Get in Touch

CONTACT US

Please enter your name. Please enter alphabets only for Name. Please enter Organization. Please enter valid email id. Please enter Phone number. Please enter numeric only for Phone number.

Overview

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

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%.

Resources

Contact us contact us