RFM - Customer Level Data

Introduction

## Warning: package 'DT' was built under R version 3.5.2

RFM (recency, frequency, monetary) analysis is a behavior based technique used to segment customers by examining their transaction history such as

• how recently a customer has purchased (recency)
• how often they purchase (frequency)
• how much the customer spends (monetary)

It is based on the marketing axiom that 80% of your business comes from 20% of your customers. RFM helps to identify customers who are more likely to respond to promotions by segmenting them into various categories.

Data

To calculate the RFM score for each customer we need transaction data which should include the following:

• a unique customer id
• number of transaction/order
• total revenue from the customer
• number of days since the last visit

rfm includes a sample data set rfm_data_orders which includes the above details:

rfm_data_customer

## # A tibble: 39,999 x 5
##    customer_id revenue most_recent_visit number_of_orders recency_days
##          <dbl>   <dbl> <date>                       <dbl>        <dbl>
##  1       22086     777 2006-05-14                       9          232
##  2        2290    1555 2006-09-08                      16          115
##  3       26377     336 2006-11-19                       5           43
##  4       24650    1189 2006-10-29                      12           64
##  5       12883    1229 2006-12-09                      12           23
##  6        2119     929 2006-10-21                      11           72
##  7       31283    1569 2006-09-11                      17          112
##  8       33815     778 2006-08-12                      11          142
##  9       15972     641 2006-11-19                       9           43
## 10       27650     970 2006-08-23                      10          131
## # ... with 39,989 more rows

RFM Score

So how is the RFM score computed for each customer? The below steps explain the process:

• A recency score is assigned to each customer based on date of most recent purchase. The score is generated by binning the recency values into a number of categories (default is 5). For example, if you use four categories, the customers with the most recent purchase dates receive a recency ranking of 4, and those with purchase dates in the distant past receive a recency ranking of 1.

• A frequency ranking is assigned in a similar way. Customers with high purchase frequency are assigned a higher score (4 or 5) and those with lowest frequency are assigned a score 1.

• Monetary score is assigned on the basis of the total revenue generated by the customer in the period under consideration for the analysis. Customers with highest revenue/order amount are assigned a higher score while those with lowest revenue are assigned a score of 1.

• A fourth score, RFM score is generated which is simply the three individual scores concatenated into a single value.

The customers with the highest RFM scores are most likely to respond to an offer. Now that we have understood how the RFM score is computed, it is time to put it into practice. Use rfm_table_order() to generate the score for each customer from the sample data set rfm_data_orders.

rfm_table_order() takes 8 inputs:

• data: a data set with
  • unique customer id
  • date of transaction
  • and amount
• customer_id: name of the customer id column
• order_date: name of the transaction date column
• revenue: name of the transaction amount column
• analysis_date: date of analysis
• recency_bins: number of rankings for recency score (default is 5)
• frequency_bins: number of rankings for frequency score (default is 5)
• monetary_bins: number of rankings for monetary score (default is 5)

RFM Table

analysis_date <- lubridate::as_date('2007-01-01', tz = 'UTC')
rfm_result <- rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
  recency_days, revenue, analysis_date)
rfm_result

customer_id	recency_days	transaction_count	amount	recency_score	frequency_score	monetary_score	rfm_score
22086	232	9	777	2	2	2	222
2290	115	16	1555	4	5	5	455
26377	43	5	336	5	1	1	511
24650	64	12	1189	5	4	4	544
12883	23	12	1229	5	4	5	545
2119	72	11	929	5	4	3	543
31283	112	17	1569	4	5	5	455
33815	142	11	778	3	4	2	342
15972	43	9	641	5	2	2	522
27650	131	10	970	3	3	3	333

rfm_table_customer() will return the following columns as seen in the above table:

• customer_id: unique customer id
• date_most_recent: date of most recent visit
• recency_days: days since the most recent visit
• transaction_count: number of transactions of the customer
• amount: total revenue generated by the customer
• recency_score: recency score of the customer
• frequency_score: frequency score of the customer
• monetary_score: monetary score of the customer
• rfm_score: RFM score of the customer

Heat Map

The heat map shows the average monetary value for different categories of recency and frequency scores. Higher scores of frequency and recency are characterized by higher average monetary value as indicated by the darker areas in the heatmap.

rfm_heatmap(rfm_result)

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Bar Chart

Use rfm_bar_chart() to generate the distribution of monetary scores for the different combinations of frequency and recency scores.

rfm_bar_chart(rfm_result)

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Histogram

Use rfm_histograms() to examine the relative distribution of

• monetary value (total revenue generated by each customer)
• recency days (days since the most recent visit for each customer)
• frequency (transaction count for each customer)

rfm_histograms(rfm_result)

aaarticlea/png;base64,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" alt="" />

Customers by Orders

Visualize the distribution of customers across orders.

rfm_order_dist(rfm_result)

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" alt="" />

Scatter Plots

The best customers are those who:

• bought most recently
• most often
• and spend the most

Now let us examine the relationship between the above.

