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EXPLICATO: PROVIDING ADDED VALUE FOR THE

RETAIL

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This deck is a top-level summary of Explicato’s capabilities and IT research, analytics and development team and our understanding how to bring value to our retail customers.

Business model: overview

Research and consulting services

DWH and BI platform: key benefits

01

03

Sample Reports and Analytics 04

Social media analytics 05

02

Intro &Content

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ExplicatoValue Added Proposition

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01

We focus on the relations between retailers and customers combining business analytics and IT technologies

Deep understanding ofcustomers’ motivationalperceptions, discoveringdifferent consumersegments by economy anddemographiccharacteristics

Precise estimation of thepotential of a business modelmeasuring customer volumesby segments, territories andsocial groups

Design of the rightstrategies based on theright statistics, analyticsand knowledge aboutcustomers and thedecision drivers thataffect them

External consultancy andproject managementstarting from the businessdiscovery to the UAT phasesensures that the IT solutionsand implementation willmeet the needs of thebusiness

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MarketResearch

o Unique research methodologyUnlike other research practices, our methodology is focused on the end customer. All

market researches in the FMCG retail field usually aim to analyze the statistics for sales of goods by: categories, brands, territories and economy environment trends. We provide the missing extra dimension - the customer. We strongly believe that knowledge of the customer as a person with his/her motivational drivers, expectations and satisfaction levels is key to retail success.

o All retail industries coveredOur research methods allow for post research analysis to discover consumer behavior

across industries. This approach provides perspective on how the customer spends his/her overall budget and discovers opportunities for cross-loyalty partner networks.

Supermarkets GAS STATIONS CLOTHES & ACCESSORIES

02

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o Customers’ motivational perceptions insightsThe customer is much more than a statistics unit. Various drivers are affecting his/her

decisions and different approaches can affect customers from one group (e.g. demographic) in a completely different ways because of uncovered reasons.

Base N=1000, All respondents

4

1

4

5

5

7

7

8

23

36

2

0

2

3

8

12

2

11

27

32

Current Loyalty…Future loyalty program

I can control my spends better

Gives me chance to buy the most modern and popular products on the market

Gives me chance to buy the best quality products on the market

Helps me feel I am part of a special group clients

Gives me chance to buy products, which are not available on the general market

Opportunity to gain bonuses which I can spent when I want

Makes me feel more sure when choosing particular items

I can use one and the same loyalty program across different entities

Makes the shopping pleasure last longer

I don’t have any benefits

02MarketResearch

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Total Participants Non-participants

0

13

18

21

23

24

28

30

3335

40

42

53

56

60

67

67

74

0 50 100

Other

Own brands

Enough…

Good…

All brands…

Products…

Parking lot

Long…

Loyalty…

Good…

Overall…

Good…

Fresh…

Good location

Product…

Big variety…

Lower prices

Quality…

1

16

20

23

23

24

28

29

36

41

4143

53

55

67

68

68

73

0 50 100

Other

Own brands

Enough…

All brands…

Good…

Products…

Parking lot

Long…

Good…

Overall…

Loyalty…

Good…

Good…

Fresh…

Product…

Big variety…

Lower prices

Quality…

1

5

816

17

19

23

25

29

29

35

36

38

46

57

60

63

69

0 50 100

Other

Own brands

Loyalty…

Enough…

Good…

All brands…

Parking lot

Products…

Good…

Long…

Good…

Overall…

Product…

Fresh…

Big variety…

Good…

Lower prices

Quality…

MarketResearch

Main drivers affecting the decision “where to go shopping

02

o Customer drivers researchThe drivers that affect customers’ decisions are not predefined constants but variables

that may be influenced through a proactive approach. You can not change the location of a store easily, but you could make other drivers more important for the customer.

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Potential Assessment

Definitely yes32%

Most probably

yes34%

Not sure23%

Most probably

not7%

Definitely not4%

Definitelyyes39%Most

probably yes38%

Not sure17%

Most probably

not3%

Definitelynot3%

0%

5%

8%

12%

23%

92%

Other

Mobile app

Client's profile…

Chip-card

Casher receipt

Plastic card

Total Already participating

Main decision triggers (top 2 boxes)

Main Communication Channels

Most preferred technical device

Base N=1008, All respondents

85% 91% 80% 75% 72%55%

3%

Pri

cere

du

ctio

n o

fp

arti

cula

r…

Pri

cere

du

ctio

n o

fal

l pro

du

cts

Acc

ess

tosp

ecia

l pri

zega

mes

an

d…

Acc

um

ula

tio

no

f b

on

us

po

ints

fo

r…

Vau

cher

s o

rp

rize

sex

chan

gab

l…

Lott

ary

and

pri

ze g

ames

Oth

ers

3%

3%

5%

6%

8%

12%

13%

13%

16%

19%

27%

28%

55%

55%

77%

Radio news

Not interested

Articles in electronic media

Special articles in printed…

TV news

Company employees

Radio Ads

WEB sites

Ads in printed media

Social Networks

Billboards/Printed ads

Internet

TV ads

Friends/Relatives

Printed materials in shops

Base N=665,Would participate in future loyalty program

02

o Drill-down into retailer-specific findingsBased on our research we can extract specific findings for a specific retailer and

determine the segments of customers which are recognized as target groups.

