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Data Innovators In Sports And Entertainment: Phizzle

Kaan Turnali

Today, sports and entertainment properties are challenged to improve business performance by increasing engagement opportunities with fans. Marketers want to connect with them through personalized outreach programs, targeted media buys, word-of-mouth campaigns and engaging digital experiences across multiple platforms.

If you are saying to yourself that this strategy is not much different than that of, for example, a phizzleretail or consumer products company, you are right on. By understanding the fan behavior using simple and integrated technology solutions, marketers have the ability to manage segmented fan experiences on all types of channels and devices in order to create a competitive advantage.

In this installment of my series on Data Innovators in Sports and Entertainment, I spoke with Ben Davis, CEO of Phizzle, an engagement automation software company.

Phizzle provides campaign and data management solution modules to address the needs of sports team, entertainment venues or brands to be successful in driving fan and consumer acquisition, activation, and loyalty over the lifetime value of a fan (LVF).

Can you tell me a little about Phizzle and how the idea was born?

Ben Davis: Phizzle has been a provider of engagement and data visualization solutions for sports and entertainment properties since 2008. Phizzle works with clients like the NBA, the NHL, as well as media companies such as Fox and HBO. We speak to our customers daily. We listen to what they are saying. We were being asked repeatedly: How do we connect with fans on a deeper level? How do we engage with the fan beyond SMS? How can we retain our fans? How do we know which fans are loyal and which aren’t so we can customize offers and promotions? How can we make the most of the data we have on our fans to drive profitability, both on and off the court?

In 2012, we saw an opportunity to develop a flexible solution that not only assists in connecting these ordinarily siloed data repositories into one single touch point, but also to provide a way to analyze and engage fans with relevant messaging on relevant channels. We help marketers understand their fans’ digital experience, buying behavior, and purchasing patterns so they can influence the customer journey across single and multiple buying cycles. Phizzle’s technology uses data to shorten digital purchase cycles by solving conversion struggles. Our platform tracks fan profile data and behaviors across offline, digital, social, and mobile channels in a single, centralized marketing data warehouse.

Our goal? We want to support marketers to deliver personalized and dynamic marketing campaigns by creating connected cross-channel experiences that engage their audience throughout the entire customer lifecycle.

The phrase “data-driven” is, admittedly, in my opinion, already a bit passé… but the implication is important: Your decisions must keep pace with your data. For that, you need an intuitive, powerful platform to access, analyze, and act upon your metrics. And as the speed of marketing gets ever faster, your decision making needs to keep up. Data is your ally in making quality marketing decisions at speed and scale.

How does Phizzle leverage data and real-time actionable insights?

On and off the field of play in sports, data is king. Data is used to make decisions that impact the speed and efficiency of every aspect of business operations. For us, we leverage fan data and the channels they use to help in not only determining fan behaviors in engaging with the properties, but determining the LVF. LVF is a metric that helps teams understand more than just spending habits; it helps uncover fan interests and how they want to ingest information, and extend retail offers when they are ready to act on it. Just as teams use data to analyze the players on the field, teams are now getting smarter in how they approach their most valuable players – their fans.

Sports teams, now more than ever, need a true 360-degree view of their fan base to increase the lifetime value of the fans. Phizzle’s fan engagement platform consolidates, analyzes, and enable teams to take actions on multiple data streams, to capture, aggregate visualize analyzing value of digital, social, and real-world fan engagements.

With actionable insights from these data troves, Phizzle enable data-driven business decisions based on the real-time behavior of the fan base and create opportunities for personalized engagements with fans.

Listening to or collecting the millions of conversations that occur daily, on Twitter, on Facebook, on Instagram, SMS, and various other channels, is a critical function to marketers. Yet, in itself, that is not sufficient to gain true insights about a fan. In order to gain actionable insights and real knowledge, marketers need to analyze the data. They need to comprehend the sentiment of the fan. They need to be able to decipher the data in such a way that it reveals who is a loyal fan and who is a fickle fan. In the end, the dollars spent on marketing campaigns are only as good as the message received by a receptive customer.

The optimal engagement solution, such as Phizzle’s Fan Engagement Platform, has both the base functions of data refinement and the more advanced analytical functions of data associations. The solution was built with the ability to satisfy an enterprise or modular-based environment, emphasizing simplicity, engagement, and affordability, catering to achieving maximum results as budget and/or business goals dictate in environments unique to sports. Phizzle’s holistic approach to engagement automation is inclusive of business intelligence (BI) and analytics.

What are some of the projects that you are working on?

Earlier this year, we partnered with SAP to help simplify the conversation around the Super Bowl XLIX. The NFL.com Stats Zone presented by SAP was a high-tech, interactive experience that transported fans into a world of numbers, images and insights about the NFL.

