What are the Types of Customer Analytics

What are the Types of Customer Analytics?

What are the Types of Customer Analytics?

1. Customer satisfaction analysis.

  • Customers who are happy with your product or service are much more likely to buy from you again.
  • Customer satisfaction analysis is the process of assessing whether your customers are getting what they want and expect from your business, product or service – in short, whether they are satisfied or unsatisfied.
  • The most common way to assess customer satisfaction is with a combination of quantitative and qualitative surveys.
  • Instead of spending a fortune on surveys, why not encourage your customers to interact with you via your Facebook page or Twitter

2. Customer lifetime value analytics.

  • If you are able to attribute a lifetime value to each customer, you can immediately see which ones are the most valuable and therefore most important to you.
  • Customer lifetime value analytics is the process of analysing how valuable the customer is to the business over the entire lifetime of the relationship.
  • Instead of looking at transaction profitability, you look at how long a customer is likely to stay a customer, how often they are likely to buy during that period and therefore how valuable they are across that timeframe.
  • This allows you to focus marketing attention on the most valuable customers.
  • This analysis can also potentially identify ways to increase the length of the relationship and the value of the customer.
  • Tip: The biggest challenge with lifetime value is finding the right formula for your business.

3. Customer segmentation analytics.

  • Customer segmentation analytics is the process of finding sub-groups or segments within the overall market.
  • Being able to assess your customers and split them up into various segments that might buy more of one product than another or buy more often allows you to tailor your marketing and communication efforts.
  • The internet is a vast source of useful customer data, helping companies identify clear segments – data mining and text analysis are useful tools for this.
  • It is possible to take segmentation too far and seek to split your customer base down into increasingly smaller subgroups. Instead, stick to some core groups who appear to behave and buy in similar patterns.

4. Sales channel analytics.

  • Sales channel analytics looks at all the various ways that you distribute your products to your market to see which channels are the most effective, allowing you to make the best use of your resources.
  • For this analysis, you need to identify all the sales channels that you currently use or could use, then attribute each sale to a channel and subtract the relevant cost of sales for each channel.
  • Keep in mind that you don’t always know if the customer was exposed to a different sales channel before purchasing.
  • In other words, a customer may have seen your product in a shop but preferred to buy online.

5. Web analytics

  • Online sales in just about every industry are increasing. Web analytics is the process of analyzing online behavior so as to optimize website use and increase engagement and sales.
  • There are two types of web analytics: off-site and on-site
  • Off-site web analytics is useful for assessing the market and opportunity whereas on-site is useful for measuring commercial results.
  • There are many web analytics tools and service providers available, such as Google Analytics.
  • The real value of web analytics emerges if you continue to do it and can see how your online performance is changing over time.

6. Social media analytics

  • Social media analytics is the process of gathering and analysing data from social media to see what people are saying about your product, service, brand or company.
  • In social media analytics, text data from social media posts and blogs is gathered and mined for commercially relevant insights using text analytics and sentiment analysis.
  • The real power of social media analytics is its real-time, immediate nature. If you can spot unhappy customers quickly then you have an opportunity to turn that situation around and create a loyal customer.

7. Customer engagement analytics.

  • Businesses are notoriously bad at customer engagement, yet it has a direct impact on a company’s bottom line.
  • Customer engagement analytics is a rapidly evolving field where businesses are trying to map the entire customer interactive journey on- and off-line.
  • Essentially it is the process of assessing how well (or otherwise) you engage your customers with your products, services or brand through these various interactions.
  • Ways of measuring customer engagement include surveys and social media analytics.
  • You can’t please all of the people all of the time but customer engagement analytics can help to identify what aspects of your product or service customers value so you can constantly improve your offering.

8. Customer churn analytics

  • Keeping your existing customers is always much easier and cheaper than trying to find new customers.
  • Customer churn analytics is the process of assessing how many customers you are losing over the course of a year.
  • It also allows you to predict customer churn in the future and take evasive action before you lose those customers.
  • Customer churn can be assessed using KPIs such as customer retention rate and customer turnover rate.
  • Pay particular attention to how you count customers and set that as a companywide benchmark for the future.
  • If you don’t then different departments may count customers differently which can pollute the data.

9. Customer Acquisition Analytics

If you don’t have enough customers your business will fail, and the same applies if you spend too much money acquiring those customers.

  • Customer acquisition analytics seeks to establish how effective you are at acquiring new customers, including how effective you are at pinching customers from your competitors.
  • There are a number of metrics that can help to establish customer acquisition, such as the cost per lead and customer conversion rate KPIs.
  • When calculating cost per lead and cost per qualified lead, calculate them separately for each marketing initiative or campaign you execute.
  • This will give you a much clearer picture of what is working and what is not.


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About the author

Nagendra Prasad Krishnam

Mr. Nagendra Prasad, MBA, M.Phil., PhD, holds Multiple Patents, Trademarks, Globally Indexed Scientific Research Journals has vast experience and expertise in the field of Data Analytics, Data Mining, Business Analytics, Lead Management, Digital Marketing, E-Commerce Management, Business Management, Learning & Development, Training, Classroom Training, Virtual Training, Research & Development, Academic Content, and Training Content at Various Organizations, Academic Institutions and Expertise in Research Data Analysis including Primary Data and Secondary Data. Also Published Multiple Books in the field of Technology in the latest Trends

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