You only need to catch a single ad break during a Premier League match to see how crowded the online gaming market is.
In a hyper-competitive industry where loyalty is supposedly dead – if you believe this tongue-in-cheek ad from PaddyPower – many companies are focusing on new technologies and data models to get ahead.
And it’s no wonder, with tech such as mobile gaming and virtual reality turning the online gaming sector into an exceptionally lucrative industry, with estimates expecting it to hit $73 billion globally in the next five years.
The trends challenging the gaming industry will be familiar to all – the need to improve customer experiences, the push to experiment with big data, the desire for a single customer view across the business – but in an industry where the house always wins, there’s the appetite to push ahead.
For other highly competitive industries, where it’s hard to differentiate from other companies – think retail – there are several lessons to be learned from how the gaming industry is adapting.
3 ways gaming companies use data to get ahead
Here are three smart ways gaming companies are using data to stand out from the crowd:
1. Personalise and target with real-time model scoring
Batch scoring has long been the algorithm of choice for marketers. Analyse hundreds of thousands of data points, and you’ll get a good audience segment to target – though it takes a long time to run the numbers.
But recently, gaming companies are turning to real-time model scoring to personalise gaming experiences much more closely. They can monitor who’s doing well and who’s losing, and identify the best way to keep people engaged – or even, in some cases, discourage them from playing.
Take poker, for example. In poker, the gaming company just takes a cut of the stakes pot, so it’s in their best interests if players stay at the table and commit a stake. Ideally, you want a reasonably equal balance of skills in poker, to keep everyone happy. But if there’s one ‘shark’ who keeps winning, it can quickly put others off.
Previously, algorithms would simply identify these players as VIPs, and reward them. But, as gaming companies explored their data more, they realised that these players were making others more likely to quit. Real-time model scoring now lets gaming companies identify these ‘sharks’ faster, and stop them playing with less confident or competent players.
You can also use the real-time capabilities of this model to catch a despondent player before they drop off. By identifying someone who’s losing frequently, and therefore likely to stop playing altogether, the gaming company can offer them personalised incentives to keep playing. However, there’s a delicate balance to be struck to ensure the company is still encouraging responsible play. Batch scoring could only monitor activity every 24 hours or so, but real-time model scoring means the company can keep an eye on how long someone’s been playing, and what their record’s like – so they can help the player know when to stop.
We won’t see the back of batch scoring for a long time yet – it still has its place for large-scale marketing efforts and getting a big-picture view of what’s going on in an organisation. But real-time model scoring is the future for marketers and data scientists that want to get more targeted in how they use data in their interactions with customers.
2. Open up a productive sandbox for your data scientists
Big data is a major trend for gaming companies, and it’s vital if they want the reward-based personalisation model to work. But as they collect more data, there’s the evergreen question of “where do we store that data?” and “what do we do with it?”
For leading gaming companies – and the leaders in many other industries – that means moving on from data warehouses to data lakes. It’s the perfect way to bring all your data points together into one place. But it’s vital you don’t get carried away.
The most important thing to ensure is that your data lake doesn’t just become a sandbox for individual developers to play in, with copies of data being added to it left, right and centre because everyone’s working in silos. You need to ensure it’s productive.
To make your data a true asset to the whole organisation, it’s important to ensure all your developers and data scientists have controlled access to the data lake, and a detailed catalogue that lets everyone know which datasets are in the lake, where they are, and what they can be used for.
(I talked in more detail about data lakes and DataOps in my last blog.)
3. Make the most of your infrastructure – and know when to branch out
Gaming companies aren’t just making big-bang investments in large-scale changes to their infrastructures and operations. Many are making small, piecemeal adjustments that improve their capabilities over time.
Optimise the infrastructure you already have to ensure you’re getting the most from investments you’ve made. Pay attention to peaks and troughs in usage, and run resource-heavy processes (such as batch scoring) during quiet periods. You can also focus on optimising and automating basic data processes to help free up your data scientists to focus on developing new models, products and personalised experiences.
Once you’ve taken your existing infrastructure as far as it can go, you might then want to consider migrating specific workloads to the cloud to make them easier to manage. There’s also the option to build out a complementary or new solution that’s tailored to your organisation’s individual data demands.
If you’d like to learn about the Adatis approach, and how our specialist Data Analytics Platform and AI Managed Service can help you get ahead in your own industry, just drop me an email at firstname.lastname@example.org.