Unlocking Precision in Marketing: How AI and Machine Learning Transform Segmentation
In the pursuit of crafting relevant marketing messages, segmentation emerges as a pivotal tool for boosting sales and enhancing customer loyalty. With advancements in technology, AI and Machine Learning are paving the way for new opportunities in this domain.
A common question I encounter is, "Why should AI and Machine Learning be considered superior for segmentation compared to traditional methods like rule-based or RFM (Recency, Frequency, Monetary) segmentation?" The short answer is that AI and Machine Learning function like highly comprehensive brains. They manage and analyze nearly unlimited combinations and potential outcomes, far beyond our human capabilities.
Key Insights:
Consider this scenario: data shows that individuals who responded positively in the past six months and those of a specific gender are more likely to respond in the future. Whom should you target in your next campaign? Respondents from the past six months, this particular gender group, or perhaps the intersection of these two groups—individuals of this gender who responded in the past six months?
领英推荐
Now, add more variables into the mix—perhaps age or recent interactions, like clicking on an email in the past 30 days. Most companies don't just work with four data variables; they handle hundreds. Manually building segments quickly becomes imprecise and time-consuming, struggling to capture the complexity and opportunities latent within the data.
This complexity is where Machine Learning excels. Defined by Oxford Languages as "computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data," Machine Learning examines all available data. It processes vast arrays of data variables and data points, combining them to compute individual scores that determine whether a lead should be included in a segment.
When traditional methods are often based on evidence that humans build (particular gender group and past six months go well together), Machine Learning can find hidden patterns and hidden correlations that we would never think about nor see through.
When traditional methods often rely on evidence constructed by humans (such as correlations between specific gender groups and behaviors over the past six months), machine learning can identify hidden patterns and correlations that we might otherwise overlook or fail to consider.
In specific instances, the presence of a contact in the 30-39 year old age group might indicate a "high score" when combined with certain data variables, and a "low score" with others. Only AI and Machine Learning can effectively navigate through these complex combinations.
While traditional segmentation methods still hold relevance—for example, trigger-based segments based on specific events like completing the onboarding process—the need for sophisticated orchestration to balance various messages remains critical. However, the real power of AI and Machine Learning lies in their ability to handle millions of data points and hundreds of variables automatically, accurately, and swiftly.
When it comes to determining the most effective methods of segmentation, A/B testing will ultimately provide the answers we need.
How are you integrating AI and Machine Learning into your marketing strategies, and what impact has it had on your segmentation accuracy and campaign results?