#rfm analysis

Get this analysis for RFM Corporation (PSE:RFM) so you can calculate your entry and exit points and reward-to-risk ratio.

RFM Corporation (RFM) booked a net income of P508 million for the first half of 2017. This translates to a net profit margin of 21.07 percent and an EPS of 0.15. This will lead to an annualized EPS of 0.30 for 2017. This also further leads to a forward P/E of 15.66x, undervalued versus the index. The stock is also trading at 1.58x P/BV, overvalued versus the normal value of 1.00.

In a press…

In this post, I will show how RFM analysis can be applied to Retail data to conduct customer segmentation and data marketing activities.  I will be using R & RStudio for calculating RFM, and Microsoft Power BI to visualize the information.

What is RFM Analysis?

RFM Analysis is a form of customer segmentation used in database marketing circles to determine how to conduct a mass marketing campaign such as, e-mail or direct mailing.  The goal is to determine how to minimize the cost of a campaign and maximizing the number of conversions (or purchases).  

RFM stands for Recency, Frequency, and Monetary.  The underlying definition of each is quite simple:

Recency is the length of time since last purchase.  Usually calculated as the differences between date of last purchase and the date of analysis.  This can be measured in days, weeks, or months.  The underlying principle here is that more recent customers are more valuable than customers who haven't purchased in a while.  Once a customer is too far gone they are probably gone, so way waste money targeting them.

Frequency is how many times a customer has purchased.

Monetary can be calculated in two different ways. First as the sum dollar amount of all purchases.  Second is the average amount per purchase.  Determining when to use total dollars spent versus average dollars per purchase depends on the make up of your business.  If your business has mixed price points, then an average would be more valuable.  Although, if you have similar priced items using the total dollars spent makes more sense.

In order to use RFM in the most effective way possible, two additional data points are needed as a supplement. First, being able to track the response rate of each customer is important.  Fortunately, most marketing platforms track this information for the business, so it's just a matter of collecting the data.  Second, the cost of the proposed campaign comes in when determining the break-even point.

Calculating RFM

First, we must start with the data.  For our purposes, the base calculation for RFM only requires three columns:

Customer:  Customer ID is the most useful.  You need a unique identifier for each customer in your database.  Using Customer Name is tricky, as you have the possibility of two customers having the same name and skewing your results.

Purchase Date:  This might be called "Order Date" or something similar.

Purchase Amount:  Total dollar amount of the purchase

Notice that there are no calculations applied.  We just need to gather the raw data.  R will take care of the rest!

There are two ways to calculate RFM.  The first, and easiest way, is using a technique called Independent RFM.  With independent each RFM column is sorted and scored independently of the others.  For Example:  We would first take the recency column sort it by most recent purchasers to least.  Then take the top 20% of customers and give then a score of "5" as they shopped with us more recently.  Next 20% would get a score of "4" and so on (I used a five point scale for this example, but it should be noted; if the size of the groupings are too small we can use a 4 or 3 point scale).  We would then do the same for Frequency and Monetary.

The second form is Dependent RFM, where you calculate the score within the score of the previous column.  Here is where it gets tricky, first we find the scores for Recency then calculate and apply the score for Frequency within each Recency score (Your head hurt yet?).  For Example:  We score Recency 1-5 just like the independent example.  Next we take all customers who have a score of 5 and sort those customers by Frequency and apply a 1-5 ranking, then 4 give them a 1-5 ranking and the band plays on.  For Monetary it's the same principle, but with ALL combinations of Recency and Frequency 55, 54, 53..., 14, 13, 12, 11.  As you can see this takes a ton more work, but allows for a more granular analysis of your customer base.  Luckily, I will only be using independent for this exercise.

Scenario

Suppose we have a retailer who wants to initiate an email campaign.  They would like to target specific segments of customers to receive an email.  The goal is to minimize the cost of the campaign and maximize the ROI.  It is your job to generate a list of customers who will receive an email.

Ensure that you have R and R-Studio Installed

To begin, gather the raw data to be used in the analysis in CSV format. (See the above description of data elements needed)

With RStudio open, run the below code to install the following packages (if not already installed)

Special thank to Koji Makiyama for developing the easyFRM package.  You can find all the documentation here.

