Big Data en Retail

Big Data In Retail Shantanu Goswami

1990 20132000 2005 2010

ANALYTICS(CIRCA 1980)

PREDICTIVEANALYTICS(CIRCA 1980)

SEMANTICANALYTICS(CIRCA 1980)

How we got to Big Data

REAL TIMEIN THECLOUD

PERSONAL COMPUTERAND CLIENT SERVER

DATABASE(CIRCA 1980)

WWW MOBILE

SOCIAL

B2B/B2C

BIGDATA

REAL TIME

1,000,000+SOLD

3,000,000people had access toInternet worldwide

More people havemobile phones thanelectricity or safedrinking water

Facebook: 1 billion users; 600 mobile users; morethan 42 million pages and 9 million appsYoutube: 4 billion views per dayGoogle+: 400 million registered usersSkype: 250 million monthly connected users

2015

Opportunity to unlock value from Big Data

* “Big Data: Next frontier for innovation, competition, and productivity”McKinsey study

Customer DataCompetitive Prices

GPS

Promotion Planning

Speed

Hot Trends

Omni Channel

Store Enhancements

Employee Records

Suppliers

Purchase Orders

MerchandiseEmails

Tweets

Planning

Social Media

Mobile

Instant Messages

Optimal Channel Assortment

60%potential increase inretailers’ operating

margin with big data*

What if you could turnnew signals into business value?

:-)Brand Sentiment

360O Customer View

ProductRecommendation

Propensity to Churn Real-time Demand/Supply Forecast

Predictive Maintenance

Fraud Detection

Network Optimization Insider Threats

Risk Mitigation,Real-time

Asset Tracking Personalized Care

4

© 2013 SAP AG. All rights reserved. 5

DIGITALLYCONNECTED

SOCIALLYNETWORKED

HIGHLYINFORMED

A new echelon of customers havetransformed the business landscape for retailers

While retail objectives remain the same

Provide seamlesscustomer experienceacross all channels

Deliver optimal offersbased on the right

product, at the rightprice, via the right

channel, at the righttime

Improve saleseffectiveness, increaserevenue potential forcross-sell and up-sell

SAP makes Big Data Real

Big DataPlatform

DataScience

Accelerate Apply Achieve

Big DataAnalytics &

Apps

Big Data successdemands full coverage

Accessible

Deep

SimpleRealTime

Broad

• Answer complex questionson granular data

• Predict the best next action

• On any device orto any user

• Self service and intuitiveinteractions

• No data preparation• No pre-aggregates• No tuning

• Real-time streams of data• Ask a question, get an immediate

answer

• Massive data scale• Many data types

Next generation businessplatform for real-time retail

One PlatformBringing It AllTogether

In-Memory

BusinessTransactions

DigitalConnections

CollaborativeBusiness

CloudSocial

Big DataMobile

AdvancedAnalytics

SAP Big Data Platform

Big Data Platform Big Data Science

Real Time Real Value Real Results

Big DataAnalytics & Apps

SAP: Real-time, with real results

10

Big Data Retail Applications

Make Big Data

insights actionable

via industry specific,

business focused

applications from

SAP and its

partners.

11

HybrisOmniChannel

Insights

:-)AudienceDiscovery

(ADT)

PromotionsManagement

(PMR)

FraudManagement

SalesInsights

Social ContactIntelligence

(CEI)

SentimentIntelligence (RDS)

Forecast &Replenishment

Planning for Retail

SAP Analytics for Big Data

ENGAGE PREDICTVISUALIZE

SAP BI SAP Lumira SAP Predictive Analysis &SAP InfiniteInsightRapidly connect

individuals, data, andprocesses to understandthe business and drive

better decisions

Intuitively explore andpresent data to reveal

new insights at-a-glance

Confidently anticipatewhat comes next to drivebetter business outcomes

EMPOWER ALLPEOPLE TO GET

THEIR QUESTIONSANSWERED

SAP MAKES BIG DATA REAL

ANTICIPATESCONSUMERBEHAVIOR


BIG DATA GIVESSHOPPERSFASHION

ADVICE TOFIT THEIR

STYLE


LISTEN.TAP INTO THE

STREAMING VOICES OFPEOPLE, PROCESSES,

AND THINGS

SENTIMENT.

PERSONAL

OFFERS AND

CUSTOMERCARE

BASED ONTHOUGHTS

EXPRESSED

PERSONALIZE.

BUILD LOYALTY WITHUNIQUE EXPERIENCES

TAIILOREDFOR EACHCUSTOMER

RECOMMEND.

PREDICT INDIVIDUAL’SFUTURE PURCHASES

ON BROWSINGAND SHOPPING

BEHAVIOUR

APPLY TOOPERATIONS.

SHOPPERSGET FASHIONADVICE THATFITS THEIR

STYLE

ACHIEVEBREAK-OUTRESULTS.

PROGRESSMEASURED

USING MODELOPTIMAL FOR

MANAGER’S KPI


How to start ?


