Discovering Data Nuances, Part 1: The CEO or Founder
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
As little as 2% of your data being messy can cause serious implications to major metrics like LTV, CAC, RPR, and more.
Data clean up that only removes duplicates addresses a very small % of bad data.
Having a single view of each customer no matter how many emails, phone numbers, or addresses they use impacts long term strategy, near-time decision making, and resource allocation for every function and department.
Leading DTC brands are seeing the impacts of clean data today.
Unified, clean data can be processed in less than 5 days
Download this page that outlines the benefits of better data for a DTC CEO or Founder
Bad data: How sound decisions, made by exceptional leaders, can still take your business down the wrong path
Enter the “fearless” leader mantra: Rapid growth. Sustainable growth. Increase valuation multiples. On repeat…like the song that never ends. Questioning second by second what’s going to help win today and wow your board, investors, or shareholders? Then, what’s going to help win whatever is next?
While we don’t have a magic elixir to catapult your DTC e-commerce brand to success, we do know data (we have already processed 100,000,000+ customer records this year). From the conversations we’ve been having, there's a major untapped opportunity here for leaders. Clean and unified customer data is perhaps the best chance your teams have at success as markets continue to be…dynamic (crazy!). Not only does unifying customer data result in getting teams aligned, but it also improves nearly every function of the business…from marketing to customer service and back.
But wait! You already have dashboards and data warehouses so why or how is this any different than what you’ve already invested in? It’s simple: unifying disparate data identifiers (email, phone, address, etc) for customers is not a standard data stack addition…yet. And to have confidence in the data, no matter how many sources or formats, requires everything to be linked and not just scrubbed source by source. The BIG problem is that it’s impossible to know when a customer is the same or different without building a single customer ID file. Which is hard. It’s not that you don’t have the talent, it’s that it’s a really hard thing to build the capability and get a real ROI. (For example, we have over 150,000 custom rules!) Nobody wants to build custom, expensive, and non-core technology.
What kind of rules are we storing? Things like:
Why hasn’t this problem been addressed yet? People are sometimes scared to ask the probing questions or will only see the data within the limits of their role. For example, the marketing team might only see email and advertising data but not subscription or loyalty segmentations.
Even if you have invested in an advanced data stack, use streaming analytics, and deliver personalized marketing or customer experiences, the data is likely handled haphazardly or minimally treated for mechanical issues like duplicate entries. After all, a lot of platforms make money on the quantity of data and not the quality.
And, as little as 2% of your data being messy can cause serious implications to major metrics like LTV, CAC, RPR, and more.
Here is the secret many data clean up companies won’t share openly: calculations can be deemed “accurate” or “correct” when customer data is only matched on exact identifiers (e.g. identical phone numbers or addresses). For example, exact email addresses will be merged into a single customer ID and the data will be called “good.” But this leaves a lot of customer data unaccounted for. Data that with a high probability belongs to the same customer will not be linked (e.g., janedoe@gmail.com won’t be associated with j.a.n.e.doe@gmail.com). This kind of deterministic linking does not result in a single view of the customer, encompassing all of their purchase history across channels.
Also, getting the data in order is a daunting task when there are pressing day-to-day matters. It’s swept aside and added to a never-ending to-do list. So why should you care? Now, today? The problem compounds. Sure, as a CEO it’s fun to talk about record-breaking holiday sales and campaigns that had a major conversion rate on promos. But what happens when those same customers buy with a different address or phone number or email (for good or bad actor reasons)? We have seen lots of examples where this influences LTV, CAC, or RPR. Treating loyal customers as opportunists or value seeking and value seeking customers as loyal results in all sorts of bad advertising, marketing, and promotional strategies.
Bad data is a big deal, good data that is disorganized or disconnected is an even bigger deal
Today, bad data is causing real problems related to growth and profitability for real companies. In May of 2022, software company Unity Technologies credited poor audience targeting due to bad data as the primary cause of a $110 million decrease in its annual revenue target, resulting in stock value declining by 37% that day. That’s not a problem that can be addressed later. But it is a problem that can be prevented.
An online retailer might use 30+ tools and systems to run their business (from CRM to chat to email marketing to online and offline POS…the list goes on). Across these systems, our analysis has shown that the average customer has 75 or more pieces of information associated with their purchase history. Clearly, there is a lot of room for mistakes, disconnection, and confusion about whether or not a customer ID belongs to the same person across different purchase environments.
While it’s become more common to centralize customer data, it’s still not being transformed into a trustworthy asset. No matter how seamlessly data moves between sources, it’s not going to result in accurate analytics, intelligence or forecasting if it hasn’t been refined and linked to a central file with all of the unified customer IDs. And even the best team on the planet can’t execute the right decisions if the tools they rely on aren’t fed with objective, clean, and unified data.
Refined data is your biggest untapped competitive advantage, it can change the entire business in less than 5 business days
To quantify who is at risk for big issues from unrefined data, we ran more than 100 million customer records from ten online retailers through our system across categories like pet, cosmetics, home goods, apparel, and consumer electronics. Through this process, we discovered that issues with just 2% of the data can lead to miscalculating critical metrics like LTV and RPR. And it’s a rare case when less than 2% of the data has problems (so rare that we’ve never seen it).
This proved true for 20,000 customer datasets from newly launched brands and 10+ million customer datasets from more established retailers. Essentially, it comes down to this: every DTC company - no matter the size - can improve metrics with clean and unified data. Since it’s the metrics driving the intelligence and strategy, this is no small matter.
In addition to better metrics, an understanding of the customer journey that you know is real, true, and accurate is a priceless asset for making the right decisions spanning from promos, to homepage design, to customer service scripts and beyond. Truly knowing the differences between customers (loyals, first-time buyers) and the products they buy will change everything about how you personalize the customer experience and marketing.
Perhaps the best part? Better decisions are available to every team with just a couple of clicks and a credit card swipe. You can have transformed data in less than 5 days and not have to look back. Imagine a board meeting, senior leadership team gathering, or monthly planning session where nobody starts by arguing about biased data.
Investor expectations are high
We’d be remiss not to address fundraising in this article, since we’ve seen clean data and accurate customer cohorts make a tangible difference. With the frenzied pace of venture capital slowing significantly in 2022, investors are increasing expectations as they become more particular about what they want. While a strong story and killer product are still crucial, investors want to be confident that LTV is strong, that your LTV/CAC ratio is in a good range and that your numbers are verifiable.
California Cowboy is a prime example of what this looks like. During their latest capital round, they had their data cleaned by Orita. When they recalculated their metrics based on the clean data, LTV increased authentically by $4.50. Not because we just removed duplicates but because we saw how the same customer increased (or decreased) their repeat purchasing over time even as they used different email addresses, phone numbers, and physical addresses. They were able to see a complete and accurate view of the customer journey. And their investors were impressed.
According to Founder Drew Clark, “Better KPIs were just the tip of the iceberg. After seeing the effects of clean data, our investors wanted a more detailed breakdown of customer behavior. And we were able to answer their questions about retention and loyalty with certainty.”
All-in-all, clean and unified data can be “the thing” that catapults your brand past the competition. That directs you toward the right decisions that lead to scaling. If you could help every team in your organization make better strategic decisions for less than the cost of a new MacBook Pro, wouldn’t you?
If you’re still not convinced, print out this page that outlines the benefits of better data for a DTC CEO or Founder and tuck it under your pillow. While we can’t promise it will result in sweet dreams, we can promise it’s a lot harder to make bad decisions when they’re based on good data.
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.
