In today’s article, I’ll talk about using advanced analytics methods to analyze customer data . Do you know who your most important customers are, what their Customer Lifetime Value is, what products they are interested in, and when they last interacted with your business?
We live in a world of increasing data, collecting customer information from many different channels, including physical stores, e-commerce, campaign systems, email, social media, merchant data, etc. So advanced analytics are in place – by creating comprehensive customer profiles from this data, you can gain insight into customer behavior and provide a more personalized experience.
As your business grows , customer segmentation can significantly improve your marketing performance, make your campaigns more relevant to customer audiences, and ultimately increase response rates and sales.
What is RFM analysis?
A common question I hear from business leaders is, “Which of my customers is the most valuable?” . While this is a relatively simple question with a straightforward answer, there are advanced ways to answer it. For example, how does a company define a “valuable customer”? They might be customers who spend the most office 365 database overall, or customers who have a high number of transactions. There are also other considerations, such as recent purchase or average shopping cart size.
Fortunately, we can use so-called RFM analysis – a framework of recency, frequency, and currency – to help us identify a customer’s transaction history and divide the entire customer base into appropriate segments.
What is RFM (Recency – Frequency – Monetary)?
Recency – How long has it been since a customer last made a purchase? Customers who have recently made a purchase will still have the product singapore data on their mind. They are more likely to repurchase the product or need it again (for example, with regular orders such as food, where we can roughly predict the next order period). Companies often measure recency in days. However, depending on the product, they may measure it in weeks, months, or even hours.
Frequency – How often has this customer made a purchase in a given period? Customers who have purchased once are more likely to purchase again. In addition, first-time customers can be a good target for follow-up advertising that would convert them into more frequent customers.
Monetary (value) – How much money did the customer spend in a given period? Customers who spend a lot of money are more likely to spend money in the future and are of high value to the company. Companies that do not accept direct payments from customers can use any other factors in their analysis. For example, behavioral data from the website/app, where they evaluate how they value readers, number of views or interactions. Instead of the standard nominal transaction value, interaction value can be used to perform RFE (recency, frequency, engagement) analysis.