You may be familiar with the terms first- and third-party data. But you might just smile and nod when somebody starts going on about second-party data. Or you may wonder which type of data can deliver the greatest ROI. Then this blog post is for you! In this post, we will break down the three types of data, explain what they mean, and help you understand the value to be gained by each data type.
First-party data is the data that you — the brand or retailer — have collected about your visitors, customers, shoppers, etc. This is the most powerful of the three data types because it is the stuff you collect directly from your customers and their interactions with your business, and it is, therefore, the most reliable.
Often called CRM data, this is the most relevant and accurate information you can collect. Some examples of first-party data include:
- Site registration data: name, email, address, gender, etc
- Website behavior: number of visits, minutes spent, products browsed, etc
- Purchase data: products purchased, purchase amounts, coupons used, frequency of purchase, etc.
- Email data: emails sent, opened, clicked, etc
- Mobile applications: location, mobile browsing, mobile purchase
- Beacons: store journey path, time in-store, in-store product engagement
A recent E-Consultancy Report, "The Promise of First Party Data," showed that companies with the highest ROI from their marketing programs used first- and second-party data more regularly than companies whose marketing programs underperformed.
Q: Which categories of data does your organization use regularly?
Generating ROI from First Party Data
Marketers use first-party data to turbo-charge every step in the customer lifecycle.
Smart marketers are seeing tremendous boosts in performance when utilizing first-party data for audience targeting in their advertising efforts. From creating an audience of lookalikes for their highest-value customers on Facebook to leveraging paid channels to reach loyal shoppers who are not responding to emails, first-party data can drive “orders of magnitude” improvements to ad spend results.
Predictive propensity modeling and data-driven persona groupings based on cluster analysis run with first-party data can help marketers anticipate what first-time buyers will be interested in purchasing next, driving up the odds of that critical repeat purchase.
Active Repeat Shoppers
CRM data can be used to personalize promotional emails to match the products, interests, and context signals that you pick up from your customers. And predictive models run on first-party data can help identify VIP shoppers long before they cross the VIP purchase threshold, helping you know where to invest your marketing dollars to significantly improve loyalty.
Advanced analytic techniques use first-party data to create an expected purchase cadence for each shopper. As shoppers veer from their expected purchase path, marketers can intervene with messages and offers at exactly the right moment for each customer to maximize retention.
First-party data is also used to prioritize outreach efforts for lost customers and to identify when at-risk customers cross the threshold into the abyss.
Second-party data is a relatively new form of data. Essentially it is another company’s first-party data that you can purchase from them. It is most often used when both brands stand to gain from data sharing.
For example, if you're a hotel booking site, you could partner with an airline to share information about customers who have recently booked. If a customer books a flight, then you can use that data from your airline partner to reach those potential customers and offer them hotel rooms. Similarly, the airline partner can reach the customers who have booked a hotel room on your site.
Amazon has vastly larger consumer insights than any other retailer on the planet. Smart marketers are crafting strategic data partnerships to shift the balance of power and leverage data not readily available to Amazon.
Generating ROI from Second-Party Data
Second-party data can be great at letting a marketer know when a consumer is in the market to make a purchase.
For example, imagine if One Kings Lane had a data partnership with State Farm. I use this example because my basement just flooded, and I wish that One Kings Lane had a partnership with State Farm! Due to the flooding, I need to replace a couch, chairs, a rug—the entire contents of my finished basement. I have a settlement check from State Farm, and it would be a perfect time for One Kings Lane to reach out to me with a great message and offer. “Basement flooding sale - 50% off select leather couches and chairs”! These insights would help One Kings Lane both acquire new customers and sell more to their existing customers.
The advent of second-party location data available from sources like Placed and Verizon take location targeting to the next level.
A deep analysis of customer traffic patterns conducted by a major non-hamburger fast-food chain showed that consumers most likely to eat at their restaurants also ate at McDonald's. They instituted geo-fenced targeting around McDonald’s stores and included McDonald's messaging in their promotional campaigns and saw a dramatic increase in restaurant visits and revenue.
Third-party data is the data collected and aggregated by someone other than the original data source who then sells that data to marketers.
In other words, the data aggregator doesn’t directly collect the data from customers/shoppers/visitors but has relationships with several companies and sources that collect the first-party data.
Third-party data is considered the least valuable kind of data. Third-party data quality can vary wildly, and it can be costly. The data is also available to purchase by anyone, so popular audience segments are inundated with promotions. If you receive an email or a coupon from some company that you’ve never heard of, it is likely that your data has been sold to them. Perhaps the email is relevant to you, but compared to first- and second-party data, the likelihood is lower.
Generating ROI from Third-Party Data
DirectTV used third-party data collected from the US Post office to dramatically improve their new home expansion initiative. They identified everyone who had filed a change of address form and presented these consumers with a special moving offer when they visited the DirecTV website. The execution was subtle — DirecTV didn’t specifically say that they knew the consumer was moving; the moving sale appeared to be a lucky coincidence...a lucky coincidence that improved conversions by 20%.
If you're interested in what you can do with your first-party data to boost your bottom line and drive growth, you can watch our webinar, Optimizing Performance Marketing with First-Party Data.
If you want to listen to this conversation on the go, subscribe to the Custora Time podcast for audio-only webinar replays on all major podcast platforms: iTunes / Spotify / Google Podcasts / Stitcher.