What is Bayesian Statistics?
In statistics, Bayesian statistics (also referred to as Bayes Theorem) is a method of statistical inference that updates the likelihood of a hypothesis as more data becomes available. To put it simply, Bayesian statistics is about making predictions in light of new information. Bayesian analysis can therefore be seen as a combination of a prediction and confidence in the accuracy of that prediction. The key difference between Bayesian and other statistical methods is that Bayesian statistics is data-driven. Bayesian statistics allows you to update a probability based on new data. Other types of statistical analysis don’t work like that. For example, if you use an average to predict the outcome of an event, that prediction doesn’t change if new data is added.
Why is Bayesian Statistics Important for Marketing?
Marketers are increasingly adopting Bayesian techniques to inform their decision-making process. Bayesian statistics provide a more complete picture of the outcome and are more efficient than other statistical techniques, such as the frequentist approach. Here is a list of reasons why Bayesian statistics is important for marketing:
Bayesian statistics provide a probabilistic approach to forecasting and prediction. This allows marketers to quantify their level of confidence in the accuracy of these predictions.
Actionable insights about customers
Bayesian statistics allow marketers to understand the level of confidence associated with various conclusions about customers. This allows them to make actionable insights about customers, such as their likely next purchase or how to get them to come back.
All-inclusive statistical analysis
Bayesian statistics allow marketers a holistic view of their data and how it can be used to inform decision-making. It simplifies the process of using data to make business decisions by providing one tool for statistical analysis.
Types of Bayesian Statistics in Marketing
Marketers use multiple types of Bayesian statistics in their decision-making process. These include:
Predictive modeling is the practice of building statistical models based on historical data. Predictive modeling is one of the most common uses of Bayesian statistics in marketing.
Segmentation and targeting
Segmentation and targeting is the process of dividing a population into subgroups based on common characteristics. Bayesian statistics are commonly used to determine the most appropriate segmentation and targeting strategy.
Attribution analysis is the process of determining what caused a particular event in the business. This typically refers to the attribution of sales. Bayesian statistics are used to determine which marketing activities are most effective.
Customer lifetime value (CLV)
CLV is a prediction of the future value of a customer. Bayesian statistics are used to determine the CLV of individual customers.
Benefits of Bayesian Statistics for Marketing
As we’ve seen, Bayesian statistics is a data-driven form of statistical analysis. Therefore, it’s very beneficial for marketers who focus on collecting and analyzing data. Here are some of the key benefits of Bayesian statistics for marketing:
Bayesian statistics produce an accuracy rate of 95% – which is significantly higher than other types of analysis.
Using Bayesian statistics ensures consistency in decision-making processes. This is important in an organization where various teams rely on data to make decisions. Inconsistent decision-making can lead to flawed conclusions and missed opportunities.
Bayesian statistics allow for real-time analysis of business data. This means that marketers can respond to changes in their business environment on time.
Bayesian statistics simplify the workflow of marketers by providing one tool for statistical analysis. This reduces the risk of human error that may occur with a serial approach.
Limitations of Bayesian Statistics
Although Bayesian statistics provide clear advantages over other statistical methods, there are some limitations to consider:
Bayesian statistics require data to be prepared and ready for analysis. This means that data-driven marketing activities, such as predictive modeling, require more time.
Depending on the type of model used, Bayesian statistics require a regular process of updating the model. This requires additional resources from marketers and may not be practical for every business.
Bayesian statistics require a certain level of mathematical knowledge and expertise. This means that marketers need to understand the underlying model and how it works to use it effectively.
Bayesian statistics is a data-driven form of statistical analysis that allows marketers to make predictions in light of new information. It’s an important part of the marketing decision-making process because it allows marketers to understand the level of confidence associated with various conclusions about customers. It’s also beneficial because it’s more accurate and consistent than other methods and provides a simplified workflow. However, it does require a certain level of mathematical knowledge and expertise to use effectively.