Factual’s VP of Agencies and Strategic Partnerships Talks Cross-Device Targeting and the Best Strategy for Real-Time Location Data

December 4, 2017 in Blog

Adelphic’s new Q&A series will shine a spotlight on the biggest challenges, questions, and trends in the programmatic marketplace with commentary from industry experts, clients, and partners.

Our first piece in this series features Ocean Fine, VP of Agencies and Strategic Partnerships at Factual. Founded in 2008, Factual provides highly customizable location-based audiences and geofencing capabilities.

As the VP of Agency and Strategic Partnerships, Ocean is responsible for driving awareness and adoption of Factual’s Location Targeting Ad Solutions.


Q: What are some of the biggest benefits you see in cross-device targeting?

Ultimately, real world actions speak louder than web browsing behavior. Cross-device targeting takes into consideration insights from different behaviors to create those contextually relevant opportunities advertisers need to really reach their audience.

With effective cross-device targeting, advertisers can reach new levels of scale by tailoring messaging across multiple devices and formats. They can build their brand story and customize content around specific stages of the consumer journey, helping deliver messaging at the right time to engaged audiences.

Q: Why should advertisers care about device IDs in the desktop environment?

Depending on the type of data you have access to, your needs will be different. Desktop behavior only analyzes someone’s browsing history to build insights. But device ID tells a more complete picture, showing movement through the physical world. For example, I love to search recipes and read them frequently. This browsing behavior might indicate to an advertiser that I am probably someone that shops for groceries and cooks often. However, I travel a lot for work and probably haven’t cooked a meal in months.

On the one hand, my device ID would have accurately given you the indication that in fact I do not frequent locations of someone who typically likes to cook, but rather I am a business traveler and frequently eat out at restaurants with friends and clients. However, on the other hand, If you observed my actions through my browsing behavior, I would have been incorrectly bucketed and targeted as someone who cooks a lot and regularly shops for food and household groceries, which as we know, is (unfortunately!) very inaccurate.

 Q: Beyond the desktop and mobile phone, what other channels and screens are emerging as important for marketers to reach their customers?

Having a single view of the consumer is critical because today’s consumers often interact in a variety of ways and are more connected than ever before. It’s important that we understand how consumers behave across all channels, at every touchpoint. It’s no longer just mobile and desktop that should be considered for cross-screen efforts, but also channels like TV and Out of Home are increasingly important.

With device matching, a marketer is able to recognize the same consumer when they interact with their brand, and target that consumer with relevant messaging at scale, across each touchpoint. For example, marketers can use cross-device targeting to understand if someone has passed by their digital elevator or highway billboard, and target that same person while online, on their mobile device, or with an advertisement on television later that day.

Q: When should real-time location be used instead of or in conjunction with past location targeting? Which is the best strategy for a cross-device campaign?

Tactics are really dependent on the goals of the campaign and the audience the advertiser is trying to reach. When advertisers craft their campaign, they need to have an in-depth understanding of a consumer’s behavior relevant to their brand, so they know what will drive the greatest ROI.

For example, Factual’s Geopulse Proximity is used to target people based on where they are in real time, using their latitude and longitude. This opens the door for broader targeting tactics to reach people at or near a place and can drive them in store to make a purchase. You can also optimize using targeting based on real world behaviors of people who visit a place frequently. Cross-device targeting allows you to message them wherever they may be, whether that’s in-app or mobile web on a smartphone/tablet, or at home or work on a desktop computer.

Q: Anything exciting on the horizon?

Yes! Something exciting for both Factual and Adelphic is that we recently completed a new component of our integration that enables Adelphic to cross device target any Factual mobile location audience. This means that Factual audiences can run on Adelphic across desktop, tablet, and mobile, using Adelphic’s patented cross-device solution. This allows for greater scale and reinforces mobile marketing with coordinated messaging across all devices.

For more information or to activate an audience for cross device, reach out to Adelphic at info@adelphic.com or Factual at strategy@factual.com



3 Keys To CPG Marketing Success: Precision, Scale And First-Party Insights

March 22, 2017 in Blog

Most B2C marketers now have more distribution channels than they did just a few years ago. Though these channels afford marketers more data to leverage and opportunities for conversion, they also create more challenges in closing the attribution loop. What’s more, brands also have to grapple with consumers making purchases both in-store and online. This means that they have to market their products in such a way that entices the customer to make a purchase in two very different environments, where they must be equally adept at closing the attribution loop and making use of the resulting data.


