Affinity Insight Retail Basket Analysis

Transcription

1 Affinity Insight Retail Basket Analysis Shantanu Goswami. SAP Data Science. 2014

2 Legal disclaimer The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP. This presentation is not subject to your license agreement or any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation and SAP's strategy and possible future developments, products and or platforms directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice. The information in this document is not a commitment, promise or legal obligation to deliver any material, code or functionality. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. This document is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP s willful misconduct or gross negligence. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forwardlooking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions. 2

3 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

4 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

5 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

6 Analytics Methodology Examples

7 Analytics Methodology A proven analysis approach leads from business problems to actionable recommendations 7

8 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

9 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

10 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

11 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

12 Algorithms.. Customer View Retailer View People who purchased this also purchased Full join or Cartesian product technique. Different Categories. Many to Many relationships. 14

13 SAP Affinity Insight Product Demonstration & Use Cases SAP Data Science

14 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

15 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

16 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) Coca Cola 330ml Diet Coke 500ml Pepsi 500ml Pepsi 600ml Capri Sun 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

17 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

18 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

19 Retail Analytics Content as CDP SAP Data Science

20 Retail Analytics extensions of Affinity Insight Overview 22

21 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

22 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

23 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

24 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

25 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

26 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

27 AI - Architecture

28 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

29 Benefits

30 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

31 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

32 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

33 Thank you Contact information: Shantanu Goswami Business Development SAP Data Sciences 2014 SAP AG or an SAP affiliate company. All rights reserved.

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