Personalized Product Recommendations Engine

Enterprises are expected to deliver a rich and consistent customer experience across multiple touch point for greater sales and brand loyalty. Personalization solutions from Happiest Minds helps you to identify the right products & product combinations for new customers using social, click stream, demographic information & product association rules.

The Solution

Personalized Product Recommendations image

Combine multiple sources of data to personalise & recommend

Social Media-to get comments, interests, activities, etc.
CRM-to get interactions with oranazations
Point of sales/e-commerce-To get Purchase behavior
Clickstrem-To understant needs and analyze behavior

Customer Profiling and segmentation

Item-item based recommendations
Customer-customer based recommendations
Item based echo system Recommendations

Integrates multiple types of recommendations

The Business Needs [Why Personalization & Recommendation]

Personalization & Recommendation (PnR) is becoming a MUST have engagement strategy for brands in customers’ journey towards conversions. Brands are looking for ways to personalize and recommend based on customer attributes like:

Interest
Hobbies
Comments
Customer Similarities, etc.

Recommendation Engines of the digital era need to look and analyze multiple source of data like CRM, POS, Social Media, Geo Positions, External Source (climate & weather, etc.). This calls for introducing a 5th ‘P’ in the proverbial 4Ps of Marketing.

Offerings

Personalization Framework

Happiest Minds in-store personalization solution is built on mobile application backed by core analytics engine running dynamic pricing algorithms. This helps in sending contextual price with product recommendations based on transactional data available from internal (loyalty data, geo spatial data etc.) and external (Neilson Spectral/IRI & store data, government & news alerts) data sources.

Features

Product recommendation based on transaction history
Personalized price incentive/matches based on the customer segments
Dynamically push ad-hoc campaign based on the customer location or any events
Reward points encashment

Business Impact – Top 5 Fashion Retailer in the US

Manage 27 Terabyte Data Ingestion
Conversion (cross/up sell) – from 5% to 8% i.e. 60% increase on overall Product Catalogues
Be Spoke Development saving $ Multi-million License Costs
Cart Volume & Value – increase potentially by 20%.

Product Recommendation Framework

This solution is designed to understand & analyze the customer from multiple sources and identify the right products & product combinations based on the historical buying pattern at real time.

Features

Analytics rule engine can identify the right products & product combinations for new customers using social, click stream, demographic information & product association rules
Recommend products based on customers’ purchase trends with price, reward points, promotion, offers etc.
Recommendation system to find product preferences on recent and frequent product purchases

Business Impact – Top 5 Fashion Retailer in the US

Manage 27 Terabyte Data Ingestion
Conversion (cross/up sell) – from 5% to 8% i.e. 60% increase on overall Product Catalogues
Be Spoke Development saving $ Multi-million License Costs
Cart Volume & Value – increase potentially by 20%.