Discovering Data Nuances, Part 4: The Finance Team
There’s not a single team leader, CEO or even seasonal intern within an online retail organization that won’t be improved by 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 today. As more articles are added to the series, they will be linked here:
Discovering Data Nuances, Part 1: The CEO or Founder
Discovering Data Nuances, Part 2: The Marketing Team
Discovering Data Nuances, Part 3: The Data Team
Get everyone on the same page about spending decisions and growth projections
In the effort to minimize risk, run an effective and efficient operation, and shape strategy, it often boils down to this question for the CFO: how do we allocate our (“limited”) resources to drive sustained profitable growth? With the added weight of uncertainty in today’s inflationary environment, the need to reduce costs often wins out in the balancing act between enabling business outcomes while keeping costs low.
But focusing too much on reducing cost is risky, and potentially short-sighted. Of course you want your business to be as lean and efficient as possible, but we all know the old adage: it takes money to make money. You must continue spending to grow, about that there is no question. So back to the balancing act…investments that enable better outcomes while also reducing costs, that’s a win-win.
Of course, there is no end of solutions and decisions that promise to do just that. Will more effective marketing automation with better personalization mean that more effective campaigns cost less? Probably. Will a new strategic hire implement processes and procedures based on expertise that are more effective, while landing new large clients? Potentially. But “probably” and “potentially” aren’t strong enough catalysts for moving forward.
In times of great uncertainty, it’s the businesses that can quickly make the right decisions and act that win-out. DTC brands that grew up in the last three years are perhaps more adept at this than brands that grew through traditional retail channels. They’ve operated in uncertainty from the beginning. But just because they’re used to making quick decisions without all of the details, doesn’t mean they have to.
Enter clean data.
Better data drives better decisions top down and bottom up
According to a new survey by CFO magazine, more than half of CFOs are looking to hire in the area of data analysis this year. They want their teams to be able to do high value tasks like model what-if scenarios, analyze trends and discover new opportunities. They know this is what will enable them to make rapid decisions that move the needle. In reality though, these analysts will waste at least half their time gathering, cleaning and manipulating data to make it usable.
Although the need for accurate data is well known across the finance organization, the impact of messy data and the risk of using it to guide decisions is likely underestimated. Specifically, when it comes to customer data…the bread and butter for the customer-centric DTC organization. In fact, according to a recent analysis of over 100 million customer records, issues with just 2% of the data means that KPIs like lifetime value (LTV), repeat purchase rate (RPR) and customer acquisition cost (CAC) are inaccurate.
For fashion brand Amour Vert, messy data had a huge impact on metrics: when recalculated based on clean data, LTV increased by $23 and repeat purchase rate (RPR) increased by 8%. Clearly, skewed metrics mean that solid, data-driven decisions can still lead down the wrong path. Not to mention, completely sink the annual budget.
Circling back to the probably/potentially example from above, investing in marketing or investing in a new strategic hire are two paths DTC brands will often take to scale. Likely, either of these options can lead to success for your brand. But will one of them result in more sustainable growth? If you choose to invest in marketing, can you be sure the campaign will increase LTV and win net new customers without too much spend on customer acquisition? In this situation, one thing is certain. Making the decision for how to spend the next million dollars based on skewed metrics won’t result in the outcomes you expect or necessarily want.
Objectivity is the name of the game
Thinking beyond financial planning and analysis, objective and transparent data is the best way to guide budget decisions (big and small) across every department. Unbiased data unification & organization leads to data that drives unbiased decision making for teams across marketing, sales, customer experience, operations, HR…the list goes on.
When data is “cleaned” or identities “resolved” by the marketing platform or the data warehouse, it begs the question: is it really in their best interest to combine multiple data points into a single customer record? Universally, companies that offer these solutions charge per transaction or customer record. It’s easy to see the bias.
If a marketing platform is reporting on their own success based on the data they have collected, would it be in their best interest to reconcile like email addresses if it makes the new customer account appear lower? Or would it make their numbers look better if every single transaction without a login was recorded as a new customer…it’s easy to see the bias.
What about your new digital sales leader, who wants to show how much they’ve improved the overall repeat purchase rate as they push for a raise after a year on the job. There are different datasets coming from different platforms, so maybe they pick the one that helps their case. It’s easy to see the bias.
What it all comes down to is this: clean and unified data can remove the bias from every situation, resulting in more effective decision making with few internal battles over the right choice.
Regular data refinement means that daily, weekly, monthly, quarterly, and annual reports are consistent and make sense over time. It means that the data you’re being charged for and the data being used for reporting is the actual data that tells the truth, not versions of department leader fiction. Even as you change tools, platforms and ways of working, spending time reconciling and excusing issues because of inconsistent data will be a thing of the past. Simple arguments are put to rest quickly because sales, marketing, product, engineering, and everyone else is using the same central logic to link any and all data in the same way, month after month.
Put your best face forward
Better strategic decisions based on unbiased data is just the first step. Eventually, you will need to raise capital, make (or be) an acquisition, or IPO. In any of these scenarios, there’s no room to get the numbers wrong.
If the next step is raising capital, you’ll need to show profit potential and a plan for spending the funds. If the next step is a strategic acquisition, you’ll need to understand if you can fund the purchase. And if you’re planning to go public, you’ll want to get the highest possible valuation based on your actual projected profit (and not be a total let down). Anyone who lived through the past year with their head out of the sand, knows what we mean with this one.
California Cowboy is a prime example of what this can look like for a brand raising a round. After having their data cleaned and unified by Orita, they recalculated metrics based on the refined data. With all other factors beyond the data remaining the same, 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.”
Bringing it back to the primary goals of the CFO - minimizing risk, running an effective and efficient operation, and shaping strategy - clean and unified customer data brings brands closer to the bullseye in each of these areas. But not only does it enable better outcomes, it also reduces cost. Paying less to all the platforms that charge based on volume will probably cover the cost of clean data, but the less quantifiable benefit of better decisions that prevent waste could be priceless to your brand. Since you’re on the finance team and want to see what all this looks like in terms of numbers rather than words, we’ve done that for you here.
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.
