Being upfront about the future of television monetization

May 12, 2017 – CIO Forget what you’ve heard about digital being the death of television. Digital and data is only strengthening the power of television.

How many times have you heard that television is dead? Gone are the days of MASH, when television shows were an event. It’s all about going digital, they say.

While many are cutting the cord, it’s not time to give up on the power of TV. TV advertising remains a highly effective advertising medium — one that digital complements well. In fact, according to ARF, out of 3,200 advertising campaigns, TV advertising was “the most effective vehicle for driving ROI, and adding digital to a TV campaign yields a 60 percent kicker effect.” This is not something brands can ignore.

Television advertising and measurement largely rely on ratings – known as the Gross Rating Points – for buying and selling. This is often referred to as the “currency” – one which has been in place for over 60 years. However, this is changing, albeit slowly, due to the availability of digital and data.

To continue reading UberMedia CEO Gladys Kong’s article for CIO, please click here

Anomalies and False Narratives: Finding Truth in Big Data

By Kerry Pearce, SVP, Product Development, UberMedia

It’s rare for marketers to find clear answers in Big Data. But for inquisitive marketers, Big Data is incredibly helpful for identifying the right questions.

Recently, a fitness apparel brand sought to identify potential customers by analyzing mobile location data. We focused on gyms, public recreation areas, and stand-alone fitness locations like yoga and cycling studios. We found the audience, but we also found something unexpected: a large overlap with audiences that frequent fast food establishments, none of which could be classified as healthy options. To the client, this looked like the kind of previously unseen and counterintuitive insight Big Data is famous for. Yes, the client reasoned, logic dictates that you’re unlikely to find a significant audience of gym rats who also have a serious French Fry habit. But with so much data, how could that conclusion be wrong?

Big data is really about picking the relevant “small” data

For all its emphasis on scale, Big Data is really about analyzing the small fraction of collected data that is relevant to the inquiry. This is because collecting Big Data, by definition, means collecting even more noise. This is why data scientists talk about “cleaning up” the data as a prerequisite to analysis – the idea isn’t to find the needle in the haystack, but rather to locate the relevant haystacks.

So is it possible that people who workout a lot also enjoy eating unhealthy food? Of course it’s possible, and we can even come up with some behavioral theories to explain why. Perhaps, these gym rats workout to offset junk food. Or maybe, the appeal is convenience, because people who workout are pressed for time. Neither of these theories is inherently wrong, but the further down the road we go with this particular data-driven narrative, the more susceptible we become to our own bias. Put simply, we want to believe we’ve discovered a new audience segment, and so we tell ourselves a story where Big Data unearthed a hidden clue. But is it truly a relevant data point; or put another way, is this insight one that will move the needle for a marketer?

When the data adds up, worry

One constant requirement of narrative is that the storyteller ties up loose ends. But that’s not how the real world works because the real world is messy. So if the data adds up to a tidy story – the audience segment for fitness apparel is huge because everyone is passionate about fitness! – it’s time to worry.

Too often, marketers seek out only data that confirms narratives they already believe to be true, or narratives they want to believe to be true. All humans are susceptible to this problem, by the way – it’s why we want to believe news reports about studies that tout the health benefits of drinking alcohol and eating dessert. But marketers can be especially prone to this type of bias because marketers are the guardians of a brand’s intangible qualities and values. They know their brands, which simultaneously makes them experts and the least likely people in the room to see the bias of their own assumptions. They believe everyone cares about fitness – whether they actually demonstrate that care or not – because everyone who works at a fitness brand is demonstrably passionate about working out. The question is not how to get rid of that bias – you can’t – but how can marketers use Big Data to seek out deeper truths that may upend what we think we know for sure?

Embrace the anomalies

The overlap between the fitness and fast food audiences is an anomaly – one we must embrace. If taken at face value, the overlap seems to prove what we want to believe: everyone is passionate about fitness. But the same data can actually be used to tell just the opposite story. People who go to the gym and frequent fast food may not be passionate about fitness at all. True, they do exercise and so may require workout gear, but their level of enthusiasm for exercise might actually be diminished by their interest in fast food. Put another way, the anomaly didn’t enlarge the fitness audience, it actually made it smaller because the deeper we dug into the data, the more nuance we found. And in that nuance we discovered a subset of the fitness segment that doesn’t really share the primary values associated with the overall segment.