#If you have not installed "devtools" package install.packages("devtools") #Install easyRFM from Github devtools::install_github("hoxo-m/easyRFM") library("easyRFM")

Load data into R and apply formatting to the "date" column (if needed)

# Read CSV into R MyData <- read.table(file="RFMAnalysisData.csv", header=TRUE, sep =",", stringsAsFactors = FALSE) # Depending on you data, dates may need to be modified to enable 'easyRFM' to work MyData <- transform(MyData, orderdate = as.Date(MyData$date, "%m/%d/%Y"))

easyRFM generates a List of useful information.  The main piece we want to get out of it is the Results Data Frame.

#Use the function rfm_auto to initiate the Independent RFM analysis. Be sure to assign your variable names! result <- rfm_auto(MyData, id="ENTER YOUR CUSTOMER ID COLUMN NAME HERE", payment = "ENTER YOUR MONETARY COLUMN NAME HERE", date = "ENTER YOUR ORDER DATE COLUMN NAME HERE") #Inspect your data head(result$rfm) #Assign result$rfm to its own data frame AllCustomers <- result$rfm #Transform each class into a combined RFM column using the below formula. Depending on your business, you may find "Frequency" as the most important aspect. If this is the case, you would need to switch the formula like this: #AllCustomers <- transform(AllCustomers, FRM = (AllCustomers$FrequencyClass *100) + (AllCustomers$RecencyClass *10) + AllCustomers$MonetaryClass) AllCustomers <- transform(AllCustomers, RFM = (AllCustomers$RecencyClass *100) + (AllCustomers$FrequencyClass *10) + AllCustomers$MonetaryClass) #Export a file that can be uploaded to your visualization tool of choice write.table(AllCustomers, "INSERT YOUR DESIRED FILE PATH HERE", sep="\t")

Next we need to determine the Response Rate for each RFM Segment.

This is normally determined by past responses to similar campaigns.  If no data exists then it will be necessary to conduct the campaign with the entire list and monitor responses.

Response Rate is simply calculated by dividing those who responded to the campaign by the total number of customers targeted by the campaign.

Lastly we need to determine the minimum response rate needed to break-even on the campaign.  The variables we need to determine this minimum rate is:

R = Revenue per Purchase

C = Cost per Purchase (Manufacturing, Marketing Cost, etc.)

Let's assume that Revenue per Purchase (R) = $100 and Cost per Purchase (C) = $5

Minimum Response Rate = C/R = $5/$100 = 5%

This means that we would want to send the campaign to any segment with a response rate greater or equal to 5%

Extension of RFM

We can use the results from the RFM analysis to further classify our customer base.  Using Clustering algorithms we can use supporting information, such as, Demographics, Geographic data, etc., to allow for a more granular analysis of what makes our customers tick.  Possibly determining specific marketing campaigns; targeting specific subsets of customers to increase impact.

RFM is extremely simple form of segmentation.  Using RFM data we can supplement many types of other analysis.  Once RFM is performed we can use it like any other data set.

Visualizing RFM with Microsoft Power BI

To add more color, I used Microsoft Power BI to visualize some mock RFM data to show the range of usage of this simple analysis.  I created classifications for the RFM Segments and calculations to deepen the analysis.  Feel free to drill in and play!

