Data-driven decision making is the gold standard in marketing. Whether that’s strategic at the top such as annual budget allocation or tactical at the day-to-day level for optimizing keyword bids or social ad targeting, without some sort of evidence-based choice, a decision is deemed to be opinion-based—and inherently less valid. Learn how marketing experiments are the key to helping organizations validate and calibrate data-driven decision making.
In a recent survey of practitioners on data-driven marketing, when asked about their most important data objectives, the top answer was “basing more decisions on data analysis”. However, when asked about the biggest data challenges they face, the top answer was “being able to make more data-based decisions” with 81% of marketers saying that they “consider implementing data-driven marketing strategies somewhat to extremely complicated.”
Many of us make what we consider to be data-driven decisions every day, but there’s an essential part of the equation that many people don’t get right or even understand how: data-driven decision making should not be taken for granted…it needs to be constantly tested and tuned via ongoing experiments.
It goes to follow: What could be more important to any brand than validating that their decision-making process is top-notch?
After all, would you want to get lost in the woods with a faulty compass?
Data-driven decision making: marketing experiments at the heart of a test-and-learn culture
According to research from Gartner:
“Having a test-and-learn culture empowers your organization to make customer experience, marketing and product decisions that cut through opinion, indecision, and uncertainty with data and insight. The results and outcomes mitigate the risk of making the wrong decisions, reduce or eliminate wasted efforts and resources and improve revenue.”
Marketers have been long-expected to absorb a chart or graph on marketing performance and make educated assumptions on how to proceed. For example, if a brand has invested in a new social channel and the ROI of the campaigns are consistently below expectations, the decision might be made to move the budget to another publisher. However, the process for vetting those decisions is often fuzzier. Did the budget perform better with the other publisher? Was it the best decision? Should it have been allocated across a handful of partners instead? Could only half of the budget have been moved?
These questions put the spotlight on the state of today’s data-driven decision making. Often only if the high-level goal goes up or down is a decision deemed good or bad. But, only a marketing organization committed to continual validation via experiments can truly say that their decision-making practice is sound.
And marketing experiments matter. Organizations that significantly outperform their competitors are almost twice as likely to make testing and experimentation a marketing priority.
Leveraging the scientific method for marketing experiments
The scientific method is a process that most of us are exposed to in middle school science class, and at its core is a simple point-of-view on testing that marketers must adopt. That is, every assumption must be coupled with a hypothesis, tested in a stable environment, given the proper time to collect significant data, and then be analyzed.
Without this approach, a data-driven decision is simply an educated hypothesis. Probably better than a wild guess, but who knows? A valid testing methodology is needed to know the difference.
Marketing experiments are a critical component to best-in-class, data-driven decision making:
- Every decision needs to be viewed as no more than an assumption that needs to be tested—whether it was just an opinion or a highly data-driven one.
- A hypothesis needs to be developed. What do we think will happen when we make this decision? Without a hypothesis of what the result might be, a marketer doesn’t have anything to compare with the final results. Just that things got “better” or “worse” should not be the standard—there needs to a quantifiable hypothesis defined to help understand how close or far off was the original guess. This will help you calibrate your data-driven practice over time. Even if most of your decisions turn out to be winners, maybe you could be winning even more if you knew that you could reach higher with your goals?
- A test needs to be defined. What needs to be tested? How long does the test need to run to enough results to be valid? Most importantly, it needs to run in a “stable environment” without too many changes going on at the same time. For example, a chemist does not test both Chemical A and Chemical B at the same time to see the reaction with Chemical C. They run one test at a time so that it is clear how Chemical A mixes with Chemical C and how Chemical B mixes with Chemical C. A control group is always needed to make sure there weren’t any external factors at play biasing the results.
- Finally, a proper analysis needs to be performed in order to determine—not if the decision was right or wrong— but how close or far the hypothesis was to the results. Closer means that the decision-making was more accurate. Further away means that it was less accurate. And then a final analysis into what factors might have led to it being close or far can start to provide calibration cues to improve every subsequent decision.
- To fully close the loop, insights from the marketing experiment should be shared across the org so that everyone can get better. There might even be an experiment performed by one group that on the surface doesn’t impact another, but it absolutely has a positive impact.
This sounds like a lot of work, but it doesn’t have to be. Yes, processes may need to change and new tools might need to be utilized, but this is what is required in a true data-driven marketing organization.
Marketing experiments aren’t “nice to have”. They are a critical component.
And there are ways to do these things fairly easily and isolated away from the bulk of the marketing so that it doesn’t cause a disruption in business. One option from Skai is Impact Navigator, a SaaS solution built specifically for test-and-learn marketing orgs to measure the incrementality and impact of their marketing programs. The platform can easily handle multiple marketing experiments to minimizes test duration while maintaining statistical significance.
Experiments are the future of data-driven marketing
Based on just how much emphasis marketers put on data-driven decision making, it’s not that hard to imagine that one day every functional group in every marketing organization will always be testing. The creative teams will be testing messaging and visuals, the media team will be testing ad formats and spend, the channel specialists will be testing optimization and automation, the CMO will be testing channel and publisher mix…the future of marketing is marketing experiments.
The most beneficial outcome for brands of ongoing testing across the organization is the improvement in their overall data-driven decision making. They will begin to learn which metrics matter, how to shorten tests as much as possible but still get valid results and at what cadence testing needs to take place in order to optimize their programs.
By leveraging marketing experiments, marketers can uplevel data-driven decision making. It doesn’t mean that every decision requires a white-lab-coat approach with years of testing—if anything, it needs to be a non-disruptive and easy approach. But with a slight change in process and a commitment to this standard, the opportunity for brands to improve their marketing ROI and performance is high.
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