• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Body Of Work_IN Touch Analytics
 

Body Of Work_IN Touch Analytics

on

  • 516 views

 

Statistics

Views

Total Views
516
Views on SlideShare
514
Embed Views
2

Actions

Likes
1
Downloads
0
Comments
0

1 Embed 2

http://www.linkedin.com 2

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Body Of Work_IN Touch Analytics Body Of Work_IN Touch Analytics Presentation Transcript

    • inTouch analytics actionable relationship analytics
    • about Relationship programs A market leading Foods & Beverages Business [India]: Customer Loyalty Program across 350 outlets – 320,000 members and 12,000,000 transactions mined; Retailer Loyalty Program across a network of 3,000 outlets A market leading Beauty / Fitness Chain [India]: Customer Loyalty Program across 110 centers in India Indian arm of a market leading global brand of Beauty products: High Value Customer panel set up and mass customization initiatives across the brand’s shop-in-shops in India Indian arm of a global Communications Service Provider: Business analytics for Revenue management and Customer Loyalty Program for the CSP’s India operations A 1,000 store global jewelry chain [UAE]: Business analytics and Customer Loyalty Program across 180 outlets in UAE, Europe and India – a consulting assignment
    • about Predictive modeling/analytics A US based satellite co: Pricing and discount modeling solution based on 5-year historical data and 10-year look-ahead (prospective) data A US based consumer marketing co: Predictive analytics solution based on historical marketing program data for the last 5 years An Ecommerce [B2B2C] brand merchandising business [India]: Web and Business Analytics – a full business solution for Online businesses A market leading Packaged Foods business [India]:Models predicting Market Share of leading Indian packaged foods brands based on Retail Audit and Panel data HR Analytics for one of the largest employers in India: People performance metrics modeling, Attrition modeling and Salary Intelligence HR Analytics for India’s largest Assessments company: Talent Pool Supply-Demand Modeling, Capability-Effectiveness models
    • about Consumer research A F&B Industry focused Private Equity fund: Consumer / Brand Perception Studies to evaluate two leading Fine Dining chains for potential investments A Global Education Major: Sizing up the BPO markets in Pakistan and Sri Lanka for the group to design entry strategies One of India’s largest fine dining restaurant chains: Consumer Perception, Feedback analysis and Mystery Customer Exercises An Indian Fortune 500 Petro-major: Retail Chain Set-up – 56 feasibility studies to date, Auto-LPG sales potential studies, Retail Outlet facility due diligence India arm of a global luxury brand: Mystery Shopper Exercise across 7 cities and 9 stores – first ever store evaluation in India An Indian Fortune 500 Petro-major : ‘Oil Conservation Fortnight’ Effectiveness Studies, Retail Audits, ‘Non-Fuel Options’ Study at Retail Outlets
    • Descriptive analytics Actionable Customer Segmentation and Profiling
    • Customer segmentation/profiling The rare breed – rarely eat out (less times than once a month) A negligible percentage of the sample (3.2%) Equal no of respondents split between impulsive and planned decision making Distance traveled anywhere between next door to > 10km Most prefer Indian Cuisine - buffet/steak & grill and prefer fine dining to casual Spend: Rs. 70 to Rs. 3,000 a meal on an outing Most are under 25 to 30 years and are from the salaried class The gregarious – eat out once a month – mostly with friends Decision equally split between impulsive and planned, most travel between 2 to 10 km Like Indian cuisine best, followed by both western and eastern cuisines Dislike fast food but have an equally good inclination to have all other food types Do not distinguish much between casual/fine dining styles Young – predominantly under 25, most are salaried Spend:Rs.1,000 to Rs.2,500 The moderates – eat out once a fortnight Most have a plan to dine out but all might not have decided on the restaurant to visit Travel between 2 to 10 km Prefer Indian but have an equally good inclination to go for the other cusines, like the buffet and the BBQ Prefer the casual to the fine dining style Spend: Rs. 1000 to Rs. 2500 Are from the business/salaried class
