If your brand is having trouble with customer churn and profitability, it could be because you’ve been relying too heavily on third-party data for your digital campaign targeting.
Certainly, third-party data is necessary when your email or ad campaign goal is new customer acquisition. But the cost per acquisition is so high in many industries that new customers don’t become profitable until year two or three. For example, insurance companies can spend as much as $500 to acquire a new policy, while new customer acquisition costs can run as high as $100 in other B2C industries.
It’s a well-known fact that returning customers spend more. In the fourth quarter of 2015, returning visitor transactions comprised 48% of all U.S. e-commerce sessions and accounted for $5.3B in spending, almost double what new shoppers spent in that time period.
Clearly, retention marketing is a worthy investment. But the targeting strategies that work for acquisition won’t work for reaching and retaining your existing customers. In short, if you’re trying to reduce customer churn, third-party data won’t cut it and here’s why.
Third-party data is widely available – but not close enough to your customers.
Third-party data and broad messages can work for acquisition campaigns when, for example, you’re trying to attract consumers who are just now aging into your category or when targeting a completely new segment.
But loyalty marketing is different. The deeper a customer’s engagement with your brand, the more precise they expect your messaging to be. Third-party data models might predict that someone is “likely” to buy your brand, but can’t tell you which of your specific products/services each customer has bought over time. Nor can it reveal that a customer recently made a complaint to customer service or left a positive review on your site. Thus, third-party data alone can’t support the nurturing “we ‘get’ you” customer experiences that build deeper emotional engagement with your brand. It’s not going to get you close enough to your own customers.
Third-party data is good for scale – but not so good for accuracy and efficiency.
Third-party data vendors love to cite mind-boggling statistics like “160+ million anonymized consumer profiles with 7,000+ dimensions” to promote their ability to reach precise audiences at scale.
But here’s the reality. A lot of those data points are outdated and inaccurate, and here is why:
Say that someone viewed a piece of online content about a new video game console. Now he’s a “millennial male gamer” for the next 180 days, according to the anonymous cookie that’s been sold to your DMP. The data associated with this cookie gets passed around multiple companies and run through various predictive models and gets re-sold as a “console game player,” a “game console intender,” and a “video game enthusiast.” The same person could be sold to you under multiple headings by multiple vendors or on separate desktop and mobile lists. The original action that put the person into a given segment is lost, so you don’t know how strong his connection to your category or brand really is. This person might already be your customer. You just don’t know. The outcome is spending inefficiency.
How inefficient is it? You’re paying a market-clearing price to buy the same people that your competitors are buying, and potentially buying the same person three times over. All the value is being absorbed by the data vendor and you’re just finding new ways to bid against your competitors. First-party data, on the other hand, gives you the power of unique knowledge and a trading advantage versus competition.
How inaccurate is it? Just take a look at what the ad-targeting cookies on your own computer say about you, using these links to Google (DoubleClick) and Oracle (BlueKai). One friend’s laptop recently revealed him as having children age 6-10, a surprise to him and his wife; speaking Spanish, for which his only qualification is eating at Chipotle; being in a blue-collar profession, but he’s a licensed civil engineer; and being interested in online dating, which he was especially eager to disavow.
Third-party data has lots of attributes – but lacks the ones you need most.
Third-party data vendors hawk the breadth and depth of their knowledge — from geographic locations visited to personal wealth and income — to promote their ability to target precise audiences. But third-party data can’t reveal customer facts like these, that can have the biggest impact on ROAS:
Ultimately, first-party data creates a much better customer experience.
First-party data refers to all of the data that your customers have shared with you, including personal attributes like their birthday along with behavioral data such as in-store purchases, online transactions, loyalty program participation and service/support contacts. When you use your first-party data to recognize individual customers in real time, you can create a much better customer experience. In contrast to the drawbacks of third-party data, first-party data enables you to:
- Recognize your customers across time and channels, so you can move beyond the transactional level and actually build a relationship with that customer that will protect your brand from competitive incursion.
- Evaluate customers based on their entire history of brand interactions — including content consumption, purchasing, service contacts, promotion response and more — enabling you to deliver messaging and offers that are more individually relevant.
- Know whether the time is right to offer a customer a complementary item, ask for a product review or entice them to come back after a long lapse.
- Know exactly what your most valued customers look like and find more like them.
Most brands already have the fundamental tools to use first-party data for ad targeting. Why not use them?
Originally published May 08, 2017