data analytics Archives | #1 Fan Engagement Platform

Fan Discovery & Analytics

December 7, 2017 by admin

FanPaaS is a comprehensive fan discovery, analytics, and management platform increasing engagement and revenue for sports teams, entertainment complexes, venues, casino, and concession companies. Fan analytics will provide the insight into the customer so you are able to make informed business decisions in real-time and continually improve the net new customer base.

FanPaaS extracts large amounts of data from your CRM, concession, merchandising, point of sale, and legacy systems so you can now drill into that data through a drag and drop interface, creating intuitive dashboards, mashups, and visualizations across numerous data elements, for fast and well-informed decision making. FanPaaS will increase revenue by 20% and save employee time producing reports to capture fan data.

FanPaaS gives you the control and data insight into customer/fan management so you are able to predict future trends and continue engagement year round. Powered by industry leading platform is a Software as a Service and will scalable to support any customer and enterprise.

Stop guessing about your fans/customers and let FanPaaS do the insight and discovery for your business.

Analytics is Overrated

April 4, 2017 by admin

Analytics is overrated in a recent article on Sport Techie Mark Cuban discusses the future waves of technology and what is the new drivers for sports teams.

‘What’s the biggest overrated and underrated technology in sports right now?’

His response?

“Overrated is analytics. Underrated is artificial intelligence and its derivative.”

Why does Cuban think the way he does?

“Because it’s become efficient now and because AI is going to kick (analytics’) ass all over the town. It’s going to be not gone because analytics becomes your AI. Your analytics department will have to learn AI, machine learning, deep learning, neural networking, etcetera, and be good at that. But historically, when analytics, and this is going back to the day I bought the Mavericks when we started using advanced plus minus, you have to make choices. And you have to be able to say, ‘OK, what are important to me? How am I going to weigh those variables and what are the conclusions I’ll draw from those variables?’ At the beginning with the Mavs at least, we had huge advantages. We would force teams to play their bad lineups that they didn’t know were bad. We forced teams to play bad combinations that they didn’t know were bad combinations, and it helped us win games and playoff series.

“Over time, teams became a lot more aware, and the market became a lot more efficient. And then it didn’t become so much about the analytics as it became, ‘OK, could you find the players you needed to play the style that you wanted to play?’ And that to this day remains the biggest challenge. You can have the greatest analytics department in the world, it’s going to be very difficult now to get an advantage over the other team. Maybe there’s a couple teams that are behind in analytics, but you’re not going to get an advantage on smart teams.”

With not many NBA teams that are behind in analytics, Cuban called it an “efficient market” now.

“Going forward, I think what data you incorporate into your models, how you clean your data, how you train your data, whether it’s structured, unstructured, how you label it, all those things will be critically important,” Cuban continued. “The hope, at least from my perspective, is we’ll learn things and see things that we never imagined, that we couldn’t perceive in a traditional analytics environment.

Data Analytics increase Fan Engagement

February 10, 2017 by admin

Data analytics increases fan engagement and provides valuable insight in the business. Even the best run companies that know their business and monitor their operations closely are often amazed at what they can gain in terms of insights from analytics. Many derive 10x and even 100x ROI on their investments in data science. All this working with the data they already have or can easily get from their own business. Typically we can use predictive analytics to model and analyze data patterns that lead to certain outcomes. We can then predict these same outcomes as data is collected and even reverse or reinforce these outcomes with additional reinforcement.

Employee retention and turnaround. Behavioral data capture from access cards, call management queues for services industries or other measures that can highlight dissatisfaction are captured. Tardiness, absenteeism, longer breaks, longer phone answer windows, customer complaints and a combination of factors specific to your business or operations. At this point a list of ‘at risk’ staff can be determined and a determination made as to whether or not to take proactive measures such as training, positive reinforcement or implementing feedback loops to help weed out bad sites, managers, or staff.

Customer churn reduction. Often characterized by complains and support calls, by data driven science and seeing how customers are using your products or interacting with your website, subtle changes in behaviors and product use can be an early detection of possible dissatisfaction and churn. A reduced level of product use can mean they are experimenting with competitive solutions or even abandoning or developing in house alternatives. Proactive measures like providing additional support, customer dedicated account reps, discounts, upgrades or other preferential treatment may provide a way of reversing these elements or at least a good customer touch point to understand and have a dialogue around them.

Deterministic product usage based roadmap investments. Are your roadmaps built on data based on how people actually use your products? Data from analytics related to screen accesses and time spent in functional areas of your platform can and should strongly influence how you spend your product development dollars and where your R&D group should invest in the roadmap. Knowing how your product is used can help you determine how to optimize customer adoption and improve satisfaction. Areas that are frequently used, areas that take the longest to complete, areas that require the most keystrokes, even subtle pauses between operations or users having to back out of transaction to correct errors are all tell tale signs of product design improvement opportunities.

Real time analytics and historical data. When the customer is on the line, on the website, or in the store is the time when real time analytics can really make you shine. It’s like the salesperson that knows your name, your spouse and kids names, and even what you like to eat and drink. Imagine that when a customer is in your store or on your shopping portal and you can say ‘ those shirts you have bought three times are now on sale. Look what we have in your size and in your favorite colors for 50% off today …

Real time response measures. When a ship or truck is coming into the warehouse or port you have a slew of preparatory measures planned in advance with highly paid staff, expensive machinery and transport crews lines up. What if there is a delay or a change in plans? Reacting in real time by reallocating resources can be a huge endeavor with the need to have data driven decision making at the ready. Rather than lose the expense of the resources reserved for the activity how can you allocate them to other tasks, how could you utilize the equipment. Again, access to your operational data in real time helps you reallocate and redistribute resources efficiently and economically.

