Predictive Analytics

Business Analytics

Business Analytics (BA) is the process of statistical analysis both exploratory and predictive to extract actionable insight to help the business manager to take decisions. Business analytics is used by company to make the decision process more scientific and backed by the data than relying on the gut feelings and domain knowledge

  • Extensive data mining to find meaningful and actionable insights from historic data
  • Statistical analysis to find out the key drivers of any customer behavior
  • To use this information to develop a predictive model
  • To present all insights and predictions in actionable dashboards.

Application of Customer Segmentation

Any campaign with budget constraints requires customer segmentation. Segmentation is used across the domains and processes to increase the ROI and effectiveness of a campaign. Right from the Customer Acquisition to Cross Sell to long term Retention till the Collection and Recovery processes, Segmentation is used throughout the customer relationship life cycle. Some business domains that use customer segmentation extensively are Banking, Insurance, Retail and Telecom. Some of the processes where segmentation is applied are-

  • Customer Acquisition: Segmentation is done to identify the profile of customers and design acquisition campaigns accordingly.
  • Customer Relationship management: Banks use customer segmentation techniques combined with predictive model score outputs to identify the customer most likely to churn. These customers are treated differentially based on their CLV (Customer Lifetime value)
  • Collections and Recovery Processes: Customers are segmented based on the propensity/likelihood of default on the next payment, or the clearing of all dues (once in collections). Different segments are exposed to different collections treatment.

Statistical techniques used for Customer Segmentation

Due to data sizes and the business complexities, various Data Mining tools are found to be appropriate under various circumstances for Customer Segmentation or Market Segmentation. Some of the popular techniques used for customer segmentation are as follows:

  • Cluster Analysis: Cluster Analysis is one of the most commonly used tools for Customer Segmentation. Cluster analysis divides customer into different distinct subgroups using both continuous and categorical attributes. Once clusters are found, characteristics of those clusters can be explored, providing insight into its members, and new members (observations) can be assigned to the clusters.
  • CHAID Analysis: The tree building approach CHAID ANALYSIS is also used to determine customer segment in the market. CHAID analysis divides the data into different nodes where the behaviors of members of different nodes are different. CHAID Analysis is famous because its output is highly visual and easy to interpret.

ScoreData Approach to Customer Segmentation

Our approach to customer segmentation begins with developing a clear understanding of the client’s objective. We define the objectives clearly and align our solution with that. We always keep the customer’s LTV (Life time Value) into consideration while segmenting the customers who are more valuable to the business. Our unique solution conducts advanced statistical modeling on customer’s transactional , profile and appended data to divide customers into groups based on shared characteristics like profitability, customer life time value, loyalty index, customer requirement and transaction history. Our customer segmentation solutions are customized to our clients’ needs and create vital insights to focus on most profitable customers throughout the relationship life time – from customer acquisition to cross sell to long term retention. We keep our solutions very simple and easy to interpret and are guided with the motto of “simple and elegant “.