Discovering Data Nuances, Part 2: The Marketing Team
There’s not a single team leader, CEO or even seasonal intern within an online retail organization that won’t benefit from better data. But data can be overwhelming (hello, omnichannel, location-based, social with no persistent identifiers!), and getting a handle on it tends to be deprioritized when things look “good enough” or if the charts are going up and to the right. In this blog series, guest author Adam Paulisick will explore how clean and unified data can benefit different functional teams and roles, leading to more effective decision making that results in growth right away. As more articles are added to the series, they will be linked here:
tl;dr
The LTV, CAC & RPR you’re trying to improve are probably higher than you think.
Your promo strategy might not be working; only clean data can tell if it does.
Unified customer data across platforms makes personalization possible at scale.
Clean data makes every tool you use for marketing work better and cost less.
Leading DTC brands are seeing the impacts of clean data today.
According to a new research report by Sequent Partners, more than 300 DTC marketers say that their greatest challenge related to budget optimization and measurement is “accessing and synthesizing accurate data from disparate sources.”
That deserves saying again: the greatest challenge for DTC marketers related to budget optimization and measurement is accessing and synthesizing accurate data from disparate sources.
Since campaign measurement and attribution isn’t our thing here at Orita, we won’t say anything more about it. But accurate data for budget optimization… we’ve got a lot of thoughts on that one. Specifically, accurate customer data. Without an accurate picture of the customer on which to create your marketing, all of your work toward “personalization” goes to waste.
But before even getting into personalization - a presumed tactic today - we have to address strategy. The big decisions about how much to spend, on which customers and in what areas. Should you pull back from Meta and start throwing money at TikTok or YouTube shorts? Have you been marketing the wrong products to the right customers? Or, marketing to the right customers the wrong products? Can you still afford to rely heavily on influencers, as they become more expensive and more choosy?
Of course, all of these decisions can be made very effectively with a reasonable assurance of success when they’re based on data. As long as the data is telling the truth.
Clean customer data can be your marketing strategy’s biggest cheerleader
Whether or not your marketing team is held accountable to lifetime value (LTV), the cost of acquiring new customers (CAC) is certainly top of mind and of course the ratio of CAC to LTV. With rising ad costs driving this number incrementally higher, DTC brands have a myriad of options. Perhaps you’re bringing advertising operations in-house, diversifying away from facebook & Instagram, or working to improve lifetime value (maybe via a subscription or other loyalty play). Regardless of what your goals are, you simply need to understand if the same customers are being counted as different people when they use slightly different combinations of data to purchase.
The thing is, data isn’t useful unless it’s accurate. Read: it’s been cleaned and unified. Inaccurate customer data can have a major negative impact on LTV, RPR and CAC calculations, meaning that all of the metrics you’re working toward improving are just plain wrong. Even worse, it could mean that customers are never even worth the cost of acquisition or the payback takes too long (more on that here).
Take Amour Vert, for example. Like most DTC brands, this sustainable fashion powerhouse relies on a bevy of third-party platforms to run its business. The solutions are minimally integrated, so it was impossible to collect and distill an accurate customer history. Although they relied on a data warehouse, the data wasn’t linked across sources. Once their data was cleaned and unified into a single source of truth, LTV increased by $23 and repeat purchase rate (RPR) increased by 8%. (Of course, lifetime value didn’t actually increase. The clean data finally resulted in an accurate calculation.) With a higher LTV, their LTV:CAC ratio would more accurately determine the potential for sustainable growth and potential profitability. And potentially create room to spend more on acquiring new customers.
** PSA: Do not spend tens of thousands let alone hundreds of thousands on brilliant business intelligences setups (shout out looker), unless, the data you are piping is clean and unified **
Another example comes from Shameless Pets. Through the process of cleaning and unifying their customer data, the brand was able to uncover unusual promo activity that had an impact of $18/order. A refined data set gave the brand confidence in shifting strategy away from welcome promos and toward a program that rewarded loyalty over conversion.
In short, bad data can lead to bad strategy. Not being able to identify the same customer across channels and platforms will lead to bad messaging, the wrong marketing mix and ineffective tactics for reach and frequency. But clean, unified data can lead to better decisions which lead to better results.
Personalized marketing that’s based on real people
Personalized marketing starts with the customer journey. The touchpoints that shoppers go through as they pass from the first brand exposure to (hopefully) becoming a brand enthusiast. It includes the order in which products are purchased, the time between purchases, the products that attract different types of buyers. There’s a lot of data and information that goes into understanding how customers engage with a brand. And this creates the foundation for personalized engagement. Customers want to be known and valued, and they want every interaction to reflect that.
