DOMINICK'S and THE UNIVERSITY OF CHICAGO Graduate School of Business. Micro-Marketing Project Presentation. March 26, 1992

Transcription

1 DOMINICK'S and THE UNIVERSITY OF CHICAGO Graduate School of Business Micro-Marketing Project Presentation for : PHILIP MORRIS U.S.A. Phase I : Shelf Management Results March 26, 1992

2 Philip Morris U.S.A. Agenda March 26, Marketing Information Revolution 2. Micro-Merchandising Re-Cap 3. Quick Review of Demographic Clusters 4. Experimental Design Rationale 5. Data Analysis Methodolgy 6. Experiment Results and Recommendations 7. Phase II : Pricing Experiment Recommendation 8. Next Steps

3 Part 1 : Marketing Information Revolution

4 Marketing Information Revolution 1. Marketing is responsible for growing the business by adapting the organization to changes in the environment. 2. The marketing environment continues to become more information intensive. 3. The GSB's focus is on understanding, developing, and implementing marketing decision technologies.

5 Part 2 : Micro-Merchandising Re-cap

6 What is Micro-Merchandising? ~l Implementation of store-by-store merchandising and promotion tactics tailored to the consumer and competitive characteristics of the individual store's trading area 0 Single chain-wide merchandising replaced by customized micro-trading region tactics

7 Why Micro-Merchandising? ~I The Information Revolution A Makes it easier to recognize and meet consumer wants and needs ~/ Intensification of Competition 0 Retailer and manufacturer competition 0 The need to better leverage existing promotional dollars

8 What Does It Take to Make Micro-Merchandising Happen? ~l Seek a common ground between retailers and manufacturers Manufacturers > Brands < Buyers. ~ I I Product Categories I I I Stores < Retailer HQs ~I A Partnership based on Fairness 0 Goal becomes category profitability

9 Strategic Partnering The Keys to Micro-Merchandising Success 1. Commitment : Both retailer and manufacturer must make long-term investments in information technology. 2. Strategy Alignment : Retailer and manufacturer must agree on product category strategies. This requires compromise on both sides to establish profit maximizing objectives for sales growth, gross margin requirements, and promotional activity. 3. Discipline : Retailers and manufacturers must adopt a longterm perspective and be willing to stick to the micromerchandising strategy. 4. Teamwork : Both parties must participate in planogram development, maintenance, and in-store implementation. 5. Information Sharing : The retailer's local market movement data can facilitate effective inventory mamagement. Manufacturers' sophisticated market research is needed to identify unmet market opportunities.

10 Costs and Benefits of Shelf-Management Costs (1) Labor (design and reset) HQ Sto re Manufacturer (2) Maintenance Category volatility Manufacturer cooperation Benefits (1) Improved shelf management Take better advantage of advanced plan-o-gram technology (2) Improved retail pricing policies Everyday pricing Promotional pricing

11 Part 3 : Quick Review of Demographic Clusters

12 Implementing Micro-Merchandising at the Store Level Clusterina of stores > Aggregate into like-in-kind store groups > Aggregate because (1) Similarity between stores (2) Limitations of historical data (3) Resource limitations at HQ or store level

13 Clustering Technology (1) Demographic Clustering (2) Category Movement Clustering

14 Demographic Clustering Methodoloay : Cluster grocery stores based on similar demographics of each store's trading area. Key demographics used : Household size Number and age of children Education levels Age of household head Income DFF price zone competition 0 Ethnic background

15 Dominick's Store Cluster Description Cluster A: Established Suburban Families Cluster B : City Dwellers Medium-sized households Small-sized households Married households (50% with children) Few married households Older children (6-17 years) Few children Few singles Lots of young singles (20-29 years) High education level (36% college + ed.) Medium education level (30% college + ed.) Some seniors Some seniors Middle-aged adults (30-45 years) Few middle-aged adults (30-45 years) Dual income families Few dual income families Higher income (45% $50,000+) Lower income (42% $20,000 or less) Moderate price zone competition Low price zone competition Substantial Black and Hispanic Cluster C : Ethnic Neighborhoods Cluster D : Prospering Suburban Families Medium-sized households Large households Married households Nuclear families Few children (older 6-17 years) Many children Few singles Few singles Low education level (blue collar) High education level (35% college + ed.) Many seniors Few seniors Middle-age adults (35-45 years) Younger adults Few dual income families Many dual income families Lower-middle income (80% less than Higher income (44% $50,000+) $50,000) Very competitive price zone Moderate price zone competition

16 Category Movement Clustering Methodology : Cluster stores based on similar subcategory movement differences. Unique cluster groups would be created for each category. (1) Determine sub-categories. (2) Determine movement for each sub-category by sto re. (3) Cluster stores on the basis of sub-category movement patterns into like-in-kind groups. (4) Gather movement data on each SKU for stores within the given cluster.

