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Cross shop analysis

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Here all you need to know about market basket analysis or Product Affinity

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  • 1. Cross Shop/Product Affinity/Market Basket Analysis http://www.Analytics-Tools.com Analytics-Tools.com
  • 2. Insights & Introduction Metrics Involved Process ApplicationWhat Are Co – Purchase Affinities ?  A measure of how likely two products are to be purchased in same transaction/order or Basket. This is why this analysis is also called Market Basket analysis  Some products have natural affinities for example Bread & butter, Shoe &Polish, hamburgers & Fries etc.  There are also some which are not the most obvious like Beer & Diapers, Tuna & Toothpaste, Barbie dolls & some types of candy bars etc. These non-obvious affinities can only be discovered by analyzing the dataObvious Items #1 & #2 Affinity is seen in Green orders Each Box is an OrderNon Obvious Item #3
  • 3. Insights & Introduction Metrics Involved Process ApplicationWhy Assess Product Affinity ?  Lot of interesting insights can be drawn out by analyzing consumers purchase basket. These insights can then be deployed across business units to improve overall profitability, productivity and consumer experience  An important thing to keep in mind while analyzing baskets is to remove the effect of impulse purchases  Driver items (items which lead to purchase of other items) identification can also be done by understanding co purchase behavior.  Purchases of other products with drivers products is mostly done in the same order but for few products (technology products) may be purchased after a certain period of time (defined by the business, more on slide 10) Insights at this 1 level help in Understand Purchase Behavior Power Category layout decisions 2 Stages of Application Develop Cross Promotional Programs Department Applicability 3 Redesigning Store Layout / Design Category Insights at this 4 Discount Plans / Promotions level help in Sub Category merchandise 5 decisions Plano gram Designing 6 Fine line / SKU Driver Items which lead to sales
  • 4. Insights & Introduction Metrics Involved Process ApplicationWhat Metrics Are Involved?  Support indicates prevalence of a product(s) Out of all transactions N Bread how often bread is bought ? N Total 1 Support Out of all transactions N Butter how often butter is bought ? N Total Out of all Indicator of transactions how N Bread  Butter prevalence often both bread of a product(s) and butter were NTotal bought ?
  • 5. Insights & Introduction Metrics Involved Process ApplicationWhat Metrics Are Involved?  Confidence Indicates predictability of one product given other product is already in the basket How predictable is Bread given Butter in same basket ? 2 Support Bread N Bread Confidence Support BreadAndButter N Bread  Butter How predictable is Butter given Bread in same basket ? Indicator of predictability of one product given Support Butter N Butter other product Support BreadAndButter N Bread  Butter
  • 6. Insights & Introduction Metrics Involved Process ApplicationWhat Metrics Are Involved?  Lift indicates the likelihood of two products going together What is the likelihood of Bread and Butter in same basket ? 3 Lift ConfidenceBread ConfidenceButter SupportButter SupportBread Indicator of likelihood of two NTotal N Bread Butter product going together N Bread N Buter
  • 7. Insights & Introduction Metrics Involved Process ApplicationWhat Metrics Are Involved?  Illustration of lift computation Metric Description Number of Transactions with Bread 100 Proportion of transactions Number of Transactions with Butter 400 Support of Bread with Bread Number of Transactions with Bread and Butter 50 Proportion of transactions Support of Butter with Butter Total number of Transactions 1,000 Proportion of transactions Support of Bread and Butter with Bread and Butter (Support of Bread & Butter) Measure Description Confidence of Bread / (Support of Bread) Support of Bread 100/1000 = 0.1 ((Support of Bread & Butter) Confidence of Butter Support of Butter 400/1000 = 0.4 / (Support of Butter) Support of Bread and Butter 50/1000 = 0.05 (Proportion of transactions with Bread and Butter) / Confidence of Bread 0.05/0.1 = 0.5 (Expected Number of Lift transactions with Bread and Confidence of Butter 0.05/0.4 = 0.125 Butter if not related) 0.05(0.1*0.4) = 1.25 (Confidence of Butter) / Lift (Support of Bread only) 0.125/0.1 = 1.25
  • 8. Insights & Introduction Metrics Involved Process ApplicationWhat is the process?Theory 1 Driver Product Product Purchased because 2 3 of Driver Product 30 Days Driver product drives purchase for this period Mar 12 Mar 13 Mar 14 Mar 18 May 011 March 12 Product and its driver product purchased in the same order2 March 14 qualifies as driver product purchased was purchased March 13 th, within 30 days3 May 01 does not qualify as the driver product was purchased more than 30 days back.Following are the steps involved in the process Transaction 1 2 3 4 5 6 Data Transaction Data is If existence Stratified Final SAS SAS codes / data is cleaned, of segments sampling is datasets are VBA macros sourced sliced diced with unique done over created for are run to to avoid purchase the dataset each generate aberrant behavior is for analysis consumer product seasonal seen, viability segment affinity maps behavior datasets are for each split for them segment