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LoyaltyStrategy Design

Retailer’s

sales data

Customer basket

analysis

DED

ICA

TED

CA

MP

AIG

NS

Other SystemsLoyalty SystemPOS System

Retailer IT

infrastructure

Transactional sales

and loyalty data

Incremental data loads

following own designed

data model Points

Earn & burn

Discounts

Promo

Information

Privilege

02

o Discovery and establishment of value chains

between retailers and customersLoyalty results from establishing strong value chains between retailers and customers.

For the customer 'value' means much more than discounts, bonuses and vouchers - it also means access to special offers designed with deep understanding for their particular needs, information, and privilege attitudes.

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ExplicatoDWH & BI

03

o Predefined database models of retail specifics Our deep knowledge in retail and IT environment helped us develop a uniform

database model giving ready-to-use solutions to retailers from the start and a platform fordevelopment of advanced analytic tools following the specifics of a particular organization. Thepredefined database model can reduce time and costs for BI integration up to 90%.

o Cloud based solutionNo need for big investments in servers, storage, software licenses and administration.

Practically no risk of data loss along with the highest standards for data security.

o ETL that is easy to adapt to rough data sourcesThe ETL extracting data from retail systems is designed to cover various retail system

standards like POS, BOS, HOIS, Loyalty, etc. Our goal is to have a pre-developed ETL for the mostcommon systems in a given market to reduce the integration time and cost to the minimum.

o Advanced analyticsOur goal is not just to provide the next-in-row reporting solution but real value for the

business by advanced analytics. We believe that Big Data should not be seen as the purpose, but as a tool to reach deep understanding of the retail business.

o Fully web-based front end panelWe offer practically unlimited ways to access the BI front end using your laptop, tablet

or smart phone. No licenses for desktop applications, no requirements for devices, no additional costs for support and maintenance. All this, with access fully secured by SSL certificates.

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Sales by Store and Product Sales of Promoted Items

Sample Dashboard for the Store Manager Activity by Cardholder

ExplicatoDWH & BI

04

o Operational report samples IOperational reports cover sales and loyalty activities and build on the reports of

existing systems like POS, while avoiding full duplication.

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Store performance by visits, sales and average purchase

Store performance by hour of the day (filter by weekday and period)

Cashier performance

Activity by Cardhoder

At-glance store status for the store manager ( to be included in the dashboard)

Customers Total Sales, Th Average Purchase Points Issued Points Burnt Points Balance

862 937,795 1,088 15,500 22,000 523,4658% 2% -5% 3% 47% 0%

04

o Operational report samples IIThe reports are designed to provide management with a full view of operations and

their efficiency.

ExplicatoDWH & BI

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Analysis of promotional activity – sales before, during and after a promo

Effect of promotion on age group by average weekly purchase

Targeting a promo campaign to specific customers Bonus points earned and burnt by age group for a time period

o Analytical report samplesAnalytical reports aim at providing the right tool for actionable insight covering the mix

of sales, loyalty and promotional activities.

04ExplicatoDWH & BI

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Coverage and Technology

Social media Analytics

05

o Integrated social media and analysis toolsExplicato and its development team can currently integrate the following social media and analytical tools:

Facebook;Adform;Gemius;Google (Adwords and Analytics);Sklik.

o How it worksDetailed data is collected from a specific social media page, e.g. Carrefour’s Facebook profile and not from the

Internet in general. The connection is established through configurable connectors (via APIs). The company providescredentials for their profile to be used by the connector and a data collection engine is set to run on a specified timeinterval (hours, days, etc.). The data is initially stored in its original form and then aggregated in accordance to therequirements of the client. Data grouping and transformation is addressed in a flexible and customized way so new dataelements can be added without additional development effort as well as to meet changes in user requirements for the dataaggregation. The processed data is stored in a database for convenient use and further analysis.

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Social media Analytics

05

Data and Analytics Potential

o Type of collected dataThe engine collects data for:

- Page likes;- Post likes;- Number and content of post comments;- User data aggregated by age group, geographical factors, gender;Note that specific user data such as login name is not provided by social media sites.

o Data usabilityAll dimensions of collected social data could be crossed in any possible way to produce valuable insight for the

specific needs of a customer. The data could be analyzed on its own or in conjunction with sales and loyalty data for deeperand broader actionable insight. Example of such analyses include.

- Popularity of the company profile and its dynamics over time;- Brand awareness;- Website user profile by demographics and locations;- Activity of web-site user groups in terms of visits, likes and comments for specific time intervals or posts;- Targeting of promotional campaigns;- Promotional campaign analytics - effectiveness and success;- Text analytics to explore the user reaction to a post (promotional message, product review, etc.);- Introduction of new products or product bundles;- Test success of a retail activity through analysis of the comments.

Combining data for sales, loyalty and activity on the social media page could produce a universe or possibilities for ananalytical-minded organization and many options to explore for the less analysis-savvy ones.

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ValueChain