Currently we are working with the Charlotte Hornets. The Hornets organization is a leader in adopting and applying new technologies to solve real-world problems. The Hornets’ management had a keen interest in eliminating data silos and implementing an end-to-end digital marketing solution that included integrated engagement channel and campaign management features.

In partnership with SAP, we pursued a radical approach to helping them accomplish these goals. The integration of Phizzle’s Single Ecosystem solution (a pioneering product) and the SAP HANA platform supports unlimited scalability and a true enterprise engagement automation solution.

Connect with me on Twitter (@KaanTurnali) and LinkedIn.

Image credit: Shutterstock

This story originally appeared on SAP Business Trends.

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About Kaan Turnali

Kaan Turnali is the Global Senior Director, Enterprise Analytics, at SAP. He is responsible for the development, oversight, and execution of strategy for the BI platform across GCO’s worldwide user base of 25,000+ registered users.

Data Analysts And Scientists More Important Than Ever For The Enterprise

Daniel Newman

The business world is now firmly in the age of data. Not that data wasn’t relevant before; it was just nowhere close to the speed and volume that’s available to us today. Businesses are buckling under the deluge of petabytes, exabytes, and zettabytes. Within these bytes lie valuable information on customer behavior, key business insights, and revenue generation. However, all that data is practically useless for businesses without the ability to identify the right data. Plus, if they don’t have the talent and resources to capture the right data, organize it, dissect it, draw actionable insights from it and, finally, deliver those insights in a meaningful way, their data initiatives will fail.

Rise of the CDO

Companies of all sizes can easily find themselves drowning in data generated from websites, landing pages, social streams, emails, text messages, and many other sources. Additionally, there is data in their own repositories. With so much data at their disposal, companies are under mounting pressure to utilize it to generate insights. These insights are critical because they can (and should) drive the overall business strategy and help companies make better business decisions. To leverage the power of data analytics, businesses need more “top-management muscle” specialized in the field of data science. This specialized field has lead to the creation of roles like Chief Data Officer (CDO).

In addition, with more companies undertaking digital transformations, there’s greater impetus for the C-suite to make data-driven decisions. The CDO helps make data-driven decisions and also develops a digital business strategy around those decisions. As data grows at an unstoppable rate, becoming an inseparable part of key business functions, we will see the CDO act as a bridge between other C-suite execs.

Data skills an emerging business necessity

So far, only large enterprises with bigger data mining and management needs maintain in-house solutions. These in-house teams and technologies handle the growing sets of diverse and dispersed data. Others work with third-party service providers to develop and execute their big data strategies.

As the amount of data grows, the need to mine it for insights becomes a key business requirement. For both large and small businesses, data-centric roles will experience endless upward mobility. These roles include data anlysts and scientists. There is going to be a huge opportunity for critical thinkers to turn their analytical skills into rapidly growing roles in the field of data science. In fact, data skills are now a prized qualification for titles like IT project managers and computer systems analysts.

Forbes cited the McKinsey Global Institute’s prediction that by 2018 there could be a massive shortage of data-skilled professionals. This indicates a disruption at the demand-supply level with the needs for data skills at an all-time high. With an increasing number of companies adopting big data strategies, salaries for data jobs are going through the roof. This is turning the position into a highly coveted one.

According to Harvard Professor Gary King, “There is a big data revolution. The big data revolution is that now we can do something with the data.” The big problem is that most enterprises don’t know what to do with data. Data professionals are helping businesses figure that out. So if you’re casting about for where to apply your skills and want to take advantage of one of the best career paths in the job market today, focus on data science.

I’m compensated by University of Phoenix for this blog. As always, all thoughts and opinions are my own.

For more insight on our increasingly connected future, see The $19 Trillion Question: Are You Undervaluing The Internet Of Things?

The post Data Analysts and Scientists More Important Than Ever For the Enterprise appeared first on Millennial CEO.

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About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

When Good Is Good Enough: Guiding Business Users On BI Practices

Ina Felsheim

Image_part2-300x200In Part One of this blog series, I talked about changing your IT culture to better support self-service BI and data discovery. Absolutely essential. However, your work is not done!

Self-service BI and data discovery will drive the number of users using the BI solutions to rapidly expand. Yet all of these more casual users will not be well versed in BI and visualization best practices.

When your user base rapidly expands to more casual users, you need to help educate them on what is important. For example, one IT manager told me that his casual BI users were making visualizations with very difficult-to-read charts and customizing color palettes to incredible degrees.

