use rfm to boost your response rate

Use RFM to Boost Your Response Rate

Arthur Middleton Hughes

Vice President / Solutions Architect

KnowledgeBase Marketing, Inc.

DMA Monday, October 17, 2005

1:00 – 2:00 PM

Georgia World Congress Center

Atlanta, Georgia

MarketingDatabase

Data Access& AnalysisSoftware

Customer Transactions

Marketing Staff

Inputs from Retail, Phone, Web

How a modern database system works

AppendedData &

Modeling

Customer Service

Web Site

Two Kinds of Database People

Constructors

People who build databases

Merge/Purge, Hardware, Software

Creators

People who understand strategy

Build loyalty and repeat sales

You need both kinds!

Responsiveness & Profitability are not the same

Recency Frequency Monetary (RFM) Analysis

• Used for marketing to customers

• Always improves response and profits

• Better than any demographic model

• The most powerful segmentation method

How to Apply Recency Codes

• Put most recent purchase date into every customer record

• Sort database by that date - newest to oldest

• Divide into five equal parts - Quintiles

• Assign “5” to top group, “4” to next, etc.

• Put quintile number in each customer record

Responsive customers may not be the most profitable

Profitable Customers

Responsive Customers

Not all responsive customers are profitable

Not all profitable customers will respond when you write them.

LTV RFM

RFM Can Predict Responders

• For product launch, select SICs with highest penetration ratios

• Use RFM to select most likely responders

• Use combination of mail, phone, and sales visits to responsive relationship buyers.

How to Apply Recency Codes

• Put most recent purchase date into every customer record

• Sort database by that date - newest to oldest

• Divide into five equal parts - Quintiles

• Assign “5” to top group, “4” to next, etc.

• Put quintile number in each customer record

Response by Recency Quintile

3.49%

1.25% 1.08%0.63%

0.26%

0.00%0.50%1.00%1.50%2.00%

2.50%3.00%3.50%4.00%

5 4 3 2 1

Recency Quintile

Res

pons

e R

ate

How to compute a Frequency Index

• Keep number of transactions in customer record

• Sort Recency Groups from highest to lowest

• Divide into five equal groups

• Number groups from 5 to 1

• Put Quintile number in each customer record

Response by Frequency Quintile

1.99%

1.56%1.31%

0.92% 0.93%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

5 4 3 2 1Frequency Quintile

Re

spo

nse

Ra

te

How to compute a Monetary Index

• Store total dollars purchased in each customer record

• Sort Frequency Groups from highest to lowest

• Divide into 5 equal groups (Quintiles)

• Number Quintiles 5, 4, 3, 2, 1

• Put Quintile number in each record

Response by Monetary Quintile

1.61%1.45% 1.46%

1.22% 1.23%

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

1.60%

1.80%

5 4 3 2 1

Monetary Response to $5,000 Product

Monetary Quintile

Percentage of households promoted who purchased

1.68

1.170.88

0.66

0.32

5 4 3 2 10

0.5

1

1.5

2

RFM Code Construction

FM

One SortFive Sorts

Twenty-five sorts

Database

5

4

3

2

1

35

34

33

32

31

335334333332331

R

Appended RFM Codes

Customer Database

Nth

Creating an Nth

300,000 Records

30,000 Records

For Nth by 10, select every tenth record.

Result will be statistical replica of database

Result of Test Mailing to 30,000

# RFM Mailed Response Rate1 555 240 20 8.15%2 554 240 16 6.56%3 553 240 13 5.62%4 552 240 10 4.33%5 551 240 11 4.51%

6 545 240 9 3.78%7 544 240 12 4.98%8 543 240 6 2.88%9 542 240 10 4.26%10 541 240 7 3.10%

11 535 240 10 4.13%12 534 240 9 3.83%13 533 240 8 3.35%14 532 240 6 2.70%

Test Response Rate by RFM Cell

-200

-100

0

100

200

300

400

500

555 455 355 255 111

Index of Response 0 = Break Even

Profit from Test Mailing

Quantity Rate Amount

Goods Sold 402 $40.00 $16,080

Mailing Costs 30,000 $0.55 $16,500

Profits (Loss) ($420)