Fortunately for CPG marketers, 86% of adult consumers prefer to purchase CPG products in a physical store. But Walmart’s acquisition of Jet.com last year is a prime example of expected growth in online shopping, and innovations like voice-activated assistants and Amazon’s Dash Buttons are making consumers more comfortable with ordering products online. Marketers truly understand the opportunity and are developing strong cross-platform strategies with focused KPIs that align with foot traffic and in-store purchases.

When it comes to cross-channel ad campaign execution, all marketers ever hear is that to achieve precision, scale must be reduced, and vice versa. This does hold some truth, as truly engaged audiences may be smaller, but extremely precise audiences can also be too small to generate ample ROI. Last summer, P&G grapple with this very same precision, scale and insights challenge when they opted to scale back their targeted Facebook ad buys in an effort to find, “the best way to get the most reach but also the right precision,” according to the company’s CMO, Marc Pritchard.

Savvy brands such as Clorox — which tested a mobile-first, end-to-end shopper-marketing program — understand the challenge and are building out their own first-party data sets to gain more scale, precision and insights to connect with their consumer. But the reality is that most CPG brands still lack the type of robust collection of first-party data they need to be successful, or they rely too heavily on partners to identify loyalists and customers who are purchasing their competitors products.

For CPG marketers to develop the type of intimate relationships that drive business, they must have a strong in-store strategy, first-party data, and alternative data sources.

Strong In-Store Strategy

CPG marketers can use their in-store strategy to their advantage. Data breeds insights, and there is a healthy selection of in-store data possibilities for marketers to choose from. For example, brands can work with a partner that provides point of sale purchase data to identify and reach a customer at a later time. In order to drive a purchase, marketers can team with a geofencing provider to deliver a coupon to a previous customer with the potential for high lifetime value to ensure they’ll repeat their behavior at a nearby retailer.

First-Party Data

The open web is home to the same audiences that are on the likes of Facebook and Google, and marketers are coming to this realization and getting smarter about building their own data stores from consumer behaviors on their smartphones, tablets, desktops and more. Brands such as Kellogg, Unilever and Kimberly-Clark are leading the way with their in-house DMPs, stockpiling first-party data to leverage as a catalyst for the type of precision required to deploy efficient cross-channel campaigns.

Alternative Data Sources

While first-party data has long been touted as a brand’s most important data asset, the importance of alternative data sets cannot be lost, as they often provide the scale required for cross-channel campaigns. For example, high-quality data sets can be augmented and strengthened with third-party data from retailers loyalty programs, but it is still internal first-party that should always be the cornerstone of any ad campaign.

Who said CPG marketers can’t have it all?

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Real-Time Banter: Adelphic and Glasswing Ventures Discuss Artificial Intelligence and Machine Learning

December 15, 2016 in Blog

This edition of Real-Time Banter features a conversation about the future of artificial intelligence and machine learning, through the lens of venture capitalist Sarah Fay, Managing Director at Glasswing Ventures. Sarah was interviewed by Jennifer Lum, Chief Strategy Officer for Adelphic.

Jennifer: What is Glasswing Ventures?

Sarah: Glasswing Ventures is a new venture capital firm entirely focused on artificial intelligence (AI) and machine learning. My partners, Rick Grinnell and Rudina Seseri felt it was the right time to launch an investment company in this specific area because AI is set to create the next big wave of value creation in the technology space, adding trillions to the economy over the next decade. AI is the third big digital tech wave. The first wave came with the advent of the Internet, which emerged in the 90’s introducing the market to ecommerce and all kinds of online activity. The second wave was Social and Mobile, which have completely changed behavior over the last decade. Technology will continue to change behavior through the connectivity that is now embedded in a growing number of our devices – some statistics point to as many as 100 billion connected devices by 2020 – and the unprecedented amount of data that can be leveraged to make all of our interactions smart. AI will be the layer that allows us to more efficiently communicate with our devices and make technology more embedded in the world around us. Instead of being handcuffed to our computers and mobile phones, we will be able to speak to our environment to receive services and information.