I had a similar experience when I was a technical writer. One of our lead writers was so concerned with readability of every sentence that he was going through the 300+ page manuals (yes, they were printed then) and manually adjusting all of the line breaks and page breaks. (!) Yes, readability was incrementally improved. But now any number of changes–technical capabilities, edits, inserting larger graphics—required re-adjusting all of those manual “optimizations.” The time it took just to do the additional optimization was incredible, much less the maintenance of these optimizations! Meanwhile, the technical writing team was falling behind on new deliverables.

The same scenario applies to your new casual BI users. This new group needs guidance to help them focus on the highest value practices:

  • Customization of color and appearance of visualizations: When is this customization necessary for a management deliverable, versus indulging an OCD tendency? I too have to stop myself from obsessing about the font, line spacing, and that a certain blue is just a bit different than another shade of blue. Yes, these options do matter. But help these casual users determine when that time is well spent.
  • Proper visualizations: When is a spinning 3D pie chart necessary to grab someone’s attention? BI professionals would firmly say “NEVER!” But these casual users do not have a lot of depth on BI best practices. Give them a few simple guidelines as to when “flash” needs to subsume understanding. Consider offering a monthly one-hour Lunch and Learn that shows them how to create impactful, polished visuals. Understanding if their visualizations are going to be viewed casually on the way to a meeting, or dissected at a laptop, also helps determine how much time to spend optimizing a visualization. No, you can’t just mandate that they all read Tufte.
  • Predictive: Provide advanced analytics capabilities like forecasting and regression directly in their casual BI tools. Using these capabilities will really help them wow their audience with substance instead of flash.
  • Feature requests: Make sure you understand the motivation and business value behind some of the casual users’ requests. These casual users are less likely to understand the implications of supporting specific requests across an enterprise, so make sure you are collaborating on use cases and priorities for substantive requests.

By working with your casual BI users on the above points, you will be able to collectively understand when the absolute exact request is critical (and supports good visualization practices), and when it is an “optimization” that may impact productivity. In many cases, “good” is good enough for the fast turnaround of data discovery.

Next week, I’ll wrap this series up with hints on getting your casual users to embrace the “we” not “me” mentality.

Read Part One of this series: Changing The IT Culture For Self-Service BI Success.

Follow me on Twitter: @InaSAP

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How Emotionally Aware Computing Can Bring Happiness to Your Organization

Christopher Koch


Do you feel me?

Just as once-novel voice recognition technology is now a ubiquitous part of human–machine relationships, so too could mood recognition technology (aka “affective computing”) soon pervade digital interactions.

Through the application of machine learning, Big Data inputs, image recognition, sensors, and in some cases robotics, artificially intelligent systems hunt for affective clues: widened eyes, quickened speech, and crossed arms, as well as heart rate or skin changes.




Emotions are big business

The global affective computing market is estimated to grow from just over US$9.3 billion a year in 2015 to more than $42.5 billion by 2020.

Source: “Affective Computing Market 2015 – Technology, Software, Hardware, Vertical, & Regional Forecasts to 2020 for the $42 Billion Industry” (Research and Markets, 2015)

Customer experience is the sweet spot

Forrester found that emotion was the number-one factor in determining customer loyalty in 17 out of the 18 industries it surveyed – far more important than the ease or effectiveness of customers’ interactions with a company.


Source: “You Can’t Afford to Overlook Your Customers’ Emotional Experience” (Forrester, 2015)


Humana gets an emotional clue

Source: “Artificial Intelligence Helps Humana Avoid Call Center Meltdowns” (The Wall Street Journal, October 27, 2016)

Insurer Humana uses artificial intelligence software that can detect conversational cues to guide call-center workers through difficult customer calls. The system recognizes that a steady rise in the pitch of a customer’s voice or instances of agent and customer talking over one another are causes for concern.

The system has led to hard results: Humana says it has seen an 28% improvement in customer satisfaction, a 63% improvement in agent engagement, and a 6% improvement in first-contact resolution.


Spread happiness across the organization

Source: “Happiness and Productivity” (University of Warwick, February 10, 2014)

Employers could monitor employee moods to make organizational adjustments that increase productivity, effectiveness, and satisfaction. Happy employees are around 12% more productive.




Walking on emotional eggshells

Whether customers and employees will be comfortable having their emotions logged and broadcast by companies is an open question. Customers may find some uses of affective computing creepy or, worse, predatory. Be sure to get their permission.


Other limiting factors

The availability of the data required to infer a person’s emotional state is still limited. Further, it can be difficult to capture all the physical cues that may be relevant to an interaction, such as facial expression, tone of voice, or posture.



Get a head start


Discover the data

Companies should determine what inferences about mental states they want the system to make and how accurately those inferences can be made using the inputs available.


