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. [1]
Purchase Frequency
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. [2]
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.[3]
Retention Rate
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. [4]
Customer Profitability
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. [5]
[1] http://www.gonewnorth.com/how-to-calculate-metrics-for-customer-retention/
[2] http://mineful.com/customer-analysis/customer-purchase-frequency.html
[3] http://mineful.com/customer-analysis/average-purchase-amount.html
[4] http://www.gonewnorth.com/how-to-calculate-metrics-for-customer-retention/
[5] http://www.wisegeek.com/what-is-customer-profitability-analysis.htm
It is tough to just pick a few metrics to follow but once you can pin point the ones that are most important to your business it is great. These 5 are very important.
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