Archives For LTV

I am a huge proponent of using analytics and other metrics to drive business decisions, but I repeatedly see people making a huge and avoidable mistake. Instead of using the data to determine the best strategy, they use data to justify their intuition. A good analyst can use data to draw virtually any conclusion and if the analyst is pushed in a certain direction by the business leader, all the data does is provide people with cover for the decision rather than leading you in the optimal direction.

The same situation applies to financial analysis. I have seen people frequently manipulate numbers, often with the approval or even encouragement of the target audience, to tell the story people want to hear. I have seen this manipulation in sales, in corp dev and in internal forecasting. In all situations, it is actually just a rationale to make a decision the person already wants to make.

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Data manipulation

The first part of the problem is manipulating the data. I am not talking Enron here, but more subtly and maybe not even intentionally. People will often select the data that supports their position while discounting the other information. If you want to greenlight a certain feature, you may look at the impact on retention while neglecting the impact on monetization and rationalize it by saying it is a retention feature. Regardless of whether it is a retention or monetization, your goal is to optimize lifetime value (LTV) so you need to look at the data holistically. Continue Reading…

A key to predicting and effectively using customer lifetime value (LTV) is to take a long-term view of your data and not just rely on the first month or even first few days. Many marketers will draw conclusions about a new product launch, a new feature or a unique customer cohort based on the initial data they generate. While you cannot wait months or years to make crucial business decisions, understand that these predictions are less reliable and thus making decisions based on this data is problematic.

The challenges

While intuitively more data is always better, there are challenges involved in looking back over a long period. First among these challenges is customer attribution. If you are determining the value of a specific growth channel, do you credit the lifetime spend of a user to the channel you used to acquire them initially or do you attribute the revenue to a channel (Facebook feed, email, A2U notification, etc.,) that brought the user back after a long period of inactivity.

The second issue is the sheer quantity of data. If you have millions of customers or players and years of data, it becomes quite a challenge to process all of that data. You may have multiple interactions with that user every day, literally for years. Think of how you interact with Amazon and consider they track all the products you look at, how often you visit, what you purchase, what you purchase instead, etc. You need the software, data warehousing and systems so that you can actually analyze this data quickly. Continue Reading…

As this is my nineteenth post about customer lifetime value (LTV), I obviously think it is very important, but I wanted to take some time to provide examples of how it can impact almost any business. Even if the examples do not cover your initiative, they will hopefully help you see how understanding, marketing and designing for LTV is crucial to any company’s success. Examples range from tech companies to business types that have been around longer than the United States. The breadth of companies that LTV is critical for shows its central importance.

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Mail order catalogs

Catalog companies, from the days of Sears and Montgomery Ward, to the current heavyweights like Restoration Hardware and Crate & Barrel, have always needed a deep understanding of LTV to succeed.

With the cost of printing and mailing catalogs, these merchants need an LTV higher than the shipping/printing costs. Thus, they have to first understand different customer segments (e.g., location/postal code, sex, age) and only send catalogs to those people who will have a higher LTV. If they sent their catalog to everyone, the average LTV would decline and make their efforts unprofitable. In addition to understanding the LTVs of each segment they have to optimize along the three key LTV variables: Retention, monetization and virality. If a person reads through the catalog once, makes an order and never picks up the catalog again, it is hard for their value to be higher than the costs of shipping them the catalog. If they, however, keep the catalog and place ten orders in a six-month period, the LTV is likely to exceed to costs of sending them a catalog. Monetization is also critical. If they love the catalog, keep it on the coffee table, but never make a purchase, the merchant loses. Even if they make very small purchases the merchant proposal loses. Successful direct marketing companies succeed by getting larger shares of wallet from their customers. Finally, virality is important even for a non-digital good. If the person shows the catalog to ten family members or friends (who have an equal potential to buy), then the costs of sending a catalog are effectively one tenth as you are reaching 10X people. Continue Reading…