Increasing click-through to RG-tools by simple re-design

To make sure you have impact, do quick experiments and redesign things based on usability principles. By doing so, we found out that displaying only one recommendation at the time makes more people click.

A click on a recommendation is a success for Playscan. It means that we’ve provoked a reaction or created interest for taking action.

Sometimes, the little stuff create a big difference. As Thaler and Sunstein writes in their bestseller Nudge: “[S]mall and apparently insignificant details can have major impacts on people’s behaviour. A good rule of thumb is to assume that “everything matters””. With the “everything matters” mindset, the insignificant details can indeed prove fruitful beyond expectation.

Back in the days, Playscan always showed two recommendations to players. The rationale was that this was a trade-off between making sure to give the player more than one choice, to make sure he could find something relevant, but not to overwhelm him with too many. From nothing but our own curiosity, we decided to test whether we were correct.

We randomly divided our visitors into three groups, presenting them with one, two or three recommendations respectively. Next, we measured the click-through rate during a two week period. At the end of it, we realized that we had left a good many clicks on the table.

Our original design with two recommendations proved a 20% click-through, measured as the proportion of players who clicked any tip. The three-tip version showed no significant difference, but our one-tip version did: 36% of players clicked the recommendation. Again: the only change was one vs two recommendations – no other design changes, the same selection of recommendation, no new content – and from this we doubled our click-through!

Lessons learned?

Hindsight is always 20/20, and there is a reasonable explanation for what we found: players are more likely to click through with fewer conflicting and maybe confusing recommendations to choose from. Still, before our test we thought we had an equally good theory of why two recommendations was the way to do things.

So while doubling our click-through on recommendations based only on simplifying things was a big lesson learned, the biggest was without a doubt that “everything matters”. Ideas and hypotheses are a good starting point, but until proven they are just that: hypotheses.

Now, getting people to use our tools is only a first step in having impact. When it comes to recommendations, the next is having relevant ones. How do we make sure that they are? Well, we will test that too.

risk_analysis_playscan

Why it is meaningful to collect and interpret player data from a risk perspective

We tend to talk a lot about consumer protection in the gaming industry. It has become a vital part, and a bit of a buzz in business since a number of online markets have matured and regulatory bodies are challenging the industry into more preventive online actions against problematic gambling.

Therefore there is an urgent need to understand players’ online behavior. Not only for creating a perfect gaming experience: but in the case of consumer protection. But yet, there is still no consistency in what consumer protection really means and the question of “how we protect vulnerable players” has still not been answered. And meanwhile as we discuss all off this, we seem to miss the target.

The player.

The information every player provides to us could give us the answer. This article will argue that if we actually value consumer protection, not only as an abstract concept that we nod at agreeably during meetings – we should make use of information available: player data that is understood from a risk perspective.

 

Identification of high risk gambling in player data

A lot of data is being generated every minute of the day when players gamble both online and within land-based facilities like Casinos or eGaming machines. Purposely designed behavioral tracking solutions can identify patterns of play in gambling data, and with current technology, combined with understandings of problematic gambling; this can be utilized for proactive consumer protection.

 

Many players, with real life problem gambling stories, express that they have experienced “an escalation of their behavior” and before they knew what was happening: they were placing increased bets, and losing more and more money. By identifying these risk factors in player data – Operators get a new dimension in “knowing your customer”.

 

Player data holds a lot of information, such as age, gender, favorite game etc. But player data also holds descriptions of a player’s behavior. Looking at data from a risk perspective means to identify possible negative behaviors or risk factors, such as;

 

  • Start playing more often

  • For longer sessions

  • Constantly changing planned spending limits

  • Chasing losses

 

These are a few examples that relate to user behavior rather than user information. With a risk analysis, it is possible to make players aware of changes in actual behavior.

This information provides direction for effective responsible gambling initiatives at an early stage, preventing problematic gambling instead of treating a problematic gambler. The risk analysis helps segment the player population into “low risk”, “increased risk” or “high risk” – leaving Operators with information for unique opportunities like customized responsible gambling communications.

 

How to understand data

Even if we have spent time on data analyses and even when players with risky gambling behavior are identified – it’s still tricky to answer the question “how can we protect vulnerable players?”

Data describes what players do, how they behave. But not really what they need.

One way to understand it and knowing what to do with data is to humanize and bring these numbers to life. For example: Wilma is a 45-year-old woman who likes gambling, especially online bingo. For the past six months she has gambled at a high-risk level, with few gambling-free days. Late nights with bingo, long sessions with lottery tickets after lottery tickets. She finds herself in a loop of wagering more and continuing to gamble, with higher stakes, even after she just lost.

This was not an ideal situation for Wilma, simply because she couldn’t afford it and lately her risk data indicates that she is trying to cut down on her gambling. For example, she is setting strict limits for her gambling that she has managed to keep within.

Should the Operator take any actions? Well, since Wilma previously has been on a high-risk journey, one thing that she does not need is to receive promotions, bonuses and commercials from her gaming company. This is were the operator can differentiate an out-going customer to one that just wants to control their gambling habits.

With customized communications, it could also be wise to inform Wilma, close to play, if her gambling sessions seem to be escalating again.

 

Why is this all meaningful?

By rethinking the way Operators use data and understand players, they can create meaningful communications that influence and engage the players. By taking the player’s risk level into consideration when communicating with player Operators can better focus on the user’s needs.

Knowing the player’s risk level is valuable through the whole chain of the gambling industry: from game design, marketing and user experience to management and business development all the way to customer support – and the player.

Simply, through understanding risk we avoid “one-size fits all” solutions and then we add true value to the concept of consumer protection. Because the point is that the answer to “how can we protect vulnerable players?” is that it varies according to each player’s risk behavior.