CAS/R enables marketing users to browser and analyze competition in terms of pricing on specific products. Beyond competition insight, CAS/R offers powerful tools for actionable decisions on your pricing policies, loyalty programs, personalized offers and more. Intelligent Online marketing CAS/R can be defined as an analytics framework for modern online retailers offering a wide range of marketing applications including product management, market analysis, Business Intelligence and predictive modeling. It is based on a powerful pricing database against your product catalogue and the ‘market’. CAS/R includes several additional components enabling real-time, activity-based proposal generation for existing customers, dynamic product pricing schemes, personalized discount models, interactive market analysis and the intelligent alerting suite.

1. Analytics for Retailers
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CAS/R

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Outline
Analytics for Retailers
CAS/R main functions
CAS/R overview
Reporting & Analytics
Integration with 3rd party sites
Dynamic pricing
Recommendation Engine
In browser proposal wizard
The marketing data mart
Campaign management

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Analytics for Retail
Loyalty platform
Specialized platform enabling personalized
offerings, bonus & rewards depending on
behavioral data and corporate policies
Competition Analysis
Tools for accessing & analyzing market data,
enabling decisioning on pricing and product
release information
Analytical models
Association Rules, Market basket analysis,
trends, seasonal patterns for management &
marketing purposes
Customer Segmentation schemes
Modelling towards a global segmentation
scheme for analysis and marketing actions
Online Components & mobile apps
Product Recommendation engines,
integration with portal/ site
Campaign Management
Tools for defining intelligent campaigns,
designing target groups, and personalized
communication for certain products
ETL components
Data handling,
transformation,
cleansing &
normalization packages
Customer DBProduct DB
Product info
Additional
data files
corporate data sources
Specialized database, enriched with
customer meta data, statistics,
optimized for analysis
Customer
Data mart
InfrastructureCustomerIntelligencelayer
Consumers
Touch-Points
POS Network
Shops
Portal/ online
Online
Communication Channels
Call center
Inbound/ outbound
Email
Electronic communication
Personalized offers, prices
Automated recommendations
Better customer handling,
personalized offers,
loyalty scenarios

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CAS/R at a glance
Online market scanner
An intelligent engine able to gather online product info
 Configurable set of target online stores
 Hosts 10s of thousands of products, scales easily to 100s
 Synchronizes products (price & status update) multiple per hour
 Maintains a rich database with product info, price and availability history
 Invisible to system administrators
Price comparison engine
Search engine, similarity matching, price analytics
 Automated product Matching using text mining, > 90% in accuracy
 Enables dynamic pricing models
 Rule-based price alerting engine
 Sophisticated reporting & Business Intelligence infrastructure
 A compact, interactive application layer
 Numerous online components and add-on's
CAS/R
 Post-processing & aggregation
 Search & comparison functions
 Analytics & Business Intelligence
The web
On-line retailers
Public domain info
Retailers
 Used for product management
 Price strategies
 Customer handling & Loyalty

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CAS/R main functions
 Organize product catalogue data into a rich, report-optimized data store
 Browse and model detailed price history
 Able to accept feeds with competitor data
 Able to scan the market (enlisted competitors) every hour
 Able to automatically match products based on fuzzy similarity measures
1
With formal specs, prices from product
catalogue
2
Fuzzy algorithm able to match products even with significantly different naming
structure and representation

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CAS/R overview
1
3
Product navigation tree, search as-you-type
functionality, list management (user defined lists
of products)
Clickable stats on matching
outcome, market availability & stock
information
2
The set of products satisfying user defined
criteria against with configurable set of
columns

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1
3
Product search & navigation engine Working product set, overview, key-
statistics
2
The set of competitors offering the product,
along with prices etc.
4
The actual product as offered by the selected competitor
CAS/R overview

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Reporting & analytics
 Standardized reporting on price history, catalogue etc.
 Competition analysis reporting
 Dynamic reporting and analysis through cubes
 Able to integrate with standard BI Environments
1
Each product is listed and highlighted against
competitor offerings and price distance

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Integration with 3rd party sites
 Automated product release every hour to Skroutz.gr or similar
 Ability to define rules/ select and monitor the products to be released
1
2
Specialized reporting enabling overview and analysis of the
products to be released to Skroutz.gr. CAS/R also generates
impact analysis/ filtering evaluation
Detailed listing of each of the product included in the list,
colored depending on release eligibility (according to predefined
rules)

