3 Companies Capitalizing on Predictive Data Algorithms

Self-optimization, Sports Gambling, and More



Matt Wujciak
09/23/2019

analytics

Whether it’s the chatty sales team, newly hired social media gurus, or strategic customer service know-it-alls, everyone is one way or another affected by data metrics. And if data is the new oil, departments are going to need to know how to refine it into actionable intelligence to generate revenue. Thanks to a ubiquitous push for digital transformation, worldwide revenue for big data and business analytics software is on pace to top $189.1 billion by the end of 2019, an increase of 12 percent over 2018.  Some companies have creatively entered the mining realm and aren’t climbing out anytime soon.

Accenture improves sales, workforce dynamic

Accenture uses predictive analytics, embedded as AI, to drive insights from huge volumes of data to predict the probability of occurrences and relationships to improve decision-making throughout different points in the sales cycle. Teams are then able to focus on opportunities more likely to be sold and walk away from investing business development cost in opportunities more likely to be lost. Originally only created for a business process outsourcing infrastructure, the tool now generates an algorithm that is able to churn through Accenture's Salesforce CRM data, taking into account 5 years' worth of deals, as well as a number of variables such as geography, demographics, price points, margins, and other metrics to predict a win/loss opportunity with 97 percent accuracy—in less than three seconds. Teams from Accenture’s sales and pricing excellence, Manage mySales and enterprise insight collaborated to train and improve the “Win Probability Predictor” model by giving sales teams’ transparency on how to alter opportunities to win, and by providing real-time scoring capabilities to sales teams as they work. 

The company is also using the algorithm to emphasize diversity and inclusion by conducting present modeling and forecast modeling to perform what-if scenarios that test out gender-mix hiring impacts and forecasts using different inputs. This will help Accenture in its attempt to reach its 50/50 gender parity goal by 2025, while having 25% of the female workforce in leadership positions

Read More: Why Agent Motivation Should Be Your Biggest Performance Goal

 

STATS is getting an edge

What happens in Vegas no longer stays in Vegas. With the Supreme Court recently overturning the Professional and Amateur Sports Protection Act, a federal law that banned sports gambling, many money-making doors within the sports industry have been opened, including sky rocketing sports media outlets, increased television viewership, tickets sales, data mining companies, and more.

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STATS, for example, currently provides its data, viewed as the fastest and most accurate in the industry according to an independent Northwestern University study, to sports media outlets such as DraftKings and FanDuel. The daily fantasy sports giants rely on the up-to-the-second updates to power their games and keep fans engaged to wager more money on their websites and apps. STATS’ award-winning predictive analytics team, marketers, and customer service agents have created a constantly improving, proprietary algorithm that has given the sports world new insights into potential outcomes, such as win probability, matchup odds, and player performance projections. Predictive insights also help personalize gambling options, such as by tailoring specific sports teams, players and propositions to players, which helps with customer retention. According to their website, STATS leverages some of the most talented data strategists in the world to provide intel to 8 of the 10 largest media companies.

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Shell fixes what ain’t yet broken

Having a digital gambling adviser via data metrics might be a nice way to fill your pocket. However, few sectors generate more actionable data than the lucrative energy industry. But for years, oil giant Shell had trouble identifying the location of products in its various facilities around the world. It didn’t know when to restock, and it didn't know when maintenance issues were occurring until parts began failing. With machine downtime costing industrial companies millions of dollars a day, Shell decided to harvest a better data platform in an attempt to alleviate customer churn. Shell built an analytics platform based on software from several vendors to run predictive models to anticipate when more than 3,000 different oil drilling machine parts might fail, according to Daniel Jeavons, general manager of Shell’s data science center of excellence. Shell’s metrics platform Databricks captures intel from Apache Shark, providing insights on when to purchase machine parts, how long to keep them, and where to place inventory items. The tool, hosted in Microsoft Azure’s cloud, has helped Shell reduce inventory analysis from over 48 hours to less than 45 minutes, shaving millions of dollars a year off the cost of moving and reallocating inventory and retaining substantial customers

 

From inventory to customer retention to sports gambling, what will data algorithms predict next? Relationships? I wouldn’t put it past Zuckerberg.

 

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