Tag: LTV

Lifetime Value Part 17: How Groupon shows the value of understanding LTV

I have written many times about customer lifetime value (LTV), but primarily from a theoretical framework. In this post, I will use Groupon to exemplify many of the key principles at work with LTV. Groupon is very well known, particularly in the United States, as a coupon or discount-deal website that normally offers 50 percent off deals with local merchants, particularly restaurants, spas and similar retailers. Started in 2008, it went public in 2011 and currently has a market cap over $4 billion.

The Groupon problem

One of the biggest issue Groupon has run into the is the perception that most retailers who run Groupon promotions find them highly damaging, and there are frequent stories of Groupon promotions that have put companies out of business. It is easy to see how this could happen, as Groupon typically gives customers a 50 percent discount and then keeps 25 percent of the remaining funds. Thus, a retailer only sees a total of 25 percent of the normal revenue they would from the consumer if the person had made the purchase without a Groupon. Since most retailers do not have a 75 percent margin, they will lose money on the Groupon. Moreover, because of Groupon’s strong distribution, they may lose a lot of money.
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If a retailer, however, understands customer lifetime value, it can make the right decision about the value of offering the Groupon and whether it is a positive to their store.

The importance of retention

The first element of LTV that is crucial to Groupon success is retention. If people normally come to your business once and never come back, the Groupon is not going to work. You will lose 75 percent of the check or bill and never see the customer again.

Conversely, if once you get a customer they come back twice a week for six months, then the Groupon is a great marketing tool. While you will lose money on the initial transaction, you will have 48 (2 times per week, 4 weeks per month for 6 months) more profitable transactions, which will more than cover the loss. As I discussed in my post about retention, retention is the key to success in any business and the Groupon examples shows the impact of weak versus great retention.

How virality plays into the equation

Virality is also a key to the success of a Groupon. If people use the Groupon deal but do not tell friends about it, then the Groupon must generate enough lifetime revenue from that user to be a new positive. While this is possible, it increases the risk that the Groupon initiative is not successful if you do not have strong retention.

If you create, however, an experience that people tell their friends about, and then their friends try your establishment and you can retain them, the value to you of offering the Groupon increases dramatically. I have written before about how to create strong word of mouth, and it is more than just offering a good product, but if you follow the STEPP model (create a product or offering with social currency, triggers, emotions, practical and public) and users bring in more users, the Groupon can have tremendous impact.

Let’s say someone buys the Groupon and comes into your store. Because of the discount the purchase is a net wash and the consumer value is no more or less than the cost of servicing him/her with the Groupon. If, however, the consumer persuades five friends to also visit, and each of those friends adds $20 of profit, then the Groupon has generated $100 in profit for you.

Why segmentation by LTV is so crucial

The other key lesson regarding LTV that is exemplified by Groupon is the necessity for understanding different segments of your user base and how Groupon users fit into these segments. Many establishments (let’s use restaurants for this example) have found out that Groupon users do not fit the same model as their other customers. While they may normally see great retention and virality, Groupon customers just go to wherever they have a coupon and do not revisit or talk about establishments. Thus, a restaurant may normally have a very high LTV for a typical user, say $100, so if the Groupon cost them $50 per user, long-term they would still make $50 from the promotion. However, if the Groupon customer exhibits different traits and only uses the Groupon and never comes back, the restaurant loses $50 per customer. In many ways, Groupons can be looked at as the equivalent of incentive installs in the app space; while there is a role for this channel you must measure the value of these installs differently than other marketing channels.

Thus, it is important to see how the Groupon demographic normally behaves for the establishment. If you expect the Groupon to generate male users with an average age of 25,  with a low income, look at the LTV of that user segment when estimating the impact of the Groupon rather than the LTV of all your customers. Also, do a test (limited number of offers) and get data on how Groupon users perform compared with other marketing channels, and divert your resources where they will have the greatest yield between marketing cost and lifetime value.

It is not about tricking your customer

One thing that you will notice I did not discuss is the common practice of trying to trick users of Groupons into spending more to lessen the cost of the Groupon. You can adjust the amount of the Groupon so the typical session cost or restaurant check is not covered, the user spends more, and you either profit from the Groupon or lose less. This technique ignores the underlying issue that you are trying to build a business with a strong long-term stream of profits. Manipulating customers short term could pay a few bills or extend your runway a month, but unless you address the lifetime value issues you will be left with nothing long-term.