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In-Browser proposal wizard
A sophisticated toolbar add-on enabling automated (rule-based) offerings to each of your existing customers, as they browse
competition:
 Customer completes the first (online) purchase with Retailer Y
 Upon order confirmation, Retailer Y promotes the ‘In Browser wizard’ providing certain benefits for the customer in
order to download it
 Upon installation, the add-on operates in a passive mode, until the user visits one of the predefined competitor sites
 Assuming that user navigates on Competitor A, for product X then the system automatically performs the following:
1. Checks the price at which product X is offered by Retailer Y
2. If the product is cheaper in Retailer Y then a friendly notification informs the user that the product is actually
available and cheaper, promoting a ‘GO’ button (which gets back the customer to Retailer Y site)
3. If the product is more expensive in Retailer Y, the system triggers the Loyalty policy and depending on
customer’s value generates a special offer for the specific customer. Suitable notification is generated
instantly
4. If the product is not available, the system finds ‘similar’ and informs the customer
Additional business rules may be defined in order to handle special cases

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In-Browser proposal wizard
Proposal generated according to (a) customer’s profile (b) timing – customer’s
web navigation (c) Retailer’s Pricing/ customer-handling policies
1
Your competitor.com
http://www.yourcompetitor.com/laptops/offers/sony/vaio
Your Brand

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In-Browser proposal wizard
1
Quick information focusing on (a) product pricing / offering
and (b) customer classification/ reasoning for the offering
2
Depending on rules combining customer & segmentation
data, the message can be formal, casual, in the preferred
language and tone
3
Configurable description of the specific proposal. Under
certain rules this may inform the user that this is the best price
in the market, or that this is a special price/ deal
4
Could be online, offline (for instance, send the customer to
specific shops with a proposal ID), with a specific offer validity
period
Able to release info/ invite friends to register to Retailer Y
Loyalty program, utilizing Proposal Wizard
5
Your Brand

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CAS/R extensions & apps
In-Browser Wizard
Automatic personalized proposals
while browsing competition
Mobile apps*
Price comparison applications for
smart phones
Loyalty extensions*
Able to setup loyalty schemes for
special offerings via In-Browser wizard
Market Analytics*
Specialized environment for analyzing
competitors strategies & market
dynamics
Data mining models*
Ad-hoc projects analyzing/ predicting
customer behaviors & market changes
Marketing datamart
Centralized database for reporting and analytical purposes able to host:
 Customer data, orders, sales, online behaviour data
 POS network, corporate organizational structure
 Product database
 Customer feedback streams
Analytical applications
Several applications/ models can be developed above the ‘marketing datamart’:
 Customer segmentation schemes/ clustering models
 Loyalty models
 Specialized KPIs denoting customer base health
 Complaint handling (featuring text mining for automated classification and
handling)
 On-going customer satisfaction monitoring (against time per POS/ channel,
product category, employee)
 Advanced data mining models
 Advanced OLAP model/ BI environment

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Dynamic pricing schemes
Design pricing policies –business rules leading to specific offerings, based on any combination of:
 Competitor Pricing: if competitor A offers products of category C1 below a threshold then change prices by Ffunction
(absolute or relative or any valid function)
 Market State: if products of category B (or specific set of) are more than D% expensive from the lower market price,
then change prices according to Ffunction
 Demand indicators: if search trends/ order trends exceed a specific threshold , then change prices according to
Ffunction
 Internal policies: priorities promotional logic for certain products, categories or channels
Pricing alerts
Design pricing policies –business rules leading to specific alerts, based on any combination of:
 Sudden massive changes in a competitor: significant changes from competitor A on certain product categories
 Pricing strategies: patterns such as cycling price changes
 Extreme price: identification of price lying outside of normal price ranges
 Market trends: alerts based on grouped (competitor level) changes on certain category products

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Applications
Analytical models
Statistical & data mining techniques against historical customer data
 Market Basket analysis, association rules, purchase behavior insight. For Cross/Up sell, marketing action design,
campaign promotions, customer analytics
 Sales Analytics, such as models depicting trends, seasonal patterns. For marketing & management purposes.
Customer Segmentation
Organize your customers through clustering and statistical modelling
 Macro segmentation schemes, a common corporate language across all customer touch-points
 Micro segmentation schemes empowering certain marketing activities or promotional campaigns
 Involves advanced profiling, thus providing customer insight and better understanding of the involved typologies
 Based on statistical modeling & business expertise
 Can be integrated via data warehouse and suitable APIs – become available throughout the enterprise

16. George Krasadakis
Head of engineering & product development
g.krasadakis@datamine-it.com
No part of this document may be reproduced. Datamine owns patents, trademarks, copyrights and other intellectual property rights in this document and the presented products. This document
does not give you any license to these patents, trademarks, copyrights, or other intellectual property. Datamine is a registered trademark. ‘use.your.data’ is a registered trademark
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