Conde Nast Brand * Database Analysis Project
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This document summarizes a light customer development project analyzing customer data to develop profiles and recommendations. It describes the methodology used, including choosing relevant variables like RFM data and demographics, analyzing correlations between variables, calculating RFM values, and validating findings. Key findings identified females aged 28-45 who own homes as most valuable customers. Recommendations to acquire new customers include TV, magazine, radio, and direct mail advertising targeted toward this profile. Maintaining existing customers is recommended through loyalty programs, renewals, surveys, events, and social media.
Conde Nast Brand * Database Analysis Project
- 1. Light Customer Development ProjectLight Customer Development Project Data Analyst Millie Statistical Analyst Christian Market Research Analyst Sarah Marketing Strategist Natvida Summer 2013Summer 2013
- 2. 2 CONTENT TABLE Methodology: •objectives •What variables we’ve chosen and why •3 Steps to analysis the data Findings •General key findings •Validation of the findings •Our best customer profile Recommendations •how can we reach new customers •How can we maintain and enhance the profit level of existing customer
- 3. WHAT IS THE OBJECTIVE? Profit is the fundamental goal of enterprises -- Karl Marx General Rule: Thus, as usual, we use RFM as the most important indicator of our analysis
- 4. WHAT VARIABLES DID WE CHOSE AND WHY • recency_from_sub_end • frequency • ACCUM_AMOUNT RFM Data • lt_duration_yr • channel_0 • PRODUCTS_RECENTLY_PURCHASE • IB_BUY_Beauty_Cosmetic_Aids • IB_BUY_Crafts_Hobbies • IB_BUY_Electronics_Gadgets BEHAVIORAL DATA • AGE • GENDER • IB_NUMBER_OF_LIFETRAITS • IB_OCCUPATION_CUSTOMER • IB_OWN_RENT_HOME • IB_MARITAL_STATUS • IB_NET_WORTH_ESTIMATOR • IB_PP_HOUSEHOLD_SIZE • IB_PP_NUMBER_OF_CHILDREN • STATE • ZIP_CODE • MAG_CUS_ACTIVE_MAGAZINES • MAG_CUS_ORIG_MAGAZINE • IB_VOTER_PARTY • personicx_group DEMOGRAPHIC DATA Why did we chose these data: Since we don’t have exact time of last purchase, recency_from_sub_end is the most close attribute Accum_amount is more significant than monetary since it’s not representative to just look at last purchase Demographic, psychographic, behavioral data are 3 sets of data that marketers ususally use to identify target customers
- 5. VARIABLES THAT WE ABANDONED AND WHY 5 • IB_WORKING_WOMAN_IN_HH • DIGITAL_INDICATOR_FLAG • LT_digital_title • lt_digital_sub_ind • lt_digital_print_auth_ind INVALID DATA • magnitude • order_year_0 • order_month2_0 • order_week2_0 • MAG_CUS_ACCUM_AMOUNT UNNECESSARY DATA • IB_PRESENCE_OF_CHILDREN • IB_LT_Cat_Owner • IB_LT_Dog_Owner • IB_LT_Other_Pets norm_renew_score • auto_renew_cc_score • auto_renew_bill_score • channel_internet_score • channel_traditional_score • EMAIL_PRESENCE_FLAG • IB_LENGTH_OF_CURRENT_RESIDENCE COMBINED DATA Why didn’t we chose these data: Life duration is used instead of order_year / month / week Mag_cus_accum_amount has the same dataset with accum_amount There are too many missing data in invalid data set Some data can be combined together. E.g. if cat/dog/other pets owner can be combined to if pets owner
- 7. STEP1: RUN CORRELATION_GENERAL General Rule: Correlation are run between RFM Data and demographic / psychographic / behavior data Didn’t use ANOVA or Regression. Since RFM may not be independent from each other First used MONOVA to test under a DPB variable, whether RFM are independent from each other Then used Discriminant function analysis to identify whether a DPB variable has influence over RFM * In order to save labor, also used Canonical analysis to run several DPB variable together Last but not least, run regression between that DPB variable and RFM
- 8. STEP 1: TAKE LT DURATION YEAR AS AN EXAMPLE SAS CODE FOR LT_DURATION & RFM
- 9. STEP 1: TAKE LT DURATION YEAR AS AN EXAMPLE MANOVA shows RFM are significantly independent from each other While CANDISC indicates that at least one factor of RFM is affacted by lt_duration
- 10. STEP 1: TAKE LT DURATION YEAR AS AN EXAMPLE The plot clearly showed that accum_amount and lt_duration is posstively correlated While the p value indicates that the correlation is significant
- 11. STEP 1: TAKE LT DURATION YEAR AS AN EXAMPLE Correlation coeffeciency between lt_duration and frequency is lower But still significantly related ( P value 0.0076 smaller than 0.05)
- 12. STEP 1: TAKE LT DURATION YEAR AS AN EXAMPLE Recency is negatively correlated as you may see from the graph But it’s reasonable since the smaller it is, the earlier you renewed the magazine. And loyalty customers tend to renew their magazine more actively
- 13. STEP 1: OTHER EXAMPLES However, it’s not always true that DPB variables are correlated with RFM For example. Recency has nothing to do with age. It makes sense, if the magazine is run by month, and customer subscribe to one year, it doesn’t matter if the customer is 80 yod or 18 yod
- 14. STEP 1: OTHER CODINGS For categorical data, use glm and class instead of regression to run the correlation