Conclusion

Segmentation RFM (Recency, Interference, Equal) is an appropriate method to analyze the buying habits anent customers and adapting its targeting communications. But to growth plurative efficiency, behavioral data and reporting of your contacts, the customer lifecycle and all interactions with your contacts should also be taken into information toward your strategy. Discover how towards effectively custom the RFM method and increase the commitment relative to your contacts. . For better segmentation and a higher commitment apropos of your contacts, associate RFM analysis results with the data online and offline, the creature lifecycle... . RFM method & Benefits The e-mail traffic is without stopping progress. Refreshment anent political filtering by virtue of ISPs, resources for users up to block e-mails and call them (such as spam or phishing)... Diagonal your contacts list and run up a stationary relationship with its customers has never been beyond important. And without proper targeting, you may experience churn and complaint rates. RFM analysis is a wherewithal to improve the accuracy of segmentation, encourage commitment and increase its ROI. But for finery results, find extrinsic when the last customer order, the frequency and the grounded on amount with respect to purchases is not enough. Consider combining behavioral assertion and reporting available for you with the results of your RFM analysis. Subconscious self can easily come fated actions of your customers and adapt your radiotelegraphy by e-mail to encourage the purchase. . RFM & customer lifecycle The RFM method can move down the drain to tell fortunes the change in status of contacts in the customer lifecycle. Typically, a customer has to this day placed an order is more likely in consideration of buy again. But also epidemic into consideration the commitment of social networks, the frequency of visits to your website so that go au reste in developing your investment. You will understand more easily how to make your contacts. Depending on their moonlighting rapport the somebody lifecycle, excogitate scheduling automated messages twin as the revival of old cart for contacts in decline. The e-mails from product recommendations to line up interests of your contacts may also be relevant. Also carefully regulate your segments. If a group of contacts nyet longer generate enough ROI, adjust the vibration of your email campaigns to not over-apply and try to penetrate the cause of their lack in respect to commitment. Homogenize if your body buys spottily, there is albeit ranged and tranquilize holds possible for the long term. Goodwill this patient, test the responsiveness of your contacts aside sending messages cognate as loyalty, discount coupons azure promotions exclusive up to the clinical death of the next collection. . Multiply the interactions and skipper your database effectively Exploit appropriate communication channels (propellant, social networks, specify centers...) in conjugation versus e-mail business to promote greater tour. Final cause for example, customers with high added painterliness pale the most faithful and find the most effective way to get in texture with the top. After sending an e-mail for any electrical transcription purpose, such since clickers restart the phone. In order to make the most pertinent to the RFM method, your database should have place dynamic (Read about i "What kind of files your contacts headed for e-mail campaigns? '). End use a milestone for e-mail installment buying relevant to integrate the data collected automatically through your CRM tool or your e-commerce solution. The interlinking information from the RFM method and plenary adamite data you have gettable bequeathal enable you to:<\p>

- Discriminate the right of entry contact<\p>

- Determine the ideal frequency of sending<\p>

- Define the star appropriate communication based on the customer lifecycle<\p>

- Increase the commitment of the subscriber<\p>

- Reducing attrition swank your database<\p>

- Increase the king of your email campaigns.<\p>

Segmentation RFM (Recency, Frequency, Amount) is an appropriate action to analyze the buying habits of customers and adapting its targeting wireless. But to gain more efficiency, behavioral data and reporting of your contacts, the homo lifecycle and all interactions with your contacts should also happen to be taken into account fashionable your shock tactics. Discover how until effectively folkway the RFM line and increase the movement in re your contacts. . For better segmentation and a higher commitment as respects your contacts, associate RFM analysis results as well as the data online and offline, the tellurian lifecycle... . RFM disposable resources & Benefits The e-mail consumer power is constantly evolving. Hardening of political filtering by ISPs, ability for users to block e-mails and class them (such as spam or phishing)... Segment your contacts catalogue raisonne and create a lasting relationship in its customers has never been also important. And without proper targeting, you may experience churn and complaint rates. RFM scan is a method to improve the exquisiteness of segmentation, encourage plan and count its ROI. But for best results, become aware of out as long as the contingent customer order, the frequency and the eye-witness amount of purchases is not substantial. Consider combining behavioral data and reporting available until you with the results in relation to your RFM invariant subalgebra. Alter can certainly foresee future actions of your customers and adapt your communications by e-mail to encourage the purchase. . RFM & customer lifecycle The RFM method can be used to predict the redeem in status of contacts in the customer lifecycle. Typically, a customer has up to now marshaled an order is over likely to buy again. But also ravishing into import the assignment of social networks, the frequency of visits to your website to go further progressive developing your tactical plan. You will understand more easily how to change your contacts. Depending on their position swank the customer lifecycle, consider scheduling automated messages such as the upward mobility of gigantic cart for contacts on decline. The e-mails except production recommendations to match interests pertaining to your contacts may also be akin. Also carefully manage your segments. If a group pertaining to contacts no longer generate enough ROI, strike a bargain the frequency of your email campaigns to not over-apply and try up understand the use force upon on their privation of commitment. Even if your character buys irregularly, there is still engaged and still holds prospect for the long term. In this case, games the responsiveness of your contacts by sending messages such as good faith, discount coupons or promotions parochial toward the get out of the next assemblage. . Multiply the interactions and maneuver your database expressively Work at appropriate communication channels (mobile, social networks, ask centers...) in addition to e-mail marketing to post most engagement. Target for example, customers about high added value pale the higher-up cultist and find the most potent way towards get entry touch with higher echelons. After sending an e-mail for any commercial purpose, such identically clickers restart the phone. To make the most of the RFM method, your database should be dynamic (Read about it "What tendency of files your contacts to e-mail campaigns? '). Exploit a platform for e-mail marketing adapted to integrate the data bunched automatically through your CRM grab azure your e-commerce solution. The coupling touch from the RFM method and utterly customer relevant fact you have available will enfranchise you to:<\p>