Recency vs Monetary Value

Customers who visited more recently generated more revenue compared to those who visited in the distant past. The customers who visited in the recent past are more likely to return compared to those who visited long time ago as most of those would be lost customers. As such, higher revenue would be associated with most recent visits.

rfm_rm_plot(rfm_result)

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" 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Frequency vs Monetary Value

As the frequency of visits increases, the revenue generated also increases. Customers who visit more frquently are your champion customers, loyal customers or potential loyalists and they drive higher revenue.

rfm_fm_plot(rfm_result)

aaarticlea/png;base64,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" alt="" />

Recency vs Frequency

Customers with low frequency visited in the distant past while those with high frequency have visited in the recent past. Again, the customers who visited in the recent past are more likely to return compared to those who visited long time ago. As such, higher frequency would be associated with the most recent visits.

rfm_rf_plot(rfm_result)

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" alt="" />

Segments

Let us classify our customers based on the individual recency, frequency and monetary scores.

Segment	Description	R	F	M
Champions	Bought recently, buy often and spend the most	4 - 5	4 - 5	4 - 5
Loyal Customers	Spend good money. Responsive to promotions	2 - 5	3 - 5	3 - 5
Potential Loyalist	Recent customers, spent good amount, bought more than once	3 - 5	1 - 3	1 - 3
New Customers	Bought more recently, but not often	4 - 5	<= 1	<= 1
Promising	Recent shoppers, but haven’t spent much	3 - 4	<= 1	<= 1
Need Attention	Above average recency, frequency & monetary values	2 - 3	2 - 3	2 - 3
About To Sleep	Below average recency, frequency & monetary values	2 - 3	<= 2	<= 2
At Risk	Spent big money, purchased often but long time ago	<= 2	2 - 5	2 - 5
Can’t Lose Them	Made big purchases and often, but long time ago	<= 1	4 - 5	4 - 5
Hibernating	Low spenders, low frequency, purchased long time ago	1 - 2	1 - 2	1 - 2
Lost	Lowest recency, frequency & monetary scores	<= 2	<= 2	<= 2

Segmented Customer Data

We can use the segmented data to identify

• best customers
• loyal customers
• at risk customers
• and lost customers

Once we have classified a customer into a particular segment, we can take appropriate action to increase his/her lifetime value.

## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

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Search:

	Customer	Segment	RFM	Orders	Recency	Total Spend
1	22086	Needs Attention	222	9	232	777
2	2290	Champions	455	16	115	1555
3	26377	Potential Loyalist	511	5	43	336
4	24650	Champions	544	12	64	1189
5	12883	Champions	545	12	23	1229

Showing 1 to 5 of 39,999 entries

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Segment Size

Now that we have defined and segmented our customers, let us examine the distribution of customers across the segments. Ideally, we should have very few or no customer in segments such as At Risk or Needs Attention.

rfm_segments %>%
  count(segment) %>%
  arrange(desc(n)) %>%
  rename(Segment = segment, Count = n)

## # A tibble: 8 x 2
##   Segment            Count
##   <chr>              <int>
## 1 Loyal Customers    10181
## 2 Potential Loyalist  9547
## 3 Champions           6477
## 4 At Risk             4660
## 5 Hibernating         3502
## 6 About To Sleep      2171
## 7 Others              1755
## 8 Needs Attention     1706

Segments

We can also examine the median recency, frequency and monetary value across segments to ensure that the logic used for customer classification is sound and practical.

Median Recency

data <-
    rfm_segments %>%
    group_by(segment) %>%
    select(segment, recency_days) %>%
    summarize(median(recency_days)) %>%
    rename(segment = segment, avg_recency = `median(recency_days)`) %>%
    arrange(avg_recency) 

  n_fill <- nrow(data)

  ggplot(data, aes(segment, avg_recency)) +
    geom_bar(stat = "identity", fill = brewer.pal(n = n_fill, name = "Set1")) +
    xlab("Segment") + ylab("Median Recency") +
    ggtitle("Median Recency by Segment") +
    coord_flip() +
    theme(
      plot.title = element_text(hjust = 0.5)
    )

aaarticlea/png;base64,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" alt="" />

Median Frequency

data <-
    rfm_segments %>%
    group_by(segment) %>%
    select(segment, transaction_count) %>%
    summarize(median(transaction_count)) %>%
    rename(segment = segment, avg_frequency = `median(transaction_count)`) %>%
    arrange(avg_frequency) 

n_fill <- nrow(data)

ggplot(data, aes(segment, avg_frequency)) +
    geom_bar(stat = "identity", fill = brewer.pal(n = n_fill, name = "Set1")) +
    xlab("Segment") + ylab("Median Frequency") +
    ggtitle("Median Frequency by Segment") +
    coord_flip() +
    theme(
      plot.title = element_text(hjust = 0.5)
    )

aaarticlea/png;base64,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" alt="" />

Median Monetary Value

data <-
    rfm_segments %>%
    group_by(segment) %>%
    select(segment, amount) %>%
    summarize(median(amount)) %>%
    rename(segment = segment, avg_monetary = `median(amount)`) %>%
    arrange(avg_monetary) 

n_fill <- nrow(data)

ggplot(data, aes(segment, avg_monetary)) +
    geom_bar(stat = "identity", fill = brewer.pal(n = n_fill, name = "Set1")) +
    xlab("Segment") + ylab("Median Monetary Value") +
    ggtitle("Median Monetary Value by Segment") +
    coord_flip() +
    theme(
      plot.title = element_text(hjust = 0.5)
    )

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" alt="" />

References

• Data Mining: Concepts and Techniques , Second Edition , Jiawei Han University of Illinois at Urbana-Champaign Micheline Kamber.
• https://joaocorreia.io/blog/rfm-analysis-increase-sales-by-segmenting-your-customers.html
• http://www.sciencedirect.com/science/article/pii/S1877050910003868