Marketers are like surfers. Every day, we must traverse the ebbs and flows of consumer behavior. Sometimes, we ride high, while other times, it feels like a period of endless waiting.
As marketers, we need to be able to read the incoming waves, while also forecasting and evaluating what tomorrow's seas look like – all to catch the perfect swell. To do so, both surfers and marketers employ a form of statistical analysis (whether they realize it or not). It’s called Bayesian statistics.
Before you get caught up on the name or intimidated by the thought of combining statistics with surfing, rest assured that we’ll explain it all for you.
Photo by Copson London
A quick definition
According to Wikipedia, Bayesian statistics are “a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a ‘degree of belief’ in an event.”
Clear as mud, right? Don’t worry. That Wikipedia explanation is the closest you’ll get to a math textbook in this article.
Explained through surfing
Rather than convoluted academic definitions, let’s think back to our surfers looking for that perfect wave. Before heading out to the beach, most surfers will check the weather and surf forecasts the day before.
They are looking for any data points on wave size, wind conditions, atmospheric pressure, well directions and tide change times. Each of these metrics can affect the quantity and size of the waves.
Now, a surf report at Noon, the day ahead of the intended surf session, may indicate a mediocre day. It may indicate a moderate number of waves and could cause our surfers to expect a few large swells, but otherwise normal seas. This might cause them to question whether it’s really worth it to head to the beach the next day.
However, an updated forecast released that evening indicates a shift in wind speed and swell direction. This new data, combined with the surfers’ past experience with these sorts of conditions, causes them to adjust their outlook and consider tomorrow to be an excellent day for surfing.
Whether they realize it or not, the surfers have used the Bayesian statistical approach to adjust their expectations. That is, they have allowed new data points to influence and update their belief in a potential outcome.
How is Bayesian statistics used in marketing?
This is all great for a couple of surfers, but how can Bayesian statistics help marketers? Well, this approach is fundamental to modern marketers. We look to data to give us clues toward future performance. We experiment with different creatives and media spend to reach our goals, and (when we see shifts in the data) we adjust our approach.
So, how can marketers make the most of this approach?
1. More responsive to real-time data
With Bayesian thinking, you’re not locked into one forecast. If a new trend emerges or campaign results vary, Bayesian statistics allows you to adjust without overhauling your strategy. For example, if a product suddenly trends due to a social media mention, Bayesian analysis helps you rapidly incorporate this new information to adjust your marketing efforts.
2. Improve marketing ROI
Bayesian models are designed to improve over time as more data is gathered, helping marketers become more precise. Instead of treating each campaign as a fresh start, Bayesian thinking builds on past successes (or failures), helping you allocate budget and resources with growing accuracy.
3. It’s great for customer segmentation
Marketers often rely on customer personas and behavior patterns. Bayesian methods let you refine these segments as you learn more, helping you reach the right people with the right message. A Bayesian approach to segmentation considers both past behavior and new interactions, creating a more accurate picture of who is engaging and why.
Bayesian vs. traditional methods
Traditional statistics rely on something called “frequentist” thinking, which assumes a fixed, unchanging truth that only becomes clear with a large data sample. While useful, this approach can fall short in fast-moving fields like marketing. Bayesian thinking, on the other hand, adapts as it goes. If you start a campaign based on your best guess but encounter unexpected results, a Bayesian model allows you to update your forecast, rather than waiting until the end to see what worked.
Think of frequentist methods as taking a single photo of the ocean at a moment in time. You might capture a decent wave, but it won’t tell you how the waves will behave as the tide shifts. Bayesian methods, on the other hand, are like watching a live video feed of the ocean, updating with every passing wave. This continuous stream of insights is far more valuable, which allows you to adjust your approach as conditions change.
How to start thinking the Bayesian way
Adopting Bayesian thinking doesn’t require a complete overhaul of your marketing strategy, but it does take a shift in perspective. Here’s how you can start:
- Start with a baseline: For any metric, define your “prior” or baseline estimate. This could be an expected conversion rate, average customer value, or engagement level.
- Look for evidence: As you launch campaigns and collect data, view each data point as new evidence. How does it compare with your prior beliefs? Is it reinforcing them or suggesting a shift?
- Update regularly: Use these insights to adjust your assumptions. For instance, if a particular audience segment converts higher than expected, direct more of the budget toward them.
- Incorporate Bayesian tools: Many analytics platforms, including Google Analytics and various marketing automation tools, now support Bayesian models. Experimenting with these features can make Bayesian thinking more accessible and help you analyze real-time data more effectively.
Staying ahead of the curve
To catch the perfect wave and feel the power of harnessing the ocean, surfers are constantly reading the waves (and forecasts) while adjusting to a fluid and ever-changing environment. Similarly, Bayesian statistics give marketers an edge by adapting their beliefs according to new data, allowing them to stay on top of a shifting market while riding the most powerful trends.
This approach helps marketers make more accurate forecasts, respond to new opportunities more quickly and create campaigns that adapt more effectively to their audience’s needs. In a marketing landscape that requires speed, precision and flexibility, Bayesian statistics helps you stay prepared for any situation.