PROVIDING ADDED VALUE FOR THE RETAIL Software-as-a-service Data processing and analytics

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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

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Intro & Content

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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

The retailers’ demand or how retailers’ demand for analytics is growing

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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%

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*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%

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

The Explicato SaaS Eco system Or how we deliver the value avoiding the obstacles

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Other business process management systems

Unified data outputs

Data loading procedures

Top-line dashboards 3rd party tools Enterprise 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 stores Core retail systems data stores

ERP/CRM/SCM systems

Social networks Online shops

Other cloud systems

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AUTOMATED DATA LOADING

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SIN

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READY-TO-USE RETAIL DIMENSIONS AND METRICS

LIVE ACCESS

PR

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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

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• 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

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• 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

Hidden insights in the data or how analytics solution impacts the business

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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

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• 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

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• 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

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%

Implementation approach what is necessary to make the system live for a particular project

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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

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Value Chain