Dream11 is a fantasy sports platform where you can create a virtual team of real sports players and participate in fantasy games. Dream11 has many use cases of Data Science that make it different from all other fantasy gaming applications. So, if you want to know how Dream11 uses Data Science, this article is for you. In this article, I will introduce how Dream11 uses Data Science for its business as a fantasy sports platform.
Here’s How Dream11 Uses Data Science
Here are some ways how Dream11 uses Data Science for its business as a fantasy sports platform.
Cohort Analysis
Cohort Analysis is an analytical technique used to group users based on their behaviour. It helps create groups of all the users based on how their common characteristics help businesses in decision-making in short-term marketing campaigns to long-term user retention strategies.
Dream11 uses cohort analysis to group users based on:
- how users engage with their application
- playing patterns of the users
- and the overall history of events generated by users on dream11
It helps dream11 to stay relevant with all its users according to what kind of sports and leagues they like to play.
Top Picks for Users
Top Picks in dream11 is the section where dream11 recommends personalised contests in a match. Dream11 creates three types of contests for every match:
- Top picks for users
- Mega contests
- Low entry fee contests
Mega contests attract users for higher engagement. As the winning amount in a mega contest is huge, the participation is huge, so the probability of winning a huge amount decreases.
That is why Dream11 has a segment of top picks where personalised contests are made by understanding user cohorts and the overtime spending capacity of the user in a match on dream11. It helps recommend the same type of contests to the users who are most likely to participate in them.
Contests Demand Forecasting
Dream11 gets a lot of new users during sports events like IPL and World Cup. So, forecasting demand for contests in such events is valuable to decide the number of spots for each contest.
Dream11 needs to forecast the number of user participation in the mega contests before and after the toss to retain all new and old users. Thus, when the demand for participation is high, many new contests are created right after the toss. All the spots are always filled because the total number of spots is decided according to the predicted demand.
Summary
So below are some ways how Dream11 uses Data Science for its business as a fantasy sports application:
- Dream11 uses cohort analysis to stay relevant with all its users according to what kind of sports and leagues they like to play.
- Dream11 has a segment of top picks where personalised contests are made by understanding user cohorts and the overtime spending capacity of the user in a match on dream11.
- Dream11 needs to forecast the number of user participation in the mega contests before and after the toss to retain all new and old users. All the spots are always filled because the total number of spots is decided according to the predicted demand.
I hope you liked this article on how Dream11 uses Data Science for its business as a fantasy sports application. Feel free to ask valuable questions in the comments section below.
