Customer Analytics

Got Business Questions, Get Answers

Advance and Predictive Analytics make it easy for you to gain insight into customer behaviour, allowing you to make business decisions based on real data in much less time.

• How should products be bundled?
• Which customers are most likely to leave?
• What are customers talking about?
• Who are the most valuable customers?
• How can we improve marketing campaign response?

Is it familiar ?

Customer Analytics

• Spot patterns and trends in data & mine data for unexpected insights or discover facts

• Based on SAP HANA and R Technologies for Advance Analytics, predictive analytics, data mining, statistics, artificial intelligence & natural language processing

Customer Segmentation

• Understand the landscape of the market in terms of customer characteristics and whether they naturally can be grouped into segments that have something in common

Product Segmentation

• Allows for the optimization of using product affinity

Upsell/Cross Sell

• Aim to provide existing customers with additional or more valued products

Advance Analysis & Visualization

Forecasting - Time Series Analysis

Forecasting enables you to adapt to changes, trends and seasonal patterns. You can accurately predict monthly sales volume or anticipate to the number of orders expected in any given month

Association Rules - Cause/Effect - Basket Analysis

Detect relationships or affinity patterns across data and generates a set of rules and select rules most useful to determine What products do customers purchase simultaneously and when ? Which customers are not buying ? What new cross selling opportunities exist ?

Decision Tree - Classify and Predict Behaviour

Classification helps you do things like select the right products to recommend to particular customer and predict potential churn

Clustering - Group More effectively

Identify segments and homogeneous groups so that precise offerings and messaging can be defined. Allows to group web visitors or prospects by specific attributes so you can more easily and accurately track customers and understand behaviors.

Demonstrations

Churn Analysis

• Identify Customers that were previously active but are currently less responsive or completely unresponsive

• Extrapolate the characeteristics of these customers using a decision tree

• Classification of these customers enables you to identify those likely to churn so you can modify marketing and sales campaign accordingly

Cross Selling

• Identify customers who are buying certain category of products or not buying at all.

• Identify cross sell opportunities

• Identify customers that represent potential sales opportunities and customize campaigns that offer them exactly the product or services they are most likely to buy

Forecasting

• Calculate the projecttion of a time series by using data that contains historic information
• Calculate number of projections selected while adjusting for seasonal fluctuations