I’ve heard people ask ‘Isn’t it a bit early for AI?’ but I believe we are right at its inflection point. AI has been around for decades. It just hasn’t had the appropriate platforms to draw from. We are now reaching a critical phase where those platforms and connected devices are available. AI applies to consumers and the way we interact within our daily lives, but also to enterprises, making companies more intuitive and smarter in how they operate. Companies like Adelphic, for example, are already using AI, allowing their systems to make faster and smarter decisions.

The Glasswing brand revolves around the Glasswing Butterfly. It’s a real butterfly species with transparent wings. The metaphor represents our transparency with our investors, the transparency we expect from the founders we partner with, and the transformation we mean to bring to these companies as well as the marketplaces they serve. The Glasswing Butterfly is actually a mechanical wonder, despite the fact that it looks delicate. We like to imagine that butterfly flying up and to the right!  We are mainly focused on making investments in the Northeastern US. AI development is very rich in this geography. We focus on Series A funding, which is a needed tier of investment capital particularly on the East Coast, and particularly in Boston where we have so many successful Seed investment firms bringing deals to life at the very early stage.

What investment areas are you focused on for 2017?

Although my background is in marketing and technology, I’m comfortable with the language of technology and how it applies to all different categories of business. As far as my role with the firm, I am reaching out to those in my network across a variety of industries to let them know that we’re looking to invest in Series A startups in the AI space. When companies are launching an AI based business, they’re looking for strategic investors and a firm where they fit into the ecosystem of a portfolio. My partners have a great track record for managing a portfolio of investment companies. I will participate in every investment that we make – not necessarily as the lead investor or board director, but as someone who helps founders across the portfolio to strategize and build their businesses.

Can you tell us about your professional experience prior to joining Glasswing?

The bulk of my career was spent as an entrepreneur and CEO in marketing and technology services. I started as an entrepreneur with a firm called Freeman Associates, a media agency specifically designed to help technology companies negotiate and spend their media budgets. I started there before the Internet launched for broad consumer use. But our area of expertise set us up for success, because when the Internet emerged, technology companies were among the first to shift advertising dollars to online. This became a key differentiator for our agency and helped us to grow quickly. The company grew 100 percent CAGR five years in a row, and we ended up being acquired by Carat, a global media organization (Aegis was the parent company). So, I went from being part of a very successful niche organization to joining a global organization with more than 14,000 people. This happened right as the Internet started to be used by marketers as a platform for digital media and marketing.

In 2000, I launched Carat Interactive, the digital media practice for Carat in the US- right into the teeth of the dot com bust, so there was a challenge at first getting off he ground. But we were nimble and able to change the business quickly. We focused on search engine marketing, CRM, creative, and service where we could capture more revenue than just a small percentage of the media budget. We started with 25 people in 2000 and by 2006 we had grown the business into a network called Isobar of 700 people and $100 million in revenue. It was a real rocket ship ride and extremely successful – the agency was filled with incredibly talented people and thought leadership, and the growth was completely energizing for the whole company. Around 2008, the decision was made to merge the traditional and digital sides of Carat, and I became the CEO of the newly formed company and managed the transition. Incorporating two very different businesses with different cultures is a huge challenge. It was probably one of the hardest things I’ve ever done, but it was the right thing to do for clients.

Shortly after that, I became CEO of Aegis Media North America, a 2,000 person organization at the time. When I left Aegis in 2009, I had a number of board opportunities offered to me. I decided that instead of taking another full-time position, I could make a business of working with startups, acting as a board of director or advisor to these companies. It gave me the opportunity to work with founders at the very cutting edge of where media was changing and re-shaping the industry, and that has been the most exciting part of my career so far. – I’m hoping the VC world will be even more exciting!

It’s funny that we were saying ‘This is the year of the mobile’ as far back as 2002.   It was very clear to me even back then, looking at usage statistics, that mobile would provide a major marketing platform,- but acceptance from marketers evolved so slowly. It was a running joke that we expected every upcoming year to be the Year of Mobile for a decade! But when it hit, it really hit. Since I lived through every stage of mobile’s journey, it was thrilling when it really took off, because I finally felt validated – and of course I have been rooting for companies such as Adelphic and Celtra. According to eMarketer, mobile accounted for $46 billion of the $72 billion spent in digital advertising in 2016. That’s amazing!