People & Skills GovernanceStandards &Processes

Information &Application ArchitectureUse Cases

How mature are your capabilities?Take our online assessment here: https://valuemanagement.sap.com/BigData2

No Big DataCapabilities

Search for asignificant UseCase in big data

Understand thebig picture fromall available data

Generate changein response to shiftsin data automaticallyor manually

Use big Data to predictoutcomes and adjustprocesses accordingly

INC

RE

AS

ING

MAT

UR

ITY

BUSINESS VALUE EXTRACTION THROUGH DATA INSIGHT

Big Data maturity model


Practical 5 step guide to getting value from Big Data

1. Define Your Use Cases

• Don’t forget to look outside, apply design thinking, prioritize

2. Discover Your Data

• Check under the bed, don’t overlook the ordinary, mine opportunities in machine data

3. Design Your Business Case and Technical Architecture

• Source, acquire, transform, stage, model, view, exploit

4. Do an Initial Deployment

• Actualize the benefit calculation; determine core and edge issues; learn and iterate

5. Make Big Data a Business Capability

• Integrate into processes, measure effectiveness


A practical example


Latency, Confusion and the lack of Granular Insights ..

Data Scientists

Detail levelNo transactional data available, only aggregated?IT support and overnight processes required?Details missing for ad-hoc analysis per stores andproduct/category?

Time & costSales analysis only available with time lag, not in real-time?Data analysis done only by specialists or externally?Time-consuming KPI calculations, low flexibility?

Business impactsReactive business decisions due totime lag in analysis availability?Deeper insights for assortmentoptimization delayed or missing?Higher costs for time-consuming dataanalysis?

CorporateOperations

Marketing

Planning

Buying

Supply Chain

Web, Mobile,Catalog

Stores

StoreOperations

SAP Sales Insights for RetailBASKET ANALYSIS. ASSORTMENT ANALYSIS . PROMOTIONS ANALYSIS

• Assortment Analysis - Spotting Unsuccessful products

• Basket Analysis - Drag along effect

• Evaluation of promotions – Vendor Analysis


Customer Challenges

Which is the best / worst selling product

Which is the best running / worst store?

How successful are the promotions ?

How to change the assortment ?

What are the Root causes for revenue changes ?

Key Queries that are difficult to answer immediately ..


SAP Sales Insights for Retail

SAP Sales Insights for Retail is an analysis toolbox tailor-made for POSdata models and retail business problems


Where can we co-innovate with you ?

Promotions Analyzer

Fraud Management

Shelf Space Optimization

Repeat Purchase Analysis

Slow moving goods

Price Elasticity

Cannibalization

Hoarding Behaviour

Zone Optimization

Size Optimization


SAP’s Retail Innovation Platform

Pricing

Fraud Detection

Assortment

Cannibalization

Hoarding

New Customers

Shelf Space

SAP CAR Data Model SAP Sales Insights for RetailLoyalty data Promotion Stock

HANA procedures, calculation view and specialized algorithms from SAPPredictive Analysis Library (PAL)

on-the-fly

Predefined Content – SalesInsights for Retail

SAP Innovation Extensions

External Extension and Innovation

Ad-hoc Analysis

POS data Vendor Funding


Business Impacts

The 3 topics of Assortments, Basket Analysis & Promotions Analysis contributes to150 – 200 BPS to the topline.

The solution addresses

• Reduced analysis effort

• Fixing problems at underperforming stores faster

• Fixing problems at underperforming product categories faster

• Identifying success & failure earlier in new product introductions

• Ability to have out-of-the-box , retail specific content

• Extensions/Innovation ideas from SAP, free to innovate independently

• Lower entry point (LREA) and can extend with value selling (Enterprise)

• Flexible deployment models ( Both On-premise & HEC )


Customer Co-Innovation Stories

Denmark

€7.5B Turnover.

43000

employees.

Departmental

store.

1000+ stores.

Customer runs a series of differentpromotion aimed at generating moretransactions and customer loyaltyacross the different store chains.Subsequently, it runs various types ofpromotions in their retail chains: Weeklyleaflets, Hit of the month product, TVcampaigns or in store promotions.

By gathering data about POStransaction and promotional data, theSAP Promo Excellence Tool tries toclassify each promotion into severaldistinct classes (Good promotion,Expensive promotion, Low impactpromotion).

This is a Co-Innovation we are pilotingon top of the Sales Insights on Hanaimplementation.


Customer Co-Innovation Stories in Mexico

Mexico

€1B Turnover

5000

employees

176

Supermarts.

Grupo Merza uses SAP Sales Insights for Retailto identify and monitor their most importantproducts, categories, or stores to relevant KPIs,such as distinct buyers, units sales, profit, orrevenue.

With this information they can better understandthe impact of each SKU in their productassortment so they can drive market basket sizeand profit.

SAP Sales Insights for Retail also helps GrupoMerza compute how key performanceindicators (KPIs) such as average basket size,number of items per basket, shopping frequency,or promotions impact each other.

By monitoring these KPIs, Grupo Merza canquickly perform root-cause analysis wheneverthey observe unexpected revenue changes in astore or product category. With rapid insight,Grupo Merza can make adjustments quickly inpromotions or product assortment to ensureprofit.

InnovationPlatform

DataScience

Accelerate Apply Achieve

Analytics &Apps

Why SAP ?


Real TimeAnalytics

MultisourceData Model

Retail Specific

Content

Customextensions

Key Differentiation

Gracias !!Shantanu GoswamiDirector – Retail Sales Motions

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Big Data en Retail

Documents

andpresent data

data preparation

streams of data

business value

sap ag

understandthe business

business landscape

retail shantanu goswami1990