Affinity Insight Retail Basket Analysis Shantanu Goswami. SAP Data Science. 2014
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TECHNOLOGY PLATFORM RETAIL ANALYTICS TOOLS ANALYTICS METHODOLOGY Analytics and Insight for Retail EXPERT CONSULTING Business-driven analysis approaches Business consulting Algorithms and dashboards Data scientists SAP HANA and SAP Business Objects Technical consultants SAP offers a comprehensive package of consulting, business content and technology to make retailers more analytical companies 3
Competitive Advantage Degrees of Analytical Maturity Advanced Analytics Optimization Forecasting Modelling Data Mining Ad-hoc Analyses Standard Reporting Analytical Maturity SAP Data Science offers Solutions for statistical Analysis, Modelling and Optimization 4
Engagement Model ANALYSIS ON DEMAND Objective: Answer specific business questions by sophisticated data analysis Performed by PIO experts, based on advanced BI content, industry expertise and scientific education ENABLEMENT Objective: Foster fact-based decisionmaking throughout the company Target group: Category managers, business managers, marketing, Guided analyses using pre-defined reports and business methodology Engagement model is a mix of enabling business stakeholders to utilize reporting platform and performing complex analyses on demand 5
Analytics Methodology Examples
Analytics Methodology A proven analysis approach leads from business problems to actionable recommendations 7
Example Analysis: Evaluating New Product Launch Hypotheses Not all flavors will be equally successful Sales volumes after product launch are indicator for success Business Problem Launch of new product line with different flavors which flavors will be successful in the long run? Repeat purchase analysis Shows that some flavors have Significantly more repeat buyers than others New hypothesis After 3 months still many first-time buyers who try a new flavor out of curiosity KPI Cockpit Shows that sales KPIs for All flavors are similar after 3 months Conclusion Flavors with few repeat buyers won t reach target shelf productivity and might reduce customer satisfaction Actionable result Remove unpopular Flavors from assortment 10
Affinity Insight Affinity Analysis 2.0 is a tool for flexible sales analysis on market basket level, calculating e.g. Likelihood for two products to be sold together Average multiplicity of a SKU in a market basket Market basket values attached to specific SKUs Affinity Insights is one of the most flexible and sophisticated reports 11
Solution Scope Affinity Analysis 2.0 is a tool for sales analysis on market basket level Ad-hoc calculation of many different market basket KPIs Maximum flexibility for analysis in product hierarchies Intuitive user interface High performance computations powered by SAP In-memory technology Affinity Insight 2.0 allows flexible computations on market basket level 12
Affinity Insight 2.0 Transaction analysis on a market basket level using TLOG data What you face What you need Market Basket Analysis for Promotion Management : Affinity Insight 2.0 Identify top-selling products (by profitability) Create better promotions & offers Increasing volumes of POS data Poor visibility into impact of promotions on overall sales Unable to determine best & worst performing stores (per basket) Maximize marketing spend & improve margin Track hoarding behavior & net new customer growth Analysis of individual customer behavior is not possible Blind-spots into local basket sizes and revenue Determine which products are driving drag-along sales Rationalize assortment 13
Algorithms.. Customer View Retailer View People who purchased this also purchased Full join or Cartesian product technique. Different Categories. Many to Many relationships. 14
SAP Affinity Insight Product Demonstration & Use Cases SAP Data Science
Analysis Example 1: Drag-along Sales Toy retailer Interested in effect of bicycle promotions on sales of other products Conclusion Affinity Insight shows: Every fifth bicycle is sold together with a helmet. Strong correlation with bicycle size. Almost two third of bicycles are sold together with other equipment. We can quantify the drag-along sales that will be generated by a promotion on bicycles Affinity Insight allows to quantify drag-along sales 16
Off promo Hoarding Cannibalization Net new customers Sales volume on promo Analysis Example 2: Customer Behaviour Grocery retailer Wants to use promotions to change customer buying habits towards high value brands Affinity Insight shows: (real life data!) Quantify how average market basket multiplicity changes during promotion, allowing conclusions about how many net new customers were reached Price Basket Multiplicity* On promo Off promo On promo Off promo Red Bull 250ml 1,05 1,46 1,91 1,24 Coke 2l 1,43 2,05 1,61 1,11 * Basket Multiplicity indicates how often a certain SKU appears on average in those transactions that contain at least one unit of this SKU Quantify different effects of promotions 17
Analysis Example 3: Effect of Brands Convenience Retailer Ask themselves: Around which brand of soft drinks should we focus our assortment? Coca Cola PET 500ml Units sold Market basket profit (K GBP) * Market basket profit per unit sold (GBP) 5396 10.6 1.96 Coca Cola 330ml 3818 7.7 2.02 Diet Coke 500ml 4746 9.0 1.89 Pepsi 500ml 2372 2.4 1.01 Pepsi 600ml 3114 3.1 1.00 Capri Sun 1140 2.5 2.19 Affinity Insight shows: (real life data!) The market baskets of Coca Cola customers are twice as profitable as the market baskets of Pepsi customers * Market basket profit = Total profit of those transactions that contain the respective SKU Find out which brands attract the most profitable customers and use this knowledge in negotiation with your suppliers 18
Analysis Example 4 : Location wise Affinity Which store in which region is displaying what affinities in which segment?.. And is that profitable? Enables you to A big question for retailers is the effectiveness of the offers at a REGIONAL level. In Devon, for example, the shorts and flip flop offers may be great. In Manchester, however, not so much. This is easy to know.. BUT - consider 10,000 SKUs across 1000 locations across 100 categories.. HQ may not always be aware of the more subtle differences. 19
Use Cases Drag-along sales Behaviour analytics - What is selling with what? Hoarding/ cannibalization Is your promotions targeting the right Customer? Effect of Brands Location-wise affinity Which brand is selling more Which brand is driving more margin Which store Which category Which region 20
