Affinity Insight Retail Basket Analysis

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Affinity Insight

Retail Basket Analysis

Shantanu Goswami. SAP Data Science . 2014

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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

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Legal disclaimer

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ANALYTICS METHODOLOGY EXPERT

CONSULTING

RETAIL ANALYTICS TOOLS

TECHNOLOGY PLATFORM

Analytics and Insight for Retail

SAP offers a comprehensive package of consulting, business content and technology to make retailers more analytical companies

Algorithms and dashboards

SAP HANA and SAP Business Objects

Business-driven analysis approaches Business consulting

Data scientists

Technical consultants

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Degrees of Analytical Maturity

SAP Data Science offers Solutions for statistical Analysis, Modelling and Optimization

„Advanced Analytics“

Competitive Advantage

Analytical Maturity Standard Reporting

Ad-hoc Analyses Data Mining

Modelling

Optimization

Forecasting

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Engagement Model

Engagement model is a mix of enabling business stakeholders to utilize reporting platform and performing complex analyses on demand

ANALYSIS ON DEMAND ENABLEMENT

 Objective: Foster fact-based decision- making throughout the company

 Target group: Category managers, business managers, marketing, …

 Guided analyses using pre-defined reports and business methodology

 Objective: Answer specific business questions by sophisticated data analysis

 Performed by PIO experts, based on advanced BI content, industry

expertise and scientific education

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Analytics Methodology

Examples

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Analytics Methodology

A proven analysis approach leads from business problems to actionable recommendations

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Example Analysis: Evaluating New Product Launch

Business Problem

Launch of new product line with different “flavors” –

which flavors will be successful in the long run?

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

New hypothesis

After 3 months still many first-time buyers who try a new flavor out of curiosity Repeat purchase analysis

Shows that some flavors have Significantly more repeat buyers than others

Actionable result Remove unpopular Flavors from assortment Conclusion

Flavors with few repeat buyers won’t reach target shelf productivity and might reduce customer

satisfaction

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Affinity Insight

Affinity Insights is one of the most flexible and sophisticated reports

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

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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

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What you need

Affinity Insight 2.0

Transaction analysis on a market basket level using TLOG data

What you face

Increasing volumes of POS data

Unable to determine best

& worst performing stores (per basket)

Blind-spots into local basket sizes and revenue Poor visibility into

impact of promotions on

overall sales

Analysis of individual customer behavior is not possible

Determine which products are driving drag-along sales Maximize marketing spend &

improve margin

Rationalize assortment Identify top-selling products

(by profitability)

Create better promotions & offers

Track hoarding behavior & net new customer growth

Market Basket Analysis for Promotion Management : Affinity Insight 2.0

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Algorithms ..

Full join or Cartesian product technique.

Different Categories. Many to Many relationships.

“People who purchased this also purchased …”

Customer View Retailer View

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SAP Affinity Insight

Product Demonstration & Use Cases

SAP Data Science

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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 allows to quantify drag-along sales 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

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Analysis Example 2: Customer Behaviour

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

Grocery retailer

Wants to use promotions to change customer buying habits towards high value brands

Quantify different effects of promotions

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

Off promo Sales volume on promo

Hoarding Cannibalization Net new customers

* Basket Multiplicity indicates how often a certain SKU appears on average in those transactions that contain at least one unit of this SKU

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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?”

Find out which brands attract the most profitable customers – and use this knowledge in negotiation with your suppliers

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

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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 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.

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Use Cases

Drag-along sales Behaviour analytics -

Hoarding/ cannibalization

Effect of Brands Location-wise affinity

Which store

Which region Which category

Which brand is selling more

Which brand is driving more margin What is selling with what ?

Is your promotions targeting the right Customer ?

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SAP Data Science

Retail Analytics Content as CDP

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Retail Analytics extensions of Affinity Insight Overview

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Customer Segment Purchase Analysis*

Customer Segment Purchase Analysis helps to understand behavior of customer segments at store and SKU-level

Functionality

Review the buying behavior of pre-defined customer segments

 Visualize business KPIs over time per customer segment

 Analyze which segments are over- or under- represented 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

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Customer Segment Attribute Analysis*

Customer Segment Attribute Analysis shows how segments differ in all available attributes

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

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Value Driver Tree*

The Value Driver Tree allows quick guided root cause analysis for performance changes in stores or product groups

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

 ...

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Key Item List*

The Key Item List shows a users most critical SKUs or product groups at a glance

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

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Repeat Purchase Analysis*

Repeat Purchase Analysis shows if customers purchase the same products repeatedly and how frequently

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

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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

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AI - Architecture

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Architecture

Loyalty, Promo, Customer segmentation

POS Data Affinity Insight Data Model

Tables, Views, Attribute and Calculation views

SAP HANA

Business rules

SQL procedures

Specialized algorithms

SAP Predictive Analysis library (PAL)

HANA XS Engine

http – based UI running on top of HANA

Extensions

Value driver tree Key Item List Repeat purchase Customer analysis

Affinity Insight

Html 5 running on any browser / mobile device

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Benefits

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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 …

Upselling Spotting

Unsuccessful Products

Forecasting of Demand*

* In combination with SAP Predictive Analysis

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

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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

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Thank you

Contact information:

Shantanu Goswami

Business Development SAP Data Sciences

[email protected]

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