And digital advertising growth has not been limited to mobile. The same can be said for companies in the programmatic, video and social marketing technologies. It is fascinating participate in those areas. That has set the stage for me to become a VC.

How have your operational experience and board work helped you transition to investing?

I’ve spent the last 7-8 years evaluating new business concepts and meeting entrepreneurs. While I haven’t before made a career of investing – I have been doing many of the things a VC does. I’ve been deciding which companies to work with, I have certainly been in the marketplace long enough to see successes and failures and to understand what factors contributed to either type of situation. You learn to recognize the significant importance of a team that can execute and evolve their company ahead of the market. Beyond evaluating companies, I have been a board member and have worked to help my companies succeed, which again will be a big part of the job going forward. In addition, I’ve done some Angel investing that includes some strong companies. Aside from all that, I have two very seasoned partners to show me the ropes in any new areas. I’m incredibly excited and ready for my new life as a VC!

What opportunities are you most excited about for AI and advertising/marketing tech?

AI has the opportunity to be a game changer when applied to all kinds of organizational or individual work processes. AI eliminates work that is otherwise done manually, making people and systems more efficient, and saving time and money. AI can have an immediate impact on a company’s growth and revenue potential. We aren’t zeroing in on one category, but it’s that type of potential that I am excited about – technology that can really move the needle.

Why hasn’t ad tech been popular among the investment community?

I think we have to recognize that it’s a crowded and complex marketplace and the buying community hasn’t changed too much over time. We have the same buyers making decisions about which of the myriad ways they can improve results against existing metrics. While the industry is oriented toward innovation, advertisers are trying to simplify their stacks and methods for targeting. It’s going to get to a point where they want to stop adding incremental pieces to their marketing technology stacks. We have also seen the industry start to really shovel money into Facebook and Google, which is boxing out many companies and new forms of innovation. Opportunities may still certainly by found in areas where big shifts happen, and we need to stay tuned for areas where the needle can be significantly moved against new returns.

For example, when Adelphic first launched, a big shift was happening. Advertisers were just figuring out how to tap into the mobile marketplace and they were trying to make mobile advertising scalable. That was a big opportunity. There continue to be new technologies and ad tech developed that add incrementally to these efficiencies. In many cases, the advertiser or publisher will be able to use these to achieve, say, another five percent ROI, which is usually nothing to sneeze at, but it’s also not necessarily a game changer – there are a lot of ways to generate that incremental five percent. The issue is that there are so many innovative solutions, and an advertiser can’t possibly do business with all of them. But a game changer may come along, and that’s what I have my eyes peeled for – especially if AI is at the core.

What are your thoughts on the current state of the programmatic market?

The programmatic marketplace is mature. A recent eMarketer statistic showed 73 percent of digital advertising dollars are spent programmatically. You hear some agencies saying their goal is to have 100 percent of advertising spent programmatically. The ambitions are to do more in programmatic, not less. I think programmatic advertising will continue to evolve. For instance, programmatic buying and selling behavior is certainly moving to TV media. The TV marketplace is very different and programmatic will evolve according to these differences and be different for that channel. Appending data to inventory will become the norm. People will not buy blindly without knowing which audiences they are reaching and how that applies to their pricing. I see programmatic continuing to be important. The fragmentation of audiences, markets and media sources necessitates automation in order to adeptly reach specific audiences, and that fragmentation is continuing.

What do you think the agency of the future will look like 5 years from now?

There are so many different types of best practices across the agency spectrum. How an agency strings these services together is where the magic will be. In the future, there will be a focus on truly understanding who the customers are and what moves them, and there will be a real-time aspect to that. Creative and media will have to dovetail to perform together. I am pointing to the future, but these practices are closer than we think.

