Lifetime Value Part 22: The need to take a long-term view

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 “Lifetime Value Part 22: The need to take a long-term view”

Analytics 3.0

As many know, I believe end-of-year predictions have zero value and I prefer to look at important trends that are already unfolding and will impact readers next year. The most important trend right now for people in the social media and gaming spaces, as well as almost anyone in the tech space, is the evolution of analytics. Thomas Davenport, author of the seminal work Competing on Analytics, recently wrote an article in the Harvard Business Review about Analytics 3.0. Just as Analytics 2.0 transformed the gaming space, allowing companies like Zynga, Playfish and Disney to leap over established competitors, Analytics 3.0 can reshape as dramatically the gaming ecosystem. Analytics 3.0 is a new resolve to apply powerful data gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products, services and games that customers buy.

Slide1

A brief history of analytics

To understand best the impact of Analytics 3.0, it is helpful to understand 1.0 and 2.0 and their impact. Analytics 1.0 ushered in an objective, deep understanding of important business phenomena and gave managers and leaders the fact-based comprehension to go beyond intuition when making decisions. Data about sales, customer interactions, production processes, etc., were recorded, aggregated and analyzed. For the first time, analytics were used to compete by creating greater efficiency: making better decisions on key issues to improve performance. Continue reading “Analytics 3.0”