Machine Learning for Transactional Analytics: Customer Lifetime Value v/s Acquisition Cost

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Machine Learning for Transactional Analytics: Customer Lifetime Value v/s Acquisition Cost

Understanding customer transactional behavior pays well for any business. With the tsunami of start-ups in recent times and the immense money flow in businesses, customers find lucrative offers from companies for acquisition, retention & referral strategies. Understanding the transactional behavior of a customer has become even more complex with the advent of new business houses every day. Although with the rise of powerful machines, one can easily manage to work with TBs of data, the complexity of business economics has made this behavioral analysis far more difficult.

Collecting and analyzing your business data on all aspects such as acquisition cost, operational cost, base profit, revenue growth, referrals, etc can help in providing the lifecycle profit patterns from a customer. But it does not help in solving many business questions such as:

What is the actual value of a new customer in Dollars worth today?

How much money business can spend to acquire a new customer?

Let’s take an example to understand it more intuitively. Firstly, to estimate the value of a new customer, we have to know the annual profit patterns or cash flow patterns if the cash flow pattern differs from the profit pattern of a customer. Secondly, we need to figure out how many years customers stay with your business.

The figure above shows customer profits for an imaginary firm based on all factors mentioned earlier. Customer value keeps on increasing with the time for which customer stays with the company. Customer who stays 2 yrs will generate $26 of profit ($80 acquisition cost balanced in first 3 years profits $40 & $66. If the customer stays for 5 years, will generate $264 in total (-$80+$40+$66+$72+$79+$87). But the differences in customer value are very large. For the same calculations, if done for 10 years, customers will generate a net worth of $760.

It would not be wise to spend $760 today for a customer who will stay with the company for 10 years as the profit generated in the future would not be equivalent to $760 today. We need to apply discount computing to take it to present value. Using a standard 15 percent discount rate will make $760 to $304. (To get the net present value of first-year profit, therefore divide $40 by 1.15, for next year divide $66 by 1.15, and so on). So for a customer who will stay with the company for 10 years, one can pay up to $304 on acquisition costs. Now we know how to calculate the value of customers based on their life expectancy of customers.

Customer Acquisition Cost vs Lifetime Value

Customer Acquisition Cost or CAC provides information about what losing a customer may cost your business, while Customer Lifetime Value or CLV shows how much revenue each lost customer could potentially bring to your business. With this knowledge, you can better plan your budget and strategy with your marketing team.

Customer Lifetime Calculations

The next question is what is the expected duration of a customer to stay with the company? To answer this, we have to find out retention rates for a customer. It is a fact that retention rates vary among customers based on age, profession, gender, acquisition source & maybe more than dozen variables. The simplest way to calculate average customer stay time is to calculate the overall defection rate and invert the fraction. First count the number of customers who defect over a period of several months, then annualize this number to get a fraction of the customer base to begin with. e.g. you lose 50 customers out of 1000 customers over three months. This works to 200 customers a year or 1/5 of all customers. Then we need to invert this number, it will become 5. So now we can say, on average, a customer stays with the company for 5 years. In percentage terms, the defection rate for customers is 20%.

Lifetime Calculation Improvements

To estimate customer cash flow accurately, we need to refine the above-mentioned calculations. Firstly, we have assumed defection rates are constant throughout the customer life cycles. In real life, such is never the case; defection rates are very much higher than average in the early years and much lower later on. Taking averages may lead to over or under-estimating the profit numbers. Additionally one more refinement we need to make to calculate the true value of a customer. Instead of trying to calculate the value of a single, average, static customer at a single moment, we need to think in terms of annual classes of customers at different points in their life cycles. In the real world, the company acquires new users each year. some of them defect early, others may stay for years. But the company invests money in the entire set of customers. So, to get the present value of the average customer, we must study each group separately over time.

Let’s take a scenario as shown in the above image, where 100,000 new customers enter at time zero. The company invested $80 at time zero making it to a total of $80*100,000= $8 million for the whole set of customers. By end of year 1, 22% of customers defected, and only 78% left, to pay back invested 8 million. By year 5, more than half people defected. To get the present value of a customer, we will estimate the set of cash flow people generate till the time they defect. Earlier in the blog, we get the current value of the customer at $304. At a constant rate of the defection of 10%, we may be dangerously wrong in deciding the money to be invested in customer acquisition whereas the actual defection rate shown in the above image makes this number only $ 172 from $304. Imagine a company spending $200 on new customers based on earlier calculated values. It would be a completely loss-making venture.

Machine Learning Scope

In the above calculations, we tried to approximate the customer lifetime value & corrected ourselves initially from $ 760 to $172. It still contains assumptions of the same cohort’s transactional behavior. Every individual behaves in a distinct way. When we plan to target a customer based on a machine learning-based marketing campaign then why not calculate Customer lifetime value for every unique customer? Based on certain pre-defined variables, one can easily predict the lifetime value of a customer & can strategize accordingly.

One can also add that every organization is getting more and more transactional data every day making it difficult to manage especially, in the presence of numerous acquisition-dependent variables, to get accurate accounting numbers. Moreover, the transactional behavior of customers has also been largely influenced by various offers and incentives from cash-burning start-ups. Making use of RFM ( Recency-Frequency-Monetary)- a “magical marketing triangle” with advanced statistical methods considering customer irregular transactional behavior, can help in creating a probabilistic machine learning model to do wonders for business economic predictability.

What is Transactional Data?

In simple terms, transactional data is the data collected from transactions. It shows information about where the transaction occurred, when it took place, the process, duration, parties involved, the channel used, and so on. Information about the payment method, coupon code/ discount applied, and the points added to the customer loyalty account are also a part of transactional data. 

Transactional data is captured at the Point of Sale (PoS), though it is also extracted from payment gateways, credit card records, ATM transactions, etc. This data is used to analyze the purchase patterns and payment preferences of customers. 

However, analyzing transactional data requires data cleaning and structuring. Transactional data comes in different formats and has to be processed before it can be analyzed. 

What is Transactional Data Analytics? 

Transactional data refers to transactions of the organization and comprises the captured data. It helps businesses use data to measure the results of their campaign and check whether they are working in the right direction or not. Transactional data analytics brings more flexibility in aligning the strategies with KPIs and increasing customer experience. It helps enterprises with the following: 

  • Create category-based recommendations
  • Introduce new products to customers
  • Customer segmentation based on multiple factors 
  • Identify the correlation between different customer categories 
  • Provide data to create customer profiles
  • Assist in building long-term relationships with customers 
  • Measure the efficiency of email marketing 
  • Calculate sales value 
  • Track the changes in conversion rate and optimize it 

Now how do we implement this in the business operations?

No need to worry, machine learning companies like DataToBiz have the right kind of experts who will help you in incorporating this technology in the best possible manner.

Transactional Analytics and Automation

Transactional data analytics can be automated to help marketers get reports based on real-time information. This helps create personalized marketing campaigns and recommendations for each customer segment. Enterprises don’t have to run transactional analytics separately. The process is automated using advanced analytical tools. The tools are integrated into the existing systems to share data with employees and help with decision-making. 

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We here at DataToBiz connect businesses to data and excel in cutting-edge ML technologies in order to solve most of the simple and trivial problems of business owners with the help of data. Feel free to Contact Us.

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