Marketers will get better at conversational marketing, which means they will have more two-way conversations with customers. As an example, chatbot marketing will become a key practice. China is ahead of us in this area. People call China the ‘messaging economy.’ Somehow consumers have really responded to interacting with chatbots. Brands don’t necessarily need to create their own chatbots, but they can find ways to insert themselves into chatbot conversations. Is chatbot going to be a dominant form of marketing? Not necessarily. But remember, at one point social media was a very small part of advertising – it was the cherry on top of the cake. If you look at the market today, social is a very big part of things. In many cases social media is central to a brand’s strategy, and ties in to all of the other channels. Social strategies will continue to evolve to include chat and voice elements. No matter what shape a campaign takes, it’s important to understand who the customers are, what motivates them and how they want to interact with the company or brand. Building loyalty is becoming the primary marketing goal, and advertising will use customer data to acquire new customers that mirror their loyal customers.

What was your first mobile phone?
This is a little embarrassing and I’m dating myself, but it was a car phone. It was a big black, lunchbox-sized phone that went under the front seat of my car. Whenever there was a new way to communicate, I had to have it. The car phone was a completely new innovation and I just had to have one as soon as it was available, so I could be in constant contact with my clients. This was back when I was in advertising sales. It was too heavy to even lift it of the car.

What is the first app you open everyday?

Email. Email leads to all of the other apps, but email is the center of my universe.

The post Real-Time Banter: Adelphic and Glasswing Ventures Discuss Artificial Intelligence and Machine Learning appeared first on Adelphic.

Real-Time Banter: Adelphic and Vistar Media Discuss Digital Out-Of-Home Advertising

November 17, 2016 in Blog

This edition of Real-Time Banter features a conversation about digital out-of-home advertising with Michael Provenzano, CEO of Vistar Media. Michael was interviewed by Jennifer Lum, Chief Strategy Officer for Adelphic.


Jennifer: What is Vistar Media?

Michael: Vistar Media is a software company that bridges the gaps between location data and media in the physical world. We do that by ingesting geo-temporal data sets – location and time  – and then we analyze that data to make michael-provenzanobetter advertising decisions by understanding where consumers are moving throughout the day and their purchase behaviors.

On the media side of our business, we built the first programmatic system for the out-of-home industry. We connect our customers to over 90 percent of the digital out-of-home assets in the U.S., and in 2017, we plan to expand into the U.K. and Canada.

We have a whole stack of products – everything from an ad server to a DSP – kind of like the old days of Right Media, where it was all in one. We have revenue management tools for suppliers, direct ad serving, reporting and billing, and inventory management tools as well as access to our exchange. On the buy-side, we have the traditional ad server for tracking, providing transparency and accountability for buyers, and our DSP for digital savvy clients to login and purchase digital out-of-home ads. We do a lot of things for one company.

Can you share the founding story of Vistar Media with us?

Vistar Media official started in January 2012.

Jeremy, my good buddy from college, and I stayed in touch over the years. He had an interesting life having lived in London and Monaco working in finance. At this point, I had wrapped up Invite Media and was taking a few months off to travel.  Jeremy was doing research in the real estate business and realized that there was a large out-of-home business. After all, it is a $7.5B industry. He didn’t know too much about advertising, but knew I had been in the tech industry and thought this would be a good opportunity.

I jumped in and started researching. I quickly realized there were no data sets or measurement solutions for out-of-home advertising. The industry was really behind in terms of media, which is fascinating considering this market is not going anywhere. People are always going to leave their homes. Out-of-home is real estate, just like an apartment in downtown Manhattan would be. It will continue to have value. A billboard will always be there and have eyeballs. That’s value and a great messaging vehicle.

This opportunity was very logical in terms of starting the business. I always had passion in software and building companies, while Jeremy is very entrepreneurial. I reached out to Mark, our Chief Architect at Invite, who was at Google, and the three of us got it started in six months and moved to Philadelphia. We had to do three to 12 months of integration work with each media owner – there’s no simple handing over an ad tag. The first year and a half was just building the pipes and getting everyone connected.

Our value proposition was to sell to digital buyers who don’t buy out-of-home ads today. It’s important to these media owners, it’s a few million dollars to some of them.

You pioneered the DSP category as a founder of Invite Media. Has the programmatic market played out as you thought it would? What changes do you foresee over the next few years?

