Post by account_disabled on Nov 13, 2023 4:42:57 GMT
Judah Phillips, co-founder and CTO of Vizadata and founder of Smart Current was very clear “We already live in a world of “real-time” predictive analytics. A simple predictive analytics is your arrival time at your destination on Waze. More complex real-time prediction occurs billions of times around the world every millisecond to match certain types of advertising.” Companies like HubSpot, Tableau, Mintigo and Versium already offer integrated real-time solutions for lead scoring , proving that the transition is technically possible. Possible, however, does not mean perfect. Sam Underwood, vice president of Futurety, recognized the complexity.
Of the integrations needed: especially in the mid-market, tools that collect data to transform into predictive modeling systems, social media aggregators, logistics and purchasing systems, often lack simple APIs or other easy mechanisms to quickly collect and interpret data. This disconnect hinders even the simplest cases of real-time predictive analytics. David Longstreet, the principal data web designs and development service scientist at Fan Three Sixty, offered a simple yet striking example. In the world of sports and entertainment, for example, most sports teams do not know how many people are in a stadium during a game. Teams know how many tickets have been distributed; however, they do not know in "real time " how many people are in the stadium during the event.
Paradoxical This knowledge gap hinders efforts to staff and supply the stadium appropriately. It's also why interest in predictive analytics is nearly universal, even as it vastly outpaces its adoption. 3. Adoption of predictive marketing and analytics is slow but interest is growing How many companies are actively using predictive analytics? According to interesting research by Dresner Advisory Services in the USA in 2017, around 23%, a figure essentially unchanged compared to the previous year. Interest, however, outweighs implementation. The same research suggests that 90% of companies “place a high importance on advanced analytics and predictive marketing.
Of the integrations needed: especially in the mid-market, tools that collect data to transform into predictive modeling systems, social media aggregators, logistics and purchasing systems, often lack simple APIs or other easy mechanisms to quickly collect and interpret data. This disconnect hinders even the simplest cases of real-time predictive analytics. David Longstreet, the principal data web designs and development service scientist at Fan Three Sixty, offered a simple yet striking example. In the world of sports and entertainment, for example, most sports teams do not know how many people are in a stadium during a game. Teams know how many tickets have been distributed; however, they do not know in "real time " how many people are in the stadium during the event.
Paradoxical This knowledge gap hinders efforts to staff and supply the stadium appropriately. It's also why interest in predictive analytics is nearly universal, even as it vastly outpaces its adoption. 3. Adoption of predictive marketing and analytics is slow but interest is growing How many companies are actively using predictive analytics? According to interesting research by Dresner Advisory Services in the USA in 2017, around 23%, a figure essentially unchanged compared to the previous year. Interest, however, outweighs implementation. The same research suggests that 90% of companies “place a high importance on advanced analytics and predictive marketing.