As a customer experience professional, do you have to be a trained statistician?
Thankfully, no. But it is important to have at least a foundational understanding of statistics. As we all know, statistics play a big role in measuring customer satisfaction and tracking progress over time. And formulas are how we develop our own parameters to capture those statistics.
There are a variety of different calculations that you should know, but below I’ve compiled a list of the ones that have been the most helpful throughout my career thus far.
From calculating Customer Lifetime Value to following the Erlang C Formula, these mathematic rules will help you better understand your customers and make more informed decisions about your CX strategy.
So, let’s dig in.
Customer Lifetime Value
Customer Lifetime Value (CLV) is a key metric for assessing the health of your customer relationships. It represents the total value of a customer to your business over the course of their lifetime and can be used to calculate how much you can afford to spend on acquiring new customers and retaining existing ones.
There are a variety of different methods for calculating CLV, but the most common one is to take the average annual value of a customer multiplied by the number of years they're expected to remain active with your company.
So, if the average customer spends $500 per year and remains active for 10 years, then their CLV would be $5,000.
CLV is best used when assessing long-term value of your customer types. For example, if you have a high CLV in a target market, then it may be safe to begin branching out and spending more time on acquiring new customer bases.
Conversely, if your CLV is low, then you'll need to focus on strategies for increasing it — especially for key customer accounts. There are a variety of different ways to do this, such as improving contact center processes or by establishing loyalty programs.
But that’s an article for another time.
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Customer Acquisition Costs
Customer Acquisition Costs (CAC) are the costs associated with acquiring new customers. In other words, it helps you assess the efficiency of your customer acquisition efforts and identify areas for improvement.
Again, there are a variety of different methods for calculating CAC, but the most common one is to take the total marketing and sales expenses during a specific period and divide it by the number of new customers acquired during that time frame.
For example, if you spend $1,000 on marketing and sales in a month and acquire 10 new customers, then your CAC would be $100.
It probably goes without saying, but if your CAC is high and your ROI is low, then you'll need to focus on strategies for reducing that initial cost.
Related Article: Customer Acquisition Really Stinks Sometimes. Here's Why
Service level measures the percentage of customer service inquiries that are answered within a certain timeframe. This calculation will help you assess the quality of your customer service.
The most common method for calculating service level is to take the number of customer service inquiries that are answered within the designated timeframe and divide it by the total number of customer service inquiries received during that time frame.
For example, if you receive 100 customer service inquiries in a day and answer 80 of them within 24 hours, then your service level would be 80%.
Utilization rate is a metric that measures the percentage of time that a resource is in use. It's an insightful metric for CX professionals to use both internally and externally.
One way to calculate your utilization rate is to take the number of hours that a resource is in use and divide it by the total number of hours that it could be in use.
For example, if your team has a meeting room that can be used for eight hours in a day and is only used for 4 hours, then the utilization rate would be 50%.
The same formula can be used for customer-facing resources as well.
Erlang C Formula
The Erlang C Formula is a mathematical equation used to calculate the number of customer service agents needed to handle a certain volume of customer service inquiries. It's important because it can help you assess the efficiency of your contact center operations.
The Erlang C Formula is derived from the Poisson Distribution, which is a probability distribution that describes the likelihood of a certain number of events occurring in a given time period.
To use the Erlang C Formula, you need to know the following information:
- The average number of customer service inquiries per hour.
- The average length of time it takes to answer a customer service inquiry.
- The desired service level (expressed as a percentage).
Once you have this information, you can plug it into the formula and solve for the number of customer service agents needed.
Here's an example:
Let's say that you receive an average of 10 customer service inquiries per hour. It takes an average of two minutes to answer each inquiry. You want to maintain a customer service level of 95%.
Plugging this information into the Erlang C Formula, we get the following equation:
10 * 2 * 0.95 = 19
This tells us that we need 19 customer service agents to maintain a customer service level of 95%.
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Little's Law is a statistical formula that can be used to predict the throughput of a system. It's a powerful tool to use throughout customer experience departments.
The formula for Little's Law is:
Throughput = Average Customer Lead Time x Average Customer Arrival Rate
For example, if the average customer lead time is two minutes and the average customer arrival rate is 1 per minute, then the throughput would be two per minute.
Therefore, if your throughput is low, then you'll know to focus on strategies for increasing it.
This one is a little more difficult to understand, but Bayes' theorem is a statistical formula that allows you to calculate the probability of an event occurring, given that another event has already occurred. Its best use case is assessing the impact of customer feedback on your business.
The basic idea behind Bayes' theorem is that the probability of Event A happening is equal to the probability of Event B happening times the probability of A happening given that B has already happened, divided by the probability of B happening.
For example, suppose you want to know the probability of a customer being satisfied with your service or product, given that they've left a positive review. The probability of the customer being satisfied is equal to the probability of the customer leaving a positive review times the probability of the customer being satisfied given that they've left a positive review, divided by the probability of the customer leaving a positive review.
So, if you have a high probability of customers being satisfied with your product, then you know that customer feedback is likely to be positive. (And therefore, the converse can be assumed.)
Conclusion: It's All About CX Data That Matters
So, while you don’t need to be a mathematical genius to succeed in this field, even just understanding the importance of statistics when it comes to formulas like utilization rate, Bayes' Theorem, and Little's Law, you can make sure that your business is providing the best possible experience for your customers.
And whether your findings are positive or not, backing them up with hard evidence is often the best approach when it comes to internal buy-in.
If you want to learn more about how to improve your own skills in this area, consider taking a statistics course or attending a conference. There are many resources available to help you become a better statistician.
With this type of knowledge, you’ll have what you need to take your business to the next level.
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