big data – The Business of Social Games and Casino

Trends in Analytics from BDA Conference

On Tuesday I went to the BDA Conference on Big Data Analytics. Conferences like these are always interesting to see at a high-level how analytics and its uses are evolving. This conference was no different and some of the trends that came through the various sessions suggest where future opportunities will be to leverage analytics:

A big challenge, and opportunity, is integrating data from multiple sources to get a more complete picture of your customers. Until recently, analyzing data in your product was the primary way to understand users (and play patterns in games) but now there is valuable data available from multiple sources. Data from social media (what people are saying about you and your product, sentiment, etc), data from beacons and other sensors, data from user acquisition, etc. When you integrate this data, you get a more complete understanding of your users and their motivations.
Data is connecting people and things, expanding the universe of data. There is now extensive data on how people interact with their surroundings and this will grow.
Using data is moving from the province of data scientists and analysts to everyone in the organization. This trend is driven by easier to use and manipulate tools, not by increased training. Designers and product managers and marketers are not becoming data experts but the tools now allow easy visualization, point and click charts, swipe and pinch access.
Top companies are now using the various data sources to understand holistically the customer journey and then driving activities to increase the value from the customer during their journey. The critical change is that you are using different data sources to pick up the user at different points (think of a race with cameras along the course and how the telecast switches between cameras).
People are now using, and expecting, data on a real time basis. Increasingly everyone in the organization has real time access to data and can drive actions based on this information. No longer are people waiting for the charts on yesterday’s activity.

Key takeaways

The universe of data is exploding, with multiple data sources and good analytics now blends this data to provide a complete picture of the customer.
Data is no longer being controlled by a few people in BI (business intelligence), user-friendly tools are allowing everyone in the organization to access and control data easily to enhance their decision-making
Data allows companies to see the entire customer journey, with different data sources filling in different parts of the journey.

Testing innovation opportunities

While almost everyone now accepts the value of analytics and metrics-driven decision making, one area where it is often neglected is in implementing innovation. Even data driven companies are hampered in implementing innovation because their data is backward looking. In the absence of sufficient data to inform decisions about proposed innovations, managers often rely on their experience, intuition and conventional wisdom, and none of these is necessarily relevant. Although many of my readers are from the mobile game space, where much is tested, even in the game space pure innovation often is not. An article in the Harvard Business Review, “Increase your chances of success with innovation test-drives” by Stefan Thomke and Jim Manzi, does a great job of showing how to test these hypotheses.

Most companies do not conduct rigorous tests of their risky overhauls because they are reluctant to fund proper business experiments and have considerable difficulty executing them. Although the concept of experimentation is straightforward, there are many organizational, cultural and technical challenges to implementing experiments. While running an A/B test on a website is simple, many business need to deal with complex distribution systems, sales territories, bank branches, etc. Business experimentation in such environments suffers from many analytic complexities, most importantly that sample sizes are often too small to be significant (e.g., only a few stores). Continue reading “Testing innovation opportunities”

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.

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”

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Key takeaways

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Sustaining competitive advantage

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The challenges

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A brief history of analytics

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