breakthrough in predictive analytics for retailers: how our recommendation engine predicts future...

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

Accurate Recommendations For Retailersof all sizes and domains

A Predictive Analytics Breakthrough(This presentation contains brief disclosure of a patent pending technology)

www.retailreco.com

http://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%

.


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.


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.


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.

Copyright 2014-15 Retail Automata Analytics www.retailreco.com 7

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.


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.


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.


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.


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.


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.


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 BUYSFOR 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.

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