The right decision making regarding Groupons

Despite the popular Techcrunch piece, “WhyGroupon is Poised to Collapse,” Groupon is not a loan shark or other immoral business. Rather it is another marketing tool that advertisers will only use successfully if they understand and can optimize their customer lifetime value. If they have a low LTV, nothing is going to help. Their Groupons will fail, but so will their advertising in newspapers or online (or the guy wearing the sign outside the restaurant). If they can create customers with a high LTV, they will see a long-term positive net return from offering Groupons.

Also notice how monetization is not the key driver here of success. It is less about how much you make or lose on the initial transaction that leads to program success, and more about how well you generate virality (and how good your virality is). Even if you improve your margin slightly, the impact of improving monetization tied to the Groupon will be much less than the impact of the other two variables (retention and virality) .

Key takeaways

  1. Groupon promotions showcase the importance of LTV. Despite negative press, Groupons can be successful if you understand and can optimize your customer lifetime value.
  2. Your Groupon promotion will only work if you can generate strong retention or great virality. If customers come back or tell their friends about your business, then the Groupon will have a positive ROI.
  3. Monetization is not a big factor for Groupon success. A change in monetization may make the program cost less but long term success of the program and your business depends on strong retention and virality.

Lifetime Value Part 15: Five ways to use data to improve customer lifetime value

I recently came across a great post in Wired by Neil Capel about leveraging data to increase lifetime value. I have written many times about how lifetime value is the lifeblood of your business. A high lifetime value allows you to spend more on marketing and thus grow your business; low lifetime value makes it impossible to acquire new users. In Capel’s post, he outlines five ways you can leverage data to increase your lifetime value.

1: Use data to understand customer interests to create relevant content

Customers and players face an overwhelming amount of information and content. They are also not looking, and actively avoiding, advertising. What they want is information that is relevant to them. Customer’s interests and needs change constantly and you can tap into that inferred nature of the data to determine which elements of your content will be the most relatable and consumable to each user. Leveraging data you can determine which pieces of content an individual wants to interact with and then use that information to deliver automatically current and relevant content to that individual.
Slide1 Continue reading “Lifetime Value Part 15: Five ways to use data to improve customer lifetime value”

Lifetime Value Part 11: How to calculate LTV

Last year, I published a series of posts on the importance of knowing your users’ or players’ lifetime value, the key components and how to impact them and techniques to increase the accuracy of your customer lifetime value (LTV) predictions. I intentionally did not publish a formula for calculating LTV—while it is always a factor of retention, monetization and virality—as it is different by product and there are many alternative ways to get to an accurate customer lifetime value. Prompted by an infographic that I came across (see below) I did want to go into some details of the mechanics of calculating LTV.

The first step is to obtain your key variable metrics as averages across all users. The ones I prefer are ARPDAU (average revenue per daily active user), day 1 retention (how many people who use or install your website, app or game come back the next day), day 30 retention and k-score (how many free/organic users does a user bring in). Continue reading “Lifetime Value Part 11: How to calculate LTV”

What is machine learning and why it is crucially important

A few weeks ago, I posed a question on Quora about the differences between machine learning and predictive analytics. I was surprised at the number of people who started following the question and actually liked it (although it sounded rhetorical, I was hoping to understand machine learning better). A recent article in Fast Company about The New York Times, of all companies, did a great job of explaining the fundamental value of machine learning.

My interest in machine learning was ignited recently as it has become the hot buzzword in the Bay Area; some have argued if you add machine learning to your PowerPoint you can add a zero to the end your company’s valuation. While that claim is obviously an exaggeration, investors are among the savviest businessmen, so their interest in machine learning shows it is a crucial emerging space.

The article discusses Chris Wiggins, a biologist The New York Times just hired as its Chief Data Scientist. Wiggins’ mandate is to build and lead the Times’ machine learning team. In Fast Company’s interview with Wiggins, it became clear exactly what machine learning is, how it is different than predictive analytics and why it is important.

What is machine learning

According to Wiggins, “Machine learning sits at the intersection of data engineering and mathematical modeling. The thing that makes it different from statistics traditionally, is far more focus on building algorithms.”

University of Utah image for machine learning

Also, while statistics is traditionally focused on explaining data, machine learning is geared to building predictive models. When Netflix or Amazon make product recommendations to you, they are using machine learning to predict what you would be interesting in experiencing. Continue reading “What is machine learning and why it is crucially important”