. For budgets in the 7 figures, this use case adds value quickly!

Not only does it make sense for most marketing campaigns to suppress customers in order to reach new customers at a higher frequency, it also creates a better customer experience. Said differently, it prevents existing customers from seeing no-longer-relevant ads and might even prevent customer complaints from those who see new promotions that they may have missed out on. This use case will have your customer care teams thanking you.

This use case can be deployed through major ad platforms (Google, LinkedIn, and Facebook), display and video networks, DMPs, and CDPs. 

If you are not thinking about this use case today, do yourself a favor and test into it.

Churn Prevention

Companies spend hundreds of dollars acquiring customers. For B2B players, customer acquisition costs might easily run into the 5-figure range. Given the investment that goes into acquiring customers, customer churn is a huge concern for all companies. 

Using your first party data to try to preempt customer churn is one of the most valuable use cases, given the challenges with acquiring new customers. Keeping customers engaged, happy, and coming back for more is critical.

In order to succeed with this use case, data work is absolutely required. First, you must become well acquainted with your customers and their data. 

Work with your data analytics team to uncover key customer behaviors that keep customers engaged. In a similar vein, find customer behaviors that signal risk. 

Create cohorts of engaged and disengaged customers based on some of the following:

• Account login trends
• App usage trends
• Contact center call volumes
• Purchases and repurchases
• Subscription status

The customers that are not engaging with your brand are probably more likely to churn than those that are indicating behaviors of highly engaged customers.

I recommend creating cohorts, based on the risk factors matter the most to your business and craft campaigns to re-engage these cohorts. 

To measure success, try to understand typical churn rates within your customer segments. Next, split each segment in two. Try churn prevention offers and messaging in one group and not the other and measure the difference. I bet you’ll notice a difference. 

Remember, it is generally less expensive to keep a customer than it is to find a new customer. Be willing to spend some money to keep good customers because it will save money in the long run. 

The key is to know when your customers are likely to leave you before your competitors do. This is the value of having and using first party data to your advantage.

Lookalike Modeling

In my last article, I talked about the value of your own customer account data brings in creating an ideal customer profile. Your customer data and the insights you can derive from your customers, should be one of the cornerstones of your segmentation strategies as you target new people and new accounts. 

In most industries, there are many prospects that share many similarities with your best customers. Prospects could potentially share similar firmographics and geographies in the B2B space and similar interests, demographics, behaviors, and needs in the B2C space. If your company and product is solving the problems for your best customers, I bet prospects that look like your best customers could eventually become some of your best customers, too. 

For B2B lookalike modeling, I recommend using a strong business data partner once you have defined your ideal customer profile. Companies like Dun & Bradstreet and ZoomInfo can help you find the right accounts and the right decision makers and gatekeepers at these accounts. Other tools like LinkedIn can also be very helpful for finding the right accounts to target. 

For B2C lookalike modeling, you can hack your way through or you can leverage a host of marketing technologies to help you. 

If you want to hack your way through this, you will be directionally on your way to reaching similar audiences. For example, if you are creating a Facebook campaign, you can manually select interests, geographies, and demographic characteristics that will mirror your beliefs about your customers. For small enterprises, this might be good enough, especially for smaller marketing budgets.

If you are a larger enterprise, there are technologies that can do this for you. For example, if you run display advertising with the help of a DMP, you should be able to simply check a box that automatically creates a lookalike audience for you. You can even determine how similar you would like your lookalike audience to be. 

The two biggest ad platforms, Google and Facebook, also support this lookalike audience functionality. In Facebook, you can indicate that you want to expand your reached audience by including the top X% of Facebook users that most look like your customers, for example. In Google, you can leverage the Similar Audiences tool, which will also find users that share commonalities with your existing Google audiences.

As with the second use case I offered, I recommend that you test into this. Test to see if lookalike audiences yield more favorable CPCs, conversion rates, etc. I think you will find that tighter segmentation and more thoughtful targeting will outperform looser segmentation and an advertising shotgun approach. 

A Few Last Words

These use cases need to be tailored to your business and your context. These recommendations are not meant to be prescriptive. Rather, these recommendations should be used to spur conversations in your own marketing teams. 

In your next team meeting ask:

1. Should we be suppressing customers from existing campaigns? 
2. What plans do we have to prevent at-risk customers from churning?
3. Should we be using lookalike models to reach new prospects?
4. Are we getting the most out of our first party data?

I hope this general overview of these three use cases will help you and your marketing teams get closer to your customers.