What will you do if you knew future buys of customers?

RETAILRECO INTELLIGENT CORE CAN PREDICT THE FUTURE BUYS OF CUSTOMER
retailreco inteligent core

Analyzing Historical Sales Data From All Sources, Our Big Data Predictive Analytics Algorithms Generate Accurate Predictions Of Future Buys Of Customers. A Unified Retail View Across multiple Business of Customers and Products can be created.

A Personalized Omni-Channel world of only relevant products is automatically created for every customer.

WHAT WORKS FOR TOP 1% DOES NOT WORK FOR 99%

Challenges of Predictive Analytics for such drastically different data sets are different.

future buys of customer
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.

frequently changing catalog problem
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.

cold start problem
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.
A new product in the catalog, because of lack of its own prior sale history can not be recommended to customers. Nor can it become part of other items "People who bought this also bought these" recommendations".

RetailReco solves both of these problems and on day one for a new item quality recommendations are available.

Five Fundamental Buy Decision Making Entities of Retail Ecology


Combining natural language processing with all five factors

So far we discussed five fundamental factors affecting the retail business. For deriving analytical outputs and predictions the data set available must have all the underlying attributes present in product data or customer preference data.

Natural Language Processing

If the product data does not have some of those attributes explicitly defined, or even if some are present. Similarity between the products can be obtained using Natural Language Processing (NLP) on various text based product attributes. Cosine similarity and KMeans clustering provides additional dimension for analytics.

Unified predictive retail eco-system

Data adapters
Store POS Data
Store POS Data
E-commerce Site
E-commerce Site
mobile ecommerce
Other Source

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.

Feature Sets

affordability

Exact values or content based approach on product present in all historical data sources, of all different feature sets and corresponding affordability price ranges forms the structure of data used for collaborative predictive analytics.

Frugality

customer evolution

Affect the strength of sales record contribution to predictive analytics data structure.

Intelligent core

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.

Result: Most accurate predictions of future buys

For retailers of all sizes and domains

Along with predictions Seasonality of Products and Frugality of customers (as well as products) is noted down. These are powerful customer segmentation criteria for RetailReco campaigning system.

A Personalized Omni-Channel world of only relevant products is automatically created for every customer.

Applicability to other domains

BFSI & Financial technology
BFSI & Financial technology

Portfolio Management, Investment Value Optimization, Personal Stock Advisor, Portfolio X-Ray

Prescriptive analytics for Medical segment
Predictive and Prescriptive analytics for Medical segment

Prediction of future most likely diseases of a person & Personal Preventive Plan.

We can schedule a walkthrough of our application in action.