WhatsApp Chat Sentiment Analysis in R

Introduction Customer Analytics

Introduction to Customer Analytics

Consumers are increasingly spending more time and money online. Business to consumer e-commerce is growing on average of 20 percent each year and has reached 1.5 trillion dollars globally in 2014. Given the scale and growth of consumer online purchase and usage data, firms' ability to understand and utilize this data is becoming an essential competitive strategy. However, large-scale data analytics in e-commerce is still at its nascent stage and there is much to be learned in all aspects of e-commerce. Successful analytics on big data often require a combination of both data mining and econometrics: data mining to reduce or structure (from unstructured data such as text, photo, and video) large-scale data and econometric analyses to truly understand and assign causality to interesting patterns.

What is Customer Analytics

Customer analytics is the systematic examination of a company's customer database to attract and retain the most profitable customers. Customers have access to information anywhere, anytime including where to shop, what to buy, and how much to pay and so on, thanks to the advent of the ever-increasing World Wide Web. This makes it increasingly important to utilize predictive analytics and data to forecast how customers will behave when interacting with brands and how we can use this information to keep our customers. Customer analytics refers to the processes and technologies that give organizations the customer insight, which is important to deliver offers that are anticipated by the consumer and are relevant and timely. 

Customer analytics comprises techniques such as predictive modeling, data visualization, information management and segmentation, all of which helps in understanding the consumer behavior with respect to the brand and the products that they are consuming. When you understand your customers, you understand what you need to do to build a better product

The goal of customer analytics is to create a single, accurate view of a customer to make decisions about how best to acquire and retain customers, identify high-value customers and proactively interact with them. The better the understanding of a customer's buying habits and lifestyle preferences, the more accurate predictive behaviors become which helps the brands.

Applications of Customer Analytics

With customer analytics, you can:

Increase response rates, customer loyalty and, ultimately, ROI by contacting the right customers with highly relevant offers and messages and retain these consumers to your brand and products.

Reduce campaign costs by targeting those customers most likely to respond.

Decrease attrition by accurately predicting customers most likely to leave, which becomes the research part of it, and then developing the right strategies to retain them.

Deliver the right message by segmenting customers more effectively and better understanding target populations.

Customer data analytics can be used in all aspects of business, from sales, marketing to support. It improves decision making and strategies in these departments using insights gained from hard and real data. This raises the probability of success and eliminates unnecessary steps that can take up too much from an employee's time and effort. 

Some specific applications of analytics would be:

Better prospecting – You can get more quality leads and better targeting with the help of data analytics. You will come up with sharper analysis of customer behavior and opportunities if you read your data well.

Data-backed customer support – When you customer care program is backed by hard data, your responses to customer needs would be more accurate, timely and relative. This raises customer and organization satisfaction.

Insight on prime opportunities – When is an account ripe for upselling? When will customer consider an upgrade? You can get the answers to these questions from proper analysis of your data collected by customer analytics research.