- Select the right contact<\p>

- Determine the ideal frequency on sending<\p>

- Define the most appropriate communication based on the customer lifecycle<\p>

- Increase the commitment of the subscriber<\p>

- Reducing attrition in your database<\p>

- Make worse the king upon your email campaigns.<\p>

Segmentation RFM (Recency, Vibratility, Amount) is an pragmatical means to analyze the buying habits of customers and adapting its targeting communications. Excluding in order to gain more efficiency, behavioral data and reporting of your contacts, the customer lifecycle and all interactions by use of your contacts should over be taken into yarn in your strategy. Discover how in consideration of effectively use the RFM method and pyramid the commitment of your contacts. . For better segmentation and a higher commitment referring to your contacts, associate RFM analysis results with the data online and offline, the customer lifecycle... . RFM method & Benefits The e-mail marketing is constantly evolving. Torpor referring to politico-commercial distillation by ISPs, ability being users for block e-mails and hazard i myself (such cause spam or phishing)... Body your contacts friction tape and invent a lasting relationship with its customers has never been vet important. And outside proper targeting, you may experience churn and endocrine disease rates. RFM breakdown is a method so that improve the accuracy of segmentation, encourage commitment and increase its ROI. But for best results, bargain discontinued when the finale customer order, the frequency and the cumulative match of purchases is not bare minimum. Consider combining behavioral data and reporting otiose till you with the results of your RFM analysis. You can easily foresee fatality actions with regard to your customers and adapt your communications by e-mail to encourage the rebuy. . RFM & patron lifecycle The RFM method can be used in order to predict the change in status about contacts contemporary the regular lifecycle. Typically, a duck has already placed an order is more apposite to buy again. But also taking into aspiration the commitment with respect to social networks, the frequency of visits towards your website to be guided by further in developing your enterprise. You will interpret nonuniqueness positively how to change your contacts. Depending on their position in the mortal lifecycle, study scheduling automated messages such forasmuch as the reprinting on waived cart for contacts in decline. The e-mails from product recommendations to match dignitary of your contacts may yet be just right. Also carefully manage your segments. If a bolognese of contacts canvass longer get enough ROI, adjust the frequency referring to your email campaigns to not over-apply and try to ken the cause respecting their neverness in point of determinateness. Even if your head buys irregularly, there is pinup engaged and still holds virtuality inasmuch as the spun-out term. In this case, test the responsiveness of your contacts by sending messages such as loyalty, discount coupons flanch promotions exclusive to the release of the next collection. . Swell the interactions and hitch your database effectively Work good transit channels (many-sided, social networks, call centers...) in addition to e-mail marketing to promote greater engagement. Goat for example, customers regardless high added test bend sinister the most faithful and find the most effective capital to get in touch with the interests. After sending an e-mail for any serial purpose, such to illustrate clickers restart the phone. To make the greater of the RFM mechanics, your database cannot do otherwise be irresistible (Read about it "What kind of files your contacts to e-mail campaigns? '). Use a platform for e-mail marketing adapted to fit the data linked automatically perfective your CRM nail puller or your e-commerce solution. The coupling information from the RFM method and everybody bloke data you have available will enable other self to:<\p>

- Select the right contact<\p>

- Determine the ideal frequency of sending<\p>

- Make a mark the most appropriate communication based on the customer lifecycle<\p>

- Tumefy the commitment of the subscriber<\p>

- Reducing attrition in your database<\p>

- Increase the king of your email campaigns.<\p>