In the past, companies that had used customer analytics had a major competitive advantage. Today most companies use customer analytics software and the competitive advantage has become neutral. Businesses are now looking to go more in depth with customer analytics. Deep Customer Analytics as its known goes beyond the basic customer analytics to accomplish such things as:
- · Customer segmentation which groups customers depending on actual behavior instead of a perceived notion of what makes customers similar to each other using averages or aggregate data.
- · Customer life cycle and how they arrived at your segment rather than only focusing on where they are in your segment now.
- · Future behaviors of customers in order to predict future spend amounts and retention ability
There are many key metrics and formulas available in order to effectively do Deep Customer Analytics. Software available from SAS, IBM, Mineful, Omniture and many others have the ability to track customer data and make it useless. Some of the most important track able metrics are as follows:
Customer Lifetime Value
Customer Lifetime Value or CLV is a prediction of the net future profits that are affiliated with the future relationship with a single customer. Customer lifetime value is a key metric because it places a monetary value on a customer and how much they are worth. Marketing departments can use this monetary value to decide if the customer is worth marketing to or how much should be spent on direct response marketing. If a customer costs $70 to market to and acquire and their CLV is $110 then the customer is profitable. 
Purchase frequency is just that; the frequency that a customer makes a purchase or uses your services. This can be compared for any time frame ranging from days to years; purchase frequency is useless if you do not have a threshold to measure from though. If the frequency is lower than the threshold then one can assume that they are spending their money with the competition. Frequency works well when compared in conjunction with recency. An example may be for a marketer to compare responses of an email or promotion with recency and frequency. Knowing when to market to a customer is key, Customers are more likely to open and view promotions when marketed to soon after a purchase or have signed up to receive promotions. This can also avoid a customer being bombarded with too much information and making the marketing more of a nuisance. 
Purchase Amount (Average)
Purchase amount is how much revenue the company gets per order. It’s a simple formula of revenue/orders and can be used to help separate customers as a group or individually. If a business knows that a certain customer only spends a certain amount per order, they shouldn’t send them promotions that are out of their usual spend but more for items that are.
The key factor in being able analyze customer retention is having good data capture. Good data capture starts with your analytic tools which were mentioned above. Although software has this ability, the formula for retention is; (Customers at EOD X – New Customers at EOD X)/Customers at BOD X. To make it useful business owners need to analyze and segment this data daily. Business owners need to set their own metrics depending on average purchase frequency, customer history and other factors that are unique to their business. For example an online sporting goods site wouldn't use the same metrics as a high-end watch dealer for reasons such as purchase frequency. The purpose for retention is to be able to track your turnover and performance over time. It should grow and be on track with performance goals. 
Customer profitability is the process of assigning revenue and profits to a segmented customer base rather than to products and departments. By breaking down the segments makes it easier to identify what is actual working for a company in ways of marketing and the selling process. It may help them identify a target market that is more profitable than others and enable them to allocate resources accordingly for different products and marketing campaigns. Over time a customer profitability analysis can identify customers or groups that may have a future impact (good or bad) on a company and allow them to act accordingly now.