Predicting Lifetime Value (LTV) Before a Prospect Becomes a Customer

Emad Hasan: COO, RETINA

How Car Salespeople Figure Out Your Value to Them

 

Have you ever noticed how car sales executives try to estimate your financial worth with small talk and the questions that precede your visits to any car dealership store? 

These salespeople tend to ask some quite simple but rather absurd questions even before they check your credit card or previous purchase history.

You might think these small chit-chats are one of the ways used to make prospective customers comfortable. Of course not! It is rather a subtle way to determine your value, hence resulting in a higher conversion rate.

What you call chit-chat can be quite revealing, as it will help them answer important decisions like; whether or not you’re leasing, or direct purchase or even the type of cars they can recommend to you.

I will never forget the customer experience I encountered at my last car purchase in a car dealership store. I was literally judged by my looks and some quite outlandish questions by the salesperson before they reviewed my application.  My first step into the dealership came with questions like – “What kind of car are you driving now?”, “Is this for you or the wife or the family?” etc.

The answers to these questions give car salespeople the necessary information to determine ones’ long-term value to their brand. Long-Term or Lifetime Value (LTV) simply refers to customers’ long-term value to a brand in dollars.

LTV gives businesses insight as to advertising and retention cost that will convert a prospect into a long-term loyal customer.

Unlike the preliminary chit-chats between salespeople and prospective customers, online businesses don’t have the opportunity to chit chat and substitute the conversation with data such as income range, demography, gender etc. These attributes help digital marketers plan better and determine metrics that can help calculate accurate Lifetime Value (LTV).

Social/Digital Marketing as A Short Primer On LTV

The revolution of digital advertising in the last decade has helped bridged the significant gap created by traditional methods of marketing. Startups can now compete with giant rivals who have deep coffers to advertise in old media i.e. (print, radio and television).

Most online business marketers focus on short-termed successes with the CPA technique (Cost Per Acquisition)-The amount required to complete a one-time business transaction, CPC (Cost Per Click)- The amount required for a user to click on your internet ad and CPM (Cost Per Mile)- The amount required for every one-thousand impressions on your online advertisements.

Pros and Cons of Online Marketing Techniques in Relation to LTV

The CPA technique is usually used for business who want instant conversion on their products and services. Hence, it is more expensive than all the other types of online ad.  Most digital marketing campaign strategies focus on short-term success in their one-time transaction with immediate customers while LTV tends to focus on the how these customers can be converted to long-time loyal customers.

CPM, CPC and CPA are incredible techniques to get new leads and convert a prospect into new customer. However, it can’t be used to get long-term loyal customers. Albeit LTV measures the value of a customer in their long-term relationship with the brand and is a more suitable retention method for customers who have shown value over time.

Peter Fader, a renowned customer marketing professor, in his book “Focus on the Right Customers for Strategic Advantage”explained in depth the “Value Per Acquisition” metrics.

He related it with inequalities amongst customers themselves and how these customers can’t be regarded as equals. In technical terms, he defined Customer Lifetime Value (LTV) as the present value of the future net cash flows associated with the customer.

In basic terms, the LTV of every customer differs depending on the value they bring for the brands and as such should not have a flat marketing rate for every customer. In Peter Fader’s word “Not all customers are created equal”.

As small and medium-scale businesses that work with a limited budget, it is normal to maximize your marketing power to a very level. Therefore, the need to prioritize LTV over any other marketing metrics should not be an option.

In CPA/CPC techniques, advertisement cost usually depends on the competitiveness of the industry vis-à-vis the standard return on investment.

Wordstream- a global advertising solution provider recently released enormous data capping advert cost on Google network. They pegged average CPA cost across all industries at $48.96 for search and $75.51 for display. B2B niches had the most expensive CPA cost at $116 for search and $130 for display. Followed by tech industries at $134 for search and $104 for display in 2018.

The increase in Google advertising cost over the years have had little effect on CPC rate when compare to CPA. The average CPC rate across industries stand at $2.69 for search and $0.63 for display. Legal and Consumer Services rocked the CPC boat with an expensive rate at $6.75 and $6.40 respectively.

The data also revealed that adword search has a higher click-through rate than the display network.  Average CTR across all industries stand 3.17% for search and 0.46% for display in 2018.

One of Bluevine’s Technologies partner, Matt Estes once insisted that CPA, LTV and ROI are the only important marketing acronym. He mentioned a subtle relationship between CPA and LTV which many marketers seem to have neglected. It is the fact that LTV gives you an enormous insight into what your CPA should look like.  The ratio of LTV to CPA should be 3:1 for best business practice.

The fact that tech companies can pay $134 in advertisement cost for a one-time transaction has attested to the usefulness of LTV or CLV as the next most important metric for businesses to survive.

According to Mary Meeker’s 2018 internet trends report, social media ad engagement is on the rise for e-commerce businesses. “With social media, ad engagement is rising, represented by Facebook’s e-commerce click-through rates which are rose from one to three percent.

Social Media advertisement cost is said to be rising faster than the reach, which means advertisers would have to spend more to reach the same number of people as before.

One might argue the effectiveness of social media and google network digital tools like remarketing, demography etc. These figures are still pointing to one fact – Online Advert Cost Are Increasing. Hence, the need to focus on Customer Lifetime Value as a credible alternative.

The importance of LTV is rising as customer acquisition costs seem to increase daily. The rate of lifetime value with customer acquisition costs might become one of the most important metrics for retailers and brands.

Calculating your customers’ LTV will tremendously transform your organizational marketing cost by working with a more centralized data which focuses on customers’ individual lifetime value.

