Pulse
IGAMING ANALYTICS OPTIMOVE
Optimove: Why hyper- targeted marketing is the Holy Grail
Player lifetime value, or PLV, is one of the gaming industry’s most popular buzzwords. Operators offer tailored promotions and suppliers boast new features designed for specific player profiles in the pursuit of improving engagement and extending coveted lifetime value.
In an interview with G3, Pini Yakuel, CEO at Optimove, explains why personalisation at scale is fundamental to increasing player lifetime value and can lead to a 33 per cent CRM contribution of overall operator revenue when done correctly.
Pini begins by detailing how Optimove has grown from a data agency founded by two academics fresh from campus into a software as a service company that now works with 30 per cent of Te Power 50.
I founded the company out of university with a fellow PhD student mastering in machine learning and data mining. We initially started the business as a data agency in 2009 before changing strategy and becoming a software company, Optimove, in 2012.
Our academic background naturally lent itself to us primarily working on modelling and predictive modelling as we were very comfortable in this field and didn’t know too much about business. We focused on finding insights from customer data and using those
P102 WIRE / PULSE / INSIGHT / REPORTS insights to better engage with customers.
Optimove’s love affair with gaming arose because we bootstrapped the business in Israel. At the time, there were three main countries performing well in gaming technologies: Israel, South Africa, and Sweden. Whilst Israel is a small country that lacked ecommerce at the time, the gaming companies based there such as 888 and Playtech created a pool of talent that appreciated sophisticated data analysis and the use of technology to drive CRM marketing and relationships with customers.
From this base, we went from strength to strength and started to get a lot of traction helping brands across Europe such as 888,
Bwin.Party, and GVC to substantially improve player lifetime value.
How has data science evolved over this period?
Machine learning has mostly evolved on the DevOps front. Te infrastructure, computing power, connectivity and network has evolved dramatically to allow companies like ourselves to deploy machine learning models easily and with greater stability, robustness, and accuracy. Tere have been advancements in the math and algorithms, but the bigger enhancements have been across DevOps.
Te likes of Google and Facebook do machine learning very well because they are gigantic
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