This article is part 2 of a 4 part article series on customer experience sponsored by Arm Treasure Data.
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 — 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.
With a complicated buyer’s journey 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.
The Allure of Easy Customer Data
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.
Unfortunately, that easy-to-come-by customer data can lead to some costly but ultimately avoidable missteps:
- Email platforms make it easy to see your highest open and click-through rate emails. But you often have to dig deeper to understand what is being clicked on. That means you may not realize that high CTR email 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 engaged 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 been pointing out 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 spending on your website or your 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.
Related Article: State of the Customer Journey: Top Challenges and How to Tackle Them
Bad Customer Data In, Bad Personalization Out
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.
Slow and Steady Doesn’t Win The Marketing Personalization Race
Not all bad personalization is off the mark due to the message content either. Sometimes it’s the timing that’s 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.
During the time between when that customer viewed the shoes on the web and had them sitting in their shopping cart and ultimately bought them, that customer 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 was popular with purchasers of the initial pair of shoes.
Related Article: Delivering the Right Content at Each Step of Your Buyer’s Journey
One Data Point Doesn’t Make a Customer Journey
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.
Related Article: The Modern Buyer's Journey Needs Multi-Touch Attribution
Don’t Lose Your First Chance to Make a Great Impression
"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.”
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.
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.
In our final two articles in our customer journey series, we’ll delve into how to structure a lengthy customer experience and how to approach multi-touch attribution.