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RFM segmentation, driven by artificial intelligence, improves retention rates


Can working on retention with an AI-based RFM approach enable ecommerce operators to identify their best customers in real time?

In recent years, marketing teams at retail companies have sought to leverage customer data to improve campaigns and conversions. Customer segmentation has proven to be an effective way to achieve message personalization and improve targeting.

However, traditional customer segmentation presents many challenges related to data quality and management, which can lead to poor decisions and wasted marketing budgets. This is where artificial intelligence (AI) technologies such as Machine Learning (ML) and Deep Learning (DL), come into play as enablers of a new, more efficient approach.


AI-driven customer segmentation: how to get consistent audiences

When artificial intelligence is added to data analysis, customer targeting becomes more precise, dynamic, and able to increase conversions. With machine/deep learning (ML/DL) algorithms, you can analyze customer data more deeply and generate results on targeted segments. You can also use this information to automate customized marketing campaigns for each group.

This approach will generate superior results compared to traditional marketing campaigns based on manual analysis.  For example, customized AI solutions can eliminate human biases in data analysis and identify hidden trends and patterns that have never been thought of before.

The main challenges of customer segmentation, however, are:

  • Data quality – One of the main problems with customer segmentation is data quality. Many marketing databases are not maintained or cleaned on a regular basis, and inaccurate data in source systems usually result in poor segmentation quality;
  • Data management – Effective customer segmentation relies on labeling data with precise terms and phrases. Many users entering data into systems do not understand segmentation definitions and use them incorrectly. Database users must be trained to understand the different customer segments identified, the actual data in the segments that the categorizations represent, and when to use the correct customer segmentation for the appropriate analysis scenarios;
  • Waste of time – Identifying target audiences, analyzing research data and developing advertising campaigns takes a significant amount of time; not only that, it is not a one-shot activity but must be recurring. Most small businesses use market segmentation to identify target customers. Market segmentation involves developing customer profiles based on demographic, behavioral, technological and other data. Marketers then determine whether their target audience is large enough to generate significant revenue. They then go about finding channels that will help them reach their core customer base.


The advantages of automatic segmentation driven by artificial intelligence

Using artificial intelligence to clusterize customers offers a number of advantages over traditional manual segmentation, including:

  • Eliminating human biases (e.g., the assumption that video game players are young males: artificial intelligence algorithms analyze data without any assumptions to build a picture of who your customers really are);
  • Finding hidden patterns in the data that a human marketer might not be able to detect;
  • Automatic updating of segments in a rapidly changing market;
  • Unlimited number and size of segments;
  • Allows a higher level of customization; 
  • Requires little maintenance or human intervention once set up;
  • Highly scalable;
  • Segmented marketing can improve the productivity, effectiveness and ROI of marketing campaigns in general. A report by email marketing software company Campaign Monitor found a 760 percent increase in revenue from segmented campaigns.


What is a clustering algorithm?

The clustering algorithm is a technique that assists customer segmentation, that is, the process of classifying similar customers in the same segment. The clustering algorithm helps to better understand customers, both in terms of static demographics and dynamic behaviors. Customers with similar characteristics often interact similarly with the company, which can then take advantage of this technique by creating a marketing strategy tailored to each segment.

Accurate segmentation is one of the cornerstones of an effective marketing campaign because:

  1. Segmenting or dividing audiences into groups means being able to target messages to customers with similar characteristics and needs;
  2. This personalization means that marketing messages will be more relevant to the individual reading them;
  3. With increased relevance, response rates are likely to be significantly better than with a single, non-personalized campaign.

Our Auience AI tool is based precisely on the RFM approach and AI-clustering, with all the simplicity of no-code inbound and outbound integration: inbound, in capturing data from the company’s sources (e.g., from major ecommerce platforms) and outbound because it allows defined audiences to be re-imported into popular online ads and email marketing platforms.


Why customer segmentation is important

Customer segmentation is important because it enables brands to better understand their target audience and create true personalization. Targeting an entire mass audience is no longer an effective strategy because buyers interact and buy differently, the user is increasingly aware, fragmented, and fluid.

Hoping to better understand their target audience, companies have implemented various segmentation tools and models to enhance loyalty and ultimately increase revenue. However, in the world of customer-centric commerce in which we live today, consumers demand a truly high level of personalization.

However, in many cases, techniques such as micro-segmentation, which categorizes customers based on a complex set of attributes, and smart segments, which use artificial intelligence tools to examine available data and use it to decide how to divide up the customer base, can create unnecessary complexity for marketers.

In contrast, ecommerce marketers can benefit more from creating large audiences of similar customers to organize campaigns and support broader business goals. By clearly defining large audience clusters, such as VIPs, Ex-Lovers or Promises, marketers and companies can focus on long-term strategies to improve business results.


Customer Segmentation with Machine Learning

As mentioned at the beginning, to achieve true 1:1 personalization, marketing teams need to leverage artificial intelligence (AI) and machine learning (ML) algorithms in their customer segmentation strategies.

AI automates segmentation and does so more effectively than a human could, as it has greater computational/analysis power. 

This means that retention and digital marketers no longer have to manually tweak campaigns to achieve incremental improvements. Optimization solutions, based on machine learning, run in the background so that brands and their retention marketers can focus on overall strategic program decisions that will deepen emotional connections with consumers.