Organizations can now make informed decisions on product pricing and development, marketing budget, sales etc.  It can also be used as a basis to target most customers with high LTV and as such can result into having smaller loyal customers’ pool but a very high return on investment.

 

The Challenges of Calculating LTV

What makes LTV computation difficult is the complex information needed for the perfect CLV. Lifetime Value needs data like amount spent by customers, its frequency and duration of purchase. Hence, LTV cannot be calculated for prospective customers nor a one-time transaction.

Consider the figure below where both the first and second customer has the same number of purchases but their lifetime value is different because one customer has stopped spending and the other is still likely to spend in the future.

In order to accurately predict LTV, businesses must have at hand;

  • Lots of customers
  • Customers with more than a year of purchasing activities
  • Lots of computation power.

Prof. Fader and his team have so far done the best job computing LTV based on lots of customer history.

 

This method of computing LTV (also known as the Pareto-NBD method) still has few limitations and pitfalls. Pareto-NBD and similar analysis heavily rely on lots of historical data about people’s purchase behaviour to predict LTV. This means that by the time you compute LTV at the customer’s level, you have already spent your acquisition dollars and computing LTV might give rise to uncertainty.

Imagine if a car dealer had to wait for several car payments history before deciding what deal to give me? The best companies today using these methods compute LTV way before the first few transactions.

How to Estimate LTV Before Leads Become Customers

What if you could predict LTV in advance of a customer’s second purchase or even before first purchase. In today’s business ecosystem, enterprise can easily predetermine certain characteristics of their customers with their actions that may be able to tell us what their LTV might look like even before a customer converts to a paying customer. These actions can be anything from the time of day and week the user lands to what products and pages they like to interact with the most. Moreover, you can add more data by connecting with data from third-party providers.

In summary, you should be able at least get an LTV estimate by the time your customer has the first interaction with your products or services.

The approach we have used does exactly that and takes advantage of the fact that we have a very rich dataset of customer profile, marketing and product interactions prior to any transaction and we can use modern classification algorithms to take advantage of this data.

LTV is counter-intuitive in the sense that it’s not only telling who your good customers are but also telling you your “one-time” customers so you don’t need to worry when they eventually do not come back.

How to Calculate Customer Lifetime Value (LTV or CLV)

The most comprehensive approach is to combine the best of Pareto-NBD models with the modern machine learning techniques.

Step 1: Identify the four types of datasets in a database. i.e. transactions history, customer demographics, profile data, marketing actions and product/website/app actions.

Step 2: Forecast every existing customers’ behavior and predict the number of future transactions, predicted churn date and expected spend amount

Step 3: split the data into two sets; training and test.

Step 4: Add hundreds of features and attributes about the customer to this data set.

Now the dataset should look very familiar and ready for machine learning models explained above.

Note: There are several variations which were not covered that normalize for cohort behavior and multicollinearity. After doing that, you can model out e-LTV (Estimate of LTV using pre-data) or predicted LTV based on attributes that are not related to customers’ transaction behaviour.

What can you use LTV for?

Lifetime Value at the customer level can drive a lot of value to a business. Here are some of the benefit of LTV to business

Call Center:

  • You can use LTV to determine how much of a refund/promotion an unsatisfied customer is worth—A $10 rebate for a customer worth $500 is certainly worth it.
  • Call centers can also be used to push the right kind of offers.
  • Always remember that keeping customers’ happy increases LTV.

Advertising: You can use LTV to;

  • Invest in ads designed to target high LTV demographics.
  • Adjust your creative to appeal to your highest LTV customers
  • Personalize email campaigns to engage customers based on their LTV segment. Personalization is key to success, and LTV // transaction history empowers you to do so

Business Insight: LTV helps you;

  • Dig into the reason why certain customer segments have low LTV
  • Helps you answer vital questions like; Is there a way you can enhance their experience?
  • Use surveys to explore low-LTV demographics
  • Compare low-LTV product interaction with high-LTV product interaction

Budget Allocation

  • You can use LTV to justify expanding/contracting budget per acquisition channel and/or promotional content

Business Strategy:

  • LTV can be used Explore markets with high LTV but low penetration.
  • Your CLV/CAC ratio helps you understand how healthy your business model is—Focus on optimizing this ratio is key to growth.  Having a 3:1 CLV to CPA level is the best
  • Loyalty programs encourage repeat purchases and retention- It costs less to retain a customer than acquire a new customer.
  • Use referral programs to reward your loyal customers and generate new ones. (A prospective customer is 4x likely to buy something recommended by a friend).
  • Use convenience/vertical integration to build loyalty, trust and a stronger customer base

Other Uses

  • LTV can be used for early value driver’s analysis, to see which behaviors are good indicators of high LTV.  You can also model your acquisition on reaching prospects who look like the highest LTV customers. 

About Retina

At Retina we obsess over increasing customer LTV and reducing CAC (Customer Acquisition Cost) through data science.

As the cost of customer acquisition skyrockets, it is important to get focused on LTV at the customer level. Companies who computed their LTV to CAC ratio wrong are failing in an increasingly competitive environment i.e. (Blue Apron, Wayfair, Chef’d). Companies that get it right are achieving sustainable profit and growth i.e. (FAANG companies, Dollar Shave Club, Ring, etc.).

The Retina Platform computes predictive-LTV and early customer behavioral drivers of LTV, at the customer level, using next-gen machine learning algorithms (Built on 30 years of academic research). Within a matter of days, Retina automatically builds audiences (Facebook, Google, LinkedIn, Snap) for your marketers to acquire new high-LTV customers and retain your existing high-value customers.

Interested in what we do? Log on to http://retina.ai/story for a more detailed presentation about what we do, how we work and how we can help your business grow to the next level.