As a DTC leader, you brand uses a dozen or more applications and deal with countless transactions, entries, and issues every day. And all that work generates the currency of the information age: data. It seems that everyone is talking about data. Big data, machine learning, analytics, and the possibilities of AI. While you get your KPIs like CAC, LTV, ROAS and NPS, what’s the point of it all of the digging in messy data? 

Put simply, the point of digging deeper into the data is to make better decisions. Finding the hidden bits and harnessing the superpowers of data is about informing the actions that you and your team will take. Insights should help you complete jobs with more data driven clarity and, with any luck, let you do it faster. The intelligence hidden in the data helps you move from “WTF is going on?” to “Here’s what I need to do next.” That’s the journey.

But the complete journey includes one more step – the “Why” step. You always go from “WTF” to “Why? to “What to do next.” The challenge is that people with data skills often communicate in vastly different ways – super mathematical ways that include R factors, regressions, and all that. 

While these techniques are at the heart of ‘data work’, business and operations folks can often lose a lot of time jumping down those rabbit holes. Using a simple WTF > Why > What do I do next framework can help you leverage more of your data’s superpowers and deliver real value to your business, every day. 

Let’s dig into it.

At first, WTF > Why? > What to Do Next may sound like a flippant, maybe even disparaging way to think about things. But give me a minute to make my case. I’ll break down and discuss each step.  

“WTF” is that feeling you get when something you thought was one thing, turns out to be another thing. You thought your daily sales were X, ACV, or CAC. You get the idea. It’s when you assume a thing is as you expected, and then … “WTF!” – that thing changes in some unexpected way. It may be positive, like when customers love a new campaign, or negative, like a new batch of products that turn out to be defective. Most Direct-to-Consumer (DTC) brands have a lot of moving parts, from marketing to products, teams and partners, and all the technologies that link them together. All these moving parts create thousands of opportunities for WTF to happen. As a result, most potential WTF moments aren’t monitored in any active way. After all, you’re not going to look at a dashboard with a thousand items or more. That’s stupid. But regardless of what you do or don’t do, those WTFs exist.

This is where data comes into play. It allows you to track and alert you about potential WTF moments. Since your data is doing its thing 24/7, monitoring for WTF moments isn’t a stretch. In the world of cybersecurity, there’s something called malicious event detection – geek talk for “scanning the network traffic for bad stuff” like viruses or ransomware. Security software is programmed to look for particular patterns in the data that are known to be bad. Known patterns are put into a catalog of security signatures, which can be thought of as the unique fingerprints of a bad thing. 

It’s important to do this because each security risk has its unique characteristics and weaknesses, infects in certain ways, and can cause specific damage. These are the WTF moments in the security world. 

This same logic applies to many other spheres where systems watch out for other kinds of WTF moments in incoming data.

Let’s imagine for a second how you might detect some WTF events in your businesses without leveraging data. A customer yelling at you? Management breathing down your neck? Disappointed investors? These are just a few examples, but we’ve all been there. It sucks. The reality is that most of those moments start as WTF events that were likely detectable in your data days, weeks, or months before they occurred. All the data in your applications contain the signals of these WTF moments, and our data can help us find them sooner if we look for them.

Once a WTF event is identified, how can our data explain the Why? This is where we start to go from a pure data problem to a hybrid one consisting of both data and knowledge. While data – whether it’s orders, service requests, returns, or a dozen other things – is always present, modern applications like Shopify and others don’t connect it in a way that makes finding the Why straightforward. So, for the Why, data is part A, and part B is the specific understanding of how the parts fit together. 

An example might help. Let’s say your sales are down. The reason – the Why – for that could be a few different things. They could be down because you had fewer orders, or they were smaller. Fewer orders could be a result of the timing of a holiday or some regional factor. The potential Whys aren’t always “in the data”. To inform the Why, we need to know how to connect data sets in different ways to indicate different Whys. This all requires external knowledge of people who know how the parts fit together in real life. After all, the goal is to make better decisions about what to do, so getting to the Why is a non-optional task that will inform what you do next. 

In reality, we usually get the proverbial knock on the door, an angry look, and a WTF moment. This results in us frantically trying to discover the Why of the matter. We download data and start trying to figure it out. The cause of the WTF might be obvious or take days to figure out. We may have a bias towards a particular root cause and get out over our skis (Not that I’ve ever done that before LOL ). We’re all human, and this happens. The challenge of leaning into a cause for expediency’s sake will usually come back to bite you. That’s because the “what to do next” to address the matter will usually only work if your assumed Why is correct.

So, astute readers like you likely see where this is going. While WTF is about data, Why is a data/knowledge hybrid, and the “What to do next” is mainly informed by past data. That said, a problem or opportunity cannot begin to be fixed or pursued until you decide what to do next. Do nothing or something? Do something, but what? Option little, medium or big? A or B? How are you even evaluating the decision? Opportunity, cost, risk? While you’ll face some easy decisions, most will fall on a spectrum. And BTW, we – the decision makers – are a variable in this equation.

Once a path is chosen and a decision for action is made, the actual work starts. Only after we’re finished can we know whether we made the right decision – which drives home the point that we have limited resources to do anything, as most of our effort goes is directed at our daily activities. All of that means that an organization’s success is in the hands of the particular WTFs we observe, the Why’s determined, and the What to Do’s taken. You’ll win some and lose some. Your mileage will vary.

As a DTC brand with all of your moving parts and uncertainty in the market, there are many WTFs happening all of the time. That’s life. The key to dealing with unknowns is consistency. Having a plan to move from WTF to Why to What do I do next, as smoothly and efficiently as possible. This is where all of that underused data and some consistency will help. It’s not rocket science, but it’s a smart thing to do.

And if you’d like to learn about a whole new way of doing this with Intelligence as a service, please contact us at Storyhub.ai/wtf