I think I was a bit naive to expect the programmatic market to be more than it is today. Everyone knows that programmatic has skyrocketed, but I’m still shocked to see people buy banner ads via ad networks, taking 60 percent margins out of a media buy or buying commoditized media in a non-transparent way – media that you can’t plan against, buy and measure very simply. Search falls into that category, video, online display, social, mobile is half and half right now, brands are still trying to find the measurement component.

I’m shocked when I hear about ad network business models “crushing it” and making tons of money off of that lack of transparency. As a marketer, how irresponsible is that? Most of the stuff I see is simply arbitrage and it’s scary that that can exist today. I don’t understand why all these ad networks exist. I’m a little disappointed.

I would like to see more agencies focusing on transparency for their clients. I get it’s hard as an agency person. Some have over 300 vendor meetings a year. It’s crazy people spend that much time in meetings. That’s an investment problem. I blame VCs and investors for that problem because they allow companies to raise large sums of money and that puts them in a nasty spot. If you raise enough money, then you can’t sell the company for a reasonable amount and you can’t merge because a VC gave them money to make the same software – so many mergers in this space don’t make sense. So companies will build a duplicate sales team to do the exact same thing. This has gone on for a while and there are so many display DSPs that are still in startup purgatory because of this. That’s an issue. We just saw TubeMogul get purchased. Focusing on one product and being great at it is important from a channel perspective, but over time I’m not sure what these companies do.

Management at agencies needs to clean that up and fix those problems. On the investor and vendor side we need to be more responsible about how we move money around.

Why did you specifically choose to focus on DOOH?

I think OOH is an under-appreciated medium. Not too dissimilar to real estate where you have distressed assets, up and coming neighborhoods – the assets are there, the eyeballs are there. Marketers have always suffered from not understanding which eyeballs are there and how to measure success for the marketer.

There’s a gap in attribution, and without attribution, we’re at a time where it won’t grow. The growth side of OOH is very small, somewhere between two and three percent a year. I have a fear that it could have the destiny of print. The reason I say that is mobile phones are just a moving, smaller billboards with a much more personal connection and data on the consumer. So if OOH doesn’t elevate itself as a measurable, attributable medium, it will get its lunch eaten by mobile. And there’s no reason for it because the same ways people can measure mobile they can measure OOH.

When you look at verticals, usually under one percent of CPG spend is spent on OOH. That’s crazy. It’s the largest vertical advertiser there is and they don’t spend nearly anything on OOH in U.S. That’s scary. You have such a small piece of the pie from one of the largest verticals. Their TV spend is off the charts, but as TV becomes more addressable or TV dollars move into digital dollars, OOH is in a really unique spot to catch that money if we upgrade our systems, prepare ourselves and work with proper measurement companies in the same way the digital vendors have in order to catch those dollars.

This transition is happening and it’s not exactly from one channel to another, it’s simply from media that cannot be measured in an efficient way to media that can be measured. We need to be in the bucket of “can be measured.” It’s very binary. At the end of the day, the goals are very logical around the business – this is a valuable piece of real estate but people don’t understand why it’s valuable because it has not been properly measured. If we can show attribution for some of the largest marketers, it’s logical that the overall investment in OOH would increase.

Are emerging measurement and attribution solutions for out-of-home similar to those in other channels? Or are channel-specific standards developing on their own?

Foot traffic is one measurement mobile has tied itself to in the past few years. It’s really important for mobile marketers as well as out-of-home. When we think about retail and QSR clients, we slap a study on to every campaign we can. If you don’t have enough impressions in a market, then it’s hard to measure something.

Vistar Media has recently partnered with name brand measurement companies to create the first-to-market out-of-home sales lift solutions for automotive and CPG products. Marketers will be able to target against segments just like you can online, then validate those who are exposed at a household level. 2016 was all about automotive and CPG. These companies believed in the opportunity of out-of-home, how big it could be and have worked with us for years. And it’s not just a Vistar effort. This is huge for the industry and should increase overall investment in OOH across the board.

Is there a publically known stat on the digitalization of billboards and out-of-home?

Most media owners work hard to digitize as much as possible. There are two factors here. One is the municipalities and government where they usually have to do trades. If you have three paper billboards, you can trade those in and put up one digital billboard. Digitizing boards requires a lot of government regulation and decision. That slows the process down.

