The document outlines the significance of big data and analytics for retailers, emphasizing that nearly 70% view these solutions as vital for business success. It also discusses obstacles retailers face in implementing such solutions, mainly related to understanding their benefits and the complexity of implementation. The Explicato platform offers a cloud-based solution to streamline data processing and analytics, thereby addressing these challenges and providing valuable insights into customer behavior and business performance.

1. PROVIDING ADDED VALUE FOR THE RETAIL
Software-as-a-service Data processing and analytics
www.explicato.com

2. This deck summarizes on top level the capabilities of Explicato Big data and analytics platform
to provide insightful analysis to retailers without the challenges to meet the complexity of the
standard business intelligence solutions
www.explicato.com
Intro & Content
01
03
04
02
The retailers’ demand
or how retailers’ demand for analytics is growing
Implementation approach
what is necessary to make the system live for a particular project
Hidden insights in the data
or how analytics solution impacts the business
The Explicato SaaS Eco system
Or how we deliver the value avoiding the obstacles

3. The retailers’ demand
or how retailers’ demand for analytics is growing
01
www.explicato.com

4. 1 Retailers’ demand of big data and analytics solutions
BIG DATA IMPORTANCE FOR RETAILERS
Almost 70% of retailers in the USA* consider Big data and analytics as important or very
important to their business while only 4% consider such kind of solutions for business
intelligence as unimportant or as little important.
These figures are pretty clear about
the importance of the capabilities
of the retailers to gather data from all
business related data sources and
to be able to analze it to support
decisions, control, forecasting and
planning.
*Based on survey covering 101 retailers in the US, performed by DataMentor (www.datamentors.com)
Of a little importance
3%
Moderately important
23%
Important
38%
Very Important
35%
Unimportant
1%

5. 1
*Based on survey covering 101 retailers in the US, performed by DataMentor (www.datamentors.com)
Why retailers need big data and analytics?
Most of the retailers need Big data and
analytics solutions to gent valuable insights
hidden in the native data sources. Asked
about which processes would be most
impacted by big data and business
intelligence they stated that optimization of
stock availabilities and supply chain along
with establishment of customers centric
merchandise and targeted offering based on
discovered behavior models are the most
important topics.
Customers-centric
merchandising
Loyalty programs provide
priceless information about
customers’ shopping behavior,
analyzing the data is crucial to
improve and adapt the
incentives as a keyf factor to
retain customers
Targeted offers and benefits
Analyzing the data from retail
systems is the key to discover
customer preferences and market
basket patterns by customers
segments to ensure that targeted
offers will deliver improved
customers satisfaction
Loyalty program management
Loyalty programs provide priceless
information about customers’
shopping behavior, analyzing the
data is crucial to improve and adapt
the incentives as a keyf factor to
retain customers
Demand forecasting and
supply chain management
Short terms demand fore-casting
by products to ensure optimal
supplies and stock availabilities
for each store.
WHY RETAILERS NEED BIG DATA AND ANALYTICS?
Demand forecasting and
supply chain modeling
28%
Targeted offers
and benefit
28%
Loyalty program
management
20%
Customer-centric
merchandising
24%

6. 1 Main obstacles preventing using big data
However the benefits and potential they see many retailers are holding back on implementing data
analytics initiatives. The biggest reason stated is that retailers need better understanding how big data
and BI can solve their business problems (46%) and the cost and complexity of implementing such kind
of solutions (42%). In the next slides you can find how Explicato can help you to gain the benefits
without challenging these obstacles.
*Based on survey covering 101 retailers in the US, performed by DataMentor (www.datamentors.com)
Obstacles preventing Retailers from using
Big Data and Analytics
7,00%
10,00%
17,00%
21,00% 22,00%
30,00%
42,00%
46,00%
Retailers
aren't
holding
on using
BigData
Other Need better
of time to
value for
Big Data
Need
Big Data
solutions to
better address
to needs of
retailers
Retailers
are sill
challanged
with basic
business
reporting and
not ready
for Big Data
Need
simplified
Big Data
solutions
that are
intuitive
to business
users
The costand/or
complexity of
implementing
of Big Data
solutions
needs to
come down
Retailers
need to better
understand
how big data
can solve their
business
problems

7. The Explicato SaaS Eco system
Or how we deliver the value avoiding the obstacles
02
www.explicato.com

8. Other business process management systems
Unified
data outputs
Data loading
procedures
Top-line dashboards 3rd party toolsEnterprise analytics Drill-down reports Custom Excel reports
2 The retail analytics eco system
Architecture and complexity of the solution
Data loading
procedures
Unified
data outputs
Core retail systems data storesCore retail systems data stores
ERP/CRM/SCM
systems
Social networks
Online shops
Other cloud
systems
DATASOURCESDATALAYER
AUTOMATED DATA LOADING
BUSINESSMODELLAYER
READY-TO-USE
RETAIL DIMENSIONS
AND METRICS
LIVE
ACCESS
PRESENTATION
LAYER