  • 9. Insights & Introduction Metrics Involved Process ApplicationWhat is the process? Digital Photography without Printers Drives & Misc without HD and USB Circuit Protection Cables Etc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Multi-Function Devices Mass Storage - Other Avg Category Sales - Portable Computers Desktop Computers Computer Monitors Avg Basket Sales - % Single Category Product Services (Cherry Picking) Single Category Multi Category Flash Memory Inkjet Printers Transactions Transactions GPS Devices PC Cameras PC Memory Networking MP3 Row # Affinity Category 1 Portable Computers 25.6% $781 $970 H M H M L H L L H M L M H M L 2 Desktop Computers 17.5% $611 $819 M H H M H M L L H H M M H 3 Product Services 8.1% $31 $325 H H H L H M M H H M H H M M H H 4 PC Memory 52.7% $107 $160 M M L H L L M L M 5 Computer Monitors 43.4% $257 $398 L H H L H L L L M M L M M L 6 Networking 50.2% $69 $125 H M M L L H L I L H L M L I 7 Mass Storage - Other 49.4% $105 $152 L L M M L L H L L L M L 8 Digital Photography without Printers 16.9% $212 $295 L L H L I H M H L L L 9 Inkjet Printers 18.9% $109 $234 H H H M L M H L H L I 10 Flash Memory 43.0% $47 $111 M L H L H L L L 11 Circuit Protection Cables Etc 33.7% $31 $127 M H H M H L H H I L M H 12 GPS Devices 58.5% $262 $296 L H L I H I L 13 PC Cameras 46.9% $61 $122 M M M L L L L L L L H M 14 Drives & Misc without HD and USB 40.4% $44 $106 H M M M M M M L M M H L 15 Multi-Function Devices 19.0% $183 $285 M H H M L L H I L H 16 MP3 50.9% $101 $143 L H L I L L I L L H Legend H High likelihood of purchasing together  One easy way to visualize product affinity is to create M Moderate likelihood of purchasing together Product Affinity Maps L Low likelihood of purchasing together May or may not be purchased together  Product affinity maps have categories as columns and I Infrequently purchased togeher rows. The color of the cells tells how often the two R Rarely purchased together categories (row-column) have been purchased together
  • 10. Insights & Introduction Metrics Involved Process ApplicationHow to separate impulse behavior with externalities? Affinity grids are superimposed over grids with information on externalities1 2 Mens Wear Swim Wear Boys Wear Mens Wear Swim Wear Sleepwear Boys Wear Sleepwear Intimates Intimates Hosiery Infants Hosiery Ladies Infants Shoes Socks Wear Ladies Shoes Socks Wear Mens Wear M M H H M M M I R Mens Wear P P P NP NP P NP P P Boys Wear M H I H H H R M M Boys Wear P P NP NP P P P NP P Shoes M H M M H M H H H Shoes P P P P P NP P NP NP Infants H I M L I H H H M Infants P NP P P NP NP P P NP Socks H H M L M M M H M Socks NP NP P P P P NP NP NP Hosiery M H H I M H H I H Hosiery NP P P NP P P P NP NP Sleepwear M H M H M H M M L Sleepwear P P NP NP P P P P P Intimates M R H H M H M H L Intimates NP P P P NP P P P NP Ladies Wear I M H H H I M H M Ladies Wear P NP NP P NP NP P P P Swim Wear R M H M M H L L M Swim Wear P P NP NP NP NP P NP P Promotion done for categories No Promotion done for categories Hosiery Intimat Sleepw Infants Ladies Mens Shoes Socks Wear Wear Wear Wear Swim Boys ear es 1 Mens Wear  Boys Wear  Shoes   Products that demonstrate impulse Infants  based higher affinity Socks    Hosiery  Sleepwear  Intimates 2 Ladies Wear   Swim Wear  
  • 11. Insights & Introduction Metrics Involved Process ApplicationHow to read insights ?Organizational focus on Profitability  Place high affinity products at an optimal High distance so that Customer satisfaction is not  Place High affinity products at a distance compromised  Place high margin / impulsive and related products in between  Irrelevant case as organization has shed $ for  Place all high affinity products together analysis Low Organizational focus on Customer Satisfaction HighLets take the example of Barbie and Candy bar. Placement- Highest margin candy can be placed near dolls or as Marketers know, that for every extra minute of quality time that one spends in a store, there is a high degree of likelihood that that person will spend one extra dollar in that store (research states that this is true for large retail stores). With this in mind, one can place Barbie dolls in one corner (Toys section) and Candy in a section that is further away from the Toys section. And, in the path that lies between these two sections, place special Kid items - which may be either promotion items, high margin items, retailers own brand items etc. Promotions- By increasing the price of Barbie doll and giving the type of candy bar free, special promotions for Barbie dolls with candy at a slightly higher margin, coupons for dolls and candy Discounts from Manufactures- Exploit discovered associations with the companies who manufacture the products with tie-ins

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