Successful customer acquisition is a basic condition for market success.
If you want to enter a business, you have to either know how to acquire your customers or find people that can do it for you. Either way, you have to spend money and have to keep spending if you want your business to be stable or even grow further.
Because of this, Customer Acquisition Cost (CAC) is one of the most important metrics in business.
How to calculate CAC
CAC is the money spent on acquiring one customer. The value can be calculated dividing the entire marketing costs spent, for example in a month, by the number of customers acquired during that period. Therefore, a business has to carefully analyze their spendings related to customer acquisition and determine the number of newly acquired users or customers.
How to minimize CAC
There are many ways to keep the CAC low. You may define your buyer persona, focus on customer retention or highlight your USP. One of the most effective ways to reduce CAC sustainably – apart from marketing automation – is diminishing customer churn.
Why are customer retention and churn prevention so important?
Churn prevention is a very important step to lower customer acquisition costs since customer acquisition is usually much more expensive than customer retention. For example, acquiring a new customer can cost five times more than retaining an existing customer. This means, if you have high churn, you have to spend much more money, in order to get a constant customer and revenue flow. This is in particular a problem for mobile apps and subscription services.
What is the best strategy to prevent churn?
There are many effective strategies to prevent churn. One of the best strategies is to:
- Recognize in time who is going to churn most likely
- Find out, why churn occurs
- Designate the most valuable customers
In the past, companies had to look for analytics and marketing specialists who could answer these kind of questions. Even today, a considerable amount of time is required in finding the right people for these jobs. Additional time is required for them to collect the necessary data to start their task. Even after spending much time, the final results sometimes lack critical information, contain human errors, or are simply outdated.
However, with the most recent advances in Artificial Intelligence (AI) and machine learning, predictive analytics is the solution to mitigate these drawbacks. In particular, tools such as churn prediction, purchase prediction, and customer lifetime value (CLV) prediction help to identify possible churners and also identify the most valuable customers ahead of time.
How AI helps to lower CAC?
Using AI-based churn prediction, it becomes easier for companies to prevent churn and lower their CAC. Nevertheless, increasing retention is still a difficult and challenging task. While churn prediction is a helpful tool and assistant, there still remains work to be done by humans to prevent the churn from actually happening.
Using machine learning, companies can analyze all available information to not only find the most important reasons why someone would churn, but also when and who this may probably be.
With this data at hand, companies can find ways to increase customer satisfaction by, for example, making good offers or providing better services to their most valuable clients.
On top of that, AI-based churn prediction will get better with time as more and more information is fed to the system and this makes the system more accurate. In the long term, an AI-based churn prediction is more profitable than the manual churn prediction and minimizes the manual labor.
You can learn more about churn prediction on our blog. For example, Better Marketing using Churn Prediction and Game Data Mining or on our product detail page.
When running or growing a business, one should not neglect the impact of retention and a low churn rate. A low churn rate can substantially lower customer acquisition costs, since fewer new customers are required to keep a stable customer base. Using an AI-based churn prediction, it becomes much simpler to retain valuable customers, as one has an efficient and effective tool at hand to be alerted about churn. On top of that, the underlying machine learning model can provide valuable insights into customer behavior and helps marketers and product managers to better understand the reasons for churn.
As we can see from this example, AI is becoming an important tool across marketing activities. It is a technology that defines the future in every company across countries and industries. It is a technology that makes the difference between failure and success. Now is the right moment to enhance your churn prevention campaigns with AI and machine learning.
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