Retail Analytics Content as CDP SAP Data Science
Retail Analytics extensions of Affinity Insight Overview 22
Customer Segment Purchase Analysis* Functionality Review the buying behavior of pre-defined customer segments Visualize business KPIs over time per customer segment Analyze which segments are over- or underrepresented within the customers of a certain product(group) Use cases Understand which customer segments are responsible for an increase or decline in revenues Analyze how well product assortment caters for different customer segments Understand the customer composition at specific stores Mock-up: Final implementation may differ * This report is specified and will be technically implemented in the HANA / Business Objects platform on customer request Customer Segment Purchase Analysis helps to understand behavior of customer segments at store and SKU-level 23
Customer Segment Attribute Analysis* Functionality Visualize numeric attributes of a segment (average age, household size, RFM score, ) in a scatter plot Display distribution of discrete attributes (gender, marital status, ) in a bar chart Use cases Understand where customer segments differ and where they overlap Mock-up: Final implementation may differ * This report is specified and will be technically implemented in the HANA / Business Objects platform on customer request Customer Segment Attribute Analysis shows how segments differ in all available attributes 24
Value Driver Tree* Functionality The Value Driver Tree computes changes in KPIs over time and makes transparent how they affect each other. Use case Allows to quickly analyze root causes for revenue and profit changes in any set of stores and on any product hierarchy level. Value drivers / KPIs Profit Revenues Gross margin Avg. basket size # of transactions Promotion share Items per basket Average price per item Shopping frequency Mock-up: Final implementation may differ * This report is specified and will be technically... implemented in the HANA / Business Objects platform on customer request The Value Driver Tree allows quick guided root cause analysis for performance changes in stores or product groups 25
Key Item List* Functionality Quickly identify and monitor the most important products or product groups in your shops or your assortment. Generate new rankings on the fly by changing weight factors of different KPIs Metrics Revenues Profit Unit Sales Distinct buyers Average basket size Average basket profit Mock-up: Final implementation may differ * This report is specified and will be technically implemented in the HANA / Business Objects platform on customer request The Key Item List shows a users most critical SKUs or product groups at a glance 26
Repeat Purchase Analysis* Functionality Shows how often customers repeatedly purchase a specific product or product group, and how many customers have purchased it for the first, second, third, etc. time in a given time frame. Example use cases Evaluate customer loyalty to specific brands or products Separate successful advertisement (first time buyers) from successful products (repeat buyers) Better understand effect of length and frequency of promotions Mock-up: Final implementation may differ * This report is specified and will be technically implemented in the HANA / Business Objects platform on customer request Repeat Purchase Analysis shows if customers purchase the same products repeatedly and how frequently 27
Product extensions as Custom Development * * This are specified and will be technically implemented in the HANA / Business Objects platform on customer request Customer Segment Purchase Analysis Customer Segment Purchase Analysis helps to understand behavior of customer segments at store and SKU-level Customer Segment Attribute Analysis Customer Segment Attribute Analysis shows how segments differ in all available attributes Value Driver Tree The Value Driver Tree allows quick guided root cause analysis for performance changes in stores or product groups Key Item List The Key Item List shows a users most critical SKUs or product groups at a glance Repeat Purchase Analysis Repeat Purchase Analysis shows if customers purchase the same products repeatedly and how frequently 28
AI - Architecture
Architecture Affinity Insight Html 5 running on any browser / mobile device SAP HANA HANA XS Engine http based UI running on top of HANA Extensions Value driver tree Key Item List Repeat purchase Customer analysis Business rules SQL procedures Specialized algorithms SAP Predictive Analysis library (PAL) POS Data Affinity Insight Data Model Tables, Views, Attribute and Calculation views Loyalty, Promo, Customer segmentation 30
Benefits
Reduction of analysis effort Reduction of manual effort by taking off dedicated analysts Reduction of effort (wait / new queries ) by business users Retirement of home grown solution Reduction of cost of outsourcing analytics 32
Other use cases that our customers find interesting Upselling Spotting Unsuccessful Products Forecasting of Demand* Finding out top-sellers and their affinities help the retailer to position the right upsell opportunity by store, and by product Increasing the basket revenue/ profit per store Finding out the best and worst performing SKU and either ensuring all stores have the best or the worst taken off the store. Helping assortment rationalization and increasing revenue Tracking temperature changes and correlating demand through modelling. Forecasting demand with temperature changes. Saving inventory costs, improving supply chain and increasing revenue * In combination with SAP Predictive Analysis 33
Business Benefits More revenue, better margins, less marketing spend Better understanding of categories, product hierarchies vis-a-vis customer segments More effective promotion management outputs can be inputs to promotion management system Creating the foundations of an analytics driven organization Championing the analytics-for-all mantra rather than only for strategic indicators and power users Future proof technology platform with expert services Fixed-price, fixed-scope, fixed-time deliverables Innovation-on-demand partner 34
Thank you Contact information: Shantanu Goswami Business Development SAP Data Sciences Shantanu.goswami@sap.com 2014 SAP AG or an SAP affiliate company. All rights reserved.
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