9. 2 The retail analytics eco system
Gaining the benefits and outsourcing the complexity
• The cloud technology resolves two of the main obstacles preventing using of big data and analytics
in retail – reduces dramatically the cost of the implementation providing to the retailer ready-to use
solution avoiding the complexity of building the tools from scratch
• Explicato BI allows the retailers to outsource their whole infrastructure for data processing in the
cloud and to get as a service the final result from deep and advanced analytics
• All layers: Physical, Business and Presentation are hosted in the cloud and ready to be delivered as a
service

10. www.explicato.com
2
• The system is ready to process all types of data from all types of specialized systems in a retailers’
infrastructure (Loyalty, POS, BOS, HOIS, etc.)
• Initial data load is fully automated based on the data model
• Incremental data loads are fully under the retailer’s management to meet all security requirements
• No changes in the IT environment are required
The retail analytics eco system
Deployed and ready to use complete solution

11. www.explicato.com
2
• The data model is in the epicenter of the development process and is based on deep understanding
of the retail business model
• The ready-to-use model of dimensions and metrics allows the user to get access the reports and
dashboards at the moment of the data processing
• The process of business discovery and needs of analytics are the basis to predesign special metrics
for advanced analytics
• The multitenant infrastructure of the solution allows customization to meet specific customers
requirements without affecting other ‘cloud’ accounts
The retail analytics eco system
Retail specifics based business model ready-to-use

12. Hidden insights in the data
or how analytics solution impacts the business
03
www.explicato.com

13. 3 Hidden insights in the data
Key performance indicators: top line dashboards
• Dashboards provide quick overview on key
performance indicators for retail business
• Drill down reports are available by hyperlinks
on KPIs to drill down the available data
• Overall performance dashboards provide
top line information for all organizational
levels managers and employees:
• Operations management
• Store management
• Marketing management
• Category management
286,346
Total
Promo Sales
27,257
vs 9,372 Avg
Daily Sales
4,351
Basket Size
Promo
5,355
Total
Discounts
1,090,278
Total Sales
1,423
vs 408 Avg
Daily
Purchases
2,337
Avg
Basket Size
2,770
Promo Items
Bought

14. 3
• Promotional analytics evaluate the effect of a promotional campaign in all asspects
• The promotional effectiveness is providing KPIs to measure the impact of a campaign on customers
segment or particular stores among with evaluation of further inventories depending on the planned
activities
• Promotional cannibalism provides deep insights on how a promotion is not only affecting the
promotional products and overall but also how affects the sales of products in the same category
• ‘HALO’ effect of promotions – evaluates how promotional campaign of one product affects the sales
of other product categories to discover product affinities
Hidden insights in the data
Promotional effectiveness analysis

15. 3
• Customers centric merchandising is based on a deep knowledge for customers behavior models
• Evaluation of promotional campaigns across customers segments is a key to discover the
customers decisions key drivers
• The output of particular customers participating in a segment or sub-segment is the basis for
CRM to communicate promotional campaigns
Hidden insights in the data
Customers centric merchandising

16. 3 Hidden insights in the data
Customers segmentation
• Customers decile segmentation
allows retailers to determine the
segments of most valuable
customers versus the segments
of the ‘cherry pickers’
• Target offers and benefits is the
best way the retailers to address
the right benefits to the segments
of the most valuable customers
Customers basket
patterns by product
categories identified
by customers
segments allows the
retailer to discover
customers
preferences.
Teas and
other drinks, 4%
Cereals and
muesli, 32%
Coffee, 11%
Dry bread
products, 3%
Spreads, 24%
Jams,
marmalades,
jellies, 7%
Instant
coffees, 8%
Hot drinks, 3%
Hot drinks, 4% Honey, 5%

17. Implementation approach
what is necessary to make the system live for a particular project
04
www.explicato.com

18. 4 Implementation approach
• Explicato approach for big data and business intelligence implementation eliminates all potential
risks for the Retailer as the whole process and all modules are deployed on Explicato’s environment
• Explicato provides dedicated User acceptance test (UAT) environment to the Retailer where the
overall project progress can be monitored
• The Pre staging phase covers the discovery of the sources of transactional and reference data that
should be processed for analytics purposes
• The data onboarding phase covers the setup of the dedicated cloud environment and the initial data
load so at the end the phase the Retailer is able to verify the data and the ready-to-use reports and
analytics using his own data
• User Acceptance test phase is intended to perform full testing of the system in the real environment
along with the incremental data updates
Pre staging
Data onboarding
and quality check
User Acceptance Go Live

19. www.explicato.com
Value Chain