Determine Break Even and Test Sizes

How to Compute the Response Rate

• Divide number of responses by number mailed. Multiply by 100

• Example: Responses = 1034

Mailed = 40,000

Rate = 1034 / 40,000

Rate = 2.59%

Test, Full File & RFM Selects Compared

Test Full File RFM SelectResponse Rate 1.34% 1.17% 2.76%Responses 402 23,412 15,295Net Revenue $16,080 $936,480 $611,800No. Mailed 30,000 2,001,056 554,182Mailing Cost $16,500 $1,100,581 $304,800

Profits ($420) ($164,101) $307,000

Test Vs Rollout Response Rates

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

554 553 552 551 545 544 543 542 541 535 534 533 532 531 525 524 523 522 521 515 514 513 512 511 455 451 445 444 443 355 354 351 344

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

Retroactive RFM Test

• Many times there is not enough time or funding to run an Nth test in advance

• Solution: apply RFM codes to your last completed outgoing promotion.

• Since you know who responded, you can determine response rates by cell

• Use previous rates to govern this rollout.

How Many RFM Cells Needed?

• Test File = (Test Budget) / (per piece cost)

• Example = $15,000 / $0.76 = 19,737

• Cells Needed = 19,737 / 274 = 72

Cell Division Determination

• To create 72 cells, some must be less than 5

• Recency most powerful. Do not scrimp.

• Example R-F-M = 6 X 4 X 3 = 72

• Is this best? Test and see.

RFM For Business Databases

• Business databases are small

• For small databases, use quartiles or thirds

• Quartile = 4 X 4 X 4 = 64 Cells

• Thirds = 3 X 3 X 3 = 27 Cells

• Custom = 5 X 2 X 2 = 20 Cells

Recent Case History

• User sells personalized product by mail

• 45,000 selected for a test

Second Recency Quintile Had More Responses.

Why?

Even so, First Recency Quintile Had Higher Sales

Recent buyers spend more per order

Lowest two recency quintiles did not break

even

Frequency was very predictive of response

Monetary did not predict response rate very well

But Monetary does predict average sales by quintile

RFM Cells clearly show who to mail to, and who to drop

When NOTNOT to use RFM

• If you use it all the time, half your customers will never hear from you

• They will be lost

• The others will suffer from File Fatigue

• Use it sparingly

• Product launch is ideal use

3.49%

1.25% 1.08%0.63%

0.26%

0.00%0.50%1.00%1.50%2.00%

2.50%3.00%3.50%4.00%

5 4 3 2 1

Recency Quintile

Resp

onse

Rate

Response by Recency Quintile

How to compute a Frequency Index

• Keep number of purchases in customer record

• Sort records in each recency quintile from highest to lowest

• Divide into five equal groups (Quintiles)

• Number quintiles from 5 to 1

• Put Quintile number in each customer record

Response by Frequency Quintile

1.99%

1.56%

1.31%

0.92% 0.93%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

5 4 3 2 1Frequency Quintile

Re

spo

nse

Ra

te

How to compute a Monetary Index

• Store total dollars purchased in each customer record

• Sort the records in each frequency quintile from highest to lowest

• Divide into 5 equal groups (Quintiles)

• Number Quintiles 5, 4, 3, 2, 1

• Put Quintile number in each customer record

Response by Monetary Quintile

1.61%

1.45% 1.46%

1.22% 1.23%

0.00%

0.20%

0.40%

0.60%

0.80%

1.00%

1.20%

1.40%

1.60%

1.80%

5 4 3 2 1

RFM Code Construction

FM

One SortFive Sorts

Twenty-five sorts

Database

5

4

3

2

1

35

34

33

32

31

335334333332331

R

Appended RFM Codes

Result of Test Mailing to 30,000# RFM Mailed Response Rate 1 555 240 20 8.15% 2 554 240 16 6.56% 3 553 240 13 5.62% 4 552 240 10 4.33% 5 551 240 11 4.51%

