RFM Analysis

Background

RFM analysis is a marketing approach that is used to statistically evaluate and group customers based on the recency, frequency and monetary value of their most recent transactions in order to spot the best customers and execute focused marketing campaigns. In order to provide an objective analysis, the system gives each consumer a numerical score based on these variables.

Each customer is ranked based on the following criteria by RFM analysis:

• Recency : When was the customer's most recent purchase? Customers who have recently made a purchase are more likely to make another buy or use the product since it is still fresh in their mind. Days are a common unit of measurement in business. However, they may quantify it in years, weeks, or even hours depending on the product.

• Frequency : How many times did this consumer buy during a specific time frame? Customers who have previously made a purchase are more likely to do so again. In order to turn first-time customers into loyal customers, follow-up advertising may make them a viable target.

• Monetary : How much did the customer spend over a specific time frame? Spending customers are valuable to a company since they are more likely to make future purchases.

Each of the three key variables receives a score in RFM analysis for customers. Typically, a score between 1 and 5, with 5 being the highest assigned. However, different RFM analysis system implementations may use slightly different scaling or value ranges. An RFM cell is a grouping of three values for each customer. In a direct approach, businesses just add up these values to find the average, then rank their customers according to value. Some companies choose to weigh the three factors differently rather than just average them.

Benefits of RFM Analysis

• By targeting specified groups of current customers (i.e., customer segmentation) with messages and offers that are more likely to be relevant based on data about a specific set of behaviors, RFM analysis enables marketers to boost revenue. Increased response rates, customer retention, customer contentment, and customer lifetime value  (CLTV) result from this .

• The ability of each of these RFM variables to forecast future consumer behavior and boost sales has been demonstrated. Customers are more likely to make a purchase in the near future if they recently purchased one. People are more inclined to interact with your brand again soon if they do so more regularly. Additionally, people who have spent the most in the past are more inclined to do so in the future.

• You can target customers with messages that are most appropriate for their relationship with your brand using RFM analysis. For instance, you might be more successful recommending expensive things to customers who make significant and frequent purchases. On the other hand, by rewarding them for their loyalty or providing referral promotions, you are more likely to increase the customer value of your connections with customers who make purchases regularly but only in little amounts.

• Create an automated drip campaign with messages that are specific to each category to increase the effectiveness of email marketing efforts.

• Promote timely promos and instructive information to recent or new customers to boost their involvement with your company. This will increase customer loyalty and user engagement.

• Reduce churn by sending tailored communications, providing discounts for repeat purchases, or offering surveys that assist you in identifying and addressing potential difficulties.

• Boost ROI and cut marketing expenses: Focusing quickly and easily on smaller groups that are more likely to generate income will help you cut costs. You can also use the knowledge gained from RFM analysis to improve future marketing initiatives.

Limitations of RFM Analysis

• RFM analysis lacks the strength of current advanced predictive analytics because it is based only on a small number of behavioral features.

• Some companies might use RFM data as justification to send out a barrage of messages to influential customers, which would lower response rates for otherwise extremely effective campaigns.

• Despite the fact that many of these customers may be worth pursuing, it can lead marketers to ignore customers with poor rankings.

Objective

This usecase aids in understanding customer behavior patterns through three key factors-recency,frequency and monetary,assigning scores based on them ,and segmenting them accordingly in line with similar trends.

Relevance of Xceed Analytics

Xceed Analytics provides a single integrated data and AI platform that reduces friction in bring data and building machine models rapidly. It further empowers everyone including Citizen Data Engineers/Scientist to bring data together and build and delivery data and ml usecases rapidly. It's Low code/No code visual designer and model builder can be leveraged to bridge the gap and expand the availability of key data science and engineering skills.

This usecase showcases how to create a rfm analysis workflow.The dataset is obtained from UCI Machine Learning Repository. Customer retail dataset is used for this purpose .Xceed will provide a NO-CODE environment for the end-to-end implementation of this project, starting with the uploading of datasets from numerous sources to the deployment of the model at the end point. All of these steps are built using Visual Workflow Designer, from analyzing the data to constructing a model and deploying it.

Data Requirements

The dataset that is used here includes :

• Cohort retail dataset : contains customer purchase information .

Columns of interest in the dataset

Model Objective

Defining the three key variables- recency,frequency and monetary,assigning scores to the customers based on them,thereby identifying customer behavior patterns, segmenting the customers with similar scores.

Steps followed to develop and deploy the model

1. Upload the data to Xceed Analytics and create a dataset
2. Create the Workflow for the experiment
3. Perform initial exploration of data columns.
4. Perform Cleanup and Tranform operations
5. Create Recency,Frequency and Monetary Scores for the customers

Upload the data to Xceed Analytcs and Create the dataset

• From the Data Connections Page, upload the the dataset to Xceed Analytics. For more information on Data Connections refer to Data Connections

• Create a dataset for each dataset from the uploaded datasource in the data catalogue. Refer to Data Catalogue for more information on how to generate a dataset.

Create the Workflow for the experiment

• Create a Workflow by going to the Workflows Tab in the Navigation.Refer Create Workflow for more information.

You will see entry on the workflow's page listing our workflow once it's been created.

To navigate to the workflow Details Page, double-click on the Workflow List Item and then click Design Workflow. Visit the Workflow Designer Main Page for additional information.

• By clicking on + icon you can add the Input Dataset to the step view. The input step will be added to the Step View.

Perform initial exploration of data columns.

• Examine the output view with Header Profile, paying special attention to the column datatypes. for more information refer to output window

• Column Statistics Tab (Refer to Column Statistics for more details on individual KPI)

Perform Cleanup and Transform Operations

1. Clean Age Column.

2. Drop Unecessary Columns.

3. Rename Target Column.

4. Update datatype of Column.

Build/Train a clustering Model