Of course, trading a large data set for a smaller, albeit more useful one, feels counterproductive because doing so forces us to abandon the story we told ourselves. In embracing the anomaly, we found hard evidence that the passion for fitness is not universal. But in exchange for letting go of that false narrative, we put ourselves in a better position to locate that passion. It’s not an easy trade, even if it is a good one. In fact, for marketers, letting go of stories that neatly summarize brands and customers is terrifying. But the alternative ought to be even more frightening, because a strategy based on a false narrative is one that is inevitably doomed to fail.

Announcing ‘Data in the Digital Age’ – A New Series on CIO.com By Gladys Kong

March 9, 2017 – CIO.com Mobile technology and data solutions expert Gladys Kong provides her views on the data landscape in new monthly series.

Here is an excerpt from UberMedia CEO Gladys Kong’s inaugural post, How Location Data can Transform your Business:

“Not far from the Jet Propulsion Laboratory, The California Institute of Technology, and scores of startups like UberMedia, you can find a typewriter repair shop. The firm has been located in Pasadena, California for more than 100 years. While it might seem out of place in the age of laptops and cloud computing, it has at least one important thing in common with its tech-savvy neighbors — the right location. The difference, however, is that you probably wouldn’t expect a typewriter repair shop to thrive in such a time and place.

That expectation comes from powerful, but sometimes incorrect or incomplete, assumptions about the world around us. Increasingly, the use of location data is challenging some of these assumptions and transforming our communities and businesses…”

To continue reading, please visit CIO.com.

How location data can transform your business

March 9, 2017 – CIO – Location data is helping cities and businesses evolve to enhance services, address challenges and plan for the future.

Not far from the Jet Propulsion Laboratory, The California Institute of Technology, and scores of startups like UberMedia, you can find a typewriter repair shop. The firm has been located in Pasadena, California for more than 100 years. While it might seem out of place in the age of laptops and cloud computing, it has at least one important thing in common with its tech-savvy neighbors — the right location. The difference, however, is that you probably wouldn’t expect a typewriter repair shop to thrive in such a time and place.

That expectation comes from powerful, but sometimes incorrect or incomplete, assumptions about the world around us. Increasingly, the use of location data is challenging some of these assumptions and transforming our communities and businesses.

A community is the sum of its data

Cities are becoming smarter.  They are increasingly exploring the use of mobile location-based data to enhance services, ease traffic congestion, and plan for the future.

To continue reading UberMedia CEO Gladys Kong’s article for CIO, please click here.

6 Tech Founders Give Meeting Efficiency Tips That Actually Work

We asked six entrepreneurs how they make meetings efficient. Check out their answers below and do away with the time wasting tradition of inefficient meetings:

Avoid Them

I only do meetings when I find there is a need for it. We have regular staff meetings, but no meetings just to have meetings. We regularly communicate on Slack. We also have an open office environment. If there is a question team members can come up and ask me, especially since I don’t have a meeting to go to.

– Gladys Kong of UberMedia

To continue reading and view more tips for efficient meetings, please visit Tech.co

Pulling Back the Curtain of Location Data

The explosion of mobile data, and specifically mobile location data over the past few years, has brought about an incredible opportunity for businesses. While data has been used in making business decisions for centuries, the type of data and its volume is changing the way we do business. Within the marketing organization, for example, direct marketing has heavily relied on location data – or simply said, household address – to drive performance for decades. However, the use of data today goes well beyond the targeting use case – it’s being used more broadly to answer question across business units and across verticals. As a technologist, it’s important to better understand the dataset with which you’re working.

There are two important factors in understanding location data. One is the quality of the data, and the other is how to apply the data to gain actionable insights. Quantity of data is important, but without quality and thoughtful processing, having a large amount of raw data is like playing a bunch of musical notes in random order – it creates noise, rather than music. Just as it takes an expert composer to put together musical notes into masterpieces, it takes data scientists to apply machine learning to turn raw data into actionable business insights.