But this obviously isn’t possible when you can’t even tell that it’s the same person who made a purchase through Instagram, on your website and in the store. Or if someone forgets their login and uses a different email address the second time around. When the data from all of the platforms you use to run your business isn’t unified around real people, everyone’s a first-time buyer.
Edutainment company Digital Dream Labs is a prime example. The brand ran a very successful kickstarter campaign, acquired another company in the space and had a gaggle of subscribers bought-in for new announcements. But out of the 3 million records in their database, how many IRL people were represented? Half. Only 1.5 million real people were represented by these 3 million records. Which isn’t too bad for a brand that rose to viral success, but also isn’t a foundation for a solid engagement plan. Give this list to any marketing automation provider and you would spend twice what you need to, without even being able to recognize who was a loyal buyer.
In order to deliver personalized marketing, you also have to be able to create accurate audience segments that align with what you know about the customer journey. You don't know that customers are 70% more likely to commit to a subscription between their 2nd and 3rd purchases, if you don’t have their purchase history linked to a single customer ID. You would be remiss to run a campaign aimed at lapsed buyers, but deliver it to customers who made a recent purchase that’s not tied to their account. When customers feel like they’re just “one of the crowd,” they aren’t customers for long.
Defend your budget with verified facts
Whether you’re presenting your annual budget or asking to sign a new vendor or service, your role is to make a case and your CFO’s role is to say “no.” Only kind of kidding on this one. As someone who’s sat in plenty of budget meetings from both sides of the table, I’ve experienced the emotional side of the conversation.
As the marketer, you know what you’re defending and you know you can execute your plan strategically with strong results. But you’re not speaking the same language as the gatekeeper, who operates from a strictly quantitative perspective. Consumer sentiment and community advocates don’t earn quite as much approval as proven increases in repeat purchase rate, decreased customer acquisition cost and metrics that show the effectiveness of your marketing operation in terms of LTV before and after you invest in a new tool.
Of course, we don’t want any of these platforms “grading their own homework.” The data being used to prove effectiveness needs to be objective and unified across sources. Reports from marketing platforms might show an increase in LTV before and after an email campaign, but the data hasn’t been unified with subscription data from ReCharge or returns data from Reverselogix. So of course LTV will be off…not properly supporting your case (or your decisions) for the next campaign.
A very tactical and immediate benefit of a clean & unified customer list is cost savings. If you’re paying per record for customer data enrichment (happy to chat about our thoughts on this one), that means you’re paying for the same customer multiple times because they engaged with your brand on different platforms or with different email addresses. Same story for Klaviyo. If the number of contacts you have in their system is reduced from 145,001 - 135,000 you’ll save $2,760 a year on their email and text program. That’s a metric your CFO will like to see (and the savings will more than pay for clean data).
Cost savings aside, all of the tools you’re relying on to deliver personalized marketing will function better if they operate on unified customer data. Every part of your marketing will not only become more efficient, but also more effective. And who doesn’t want the tools they spend their budget on to work as promised?
Focus Funnels is an ad firm aimed at helping female founders grow their DTC business with creative digital content. Their experience working with brands like Lily Jade, Primally Pure and Tubby Todd to scale their paid media has resulted in happy clients that have seen measured growth. According to co-founder Taylor Frame, "Clean and unified customer data can not only help DTC brands save money on their advertising, but also see better results. With accurate customer lists, customer acquisition cost goes down while average order value, repeat purchase rate and lifetime value increase. Every brand should be marketing on clean data."
One major key to success here is to enable clean data in the center. If the marketing team is the only one relying on a unified customer list, the metrics and decisions just won’t make sense to anyone else. Of course, tools will still work more efficiently and effectively. Of course personalization will still be much better. But you can’t be responsible for increased LTV or improved LTV:CAC ratio if your LTV calculations have different results than the executive team. And they will, because a dataset with only 2% issues (which is all of them before they’re refined) will return different metrics.
If you need help convincing the executives in your organization that clean data will improve the way you (and they) work, check out this article just for CEOs and Founders. Then download this document to help you use clean data effectively across your marketing team.
Adam Paulisick is an Adjunct Professor of Entrepreneurship at Carnegie Mellon University and an Advisor to Orita. Adam was previously the Chief Product Officer at the Boston Consulting Group and a Senior Vice President at The Nielsen Company specializing in advertising attribution, identity resolution, and clean room data matching.