How data analytics is running the sports world

February 7, 2017 by admin

This was a great article on how data is running the sports world

How Data Runs The Sports World

Data often informs the way we consume and relate to sports, and since the publication of Michael Lewis’s Moneyball sports leagues, teams and fans have all become more aware of the inherent capabilities of sports analytics.

For instance, you probably know that there are a series of algorithms and analytic programs driving your live gamecasts and fantasy football projections, just as there are a host of 1’s and 0’s determining the advertisements during your live stream.

But according to Nick Maywald of Genius Sports, there is more data being used in sports than you know, and in ways you could never imagine.

“There is an enormous amount that goes on behind the scenes that the average fan definitely would not be aware of,” said Maywald.

To best understand this background data, Sam Ebb, co-leader of the upcoming 2017 MIT Sloan Analytics Conference, says we can consider the two main ways we interact with sports data as individual phenomenon.

First are the data sets and analytic techniques that inform business decisions.

Jim Tobin, National Sales Executive of SAS Sports, says one of the best examples of data-informed business decisions is the New York Mets recent decision to alter their weekend schedule.

“For years they always thought that families that went to Citi Field wanted to go to 1 pm afternoon games,” Tobin said. “After researching and analyzing the data, they found that families preferred to go to games in evenings on weekends, not during the day. So they changed their marketing platform to support that.”

In order to make this decision, the Mets needed to understand a digitally connected and involved fanbase.

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“Fans are definitely more empowered and connected than ever before,” said Tobin. “They have access to information that spans across multiple channels and can be accessed anytime across multiple devices. Fans have the ability to collect information on-demand, especially related to where to buy tickets or merchandise, from whom and for how much.”

So to both meet fan expectations and gather the necessary information, teams and leagues need to understand fan habits and lifestyle preferences as well as predict future behaviors. This type of fanbase comprehension has become one of the major ways that teams and leagues use data to inform their business practices.

Major League Soccer (MLS), for example, has been using SAS to collect data from their individual clubs with the goal to create a centralized data warehouse so that the clubs can analyze and predict customer behavior, allowing them to better serve and better market to their individual fans.

MLBAM, meanwhile, uses SAS to target specific fans who are more likely to cancel their season ticket packages.

“They score their season ticket database with SAS to predict season ticket renewals and determine which fans are more likely to churn,” said Tobin. “It’s valuable information that individual teams can leverage in their sales and retention efforts.”

Another way teams and leagues are using data to inform marketing practices is with targeted advertising. Nick Stamm, director of marketing and communications at Sportradar US, explains:

“If Anthony Rizzo or Kris Bryant hits a home run during the World Series, the power of promoting a Cubs hat or their jersey in the moment of the home run is so much more powerful than just a random MLB paraphernalia ad or a digital banner that isn’t connected to the game action,” said Byrd.

Just as data helps inform business decisions and marketing practices, data also allows coaches and scouts to make front office decisions.

Benjamin Alamar, director of sports analytics at ESPN, says one example of front-office data use is Stanford University’s use of virtual reality to train its quarterbacks against particular defenses. Vijay Mehrotra, a professor of business analytics and information systems at the University of San Francisco, elaborated that at the heart of these simulations is data.

“Analytics are buried in the design of that simulation,” said Mehrotra. “It crucially depends on it.”

Tom Davenport, a research fellow at the MIT Center for Digital Business and a professor of IT and Management at Babson College, says this sports analytics movement in a front office capacity began with baseball and was slower to catch on in sports like basketball, football and hockey, sports with greater interdependency between players.

Sam Ebb of the MIT Sloan Analytics Conference echoed Davenport’s sentiments, and expanded.

“Different leagues have moved at different rates,” said Ebb. “Some of that is because of things that are difficult to quantify, so for a while people have had a hard time quantifying goalie metrics in hockey, whereas a sport like baseball where you have more isolated events and less interdependencies in the data there was a little more kind of initial success in the ease of analysis and the computational intricacy in pulling it together.”

Now, thanks to technological advances, particularly in player tracking, more advanced analytics are possible.

“You [now] know about distance run, speed, burst speeds, impact, strength, recovery times…there’s more and more capability to analyze not just present stats but an enormous amount of historical stats which then creates a whole new range of benchmarking opportunities for performance and coaching,” said Maywald.

But according to Maywald it could cost a top tier European football team upwards of 100,000 dollars to run standard analytics for a single season, and few teams can afford such services.

To take advantage of the available analytics, teams and leagues often turn to data businesses like Genius Sports and Sportradar – companies that can tailor programs and services to meet individual needs.

“If you’re sitting on the sports side, the purpose of Genius Sports would be very much to help them collect sports data, particularly live statistics but also a range of other federation league services around membership and helping them manage their federations and competitions more effectively,” said Maywald.

This means that a team can do everything from analyze player biometrics to enhance scouting reports and help players better prepare for opponents with data-informed virtual reality systems to tailor their marketing to a single fan’s interests and ensure that fans are engaged with the most relevant, exciting possible experiences.

So when Genius Sport’s Maywald says there’s more going on than the average fan is aware of, he isn’t exaggerating.