17 Part 4 : Experimental Design Rationale

18 Cigarettes Historical Perspective 1. Background Data a Historical "Individual Pack" scanner data was not available at Dominick's. ~l Prior to July Individual packs were entered under a PLU code. b. Philip Morris had access to A. C. Nielsen data that indicated urban/suburban consumer behavior differences among individual branded packs. 2. Current Dominick's Finer Foods POG ~l RJR was currently renting the cigarette fixtures from Dominick's. ~l One POG for entire chain blocked horizontally by brand by RJ R. ~1 Fixtures were located in 36 of the 60 designated micromerchandising stores. 3. Relatively stable category ~ Moderate promotion/on-pack premium activity

19 Cigarettes Experiment Objectives Objective #1 Match product assortment to buyer preferences ~l Allocate space based on cluster movement differences from A. C. Nielsen data. ~l Three clusters were chosen : two suburban based clusters and one urban cluster. Objective #2 Manipulate display space on top of fixtures to match urban/suburban preferences.

20 Cigarettes Experiment Designs Objectives Experiment Designs Match cigarette brand SPACE-TO-MOVEMENT assortment to movement Feature alternating MICRO-MERCHANDISING brands on top of rack DISPLAYS every two weeks based on urban/suburban differences

21 Cigarettes Basic Shelf Configuration for Treatments SPACE-TO-MOVEMENT TREATMENT More Marl boro Newport Carl ton Other Left Side Winston VirSlims Salem B&H Other Front CONTROL TREATMENT Kool Merit Other Right Side RJR POG RJR POG RJR POG Left Side Front Right Side Facings and product positions varied across the Space-To-Movement (STM) treatment POGs. The control R. J. Reynolds POG was constant across all control stores.

22 Cigarettes Experiment Execution Step 1 : Thirty-seven of the sixty micro-merchandising Dominick's stores had cigarette fixtures. Step 2 : Clusters A & D were treated as individual suburban clusters (28 stores total). These stores were randomly assigned to two cells : a) Suburban space-to-movement (STM) set b) Control set Clusters B & C were designated as one urban cluster (8 stores total). These stores were randomly assigned to two cells : a) Urban space-to-movement (STM) set b) Control set 'STM' Control Set Test Set Suburban stores Cluster A 8 7 Cluster D 6 7 Urban stores Clusters B & C* 4 4_ Total Stores *Store 93 was assigned to the test set originally, but was dropped from the study due to store remodeling which prevented cigarette fixtures from being used in that store.

23 Cigarettes Experiment Execution (continued) Step 3 : Based on A. C. Nielsen data for the assigned stores in Clusters A, B, C, and D. Philip Morris developed 3 planograms : ~l Cluster A POG ~l Cluster D POG ~1 Urban POG : Based on Clusters B & C movement combined. Step 4 : Nineteen resets were conducted by Philip Morris. (June 7 - June 10, 1991). Step 5 : Data analysis : 16 weeks (July 25 - November 13, 1991) Historical analysis : 97 weeks (September 14, July 24, 1991) ~l Based on PLU scanner data for pricevalue, generics and premium brands. Note : Promotional Displays were unreliable based on in-store weekly audit checks. Therefore, the promotion display test was not analyzed.

