Why Bad Customer Data Is Worse than No Data at All

Why Bad Customer Data Is Worse than No Data at All

Is something off about the responses you get to your marketing campaigns? Do you sometimes get odd or conflicting results? It could be bad or incomplete data, and if it is, you’re not alone. Our recent research on the state of the customer journey found companies are still struggling with data integration. More than half of those surveyed (54%) say their biggest barrier to leveraging data is fragmented or siloed data, which makes it difficult to get an accurate, integrated view of the customer journey.

And it’s no wonder—customer journeys are often long and complex. Most of our survey respondents (61%) report having three or more pre-purchase customer touchpoints, with about a third of all respondents (32%) reporting six or more touchpoints. And these touchpoints frequently happen over a course of several months.

Why Bad Customer Data Is Worse than No Data at All

With complicated buyer’s journeys becoming the new normal, you’d expect to see an increase in the use of multi-touch attribution strategies to ensure companies understood the path to purchase for their customers. (A multi-touch strategy divides up credit for sales or conversions among lots of touchpoints, rather than just using the last customer touchpoint as “the cause” of the conversion.) Yet nearly half (48%) say they are not using a formal attribution strategy at all, let alone one that can track multiple omnichannel interactions with the customer. This makes it increasingly difficult to determine which sales and marketing efforts produced a sale.

Relying on the Unreliable

Unfortunately, in the absence of a reliable source of integrated customer data, people tend to rely on unreliable sources—even though they know better. That’s why we see marketers reporting on easy-to-access vanity metrics or only tracking the final touch before a sale, and guessing at what came before it. That easy-to-come-by customer data can lead to some costly but ultimately avoidable customer data marketing missteps:

  • Email platforms make it easy to see which emails garnered the highest open and click-through rates. But you often have to dig deeper to understand what is being clicked on. That means you may not realize that your high CTR email, the one that shows up in the top-line reporting, is driving people to unsubscribe or to view the email as a web page due to poor formatting or image sizes.
  • Social media platforms make it easy to identify the customers who engage the most with content you post and your most engaged followers. But without a layer of sentiment analysis—and looking at what the engagement actually entails—you can end up boosting content that your ideal customer has actually been annoyed with, or showcase content that’s trending, but as an example of what not to do.
  • Website analytics can show you how a customer that was ready to make a purchase found you. But, if you aren’t using advanced tracking, and integrating other channel data, you may decide to double down on your website or search engine advertising and not realize those customers were hearing about the product in a podcast or through influencer word-of-mouth and searching for your brand name specifically as a result of that initial engagement.

When we use easy customer data to make decisions, it makes us feel better than just going with our gut. But it may actually cause us to just make bad decisions with more confidence.

Gartner Estimate Says Poor Data Costs $15 Million

Gartner research found organizations believe poor data quality to be responsible for an average of $15 million per year in losses. Marketers at Shutterfly ran into this when they made some big assumptions presumably based upon browsing data. They sent emails congratulating new parents on the addition to their family—only, many of the recipients definitely hadn’t just had children. While some of the people on the receiving end were amused by it, and took to social media to post at the brand’s expense, that email also likely ended up in the mailbox of customers who weren’t able to conceive, had miscarried, or had lost a child.

Get Treasure Data blogs, news, use cases, and platform capabilities.

Thank you for subscribing to our blog!

Timing Is also Everything

Also, not all bad personalization is off the mark solely due to its message. Sometimes timing is an issue. Like the pair of shoes that follows you all over the web…starting the day after you purchased them from that retailer online. This sort of customer journey mismatch is caused by a lack of real-time data integration.

Between the time that customer viewed the shoes on the web, had them sitting in their shopping cart, and ultimately bought them, that customer’s data had to make its way through your internal processes and systems to eventually fuel a retargeting campaign.
If your data had been up-to-the-minute, you could instead be pitching that same customer on buying the shoes in another color, or on purchasing a completely different pair that your customer data shows is popular with purchasers of the initial pair of shoes.
Subaru, for example, used a customer data platform to unify all of its marketing and sales efforts. Not only was the company able to easily distinguish those who were ready to buy from those who weren’t, but the buying process was streamlined and accelerated, and when the sale was finally closed, Subaru didn’t have to waste resources on someone who had already bought a car. Rather, the company could begin automated marketing efforts steering customers back into dealerships for service, upgrades, and after-market products.

Don’t Let Bad Data Blind You in Making Critical Decisions

When you rely exclusively on one data point—or one customer data source—without context, you have a good chance of turning that right place, right time, right message opportunity into a brand turnoff.

The marketing team at Target found this out the hard way. Using predictive analytics based on purchase history, they would send out pregnancy-themed coupons and mailings to customers based upon their purchase history of items such as unscented lotion, specific prenatal vitamins, etc.

When one such mailing outed a teenager’s pregnancy to her father, it became clear that although that purchase data was right about the impending pregnancy, not combining it with age or other demographic data led to an unwanted media frenzy that stirred up privacy concerns among its customer base.

“Blasting Emails to Everyone” Doesn’t Work

Good, complete customer data, on the other hand, helps you tease out subtle shifts in customer attitudes and behavior, something Shiseido learned when it began using its own customer data platform (CDP) to unify its loyalty, browsing, and ad campaign data.
“Our new customer data platform built on Treasure Data is fundamentally changing how we communicate with our customers,” says Kenji Yoshimoto, Chief Analyst for Direct Marketing, Shiseido. “Blasting emails to everyone who tried samples or bought a particular product won’t lead to customer delight. Detecting a mood swing in each customer and changing the tone of push notifications does.”

And of course, when you rely on bad data to make significant business decisions, you not only miss out on that opportunity to delight the customer, you may even permanently turn them off to your brand. You’re not only missing today’s opportunity; you’re risking an unsubscribe or losing to a competing offer that forecloses on tomorrow’s opportunities.

Consumers want to buy from brands who provide them with an omnichannel “know-you” experience. But to deliver on that expectation, brands must invest in both data integration and tracking customer activities throughout the purchase process to ensure accurate attribution to use for future marketing decision-making.

Tom Treanor
Tom Treanor
Tom Treanor was head of marketing at Treasure Data. He focuses on marketing, martech, CDPs, and digital marketing. Follow him on Twitter @RtMixMktg.
Related Posts