To read more from UberMedia CEO Gladys Kong, please visit insideBIGDATA.

UberMedia: Brands to Evaluate Agency Options & Refocus on Strategy in 2017

At the end of 2016 we asked members of the extended LSA community to tell us what they expect in 2017 for location-based marketing and media. We received a broad mix of predictions from over 50 industry professionals and compiled them into a free report.

Below are the predictions submitted by Michael Hayes, CMO and CRO at UberMedia:

Agency Review-Mageddon 3.0

After two years of agency review upheaval, we can expect yet another avalanche of advertisers putting their business up for review in 2017. One recent survey found that more than two thirds of advertisers said they are seriously evaluating their advertising agency partners.

A New and Complicated Era between Marketer, Agency & Publisher

We saw hints of this in 2016 when, after a long and much publicized pitch process, McDonalds and Omnicom created a jointly operated “agency of the future” where the client marketing team is embedded within the agency and remuneration is tied to advertiser performance.

The takeaway is that in 2017, as marketers search for new ways to grapple with the velocity, complexity, and data deluge of marketing, we will see marketers demand to be more integrated with their agencies, publishers, data partners, and tech suppliers – all embedded into one multifunctional unit.

“Strategery” Will Return After a Brief Hiatus

Over the last few years, agencies have been focused on agency-wide buying efficiencies, supplier consolidation, and programmatic automation. These efforts have been worthwhile, and yet they have left an important client strategy gap. As the ad ecosystem consolidates, agencies will turn their focus to a more strategic market and audience planning era.

To continue reading UberMedia CRO/CMO Michael Hayes’s 2017 predictions, please visit LSA Insider

Retail Space is up for Grabs: Why the Physical Future Will be Data-Driven

Once a sign of a prosperous community, many of today’s malls face strong economic headwinds. But that doesn’t mean the mall will be relegated to the history books. In fact, there may be a new chapter in the mall’s story. As The Wall Street Journal recently reported, a diverse group of players are investing in retail locations, or buying retailers outright, because they see an opportunity to shape the future of brick and mortar. But who will win that future?

Unlike in years past, where investors often banked on the cyclical nature of real estate, the winners this time around will be firms that understand how to leverage data to better analyze the connection between geography and consumer behavior. Yes, location still matters, but just as important is what you know about that location and the people who are likely to frequent it.

To read more from UberMedia CBDO Eric Aledort, please visit The Drum

Why the Food Court is the New Department Store

But one reason some upscale malls have done a better job of weathering the economic headwinds of the past few years, as well as the decade-long digital disruption of retail, is the fact that their food courts have done a better job of keeping pace with our changing food preferences.

Put simply, people need a reason to go to the mall, and the quick serve restaurants that make up the food court, along with the larger sit-down restaurants, are fast becoming the anchors of today’s malls, replacing the drawing power of the department stores.

Of course, our taste in food is subjective, culinary trends change quickly, and dining preferences vary by locality.

Retailers and mall owners can’t learn what they need to know about consumer behavior just by taking inventory of the dining options in the area. Instead, retailers and mall owners can dig deeper into consumer behavior by using mobile location data to understand the patterns and preferences that govern retail foot traffic. Here are some questions retailers should be looking to mobile location data to answer.

To read more from UberMedia CBDO Eric Aledort, please visit GeoMarketing

Why The GRP Must Die: And Why It Won’t

August 23, 2016 – MediaPost – Is it really still true, in the age of digital, that if you blast enough eyeballs with an ad for a fast-food burger, you’ll sell more beef?

The answer to that question is a qualified yes, but the size of the asterisk grows bigger by the day. On the one hand, Gross Ratings Points formulas have been the bedrock of advertising for decades. They play a big role in the media mix models advertisers see as crucial for understanding omnichannel performance.

Conversely, the GRP model is breaking, thanks to the Internet and mobile and trends like cord cutting and media fragmentation. Once, advertisers could comfortably rely on the GRP—spend enough to max out reach and frequency and inevitably the needle will move.

Today, however, that spend doesn’t deliver the same bang for the buck. Although we collectively acknowledge this shift, all advertisers can really do is lament that things aren’t what they used to be.

To continue reading, please visit MediaPost