This document introduces a predictive analytics solution called RetailReco that can generate accurate recommendations for retailers of all sizes. It addresses challenges typical retailers face, such as lack of sufficient purchase history data for most items and customers. RetailReco takes a unified approach, abstracting customers and items into standardized entities based on decision-making factors like product features, price ranges, and discount sensitivity. It combines and analyzes all available customer preference data to build a predictive retail ecosystem and generate the most accurate predictions of future purchases.

1. Copyright 2014-15 Retail Automata Analytics www.retailreco.com 1
Accurate Recommendations For Retailers
of all sizes and domains
A Predictive Analytics Breakthrough
(This presentation contains brief disclosure of a patent pending technology)
www.retailreco.com

2. Copyright 2014-15 Retail Automata Analytics www.retailreco.com

3. Copyright 2014-15 Retail Automata Analytics www.retailreco.com
WHAT WORKS FOR TOP 1% DOES NOT WORK FOR 99%
Challenges of Predictive Analytics for such drastically different data sets are different.
1% 99%
.

4. Copyright 2014-15 Retail Automata Analytics www.retailreco.com
GENERATING PREDICTIONS FOR 99% RETAILERS IS TOUGH
Process of generating future buys of
customer involves analysis of past
purchase histories of customers and
items. To derive meaningful
analytical insights repeat sales
history of same item is required.
While huge repeat sale history of
any single item is present for top 1%
like Amazon.
For most of the retailers this is an
uncommon luxury.
The problem of Predictive Analytics is much more difficult for most of the retailers
than for the top 1% of retailers.
At the same time bandwidth and $resources necessary for research into Predictive
analytics is also not available for SME retailers.

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COLD START PROBLEM
For a new item or a new customer there exists no previous data.
Hence no predicted buyers of the item or item to item recommendations can be
made on day one.

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FREQUENTLY CHANGING CATALOG PROBLEM
By the time enough sales history of an item is recorded. That item is no longer in
the inventory.
An out of stock item can not be recommended to customers. Nor can it become
part of other items "people who bought this also bought these"
recommendations.
One of kind sellers like jewelers where every item is unique can be an extreme
case of frequently changing catalog problem.

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THE RETAILRECO SOLUTION TO RECOMMENDATION PROBLEM
As noted down the most fundamental problem of 99% retailers is the lack of
sufficient sales history.
Products come and go, For every customer characteristic Unique Blue Print of
Relevancy Remains the same.

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THE ABILITY TO DERIVE MEANINGFUL INSIGHT STARTS WITH
DEFINING THE RIGHT ABSTRACTIONS AND MODEL FOR ANALYSIS
WE STARTED WITH IDENTIFICATION OF ENTITIES WHICH
MATTER THE MOST FOR A RETAIL BUSINESS
We form The blueprint or retail DNA of every customer on the basis of most
fundamental buy decision making entities of retail.

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FIVE FUNDAMENTAL BUY DECISION MAKING
ENTITIES OF RETAIL ECOLOGY
Decision Making Feature Sets
A retail business is characterized by one or more types of
products with different features.
Exact values of different features combined together is
the primary buy decision making factor for customer.
Unlike the content based recommender system where
every feature value like “color” is treated as independent
decision factor. In our model the exact values of all
features that has occurred in all historical sources of
customer preference data forms the character of retail
business.
Not many like the “Navy Blue” shirt, while even fewer
people have the preference for “White” jeans.

10. Copyright 2014-15 Retail Automata Analytics www.retailreco.com 10
SECOND ENTITY: AFFORDABILITY OR PRICE RANGES
Price of an item is a critical buy decision
making factor
Affordability of the same customer can be
different for different types of products.
A person who likes to buy most expensive
Diamond Solitaire ring may be willing to buy
quartz watches only in the lower price ranges.
For predictive analytics purposes a
classification of buy within just a single defined
price range is also not sufficient. Just above or
below the defined price range the person is
still has interest in that specific product type
having same decision making feature set value.
Affordability Price Ranges for any specific
feature set values is given a very special
consideration in our approach of prediction
generation.

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THIRD ENTITY: FRUGALITY OR DISCOUNT SENSITIVITY
Some customers’ have a tendency to be more
sensitive to the amount of discount in making
the buy decision compared to others. This
tendency is not only restricted to the customer
dimension but can be generalized to include
the product dimension also i.e. some products
have the higher likelihood of being bought
with deep discounts while others are not so
sensitive to discounting.
A discounted sale record should have lesser
impact on process of generating predictions,
compared to a sale record at full price.
Furthermore for marketing purposes customer
segmentation on the basis of Frugality
consideration can be very effective for
different business objectives like clearance sale
or premium product launch.

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CUSTOMER EVOLUTION AND SEASONALITY
Buying behaviour of customer changes
over period of time. This is intelligently
captured in our system.
Seasonal purchasing of different
products is different. We note it
down so that retailer can utilize
this information.

13. Copyright 2014-15 Retail Automata Analytics www.retailreco.com 13
UNIFIED PREDICTIVE RETAIL ECO-SYSTEM
+
Exact values present in all historical data sources,
of all different feature sets and corresponding
affordability price ranges forms the structure of
data used for predictive analytics.
DATA ADAPTERS
Unify the customer preference data from all
historical sources including offline and online
store sales history, shopping cart, browsing
history, social media etc. Each data source can
have different importance or weight.
+ Affect the strength of sales record contribution
to predictive analytics data structure.

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INTELLIGENT CORE
All the disclosure of the invention presented in this presentation are covered under patent application
“Unified Predictive Retail Eco-System” : (number: 2465/MUM/2015).
RESULT: MOST ACCURATE PREDICTIONS OF FUTURE BUYS
FOR RETAILERS OF ALL SIZE AND DOMAINS
Along with the Seasonality of Products and Frugality of customers (as well as
products) noted down. Which are powerful customer segmentation criteria for
RetailReco campaigning system.
A Personalized Omni-Channel world of only relevant products is automatically
created for every customer.
Applies Big Data technologies to handle scalability.
Sparsity in Unified abstracted predictive analytics data
structure is handled by Dimensionality reduction
techniques.
Effect of frequent purchases of a small set of products
or customers buying all products is handled by
normalization techniques.