6 545 240 9 3.78% 7 544 240 12 4.98% 8 543 240 6 2.88% 9 542 240 10 4.26% 10 541 240 7 3.10%

11 535 240 10 4.13% 12 534 240 9 3.83% 13 533 240 8 3.35% 14 532 240 6

2.70%

Test Response Rate by RFM Cell

-200

-100

0

100

200

300

400

500

555 455 355 255 111

Index of Response 0 = Break Even

Profit from Test Mailing

Quantity Rate Amount

Goods Sold 402 $40.00 $16,080

Mailing Costs 30,000 $0.55 $16,500

Profits (Loss) ($420)

What is the break even rate?

• Each test segment must be measured

• A segment breaks even if the profit from sales exactly equals the cost of the promotion

• BE = (Per Piece Cost) / (Net revenue from one sale)

• BE = ($0.48) / ($28) = 1.71%

How large must test segments be?

• Large enough for predictive accuracy

• Small enough to keep test costs down

• Size = 4.00 / (Break Even Rate)

• Size = 4.00 / 1.71% = 234 pieces mailed

• You should adjust the “4.00” based on your experience -- up or down.

How to Compute the Response Rate

• Divide number of responses by number mailed. Multiply by 100

• Example: Responses = 1034

Mailed = 40,000

Rate = 1034 / 40,000

Rate = 2.59%

Test Response Rate by RFM Cell

-200

-100

0

100

200

300

400

500

555 455 355 255 111

Index of Response 0 = Break Even

Test, Full File & RFM Selects Compared

Test Full File RFM SelectResponse Rate 1.34% 1.17% 2.76%Responses 402 23,412 15,295Net Revenue $16,080 $936,480 $611,800No. Mailed 30,000 2,001,056 554,182Mailing Cost $16,500 $1,100,581 $304,800

Profits ($420) ($164,101) $307,000

Test Vs Rollout Response Rates

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

554 553 552 551 545 544 543 542 541 535 534 533 532 531 525 524 523 522 521 515 514 513 512 511 455 451 445 444 443 355 354 351 344

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

RFM Deals With Very Small Numbers

• Only a small percentage (such as 5%) of customers respond to the typical offer

• 95% or more will not respond at all

• RFM tells you which customers are most likely to be in the responsive 5%

• Those who respond may not be your most profitable customers

Retroactive RFM Test

• Many times there is not enough time or funding to run an nth test in advance.

• Solution: apply RFM codes to last year’s completed outgoing promotion.

• Since you know who responded, you can determine response rates by cell.

• Use last year’s rates to govern this year’s rollout.

Recent Case History

• User sells personalized product by mail

• 45,000 selected for a test

Second Recency Quintile Had More Responses. Why?

Even so, First Recency Quintile Had Higher Sales

Recent Buyers Spend More per Order

Lowest Two Recency Quintiles did not Break Even

Frequency was Very Predictive of Response

Monetary did not Predict Response Rate Very Well

But Monetary does Predict Average Sales by Quintile

RFM Cells Clearly Show who to Mail to, and who to Drop

When NOTNOT to use RFM

• If you use it all the time, half your customers will never hear from you

• They will be lost

• The others will suffer from File Fatigue

• Use it sparingly; when you need a boost

• Use it to identify your best customers

• Don’t go hog wild!

Half Life Data

Graphing Half Life

Half Life by Revenue

What should you do?

• Maintain a customer database

• Maintain the most recent date, frequency of orders and total dollar amount

• Put RFM cell codes into your records

• With each mailing, see which cells respond.

• Increase response and profits by NOT MAILING non responsive cells

Books by Arthur Hughes

From McGraw Hill. Order at www.dbmarketing.com Contact Arthur: arthur.hughes@kbm1.com