Purchase frequency

Purchase frequency is the average number of purchases made by a customer over a defined period of time.

Customer activity

Customer activity Activity performed by customer within a limited period of time.

Loyalty prediction

Customer loyalty is predicted using his/her past activity.

Segmentation

Market Segmentation is a technique applied to collect greater marketing force to a subdivision of a market.

Group analysis

Churn prediction

Preventing a customer from churning is often more cost-efficient than acquiring a new customer.

Purchase Propensity or Propensity to Purchase or Propensity to Buy uses results from previous buying history of customers to generate scores. The scores indicate which contacts are most likely to make purchase decision.

Recommending the right products at right time and right place can dramatically increase the revenue. Up-sell is when a retailer suggest a better products to customer and Cross-sell is the complimentary product customer buy at checkout. 

Retailers can attract huge number of customers on social media by analyzing the users interest over a period of time and placing the right offers at right times. It has helped retailers to influence consumers to buy something while they are watching videos, reading blogs, listening podcast,etc.

ML-AI models are used as a tool for customer’s sentiment analysis. Retailers are using such models to evaluate and enhance their brand reputation in online/offline world. Customer data is collected from social media or any survey or feedbacks.

Timing is extremely critical in sales. We have experience of building and implementing ML models that can predict best product that a customer will buy, best suited offer for the customer at best times. It has has shown a significant uplift in ROI.

Personalized, unique cosmetic recommendations for each customer using a variety of features such as location / weather, skin tone (color), Skin type ( normal, dry, oily, allergic / sensitive), price sensitivity, similar consumer behavior and past purchase data.

Use ML Models for hyper personalized engagement with your customers across web store, mobile store and physical stores based on their preference, location, gender and various other parameters.

Shipment timelines are extremely critical in successful conversions.. Logistics and delivery teams understand the value of speed, efficiency and visibility product store and shipment. AI powered with GIS location data, warehouse data etc can predict accurate shipment details and meet customers delivery expectations.

Business Problems​

How many customers does the firm still have?

How many customers will be active in one year from now?

How many transactions can be expected in next X weeks?

Which customers can be considered to have churned?

Which customers will provide themost value to the company going forward?

Outcomes​

The project deals with forecasting purchase predictions for customers in a non-contractual, variable spending environment using minimal datasets. Dataset used are only Customer ID and date of transaction ( Non PIIA data)

Customer Churn / Inactivity Prediction

Individual Customer Purchase Frequency Prediction (Weekly / Monthly / Quarterly / Half-yearly)

Aggregate Customer Purchase Frequency Prediction (Weekly / Monthly / Quarterly / Half-yearly)

Customer Segmentation/ group analysis

Customer Loyalty Prediction

Every outcome is applicable for individual customers as well as for groups of people.