by Nikita Agrawal
Companies in nearly every industry are feeling the heat from more observant and often less loyal consumers. Therefore, the current businesses should have a careful and comprehensive understanding of their customers to get a hold of them, grow them, and sustain them.
Customer Analytics gives valuable insights to understand the customer’s purchase & usage patterns, demographic distribution & profitability. Organizations based on customer analysis insights can segment customers, understand their behavior, create loyalty programs, decide the best pricing, and optimize cost.
As your business grows, segmenting your customers can significantly improve your marketing performance, making campaigns more relevant to most of your customers ultimately, increasing response rates and sales.
Recency -Frequency – Monetary (RFM)- RFM Analysis is a proven direct marketing model for behavior-based customer segmentation. RFM helps to group customers based on their transaction history – it segregates the customers based on how recently, how often, and how much they paid for their purchases.
Customer Churning refers to regaining the customer who has ceased his or her relationship with the company. Businesses typically treat a customer as churned once an amount of time has elapsed since the customer’s last interaction. The full cost of customer churn includes both lost revenue and the marketing expenses involved with replacing those customers with new ones.
The ability to predict that a specific customer is at high risk of churning, while there is still time to do something about it, represents a considerable additional potential revenue source for every business. Besides the direct loss of revenue that results from a customer abandoning the business, the costs of initially acquiring that customer may not have already been covered by the customer’s spending to date. Moreover, it is always difficult and expensive to acquire a new customer than it is to retain a current paying customer.
In Marketing, Customer Lifetime Value is a prediction of net profit attributed to the entire future relationship with a customer. It estimates a customer’s monetary worth to business after factoring in the value of a relationship with a customer over time.
CLV helps you make confident business decisions about sales, marketing, product and customer support.
Sentiment Analysis is basically Opinion Mining. The process of computationally identifying and categorizing opinions expressed in a piece of text, primarily to determine whether the writer's attitude towards a topic, product, etc. is positive, negative, or neutral.
It is important to classify incoming customer conversation about a brand based on 2 factors:
These concepts when used in combination, become a vital tool for analyzing millions of brand conversations with human-level accuracy.
Description: The workflow takes tweets data as input and Classifies these tweets as positive, negative, or neutral based on LSTM based NN.
Algorithm Used LSTM Neural Network
Brand Sentiment Analysis
Buying pattern refers to the typical way in which consumers buy goods or avail services- encompassing frequency, quantity, duration, timing, etc.
Customers have different agendas and different concerns as that of a salesperson.
Algorithm Used Multinomial Logistic Regression
Most of the business decisions of an organization are made under the conditions of risk and uncertainty.
Demand forecasting is a systematic process that involves anticipating the demand for the product and services of an organization in the future under a set of uncontrollable and competitive forces.
Considering the competition between brands in the market, it is often of interest to model market-shares.
Customer analytics is among the most compelling enablers companies should have for translating those signals which they are unable to convert into useful insights about their customers. BDB Customer Analytics helps to deliver better customer insights, in a format that makes it easy to understand and act on them.