Work with IT

Involve IT and engineering groups to figure out the challenges of integrating with existing systems for collecting, assimilating, and analyzing large volumes of emotional data.


Consider the complexity

Some emotions may be more difficult to discern or respond to. Context is also key. An emotionally aware machine would need to respond differently to frustration in a user in an educational setting than to frustration in a user in a vehicle.

 


 

download arrowTo learn more about how affective computing can help your organization, read the feature story Empathy: The Killer App for Artificial Intelligence.


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About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

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Predictive Procurement Gets Real

Marcell Vollmer

The physical and digital worlds have officially collided. In the old days, we’d have the morning paper delivered to our doorsteps and read it on the way to work while sipping coffee we made at home. Today, the news stories we care about are automatically delivered to our mobile devices, and we scan them while enjoying the beverage that was ready and waiting for us at the local coffee shop after we ordered it via mobile app. In years past, we attended events after work to expand our professional networks. Now we link to our peers — and their peers — around the world, online in real time.

Connecting the dots

As a society, we are more connected than ever. Thanks to the Internet of Things (IoT), we can see and be seen like never before. We can learn about the future and use this information to shape it to our advantage.

There are plenty of examples of this in the consumer world—for example, refrigerators that predict when you’re about to run out of milk and automatically order and have it delivered before you even notice, and devices that know you’re on your way home and turn on the lights before you get there.

It’s happening in procurement as well, and transforming the function as we know it. Procurement is complex and involves lots of moving parts, from sourcing and manufacturing to transportation and logistics. It’s an intricate web of systems, processes, and relationships that must be coordinated and managed, both internally and externally, to ensure that goods and services get delivered on budget and on time.

Predicting the future

Over the years, procurement has made great strides, leveraging disruptive forces such as business networks and cloud technologies to evolve from a tactical manual process to a strategic digital one. Paper orders and invoices are all but dead. Electronic payments are taking hold. Buyers and sellers are meeting and collaborating online.

Yet the transformation has only begun. Aided by Big Data and the IoT, procurement is becoming smarter and more predictive than ever.

Data is the lifeblood of any organization. From structured information on production, marketing, sales, HR, finance, facilities, and operations to transaction-level data on suppliers, customers, and partners, it tells the story of a business. For years, companies have been mining data simply to figure out what it all means—essentially, to learn from the past and perform better in the present.

Now they are leveraging advances in technology such as in-memory computing, real-time analytics, and the IoT to create assumptions about what will happen in the future and take actions that drive optimal outcomes.

Eliminating risk

Supply chains are more global than ever, and as a result, fraught with more risk. Many companies are turning to the IoT to anticipate and mitigate this risk before it disrupts their business. Consider the mining industry. Trucks are the critical link to transport raw materials to either further process or sell them on the market. If one of these trucks stands still due to maintenance issues, losses to the company could run into the millions, as they only can sell what they get out of a mine and deliver.

With the help of sensors, companies can continually monitor their fleets and receive notifications on upcoming maintenance needs to prevent breakdowns before they occur. Critical components such as engines and braking systems, for example, can be connected by small IoT sensors that monitor their temperature, hydraulic pressure, container angle, position, and vibrations. The sensors transmit all data to a live dashboard, and if a key parameter such as temperature changes, it will trigger an alert for the radiator. This information is then automatically routed to the procurement system, where a replacement order for radiator hose and radiator cleaner is automatically processed in line with the company’s procedures and policies. Related maintenance service is scheduled with a qualified technician who will arrive as soon as the material arrives and perform the work before a fatal defect of the radiator causes the truck to literally stop in its tracks. Risk avoided.

Delivering value

Supply chains are no doubt complex — and the data within them even more so. But data is the new global currency. And the IoT holds the key to unlocking its value. With the IoT, companies can not only spot patterns and trends in their business but anticipate risk and changes and adapt their businesses to gain advantage.

For more on how data analysis is transforming business, see Living The Live Supply Chain: Why You Need Data Scientists.

The article originally appeared in Spend Matters. It is republished by permission.

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Marcell Vollmer

About Marcell Vollmer

Marcell Vollmer is the Chief Digital Officer for SAP Ariba (SAP). He is responsible for helping customers digitalize their supply chain. Prior to this role, Marcell was the Chief Operating Officer for SAP Ariba, enabling the company to setup a startup within the larger SAP business. He was also the Chief Procurement Officer at SAP SE, where he transformed the global procurement organization towards a strategic, end-to-end driven organization, which runs SAP Ariba and SAP Fieldglass solutions, as well as Concur technologies in the cloud. Marcell has more than 20 years of experience in working in international companies, starting with DHL where he delivered multiple supply chain optimization projects.