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
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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
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Additional Retail Content
Retail Analytics extensions of Affinity Insight Overview
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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
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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
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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.