Customer segmentation using AI
Good businesses know that customer segmentation is a necessity. It’s used for everything from market strategies to discount offers to development loyalty programs. The more precise the segmentation, the more likely the company is to target customers in a way that encourages them to come back more often.
To do this, many organizations are turning to RFMa segmentation model based on three factors: recency, frequency and monetary analysis. Under this system, companies rank customers based on how long it has been since they purchased something; how long they wait between purchases; and how much they spend over time.
Now that AI-based tools and platforms are becoming essential in businesses, with 72% use AI for at least one function, some are looking to update their RFM strategies with this technology. But as mind-blowing AI stories still abound as often as 27% time or making other mistakes, some business leaders are understandably hesitant to trust it to perform such a crucial function.
A team of researchers set out to evaluate the effectiveness of AI in customer segmentation. Malay Sarkar, Faiaz Rahat Chowdhurv and Aisharyja Roy Puja performed an experiment by asking a popular algorithm to perform RFM analysis. They then reconstructed the results.
“The experimental results provided convincing evidence of the algorithm’s performance in terms of consumer segmentation,” they said. wrote in an article published this year by the Journal of Business and Management Studies. They found the “cluster purity rating” to be 0.95. In simpler terms, this meant that the analysis achieved “a relatively high accuracy rate of 95% in terms of precisely and accurately segmenting consumers based on their common behaviors and characteristics… This showed that the algorithm was effectively organizing and identifying consumers into distinct groups based on their similarities, facilitating targeted marketing strategies and personalized approaches.
Because my work focuses on helping businesses deliver the best possible customer experience, I know that many organizations are looking to do deeper segmentation that includes additional factors. This also proves to be an asset for AI.
In a separate study, six researchers proposed “an extended RFMD model,” in which the D stands for demographic. Under this system, the three financial measures were combined with demographic information about each consumer, such as age, gender and the region in which they live.
Still using algorithms, the researchers (Thanh Ho, Suong Nguyen, Huong Nguyen, Ngoc Nguyen, Dac-Sang Man and Thao-Giang Le) found that they were able to divide consumers into several groups, and the results been beneficial. “Businesses can apply this model to deeply understand customer behavior based on their demographics and launch effective campaigns,” the group said. wrote in the Journal of Business Systems Research.
Beware of the “RFM Trap”
While these types of analytics are useful, businesses should be careful not to rely on them too much. In a blog postData Science Logic – a team of specialists in data analysis and the use of machine learning – has warned that businesses can fall into an “RFM trap”.
This most often occurs for one of two reasons: it is the primary, if not only, tool an organization uses “to analyze and plan communications strategies and activities”, or those responsible for Drawing insights based on analysis go too far and jump to conclusions that aren’t really supported by the data.
Businesses should always keep in mind that each customer is a unique individual. The predictions and suggestions that AI can offer can have great potential. But as soon as a customer indicates a preference that does not match a prediction or expectation, all marketing and sales efforts aimed at that person should be modified accordingly.
This is another reason why having a omnichannel configuration is essential. By bringing everything about a customer’s journey together in one place and using AI to derive instant insights, an organization can ensure it treats each customer as an individual. Whether the customer is interacting with a human or a chatbot, this understanding of who they are is proven to increase the sense of belonging. empathyleaving people with warmer feelings toward the organization.
AI tools are improving and evolving rapidly. Their success with RFM is just the latest sign that this technology will deliver untold benefits, enabling organizations to serve their customers better than ever before.