SNUG - 27TH FEBRUARY 2014 RETAIL MARKETING ANALYTICS OVERVIEW

Transcription

1 SNUG - 27TH FEBRUARY 2014 RETAIL MARKETING ANALYTICS OVERVIEW

2 WHO IS THIS GUY? Tim Manns a) Principal Analytics Consultant b) Since 1998 (employment history: Clementine/Modeler, SPSS, Optus, SAS) c) All industries, all analytics.

3 PROOF OF CONCEPT - PROJECT OBJECTIVES Proof Of Concept (POC) project. Project duration: 25 days effort. - using SAS software, analyse point-of-sale data with a focus on member activity. The project deliverables fell into three principal phases: 1. Member Insights & Profiling Census demographics surrounding stores Member segmentation 2. Predictive Analysis Forecast future member spend Predict member churn Recommend next best product 3. Market Basket Analysis Product associations Price sensitivity

4 HIGHLIGHTS FOR SNUG 1. Project Overview 2. Demographics & Summary 3. Predictive Analysis 4. Basket Analysis 5. Customer Insights

5 ANALYTICS OVERVIEW Basic demographic analysis was conducted using publicly available data from the 2011 National Census, and Australian Taxation Office postcode summary tax returns Data includes age, gender, income, rent & mortgage amounts etc s are compared in relation to the demographic profile of the households in surrounding postcodes

6 DEMOGRAPHICS SURROUNDING STORES For some stores there are significant differences in the demographic profiles of surrounding postcodes. For example the average % of individuals aged 0-4 in surrounding postcodes of the Redbank store can be compared to the average store nationally. In this case there is a 49% greater proportion of 0-4 year olds. Difference From 60% 40% 20% 0% 20% 40% 60% Redbank Age 0 4 Age 5 14 Age Age Age Age Age Age Age 65+ Distribution Of Age Groups In Nearest Postcodes Above Below

7 DEMOGRAPHICS SURROUNDING STORES Difference From Difference From 140% 120% 100% 80% 60% 40% 20% 20% 0% 40% 60% 140% 120% 100% 80% 60% 40% 20% 0% 20% 40% 60% Brisbane Age 0 4 Age 5 14 Age Age Age Age Age Age Age 65+ Distribution Of Age Groups In Nearest Postcodes Redbank Age 0 4 Age 5 14 Age Age Age Age Age Age Age 65+ Distribution Of Age Groups In Nearest Postcodes Above Below Above Below

8 STORE DEMOGRAPHICS VS SALES BY CATEGORY Brisbane 103% more (over double) Age in surrounding postcodes than average store. Difference From 120% 90% 60% 30% 0% 30% 60% Age 0 4 Age 5 14 Age Age Age Age Age Age Age 65+ Distribution Of Age Groups In Nearest Postcodes Above Below 65% greater proportion of Sundry purchases than average store. 37% greater proportion of Intimate Apparel purchases. % Of Sales By Category 80% 60% 40% 20% 0% 20% 40% 60% SUNDRY SECTION INTIMATE APPAREL LADIESWEAR GENERAL MERCHANDISE FOOTWEAR & ACCESSORIES MENSWEAR ENTERTAINMENT HOMEWARES TOYS BABY & BASICS CHILDRENSWEAR Above Below

9 STORE DEMOGRAPHICS VS SALES BY CATEGORY Redbank 49% more Age 0-4 in surrounding postcodes than the average store. 43% more skew to Baby & Basics in store (as percentage of overall store sales) than average store. Difference From % Of Sales By Category 60% 40% 20% 0% 20% 40% 60% 50% 40% 30% 20% 10% 0% 10% 20% Age 0 4 Age 5 14 Age Age Age Age Age Age Age 65+ Above Below Above Below

10 PREDICTIVE ANALYSIS

11 MEMBER PREDICTIVE ANALYTICS Deliverable 2: Predictive Analysis. Predict customer value using time series forecasting. In order to accurately forecast future member spend we utilise multiple inputs including: The number of prior visits/baskets (recency & frequency) The cumulative prior spend (monetary) The number of items/products purchased in prior visits Customer segment (applied basket & product affinities)

12 MEMBER SPEND FORECAST Prediction of future $ spend for every member Churn risk is the difference between average forecast and recent forecasted spend Members with greatest decreasing spend can be targeted in campaigns. Optimises $ Member $ Spend Per Quarter Forecast is below average. Therefore lost revenue. High churn score Year Quarter Actual $ Sales Forecast $ Spend Forecast $ Spend Per Quarter Churn Score 50 is $ Lost Spend Forecast above average. Higher spend than historically. Low churn score Year Quarter Actual $ Sales Forecast $ Spend Forecast