They’re controlling utilization. Typically, they take their highest grossing assets in the best location and they digitize those first so they can show eight adds instead of one in that location. At the same time, if you were to take a location only 50 percent full out throughout the year and digitize it, now you’re talking about an even lower utilization rate – 50 percent divided by eight. It’s a much more drastic utilization problem, lowering the share of voice with a specific advertiser, CPM, and the rate. So it’s a balance. If the amount of eyeballs in one specific location at specific times aren’t growing then there is no reason to digitize more because you’re sitting on an investment for which you can’t prove ROI.

That’s the balancing act.

How should marketers be thinking of leveraging DOOH as a component of their cross-channel plans?

It goes back to measurement. I wouldn’t tell a marketer to invest in media that they can’t measure. That would be irresponsible to recommend. There are measurement solutions for all major verticals – retail, QSR, automotive, CPG. They shouldn’t think of out-of-home as a just a tonnage platform. We’re past that now. We’re able to look at true sales lists and in-store traffic. This is what we’re focused on and it’s the core to revenue and sales marketers.

I would urge them to think of out-of-home. Don’t just buy the medium, but look at how to measure and validate it’s working for your brand. A lot of brands can question that across the board. There is still a lot of vanity buying of out-of-home. I’m not a big fan of people investing in that way because we know data platforms exist that tell us who we’re reaching and if it’s working. I think people are scared of the results. They don’t know if they will be good or bad. But we can’t let fear dictate our investment decisions.

What other standards are developing for programmatic buying of DOOH media?

We’ve been working with the IAB and  DPAA, and OAAA to help develop standards in programmatic advertising. We wrote the first OpenRTB spec and we’ve written a lot around creative in points as well as how to do approvals for creative. We’ve been very open about our API and how it works in terms of communicating ads and making media more transactional than it was prior to us. Being a younger company, it’s hard to tell the big guys how to do things or what’s right versus wrong. It’s hard to build standards when there are a handful of big players in the space and they dictate what’s going on.

The final questions are personal and mobile-based and we ask them of all of our guests:nokia-snake-game

What was your first mobile phone?

It was probably a gold-plated Nokia with the game snake on it. It worked. I loved it! I was able to go online and create my own ringtone.

What is the first app you open every day?

Gmail. No question about that one.


The post Real-Time Banter: Adelphic and Vistar Media Discuss Digital Out-Of-Home Advertising appeared first on Adelphic.

How Cross-Device Identity Matching Works (part 2)

September 20, 2016 in Blog

Martin Kihn at Gartner continues a great series of posts describing Adelphic’s patented methodology for cross-device identity matching.

Cross-device identity matching is the way marketers try to map devices and browsers to the same consumer to improve personalization and measurement. in our super-popular Part 1, we got half-way across the bridge by describing one common way this is done. Our case study was a particular patent issued to the mobile demand side platform Adelphic.



The post inspired many thoughtful squibs and expansions, including some builds from our friends at Drawbridge, who are well-known for their cross-device identity matching solution. Drawbridge pointed out to me a distinction that might seem obvious but isn’t.

Namely, there are two problems the marketer must solve to perform a successful cross-device identity match:

  • (1) Identify a singular device
  • (2) Match that device to a person

Of course, you probably can’t do (2) without successfully doing (1). And either or both can be attacked using deterministic or probabilistic methods. It’s possible to use a deterministic (one-to-one) method for (1) and a probabilistic method for (2) or vice versa. Which opens up a wide gate through which parties can march their dogs and ponies.

I have heard vendors say “we use only deterministic methods,” when they were referring only to step (1). A device-matching vendor who doesn’t do any probabilistic modeling at all does not have a model, it has a lookup table — one it likely acquired from someone else who does probabilistic modeling.

There is nothing wrong with probabilities, friends; they are probably inevitable.

All of the major stand-alone third-party matching vendors — TapadDrawbridge, Oracle’s Crosswise, Adbrain — use both deterministic and probabilistic methods. Which combined or hybrid approach is exactly what Adelphic describes in its patent.