    • Customer segmentation/profiling Email SMS Mobile Home Number Office Number Number of Respondents Others Weekday Weekend Time of week
    • Customer Loyalty Analytics Micro-segmentation, Attrition, Reward Milestone Modeling
    • Attrition Modeling Interval between visits 11 days 22 days 36 days 52 days 66 days 82 days > 100 days October 630 61 November 1252 423 29 December 1874 1000 189 29 January 1493 1608 462 146 26 February 862 1639 658 248 99 19 4 March 958 1878 842 383 168 73 25 April 914 1870 779 376 202 88 76 Base: 21,383 High frequency visitors made more visits before attrition Member visits numbered between 3 and 18 Assumptions: 1. Enrollments up to April considered 2. Members not transacting for the second time after being enrolled, until July ’04
    • Attrition Modeling •Worrying Non-usage activity linear 5000 180.00 trend, a 4229 4500 4302 160.00 steady churn 163.78 4000 3735 of 3,500 to 3525 140.00 4,500 per 3500 3223 138.77 120.00 month could 3000 119.87 Members 100.00 be Days 2500 anticipated 90.36 80.00 2000 1751 •Rising ave 60.00 1500 65.08 lifetime 40.00 1000 691 suggests 44.36 early 500 26.64 20.00 members 0 0.00 losing October November December January February March April interest No of churns Ave Lifetime (Days)
    • Redemption Ready Members – 300 to 700 reward points 60000 End of gestation for a majority of members 50000 40000 30000 Equation predicts a rapid decline 20000 Surge in redemptions at this level 10000 0 Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul '03 '03 '03 '04 '04 '04 '04 '04 '04 '04 '04 '04 '04 '04 '04 '05 '05 '05 '05 '05 '05 '05 -10000
    • The next 2 months across milestones Member Eligibility: Predictions for the next two months 160,000 Flatter, steady growth 140,000 134,687 140,313 129,479 124,682 Number of eligible members 120,000 Rapid decline at low end 100,000 Faster growth enables higher level sweet spot 80,000 81,494 70,436 62,533 60,000 57,144 50,934 46,662 40,000 39,355 28,222 26,547 21,540 25,992 20,000 22,447 11,809 17,876 10,783 3,104 14,889 628 830 0 340 Nov '03 Dec '03 Jun '05 Jul '05 Aug '05 Sep '05 All eligible 300 to 700 700 to 2,000 2,000 to 5,000
    • Advanced Analytics for Decision Support Marketing Effectiveness modeling for a 8 billion USD company
    • Marketing Program Performance Incremental Sales, Volume and Margins Incremental Sales from the National Sales program (2002 through 2006) Each of the programs comprising the NSP has 1,300,000,000 contributed 1% or less to annual sales 1,200,000,000 All but two programs – Fall 1,100,000,000 Rebate 2002 and Spring Rebate 2006 – have 1,000,000,000 contributed significantly to sales but individual 900,000,000 programs are yet to yield 800,000,000 consistent y-o-y increments 2002 (1.42%*) 2003 (2.03%) 2004 (1.59%) 2005 (1.6%) 2006 (0%) Fall Warranty $3,795,387 Annually, off-season Fall Rebate $0 $9,313,176 $6,001,539 $11,947,742 programs add between $12 Spring Warranty $9,991,971 Spring Rebate $8,494,085 $10,735,324 $8,473,328 $0 million and $20 million to Annual $866,918,809 $950,862,630 $1,051,523,573 $1,274,812,840 $1,414,647,693 the top line
    • Marketing Program Performance Incremental Sales, Volume and Margins Incremental Sales from the National sales program (2002 through 2006) $160,000,000 $140,000,000 $120,000,000 $100,000,000 Sales ($) $80,000,000 $60,000,000 $40,000,000 $20,000,000 $0 Incr. Sales Program Sales Incr. Sales Program Sales Incr. Sales Program Sales Incr. Sales Program Sales Spring Rebate Spring Warranty Fall Rebate Fall Warranty 2006 $0 30,713,129 2005 $8,473,328 $80,833,729 $11,947,742 $24,596,407 2004 $10,735,324 $24,483,535 $6,001,539 $42,806,992 2003 $9,991,971 $30,576,490 $9,313,176 $37,921,499 2002 $8,494,085 $39,547,183 $0 $4,853,516 $3,795,387 19,499,789 Incremental sales due to the programs have grown over the years but for a dip during Fall 2004 As a proportion of sales tracked through respective programs, incremental sales have varied between 10% and 49% Except for the first Fall program (2002) and the 2006 Spring Warranty program, all others have generated significant incremental sales