13 BASKET ANALYSIS

14 BASKET ANALYSIS CHILDREN S UNDERWEAR Product affinities across the second highest product category grouping skuplus5 show that the strongest product pairings occur amongst similar products. For example product affinity for Children s Underwear is strongest to Children s Hosiery (although this is part of Intimate Apparel, not Children s Wear ). Skuplus6 Key Children s Wear Intimate Apparel Baby & Basics NURSERY CHILDRENS HOSIERY CHILDRENS UNDERWEAR CHILDRENS NIGHTWEAR BABYWEAR Examining product affinities (at skuplus5 category level) - within and across different product skuplus6 categories.

15 BASKET ANALYSIS FOR CHILDREN S WEAR Ideal input for activities to promote Next Best Product or increase basket diversity Skuplus5 Code Skuplus5 Description Skuplus6 Description Affinity Order TG CHILDRENS UNDERWEAR CHILDRENSWEAR 1 TG CHILDRENS HOSIERY INTIMATE Best APPAREL next product 2 TG BABYWEAR CHILDRENSWEAR Second next product 3 TG CHILDRENS NIGHTWEAR CHILDRENSWEAR Third next product 4 TG NURSERY BABY & BASICS 5 TG LICENCE CLOTHING CHILDRENSWEAR

16 PRICE ELASTICITY AND SENSITIVITY Price Elasticity Consumer demand of products varies depending upon price. The relationship between price and demand of goods if often referred to as Price Elasticity. Price Sensitive Members A member who typically purchases a product at its lower end price range can be considered Price Sensitive.

17 PRICE ELASTICITY & BASKET ANALYSIS One specific product (Children s Wear -> Girls > Pleated Dress -> TG ). Product price varies from $6.01 to $22.40 (mean) price of $15.40 Unit Price $25 $20 $15 $10 $5 Price Range 3 (15%) Some members buy when priced at Price SKU Price And Range Demand 2 (70% unit sales) Price Range 1 (15%) Some members buy when priced at $0 0% 9% 17% 26% 34% 43% 51% 60% 68% 77% 85% 94% % Of Units Sold

18 CUSTOMER

19 MEMBER SEGMENTATION Why Segment? -> differentiate members by product preferences and behaviours. Members can be allocated into segments based upon their basket purchases, and other metrics such as visit frequency, spend, and price sensitivity. 3.4 million members (all members that made transactions during and were tracked over time) were allocated to an behavioural segment. Members placed into one of 25 segments. Each segment is unique in some way.

20 MEMBER SEGMENTATION CHILDREN S WEAR $150 $125 Big Basket For Mum And Kids Monthly Kids Monthly Spend $100 $75 $50 $25 Churned One More Time Single Lady With Discount Undies Inactive Discounted Baby Monthly Family Every Two Weeks $0 Inactive Menswear Churned Bargain Hunter $25 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 16% 17% 18% 19% 20% Basket % Children's Wear

21 EXECUTIVE SUMMARY

22 NEW MEMBERS HONEYMOON OFFER During ,250 Members 1 st visit or >3 months since previous visit $64.61 (4.3 items) Currently no tailored communication vs Honeymoon Offer is presented 435,207 Members (50%) 2 nd visit within 3 months $ ,750 Members (60%) 2 nd visit within 3 months $63.29 Total $27.5m revenue from 2 nd visit Total $32.5m revenue from 2 nd visit Additional $5m revenue for 2 nd visit alone member visit frequency is monthly >$60m additional revenue over 12 months

23 NEW MEMBERS HONEYMOON OFFER During ,250 Members 1 st visit or >3 months since previous visit $64.61 (4.3 items) For example: Member 435, Members Currently no First visit: 23 rd Sept (50%) tailored nd visit Total $27.5m within 3 months revenue communication Purchased: Ladies Briefs; Free Fusion; Ladies Denim. $63.29 from 2 nd visit Spend: $68.20 (3 items) Next vs Best Offers: Bras; Hot Options Knits; Hot Options Woven; Ladies Casual Bottoms Honeymoon Offer 513,750 Members (60%) 2 nd visit Total $32.5m revenue is Second presentedvisit: 23 rd within Dec months from 2 Purchased: Bras; Hot Options Woven; Ladies Fashion nd visit $63.29 Accessories (x2) Spend: $75.90 (4 items) Next Best Offers: Ladies Briefs; Hot Options Knits; Women s Shoes

24 THANK YOU