4-Step Framework for Data-Driven Decision Making With Case Studies

We are in the middle of the COVID-19 pandemic and it’s been fascinating to see governments make significant policy decisions on a daily basis. These policies are a great example of data-driven decision making in a time of crisis. 

Every government has its own model projecting the impact of the virus and what it could do to minimize it. Having the right data for their model is critical even if the data is imperfect.

Beyond the virus, all companies can benefit from a more data-driven approach to making their most important decisions. In this article, I’ll show you the practical day to benefits of using data to influence your decisions, walk you through a few case studies and tie it all together in a simple framework that you can apply to your business.

Benefits of Data-Driven Decision Making

Decision making groups (or tribes?) seem to be split in two major branches. Those who think that “gut feeling” is the most important thing and those that think “facts” are the most important. I personally think that both groups are right and these two approaches work better together than separate.

There are 3 benefits to adopting a data-driven methodology for making decisions and at least one plays nicely with the “gut feeling” crowd.

Benefit #1: Data or computer can process information quicker

If you spend any time in the world of data (or business really), then I’m sure you have come across concepts like Big Data, Machine Learning, and AI. It can be hard to separate the hype from reality but there is one area where we are seeing a clear benefit from these technologies: pattern recognition.

Humans are incredibly patterning recognition machines but software is even better at it and it can do it as scale and non-stop for 24 hours a day. You may notice that there are similar patterns in buying behavior from your customers but data (and software) could tell you exactly what those patterns are and how they are changing over time.

This can be done to a statistically significant level quite quickly and for a relatively affordable rate. Better yet, software can uncover these patterns and surface them to you instead of you having to go find them or stumble upon them by accident. 

Benefit #2: Data can help overcome biases

Data-driven decisions can also help overcome the many biases that plague us. These are things like the sunken costs theory, confirmation bias or anchoring. These biases are part of being human and data can be used to manage them over time.

Ray Dalio, the founder of Bridgewater Associates, uses data to overcome the biases of their organization. They implemented radical transparency policies where people can openly share their criticism and where you can see critical information about coworkers such as communication preferences and personality quirks. By making this data publicly available within the company, it can help balance out the need to hide or work in your self-interest.

The same can be seen in other forms of reporting. It’s hard to pretend that your experiments are working if your KPIs aren’t budging. It’s also hard to assume that a specific marketing channel is working if you can clearly see the cost of acquiring users through this channel.

Benefit #3: Data can help refine your gut feeling

Finally, data itself can be used to refine your “gut feeling”. I think this is an unspoken benefit of data but perhaps one of the most impactful over the long term. My clients are typically quite perceptive but they don’t always make the right decisions (as it happens to all of us). 

However, because they have access to the right data, they are able to use it as a feedback mechanism for their decisions. If they decide to launch a new product or campaign, they can quickly get feedback on how well this initiative performed and adjust appropriately. 

If things didn’t go as planned, they are able to take a few minutes and think about the assumptions that led them to the wrong decision Repeating this process over time leads to refinement of their internal compass. This is what I think “experience” boils down to. 

Let’s now look at how companies as a whole are using data-driven decision making.

Case Studies of DDDM in Practice

One of my favorite case studies is Amazon which is slowly taking over the world. Everyone knows that Amazon started by selling books but few people know why Jeff Bezos chose this specific product type. This decision would lay the foundation for how Amazon would make future decisions.

“After reading a report about the future of the Internet that projected annual web commerce growth at 2,300%, Bezos created a list of 20 products that could be marketed online. He narrowed the list to what he felt were the five most promising products, which included: compact discs, computer hardware, computer software, videos, and books. Bezos finally decided that his new business would sell books online, because of the large worldwide demand for literature, the low unit price for books, and the huge number of titles available in print.”

Source
Amazon Website in 1997

Amazon continues to use data to influence what products or services they should offer. We also saw the same principles apply to the release of AWS (Amazon Web Services). Amazon realized that their ecommerce platform had value so they started working on making it accessible to other merchants through merchant.com. During the process, they realized that they need to make their code cleaner and easier to manage. This process eventually led to the creation of AWS.

Netflix is another company that relies on data quite a bit. Their first major hit, House of Cards, came to be because of data. Netflix was able to see that Kevin Spacey was popular with its users (based on his movies) and that the political thriller could be a good fit for their audience. Netflix doesn’t have to guess what is likely to do well, instead, they just have to look at their data and combine that with experienced content creators.

Data isn’t just for massive public companies though. My clients use them in small and large decisions. One of my clients, a payment processor, was able to truly understand the impact of their product redesign and show the impact of months of hard work. 

Another one of my clients was able to uncover the behaviors that led them to become paying customers. They could then guide users towards these behaviors through emails and changes within the product itself.

Regardless of your company size, you can take advantage of your data. I’ll show you a 4-step framework in the next section for how to get started.

Framework for Becoming More Data-Driven

Every company is unique but they can approach their data in similar ways. The goal of the 4-step framework below is to help you uncover insights hidden in your existing data and figure out what gaps you should be working to fix.

Step 1: Identify High Impact Areas

We need to start by prioritizing what areas could benefit from data. The low hanging fruit tends to be any team that is directly tied to revenue such as sales or marketing or any team that is overwhelmed such as customer success dealing with onboarding.

Step 2: Audit of Existing Data & Gaps

You then need to analyze what data currently exists and what gaps need to be addressed over time. The goal here is to have enough data to understand what is going. For example, if we are looking at sales, we would want data around where deals come from, how long they take to close, why do deals fail, common attributes to the best deals, and so on. If you can’t answer important questions, these are gaps to be solved.

Step 3: Stress Test Existing Tools & Reports

The next step is to stress test your existing tools and reports. Are you able to easily generate the reports that you need? Is there a better tool for tracking data? We want velocity when analyzing data so any bottlenecks should be removed.

Step 4: Close Skills Gaps With Training

Finally, you want to close any gaps in analysis capabilities with training. This means that your team should be able to query the data they need, understand their reports and know how to mine the data for insights. This is the last step because we need to know what team to focus on (step 1), what data we have (step 2) and what tools are available (step 3).

Moving to a more data-driven decision making isn’t rocket science. This world might be full of jargon and hype but the benefits are rooted in reality. Use the framework to systematically work through different business units and over time, you’ll find yourself making fewer decisions based on anecdotes and more on facts.

One more thing before you go! Do you know how to get more insights out of your data? 

All companies are sitting on a goldmine of data that they haven't fully explored. It's not about technology or capturing more data. The key is to learn how to make the most of your current data and convert it into actionable insights. This is the main idea behind my first book, The Data Miage: Why Companies Fail to Actually Use Their Data

I'm excited to announce the release of the book through all major retailers. If you're interested, you can download the first chapter for free using the form below. You'll learn what the best data-driven companies do differently and how to make sure you're playing the right data game.