24 Part 5 : Data Analysis Methodology

25 Analysis of Experimental Data Methodoloav : Separate promotional sales from every-day sales in order to isolate effects of plan-o-gram changes. Goal : Build a sales response model calibrated on preexperimental data for each category. > Focus on biggest selling UPC's (e.g., the top items in a category)

26 Sources of Data (1) Dominick's Weekly store-level scanner data by UPC > Sales > Retail price > Margin > DFF deal code (2) Information Resources Inc : (Chicago info Scan Data) > % ACV feature & display (at DFF) > % ACV display (at (DFF) > % ACV feature (at DFF) > Incremental sales of the subcategory in DFF > Incremental sales of subcategory in competitive stores (3) University of Chicago_ : (Weekly store audits) > In-store display > Weekly POG integrity

27 Model specification ~1 Log-log model of unit movement for - 25 categories 0 Price elasticities for each U PC (top 10-20) 0 Price elasticities for each store (86 stores) 0 Promotional activity variables 0 Incremental sales variables d - 85 weeks of pre-experimental data

28 Summary of Results All Categories (1) Good fit (R2=.76 to.94) (2) Large promotional effects in most categories Feature & Display : >500% increase (frozen entrees > canned soup) Display Only: >120% increase (soft drinks > paper towels) Feature Only : 35% increase (frozen beverage > toothpaste) Price Reduction Only : 7% increase (bottled juice > bath tissue) (3) Price elasticities vary by sku cookies more so than crackers brand elasticities are sensitive to model specification (4) Price elasticities vary by store high variance : toothpaste, tuna, fr juice, paper towels low variance : analgesics, auto dish, HDL, candy (5) Small substitution effects

29 Part 6 : Experimental Results and Recommendations

30 Analysis of Experimental Data Compare average sales in the experimental period to average sales in the historical period 0 Experimental - historical average weekly sales Experimental period : July 25, 1991 to November 13, 1991 Historical period : September 14, 1989 to July 24, 1991

31 Cigarettes Average Weekly Historical Sales Levels* Cluster Control Stores STM Test Stores A $2220 $2520 B&C D Weighted Average $2520 $2480 Across Stores *Sales data is based on 36 Dominick's stores that were observed in the study. Historical time period is 09/14/89 to 07/24/91.

32 Cigarettes Experiment Results by Cluster B (Experimental - Historical) Sales Control Space-To- Movement Test Test-Control AF %02 A$ %A A$ %0 Cluster A (suburban) % % % B & C (urban) % % % D (suburban) % % % Average % % % (across all clusters) 10$ = Average Weekly Sales : Experimental - Historical Weeks 2 0% = (Experimental Sales - Historical Sales)/Historical Sales

33 Cigarettes Test Results by Manufacturer Adjusted Experimental Period Sales* Manufacturer Control $Sales %Sales Test $Sales Difference %Sales $Sales Philip Morris 1, % 1, % 198 R.J. Reynolds % % -5 Other Manf % % -15 Total Category $2, % $2, % $178 *Adjusted for: (1) historical differences in sales ; (2) unscanned single packs registered under plu keys.

34 Cigarettes Test Results by Brand Adjusted Experimental Period Sales* Control Test Difference Brand $Sales %Sales $Sales %Sales $Sales Benson & Hedges % % 0 Marlboro % % 97 Merit % % 5 Virginia Slims % % 40 Other PM % % 56 Total PM 1, % 1, % 198 Camel % % -20 Now % % 30 Salem % % -34 Winston % % 29 Other RJ R % % -10 Total RJR % % -5 *Adjusted for : (1) historical differences in sales ; (2) unscanned single packs registered under plu keys.

35 Cigarettes Test Results by Brand Adjusted Experimental Period Sales* (Continued) Control Test Difference Brand $Sales %Sales $Sales %Sales Sales Newport % % -8 True % % -3 Kool % % -15 Carlton % % 8 All Others % % 3 Total Others % % -15 Total Category % % $178 ~ 0 ~ *Adjusted for : (1) historical differences in sales ; ~ (2) unscanned single packs registered under plu keys. c~ w

36 Cigarettes Summary of Results Results : Matching space allocation to consumer buying preferences matters in the cigarette category. ~1 Overall sales dollars increased by 6-7% within each cluster, (on average $178 per store per week). ~I Philip Morris market share increased by 4%, ($198 sales per store per week). ~I R. J. Reynolds market share decreased by 2%, ($-5 sales per store per week). Recommendation : ~ Keep the current test POGs and continue to use spaceto-movement technology to design cluster based plano-grams. ~l Put respective test POGs in control stores that were assigned to Clusters A, B, C, & D.

