In Gambling, Games Analytics

I know, you’re thinking job done. You’ve got a BI system, everyone in the company is excited about it and you’re on top of your KPIs and finding out some really interesting stuff. Your accounts team has never been happier and, your board are making decisions on reports that aren’t a month old, and your IT team are strutting about like Steve Jobs when he launched the iPhone. All is hunky dory!

Or is it? Actually, I’m a big fan of BI. Companies that don’t have BI really need it, but those that have it, and that have a significant mobile gaming income, need to recognise that they’ve just reached basecamp. They should congratulate themselves, admire the view, then start planning for the more significant challenge ahead, as the data is going to become immersive!

What’s the next step and why should you take it? BI can give you a single view of your company data, up-to-the minute KPI reports and the ability to drill down into the results. It gives you answers to company questions, but it doesn’t give you the ability to understand player interactions, tell you why they are leaving or let you engage with individuals to make them stay by targeting micro-segments in real-time. This type of data-led capability goes beyond traditional BI.

By far the biggest potential for improved operational efficiency lies in player relationship management. With a staggering amount being spent on marketing and player acquisition, prioritisation needs to be given to player retention and plugging the sieve.

So, is your data infrastructure capable of meeting this challenge? Where are you on the data spectrum?

Data 1.0

Business and operational reporting of performance; includes consolidated dashboard reporting of what has happened. You may link CRM, finance, clickstream and operational data to get a single view of the truth, but you will only know that you have a problem, and broadly where the problem lies without having the capability to interrogate raw data.

Data 2.0

Is the ability to probe and query data from multiple sources, join it up and query it using incredibly powerful database technology that supports train-of-thought interrogation. This is where the top performing BI systems used in the industry currently reside. In reality, most still aspire to reach this level of capability. They provide a much clearer idea of where the problems lie, but while your data is real-time, your analysis is still historical and your intervention is slow.

Data 2.1

By setting custom events and using data mining, it can provide you with greater tailored insight into specific problems like optimizing the first time user experience undergone by new players as they start out, or discovering the pathway to becoming a VIP. This advanced level of data also provides the fuel for predictive analytics to effectively optimise marketing and acquisition engagements.

Data 3.0

With Data 1.0, you know whether you’ve got a problem or an opportunity, with 2.1, you know where it is and who it is affecting, but with Data 3.0 you can identify those players, create target lists and real-time marketing campaigns that trigger as a consequence of the player’s in-game actions, and/or their engagement with previous marketing.

BI systems can extend as far as Data 2.0 and are more internally focused on operational data, whereas app analytics extends to real-time marketing and player relationship management. App analytics and marketing systems are usually more outwardly focused, on specific game derived data.

There is most certainly a cross-over between these two systems where it comes down to recording revenue and user acquisition costs, but app analytics comes to the fore when it comes to optimising the opportunity from mobile gaming.

At Board level within the industry, the optimisation of growth opportunities afforded by mobile are high on the agenda. Putting the right infrastructure in place to meet this challenge will be vital, but in doing so, Executives will also need to consider whether their businesses have the skill-sets and the internal culture to rise to the challenge. Just as app analytics represent a paradigm shift, the associated business challenges brought about by mobile will require much more than incremental change.

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