Affinity Insight Retail Basket Analysis

Affinity Insight Retail Basket Analysis Shantanu Goswami Analytics and Insight for Retail ANALYTICS METHODOLOGY EXPERT CONSULTING Business consult...
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Affinity Insight Retail Basket Analysis Shantanu Goswami

Analytics and Insight for Retail ANALYTICS METHODOLOGY

EXPERT CONSULTING

Business consulting

Algorithms and dashboards

Data scientists

SAP HANA and SAP Business Objects

Technical consultants

TECHNOLOGY PLATFORM

RETAIL ANALYTICS TOOLS

Business-driven analysis approaches

SAP offers a comprehensive package of consulting, business content and technology to make retailers more analytical companies © 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Example Analysis: Evaluating New Product Launch Hypotheses • Not all flavors will be equally successful • Sales volumes after product launch are indicator for success

KPI Cockpit Shows that sales KPIs for All flavors are similar after 3 months

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

Conclusion Flavors with few repeat buyers won’t reach target shelf productivity and might reduce customer satisfaction © 2014 SAP AG or an SAP affiliate company. All rights reserved.

Actionable result Remove unpopular Flavors from assortment

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What are we Offering ?

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) Increasing volumes of POS data

Poor visibility into impact of promotions on overall sales

Create better promotions & offers

Unable to determine best & worst performing stores (per basket)

Analysis of individual customer Blind-spots into local basket sizes and revenue behavior is not possible

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

Maximize marketing spend & improve margin

Track hoarding behavior & net new customer growth

Determine which products are driving drag-along sales

Rationalize assortment

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Business overview

Main Points Predefined content designed by retail experts and data scientists

Business Methodology of analysis

Fast track of 6 weeks of start to finish of implementation

Workshops with retail experts and data scientists analyzing your data, offering additional content available

Affinity Insight is a starter pack of market basket analysis easily extendable with extensions.

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Technical overview

Main Points Minimal disruption of the IT landscape – one HANA machine, minimal interaction with other systems, no need for web-server Modular design, build for accommodating new extensions.

Designed for large data volumes (> 4 TB of data)

Up to date technology, HTML 5 and mobility readiness

In memory data model – no need for cubes and pre-computed values

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Analysis Example 1: Drag-along Sales Toy retailer Interested in effect of bicycle promotions on sales of other products 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. Conclusion We can quantify the drag-along sales that will be generated by a promotion on bicycles Affinity Insight allows to quantify drag-along sales © 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Analysis Example 2: Customer Behaviour Grocery retailer

Sales volume on promo

Net new customers

Cannibalization

Off promo

Hoarding

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 © 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Analysis Example 3: Effect of Brands Convenience Retailer Ask themselves: “Around which brand of soft drinks should we focus our assortment?” Units sold

Market basket profit (K GBP) *

Market basket profit per unit sold (GBP)

Coca Cola PET 500ml

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 © 2014 SAP AG or an SAP affiliate company. All rights reserved.

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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 Rio, for example, the shorts and flip flop offers may be great. In Sao Paulo , 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. © 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Use Cases Drag-along sales What is selling with what ?

Behaviour analytics Hoarding/ cannibalization Is your promotions targeting the right Customer ?

Effect of Brands Which brand is selling more Which brand is driving more margin

Location-wise affinity Which store

Which region Which category

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Additional Retail Content

Retail Analytics extensions of Affinity Insight Overview

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

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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 © 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Architecture

Architecture

Extensions Affinity Insight

SAP HANA

Html 5 running on any browser / mobile device

Value driver tree Key Item List Repeat purchase

HANA XS Engine

Customer analysis

http – based UI running on top of HANA

POS Data

Business rules

Specialized algorithms

SQL procedures

SAP Predictive Analysis library (PAL)

Affinity Insight Data Model Tables, Views, Attribute and Calculation views

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

Loyalty, Promo, Customer segmentation

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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

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Other use cases that our customers find interesting … Spotting Unsuccessful Products

Upselling

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

Forecasting of Demand*

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 © 2014 SAP AG or an SAP affiliate company. All rights reserved.

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Key take aways



Standalone on Hana – can work on SAP or non-SAP (POS) data



Easy & Fast to implement



Easy roadmap – start small and grow after realizing benefits



Works on transaction level and NOT aggregate data ( NO batch Jobs !! )



Used by category managers ( business ) and promotions managers

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

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© 2014 SAP AG or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG (or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additional trademark information and notices. Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP AG or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP AG or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP AG or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP AG or its affiliated companies have 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 AG’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP AG or its affiliated companies 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. 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 forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

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Thank you Contact information:

Shantanu Goswami Business Development SAP Data Sciences

[email protected]

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

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