    • Marketing Program Performance Incremental Sales, Volume and Margins What volumes do programs drive? 2006 0 [Spring Warranty] Spring Warranty in 2003 generated the most volumes over the 2005 8,829 10,450 Spring Rebate 4 years Spring Warranty Fall Rebate Again, Fall 2004 sees a 2004 9,890 5,329 Fall Warranty dip in volumes but the Fall season next year 2003 12,461 9,320 does very well 0 [Fall Rebate] Except in 2005, Spring 2002 9,685 4,993 programs have performed better than 0 5,000 10,000 15,000 20,000 25,000 Volume (number of units) the ones during Fall
    • Marketing Program Performance Programs that generate maximum Rate of Returns The 2 warranty programs Margins and Returns from the Programs have yielded good returns 50.00% 5,000,000 though, the second among 40.00% 35.52% 36.95% 4,000,000 the two works well on both Rate of Return (Net Margin / Incremental 30.00% 26.22% 31.91% 3,000,000 margins and returns 18.58% 20.00% 14.70% The 2005 Spring Rebate 12.31% 2,000,000 10.00% program yields Net Margin ($) 1,000,000 0.00% considerably high negative Sales) Spring Fall Spring Fall Rebate Spring Fall Rebate Spring Fall Rebate 0 -10.00% Rebate Warranty Warranty 2003 Rebate 2004 Rebate 2005 net margins and returns -20.00% 2002 2002 2003 2004 2005 -1,000,000 despite generating decent -2,000,000 incremental sales and -30.00% -3,000,000 margins -40.00% -53.15% -50.00% -4,000,000 The 2003 Spring Warranty program has, so -60.00% -5,000,000 far, fetched the best Rate of Returns NetMargin returns
    • Impact if program was not offered Change in mix of products sold Change in the mix of products sold if incentives were withdrawn 6.00% % drop in the number of units sold 5.00% 4.00% There is a likelihood of 3.00% about 4% to 5% lesser number of Premium 2.00% products being sold if the 1.00% programs were unavailable to 0.00% consumers. The drop is 2002 2003 2004 2005 Drop in Premium Products sold 2.78% 4.69% 4.99% 3.76% not as much for non- Drop in Non-Premium Products sold 1.42% 1.91% 0.88% 1.54% Premium products
    • Advanced Analytics for Decision Support Revenue management for a global B2B C.S.P.
    • PPU business concerns Usage Variance – Corporate Corporate Customers by Weekly Customer Code 5000 3782 6654 • Fluctuations are wild 8041 8084 across the 4000 9231 board, irrespective of quantum of usage Usage in minutes • Dips are prolonged in 3000 many cases; few large peaks 2000 • Trend observed in some cases while in others, growth is flat 1000 over the year 0 1 3 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 weeks
    • Forecasting Model Enterprise Observed 250,000 Fit Forecast 200,000 Usage in minutes 150,000 Minutes-Model_1 100,000 50,000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Month
    • Scenarios Forecasting Usage for an Enterprise Customer 350,000 300,000 250,000 200,000 Usage in Most Likely minutes 150,000 Optimistic Pessimistic 100,000 50,000 0 1st Month 2nd Month Next months
    • Statistics Correlations Correlations Meeting Minutes Port Enterprise customers Meeting Pearson Correlation 1 .969** .990** Sig. (2-tailed) .000 .000 • No of attendees (Ports in use) N 352 352 352 have a very high correlation with Minutes Pearson Correlation .969** 1 .957** Meetings (in the case illustrated Sig. (2-tailed) .000 .000 alongside) for Enterprise N 352 352 352 Port Pearson Correlation .990** .957** 1 customers. And in many cases, Sig. (2-tailed) .000 .000 they very strongly drive minutes N 352 352 352 as well **. Correlation is significant at the 0.01 level (2-tailed). • Meetings drive Minutes strongly Correlations in case of Corporate customers Meeting Minutes Port Meeting Pearson Correlation 1 .808** .783** • In essence, teams may drive Sig. (2-tailed) .000 .000 meetings in the enterprise N 196 196 196 segment while initiators drive Minutes Pearson Correlation .808** 1 .753** meetings – and hence minutes – Sig. (2-tailed) .000 .000 in case of corporate customers N 196 196 196 Port Pearson Correlation .783** .753** 1 Sig. (2-tailed) .000 .000 Corporate customers N 196 196 196 **. Correlation is significant at the 0.01 level (2-tailed).