37 Part 7 : Phase II - Pricing Ideas

38 Phase I! : Pricing DFF currently makes pricing decisions based on the competition. DFF should take into account differences in price sensitivity. Two part pricing decision (1) Price individual UPC's according to observed differences in price elasticities (2) Adjust individual store's prices using differences in price sensitivity by store

39 Cigarettes : Single Packs : Weekly DFF Dollars Sales N Weekly DFF Dollar Sales 10 week moving averi ge I I N Y 0 0 J N 1T sst0g Week /20/91

40 Cigarettes : Single Packs : Weekly DFF Sales in Packs /14/ /20/91 39M88M Week

41 Cigarettes : Single Packs : Unweighted Average Price per Pack Unweighted Average Price per Pack 10 week moving average : Experimen~ N N 0 N...,.. : :.::::. :< :.,,.:, :.:.,:..:,:.....,:5 ::, t ` 1 CD /14/89 69M8SP0Z /20/91 Week

42 Cigarettes : Single Packs : Weighted Average Price per Pack Average DFF Price per Pack 10 week moving average N N O N CD /14/89 b~~9888~0~ Week /20/91

43 Cigarettes - Cartons : Weekly DFF Dollars Sales /14/89 %M88t0g Week /20/91

44 Cigarettes - Cartons : Weekly DFF Sales in Packs

45 Cigarettes - Cartons : Unweighted Average Price per Pack CV 0 CV Unweighted Average Price per Pack 10 week moving average : Experimen~l 0) I : :. :.., :....., :_..: :.. :.:., r- (0 LO /14/89 E 4 ~9mf U g Week I 80 T- -] /20/91

46 Cigarettes - Cartons : Weighted Average Price per Pack 0 CV Average DFF Price per Pack 10 week moving average ; Experimen~- 0) M I 0 9/14/89 - I tmsmg Week I I /20/91

47 Cigarettes : Weekly DFF Dollars Sales i I Weekly DFF Dollar Sales 10 week moving average LO I N I I A N 0 0 ~ 0 J /14/89 ~~~9888~b0~ Week /20/91

48 Cigarettes : Weekly DFF Sales in Packs 0 9/14/89 94"98s5f0g Week /20/91

49 Cigarettes : Unweighted Average Price per Pack N CV Unweighted Average Price per Pack 10 week moving average : Experimen~- N E 0 N m OD 0 9/14/89 ' 4~'98~8~0~ Week /20/91

50 Cigarettes : Weighted Average Price per Pack N CV I Average DFF Price per Pack 10 week moving average N 0 N (Y) 00 r /14/89 84~'98s8f0g Week I ~ /20/91

51 Cigarettes Historical Model Price Sensitivity Observations 1) We estimated price sensitivities separately for cartons and single packs across brands. Product Form Price Elasticity Cartons Single packs A 10% price increase on cartons leads to a 20.4% decrease in unit sales. o A 10% price increase on single packs leads to a 11.5 /o decrease in unit sales. 2) Conclusion : Cartons are more price sensitive than single packs by a magnitude of 100%.

52 Cigarettes Store-Level Price Elasticity 1) For the 22 product categories in the DFF/UofC Micro-Merchandising Project, we estimated separate store-level price elasticities. 2) Across the categories, we find distinct price sensitivity profiles for each store. > Some stores are very price sensitive, while others are price insensitive 3) The major determinant of store price "personality" is the characteristics of the consumer population in the trading area, not competition. > Elderly - more price sensitive > Higher education - less price sensitive > Lower income - more price sensitive > Large households - more price sensitive > Ethnics - more price sensitive

53 Cigarettes Everyday Pricing Experiment #1 Changes in Single Pack Prices Changes in Carton Prices 10% 0'% -10% *n = 10 stores per cell ~1 This design simultaneously manipulates two factors : 0 Overall category price levels o The relative price of single packs vs cartons

54 Cigarettes Everyday Pricing Experiment #2 1) Determine cross-category store-level price sensitivity. 2) Assign stores to high, medium, and low price sensitivity clusters. 3) Change everyday prices by +10/-10% of cartons and single packs. Category Level Price Change Store Price Sensitivity Low Medium High n-* *n=15 store per cell ~l This design tests whether Philip Morris and DFF can micromarket on the basis of store-level price sensitivity.

55 Part 8 : Next Steps

56 NEXT STEPS PHASE I : SHELF MANAGEMENT 1. Final Shelf Management Report will be delivered in April, PHASE II : PRICING EXPERIMENT 1. Philip Morris needs to discuss the GSB's pricing ideas and other pricing objectives internally in April, We will regroup with appropriate Philip Morris designers to finalize the pricing experiments in May, Roll-out pricing experiment in July, 1992.