- 15. STEP 2: RFM VALUE CALCULATION RECENCY FREQENCY MONETARY • If smaller than -1, rate it 5 • If smaller than -0.5, rate it 4 • If smaller than 0, rate it 3 If smaller than 1, rate it 1 If smaller than 2, rate it 2 If smaller than 3, rate it 3 If smaller than 4, rate it 4 • Divide accumulative amount by 10000 • If bigger than 7, use 7 • If equal to 0, rate it 2, • If smaller than 1, rate it 1 • If bigger than 1, rate it 0 Through SAS analysis, we found that 50% DPB variables are related to monetary factor, while 30% are related to frequency, 20% related to recency. We use the ratio and calculated a new RFM Value, we call it customer value And then run pivot table analysis between customer value and DPB data 50% 20% 30%
- 16. STEP 3: CUSTOMER VALUE & DPB VALUE PIVOT TABLE ANALYSIS Then we run pivot table analysis among the variables and customer value to visualize the findings
- 17. 17 CONTENT TABLE Methodology: •What variables we’ve chosen and why •How did we analysis the data •What tools did we use Findings •General key findings •Validation of the findings •Our best customer profile Recommendations •how can we reach new customers •How can we maintain and eenhance the profit level of existing customer
- 18. KEY FINDINGS: 28-45 IS THE BEST RANGE FOR TARGET CUSTOMER
- 19. KEY FINDINGS: FEMALE CUSTOMERS ARE 15.6 TIMES MORE VALUABLE THAN MALE CUSTOMERS
- 20. KEY FINDINGS: CUSTOMERS WHO OWN THEIR HOUSE ARE 19.8 TIMES MORE VALUABLE THAN THOSE WHO RENT
- 21. KEY FINDINGS: MARRIED COUPLES ARE MORE VALUABLE THAN SINGLE CUSTOMERS
- 22. KEY FINDINGS: DEMOCRATS ARE MOST VALUABLE If independent generates 1 unit of RFM value, democrats will contribute 7, while republican gives 17, vacancy gives 10 Vacancy Independent DemocratsRepublican
- 23. KEY FINDINGS: GEO DISTRIBUTION KEY MARKETS THAT SHARE THE HIGHEST VALUEABLE CUSTOMERS: Tier 1: California, Texas, New York Tier 2: Illinios, Pennsylvania, Florida Tier 3: Arizona, michigan, Ohio, Tennessee, Georgia, North Carolina
- 24. KEY FINDINGS: HIGH VALUALBE ZIP-CODES Top Zip code Area is as follows: New York City has the top 2, which is coordinate with the geo distribution San Jose has relatively high income residents due to the insensitivity of high-tech companies Lancaster has well-educationed population, so does chicago, near northwestern univ.
- 25. SAS VALIDATION
- 26. SPSS VALIDATION Findings Via Skype: 53% of its circulation in the top ten U.S. metropolitan areas Average Income is $109,877 (2009)
- 27. BEST CUSTOMER PROFILE: CAREER WOMEN CINDY Gender: Female Age: 40 years old Marital Status: Married (no kids) Occupation: “Occupation 4” Income level: High Area of residence: Manhattan, New York Zip Code: 10021 (Upper East) Owner of home Political Affiliation: Democrat Beliefs: Liberal: Pro choice etc. Favorite News Channel: MSNBC Hobbies and Interests: Traveling, Reading, Zoomba, Art Museums, Jazz Clubs
- 28. RUN CUSTOMER DEMO/PSCHO ON ETELMAR 28
- 29. 29 CONTENT TABLE Methodology: •What variables we’ve chosen and why •How did we analysis the data •What tools did we use Findings •General key findings •Validation of the findings •Our best customer profile Recommendations •how can we reach new customers •How can we maintain and enhance the profit level of existing customer
- 30. ACQUISITION: TV COMMERCIAL How to enhance the influence among target audience most efficiently: TV Advertising in California, New York and Texas Buy commercial in tv programs our target customers are watching
- 31. How to enhance the influence among target audience most efficiently: Top magazines our target audience is reading Use up-seling and cross-sell to increase profit CUSTOMER ACQUISITION: MAGAZINE
- 32. 32 Copyright © 2007 Accenture All Rights Reserved.Copyright @ 2007 Accenture. All rights reserved. How to enhance the influence among target audience most efficiently: Radio is still one of the strongest influencers Commuting Radio in car is the best choice for our target audience ACQUIRE NEW CUSTOMER: RADIO
- 33. 33 Copyright © 2007 Accenture All Rights Reserved.Copyright @ 2007 Accenture. All rights reserved. How to enhance the influence among target audience most efficiently: Direct Mail Campaign in top 6 zip code areas Also, NCOA provide great opportunity to reach out to potential customers ACQUIRE NEW CUSTOMER: DIRECT MAIL
- 34. ACQUIRE CUSTOMERS: OTHERS 34 Other ways to enlarge customer database: Invite friends, earn credits Enable Subscription in store
- 35. RETAIN LOYAL CUSTOMER: 5 KEY METHODS How to maintain customer : Loyalty Program ( Offers & Discounts, Benefits) Insert in magazine for renew Survey & Questionnaire Event / Experiential marketing Social Media Enagement
Editor's Notes
- #31: TV Advertising Cross-Sell opportunity with New Yorker Magazine Geo-targeted advertising TV advertising directed at California, New York & Texas Direct Mail campaign targeted at residents living in area with postcode 10021, 10012, 95123, 17601, 60047, 90803 Coupon Booklets Referral Program Change of Address (COA) Opportunity Offer magazine subscriptions in shops
- #36: Customer loyalty programs based on RFM scores - Offers and Discounts - Benefits correspond to customer loyalty Renewal inserts in currently subscribed magazine Questionnaires and surveys Experiential/ event marketing Social Media: Facebook, Twitter