For an industry to progress consistently or for that matter phenomenally and stay relevant for ages, the incumbents must keep searching for fresh ideas and come up with some extraordinary innovations. Otherwise, its existence will come to a grinding halt. This is where the gaming industry leads the way as it is able to clinically perform predictive analytics for its future to look ever brighter. Predictive analytics is nothing but assessing the future outcomes by duly analyzing the historical and current data available. Let us now see how such predictive analytics thus support online gaming industry.
What predictive analytics achieve?
As per an article published in “Business Wire” there are as many as 1 lakh mobile games available in both IOS and Android play stores. With such a huge competition in the gaming industry, it is indeed an uphill and a challenging task for the game developers not to lose their respective customers as the latter are under no compulsion to stick on with a particular gaming company. But with the help of predictive analytics, the developers are able to retain their customers by providing them exactly what they want. By using the past data, the analysts are able to get an insight on the needs and wants of the customers with respect to a particular game, thereby meeting their expectations. Precisely by using this method of predictive analytics, games like Candy Crush, play Rummy online and clash of clans are able to still maintain their popularity in the gaming industry although they are subject to stiff competition from other gaming companies.
What type of data gets collected?
The moment a person logs in to play a game, he leaves a large amount of data for the developers to use it for machine learning. There are so many data available for the incumbents to collect. By collecting the data, many useful information gets ascertained like the time that an user prefer to play, the duration of his visit, the type of games he is interested in, up to which level he is able to play with ease, how much time one spends on a particular game before moving to another game, the promotions and bonuses that an individual is interested in, win-loss ratio of the users and their proportionate presence online subsequent to wins and losses. These data so collected are used by the developers to create a profile for an individual user. For example, if the developers find a decline in the number of users suddenly at a particular time in a day, they may immediately find out the cause for the same and repair the situation as soon as possible so that their company is saved from further fall outs. They may satisfy the customers by addressing the reason for their disinterest which caused that decline. This kind of personal touch induces the customers to stay with one particular gaming company who will feel it is worth spending money over there rather than taking a stand of searching in for two in the bush. For them, there is no reason to hesitate to play real cash rummy or any other money spending games, since they know their choices are taken care properly.
How does data collection happen?
The data collection is not an easier process and on the other hand it involves a very complex procedure which includes many stages. Let us look into the same very briefly.
The first step is data collection that results in the creation of a model scenario. The second step is about detecting and eliminating the parameters that can affect the happening of a predictive event and detecting the patterns by applying suitable algorithms. However, if only a very a smaller number of factors are seen remaining after such eliminations, the lower will be the accuracy of the resulting model.
Then as a next step, the accuracy of the model must be determined and established. Once it is done, it has to be seen if the reality is in par with the prediction and if the former and latter coincide at least by 95%, then the model can be applied to the new data.
Areas in which predictive analysis can be applied:
As per a survey by Academia 46% of people use predictive analytics for campaign management, 41% for customer acquisition in addition to forecasting and budgeting, 32% for the detection of fraud, 31% for promotion, 30% for pricing, 18% for surveys, 26% for customer service and so on. This data shows that predictive analytics have got numerous topics to cover and regularize them.
The predictive analytics is therefore, not a wider guess. It is an educated guess that involves a very detailed and complex study aided by big data, Machine learning and Artificial intelligence. That is the reason as to why most of the outcomes predicted do not fail to hit the target. If the gaming industry has a strangle hold on its customers for more than a decade now, it is only because of the inputs obtained through predictive analytics.