No more leaving money on the table: A KPI-driven data strategy for the gaming industry

kpi-data-strategy-gaming

Business strategy or Data strategy? With gaming, it’s both

Monetization and engagement: the northern stars of business success in the gaming industry. Business strategy should optimize for those, and integrate the data element at its core.

It’s simple, for business users, having a data strategy in place equals making informed business decisions, where data is the main driver of success.

Successful gaming companies are the ones capable of monetizing their data based on an understanding of their customer’s preferences. This course allows them to target the relevance of their product/ service offerings to their audiences and create that competitive edge.

KPIs are the mean through which data informs business decision-making. They are required to determine how an organization successfully meets its business goals. KPIs also help organizations understand if they are headed in the right direction or whether the attention needs to be diverted/ re-directed.

It is, however, essential to measure the right kind of KPIs based on your data. Before your data becomes actionable, you must define attributes that matter to your performance. Good KPIs, according to KIP.org, are said to:

  • Provide objective evidence of progress towards achieving the desired result.
  • Measure what is intended to be measured to help inform better decision-making.
  • Offer a comparison that gauges the degree of performance change over time.
  • Can track efficiency, effectiveness, quality, timeliness, governance, compliance, behaviors, economics, project performance, personnel performance, or resource utilization.
  • Are balanced between leading and lagging indicators.


KPI cluster frameworks

From work done with our customers in the gaming space, we’ve identified two metric clusters for analytics we found to be most compelling according to our aforementioned northers stars: user and user engagement and business monetization metrics. The user and usage metrics are crucial data about your audience, illustrating which users your game appeals to.

Engagement metrics are insights into user behavior on how they interact within the game. This will, in turn, affect the retention of a loyal customer base and optimization of your monetization. Monetization metrics track user acquisition providing data that informs you on how a game is fit for the market and what you can do to optimize for revenue. Essentially the 2 clusters are linked, and the KPIs themselves are interconnected as the definition of one affects the other.

*See the engagement and monetization funnel:

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Engagement:

DAU/MAU: Daily/ Monthly Active Users are the “genesis metrics” and represent the number of unique users that log in to or start a session within 24 hour/ or 30-day period. These metrics are fundamental as they affect other metrics within the same monetization cluster: they help determine retention and lifetime value.

Retention: can be broadly understood as the percentage of users returning to the game after a given period. In free-to-play/ subscription models, this is a critical metric as it helps highlight the relevance of a game. The simplest way of finding retention is by taking the total number of users in a specific period and dividing it by the total number of users in a previous period (i.e., using DAU/MAU).

Churn: churn is the number of people who downloaded and installed a game but are no longer playing it or the rate at which users uninstall or unsubscribe. “High churn is bad and low churn is good,” but beyond this vanilla statement, high churn means something needs to be fixed with the experience to ensure people don’t abandon after downloading. What is most important about the metric is how it connects to retention and how many days of no play are indicative of abandonment. Generically, this metric is calculated as lost gamers over a period divided by the number of total gamers (x100).


Monetization

ARPU/ARPPU: ARPU is the Average Revenue Per User. Knowing this is key to business plans and future revenue projections. It indicates the perceived value of a game and can almost be called the “investor metric” The main purpose of this metric is to understand how to engage players and encourage them to spend more time on the game with in-game purchases.

It is essentially the average amount of money generated by one user over a period of time, and it is calculated by dividing the total amount of earnings by all your users (i.e., one-time downloads, monthly subscriptions, in-game purchases, content downloads, etc.)  by the total number of users in at a given time. Average Revenue Per Paying User is the average revenue generated from each paying user during a specific period. Although it is similarly calculated by dividing the revenue by the number of paid users, this metric will always be higher than the ARPU since it only accounts for active paying customers rather than the entire customer base.

LTV: The lifetime value in gaming is the total value of a single user over the life span of playing a game. LTV represents the revenue earned from a single user through the entire lifecycle of a game. Hence this metric is an insightful prediction of a player’s financial contribution, monetary value, and relationship with the game. This is fundamental since it will inform how much to spend on acquiring customers… We found this metric to be used for measuring a company’s growth.

CAC: The good old customer acquisition cost. This last metric shows, of course, how much it costs the business unit to acquire a paying customer. Understanding this allows us to match business requirements with strategies to attract new customers. It can include all ad costs and the time spent marketing the game. Calculating it entails dividing the total marketing costs by the number of new customers.
Having said that, if the goal is to understand how to improve business processes or for business functions to distribute spending across marketing’s strategic assets, the conversion would be a more important metric instead. Conversion measures the number of users that have made a purchase during a specific period. This means knowing the percentage of users who convert, including when and where.


The costs of not knowing what’s “under the hood”?

Now, imagine a gaming company that is going through a few gaming acquisitions…

…Now, few product lines are operating independently on many levels.

Regulations and compliance make the gaming company report on different financial metrics and KPIs, but “under the hood,” quite a few assumptions are being made.

Is the MAU of game “A” calculated the same way the MAU of game “B”? Different game divisions use the same naming conventions but not necessarily the same formulas or similar data aggregation. Game “A” division defines MAU as a user who plays once a month, while Game “B” defines MAU as someone who spent at least x credits a month.

Sometimes those definitions are documented in shared excel files or organizational Wiki pages, but, most of the time, this is tribal knowledge, which is hard to reverse engineer.

What is the damage of this misalignment? Well, firstly, management, who makes decisions based on those KPIs, is not always aware of the differences in calculations and aggregations. This can lead to many wrongly data-driven decisions since the learnings from one KPI’s behavior cannot be applied to another.

Secondly, static documented definitions of KPIs could be outdated, which usually leads to a huge overhead in finding the right person to ask questions about the definition of the KPI.

Thirdly, lots of missed opportunities for collaboration by analytics teams in different departments.

Peter Drucker once said “If you can’t measure it, you can’t manage it”.
Well, you cannot manage what you measure differently under the hood…

The good news is that the tech is not standing still. Tools like active metadata management and connected governance are used by organizations to scan data used and map the relevant clusters automatically. Ideally, a tool would help surface the misalignments in definitions and calculations automatically, allowing the organizations to decide on the correct way to calculate KPIs per division, and document it all transparently.

The game of business may be complicated, but if you are playing it right, you might just score a jackpot.

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