    • XYZ Loyalty Program Enterprise Incentive Strategy Group Bonus: 5 additional XYZ marginal scope for improvement Loyalty Minutes for a High group meeting 4262 incentivise group meetings Activity Reward: 10% extra Port Utilization in Loyalty Minutes if > x ports are active in a month (could be relaxed in specific cases) 2226 9140 incentivise by number of ports used in a month 3372 4323 Low Low Number of Ports in Use High
    • Market Baskets for a Beauty & Fitness and an Online Business
    • Multivariate model Estimation Results Variable Sessn_Pkg Target_Pkg Bod_Frmr Full_Wxg FMP Direct Effects Intercept 0.89 1.79 2.04 0.83 -1.69 Loyalty 0.73 0.71 1.29 0.86 0.91 Time -0.21 -0.33 -0.26 -0.19 0.87 Price -0.76 -2.00 -0.88 -0.01 0.58 Offers 2.19 0.13 -0.39 0.38 0.09 Cross-Category Effects Size 2.12 2.12 2.01 2.27 2.27 Sessn_Pkg - -1.89 -1.93 -2.08 -1.67 Target_Pkg -1.89 - -3.02 -1.39 -1.72 Bod_Frmr -1.93 -3.02 - -1.70 -0.95 Full_Wxg -2.08 -1.39 -1.70 - -1.93 FMP -1.67 -1.72 -0.95 -1.93 -
    • Market Basket Analysis Suggestive Selling Tools for an Online Business Business Challenge arrive at purchase likelihood estimates across all products at unit and category levels visualization of the model along with prioritization of product pairs and triplets that sold well together
    • Market baskets – Category Confidence Stats for associations Supposed direction of cause-effect Stronger lines show higher degree of association Computers and Accessories Stationery 0.11 0.69 Watches & Clocks 0.37 0.06 0.12 0.58 Apparel and accessories Bags Electronics 0.29 0.48 0.19 0.22 Utilities Travel Bag, T-Shirt & Travel organizer
    • Market baskets – Products Confidence Stats for associations Photo Pen Holder Photo Frame 0.27 0.73 I Don't Sleep Round Neck T-Shirt - (Men) I Didn't Get Smarter 0.324 0.622 Round neck T-Shirt - (M) Arrow Shirt White 0.35 0.567 Black Polo Neck Van Heusen - Blue T-Shirt - (Men) 0. 35 Black I Didn't Get Smarter 0.54 Round neck T-Shirt - (M) Leather wallet with white stitch 0.35 0.486 Leather Pen Stand 0.432 White Polo Neck T-Shirt - (Men) Magic Calculator with Pen
    • Attrition Modeling Employee Attrition Models for one of India’s largest employers
    • Client-wise Profiling Distribution of clients and attrites across the attrition range 450 70.00% 400 60.00% 350 # of clients 50.00% attrites as a % of all associates % contribution to total attrition 300 250 40.00% % contribution to # of clients total attrition 200 30.00% 150 58 clients with modest attrition rates… 20.00% 100 10.00% 50 0 0.00% 0% 0.1% to 4.99% 5% to 14.99% 15% to 24.99% 25% to 34.99% 35% to 44.99% 45% to 54.99% 55% to 64.99% 65% to 74.99% 75% to 84.99% 85% to 94.99% 95% to 100% % Attrition (Range)
    • Client-wise Profiling Distribution of associates & attrites across the attrition range 1,800 …contribute the highest to overall attrition 70.00% by sourcing in and losing massive numbers… 1,600 Mean # of 60.00% associates 1,400 50.00% attrites as a % of all associates % contribution to total attrition 1,200 % contribution Mean # of associates to total attrition 40.00% 1,000 800 …while 19 clients sourcing the second highest 30.00% average number of associates have relatively 600 higher attrition rates but contribute less than 20.00% 10% to overall attrition numbers 400 10.00% 200 0 0.00% 0% 0.1% to 4.99% 5% to 14.99% 15% to 24.99% 25% to 34.99% 35% to 44.99% 45% to 54.99% 55% to 64.99% 65% to 74.99% 75% to 84.99% 85% to 94.99% 95% to 100% % Attrition (Range)
    • Client-wise Profiling Clients with the worst attrition rates: 100% to 50% 100% attrition on a sizeable base of associates 3,000 110.00% 100.00% 2,500 90.00% 2,000 Attrition rates climb down with larger offtakes 80.00% 1,500 70.00% 1,000 R2 = 0.9603 60.00% 500 50.00% 0 40.00% Total # of associates Percentage_Attrited Poly. (Percentage_Attrited)
    • Designation-wise Profiling Designations with the worst attrition rates 6000 55.00% Attrition drops progressively, with relatively more popular designations… 50.00% 5000 45.00% 4000 40.00% 3000 R2 = 0.9906 35.00% 30.00% 2000 25.00% 1000 20.00% 0 15.00% Total # of associates Percentage_Attrited Poly. (Percentage_Attrited)
    • Contact: Arun Prabhu email: arun@be-in-touch.com Phone: (91)96202-71950; (91)(80) 3292 9411