What are the most popular RFM segmentation models for e-commerce marketing?

RFM segmentation is crucial in e-commerce marketing, allowing businesses to categorize customers based on transactional behavior. Below are the popular RFM segmentation models I believe works nicely: • K-Means Clustering: Groups customers based on RFM values' similarity, suitable for large datasets with complex patterns. • Predictive RFM Models: Use ML to forecast future customer behavior, enabling proactive targeting with personalized marketing campaigns. • RFM-LTV Segmentation: Combines RFM with customer lifetime value (LTV) metrics, prioritizing resources towards customers with the highest long-term value. Apart from these, we have Basic RFM Segmentation, RFM Grid Analysis, RFM Percentile Ranking, and RFM Score Model.

RFM analysis assesses customers based on Recency (last purchase), Frequency (number of purchases), and Monetary value (total spending). Popular RFM segmentation models in e-commerce include Pareto/NBC, K-means clustering, and decision trees, helping tailor marketing strategies effectively.

OBS: Você poderá encontrar as siglas "RFV" no lugar de "RFM" em literaturas ou cursos a fora. A diferença fica apenas pela referência ao "V de Valor" no lugar de "M de monetário". Ponto interessante: A métrica é muito indicada para mercados B2C, porém no B2B sendo vendas de alto valor agrado e/ou alta complexidade, caso não haja um histórico de longo prazo, é interessante optar por outras métricas para classificação de clientes, por exemplo, "Share of Wallet", que é flexível quanto à variáveis e pontuações para serem utilizadas ou mesmo a tradicional "Curva ABC". O motivo é por conta da Frequência ficar com baixo volume não sendo uma amostra suficiente nestes cenários.

RFM analysis is a straightforward yet potent method that assesses customers based on three key factors: recency, frequency, and monetary value. These metrics help gauge how recently a customer has made a purchase, how often they make purchases, and how much they spend. By assigning scores to these metrics, businesses can identify their most valuable customers and tailor marketing strategies accordingly. By segmenting customers based on their RFM scores, businesses can create targeted marketing strategies for different customer segments: High-Value Customers. At-Risk Customers. Low-Value Customers. Dormant Customers.

RFM analysis is a method used to identify valuable customers by examining three quantifiable factors: how recently a customer made a purchase (Recency), how often they make purchases (Frequency), and how much money they spend (Monetary).

Capturing and collating the data is the key here. First of all make sure this is GDPR compliant then extract the data as a csv file and import into excel. Keep scoring simple (1-5) as stated in the article. You can explore other behavioural traits such as the customers regular purchases later. You can also build in lengthy periods of lapsed behaviour to recognise a change of behaviour in frequent, large spend customers. This then gives you the ability to send surveys to identify why they have lapsed and then target with appropriate offers, content to recapture their business.

To calculate RFM scores: Recency: Days since last purchase. Frequency: Total purchases within a defined period. Monetary: Total spending over the same period.

Recency Score (R): Determine the time since the customer's last purchase or interaction. Assign a score based on recency, such as 1 for least recent and 5 for most recent. Frequency Score (F): Count the total number of purchases or interactions within a given period. Assign a score based on frequency, such as 1 for least frequent and 5 for most frequent. Monetary Value Score (M): Calculate the total monetary value of purchases made by the customer. Assign a score based on spending, such as 1 for lowest spending and 5 for highest spending. Combine these scores (e.g., R=4, F=3, M=5) to create an overall RFM score (e.g., 435). Segments can then be created based on these combined scores for targeted marketing strategies.

Typically, customers are scored on each RFM factor on a scale (e.g., 1-5). Recency score considers how recent their last purchase was, Frequency score looks at how often they purchase over a certain period, and Monetary score assesses the total amount spent.

To calculate RFM scores: Recency: Measure time since last purchase. Frequency: Count number of purchases over a period. Monetary: Calculate total spending. Assign scores based on quartiles or other criteria. Combine individual scores to generate an overall RFM score for each customer.

Two popular RFM segmentation models in e-commerce are "RFM Score" and "RFM Grid." To create RFM segments, assign scores based on recency, frequency, and monetary value of customer transactions, then group customers into segments according to their scores.

To create RFM segments, assign scores for Recency, Frequency, and Monetary Value individually. Combine these scores to form segments such as "High-Value," "At-Risk," "Low-Value," and "New Customers" based on predetermined score ranges.

Customers are segmented based on their RFM scores. For instance, customers with the highest score in all three categories (e.g., 555) are considered the most valuable, while those with the lowest (e.g., 111) are the least valuable.

After calculating RFM scores for each customer, combining them yields an RFM code representing overall behavior. For instance, a customer with scores 5 for recency, 4 for frequency, and 3 for monetary value has an RFM code of 543. Segmenting customers based on RFM codes allows tailored strategies. The quintile method divides customers into five groups based on scores, but it lacks granularity. Conversely, the custom method lets you set thresholds for RFM segments. Meanwhile, the cluster method uses cluster analysis to group customers, demanding technical expertise. For example, using the quintile method, customers with RFM codes 543, 442, and 431 would fall into different segments based on their relative scores within each criterion.

If this is the first time you have used this technique, group them by code. Add a simple description to each group as an identifier. For example 145 RFM - Lapsed, frequent high spenders. This will help you when identifying what techniques to use in targeting them. Also avoid making the groupings complicated by putting similar codes together for example 251 and 142. There behavioural patterns are so similar it can be recognise that their purchasing habits are similar. As you get more familiar with this technique start to segment further by the types of products they purchase, are there certain patterns e.g dates when they purchase related to holiday periods? Start of simple then get smarter with your data if required.

To use RFM segments for marketing, target high-value customers (Recency), frequent purchasers (Frequency), and big spenders (Monetary) with personalized offers, loyalty programs, and targeted campaigns based on their segment characteristics.

Tailor marketing strategies to each RFM segment. For Champions, consider loyalty programs and upselling. For At Risk customers, re-engagement campaigns with special offers might be effective. Personalize communication to match the characteristics of each segment.

Different strategies are employed for different segments. High RFM score customers might receive loyalty programs or exclusive offers, whereas lower score segments might be targeted with re-engagement campaigns.

To measure RFM performance, track changes in customer segments over time based on their RFM scores. Monitor shifts in high-value, at-risk, and low-value segments to assess the effectiveness of marketing strategies and customer engagement initiatives.

Assess the success of your RFM-based campaigns by tracking metrics like changes in customer purchasing behavior, increased customer lifetime value, or improved engagement rates.

One interesting insight from RFM analysis is discovering that a group of infrequent buyers actually has a high monetary value due to large purchases when they do buy. This challenges the conventional wisdom that frequent buyers are always the most valuable customers, showcasing the importance of analyzing both frequency and monetary value together for a complete understanding of customer behavior.

Go beyond basic RFM segmentation by integrating personalized marketing approaches and predictive analytics. This can involve using machine learning algorithms to predict future customer behavior based on their RFM scores.

RFM Analysis has been in practice for many years since the Direct Marketing Revolution, which led to digitalization. However, many more accurate and newer AI and Predictive analytics methods make thinking about just a business rules-based approach such as RFM unnecessary. Why fly the 737 when you can fly Space X and take advantage of the full multi-dimensionality of more advanced methods that Data Science Brings? Just another way of thinking about this.