Problem (1) may seem simple to solve but is not. Imagine you are a publisher or marketer and a device communicates with your site. You know this device exists — after all, it’s talking to you. But what is it? Would you recognize it if it appeared again? Does it have an identity?

Apple used to communicate a unique device ID with eace server call, but it stopped doing this three years ago (citing privacy concerns). In its place, both Apple and Android created a unique, consumer-controlled ID available to apps selling ads. This is called IDFA and AdID, respectively. So apps can choose to share this ID if they want, but only if (1) the consumer downloaded their app, (2) is using it, (3) has not manually opted out of ad tracking (which they can do with both IDFA and AdID), and (4) the app really sells ads.

So it’s not available to mobile browsers, people the app doesn’t want to share with, opted-out consumers, and non-ad-sellers. In other words, it’s not a cure-all. And it is tied to a device, not a person. The IDFA is different on my iPad Mini and on my iPhone 6.

IDFA and AdId are often called “deterministic” IDs, because if you know them, you know the device. What is the long-suffering marketer to do if she doesn’t have either IDFA or AdID? Give up? Well, if the consumer is in a Chrome browser it can be cookied, of course, but what if he isn’t, or it can’t?


Here we venture into the territory that used to be called “fingerprinting” and was associated with firms like BlueCava. As we’ve said, this is a form of probabilistic device identification, meaning it ID’s unique devices within a range of probability. Setting aside the right name, let’s describe how it works, again using Adelphic’s patent as our guide.

The patent refers to something it calls a “signature.” This is a combination of attributes that collectively may be used to identify a unique device. These attributes are pieces of information that are shared in the course of routine communications with the app publisher or mobile website owner.

We described some of these attributes last time. They include:

  • “system-type” data such as OS version, local time, phone model
  • “usage-type” data such as headers, user query parameters, referrer, plug-in data, location, URLs viewed

How is this “signature” created? The patent describes it as a kind of list that contains some or all of the above-mentioned system and usage attributes that have been encoded in such a way that they can quickly compared to similar signatures sitting in the master ID database. The attributes can be encoded as numbers, categories, or even distributions.

Of course, it is not simply a list of all attributes. Much of any data set is noise. It is a selection of those attributes most likely to mean something to the system (the selection process is described below).

So we can think of the “signature” as a streamlined version of the attributes that includes the good ones and not the noise. The patent puts it this way:

“The entity identity is generated by applying to the feature data [i.e. attributes] one or more rules … identifying which of the feature data to use to generate the entity identity …”


All this talk of “entities” brings us to a rather subtle point about cross-device matching. In the Adelphic patent, explicitly, an entity can be a device, a person or a household. So in the same way a probabilistic signature for a device can be created from a weighted subset of attributes, a “signature” can be created for a device — or for people.

Why? It’s simple, really. The attributes we get are the only ones we’ve got. We can use them in a step (1) way to identify an unknown device without a deterministic ID in a sea of devices … OR we can use them in a step (2) way to try to link devices we have already identified as belonging to the same person (or household).

All of these IDs and attribute signatures are going into the master ID database. Even if a deterministic ID is available (like AdID), the database will include a signature based on probabilistic attributes. Why? Think about the app world. What if the person of interest pings you from the same device but a different app, one that doesn’t sell ads or otherwise lacks access to the AdID?

What if they use their browser to hit up your Bernese mountain dog tea cozy shop rather than your amazing BMD app? You’re going to be glad you maintain both deterministic and probabilistic device identifiers.

Now, you have been very patient. Some of you have abandoned the ship and are well within site of the cabana. To the rest, I say: It is time to discuss how to match a device to a person.

First, the system will do (1). It has the device. Next, step (2). Who is it? The system will try to find out if there is any personal determinsitic data available. Data that can be linked to a person include phone number, email, customer ID. Usually, personal deterministic data is known only if the person has a relationship with the app or site owner or has provided it in the session.

The matcher takes the device ID and the deterministic person ID and sees if there is a match within its master ID database. If so, it will look up to see what it knows — e.g., that this person has been flagged by Target as a super-shopper to get massive deals now! or whatever.

if not, the matcher will try to see if it can match the device ID to someone it has in its master ID database some other way. and you all see this one a-coming … yes, it’s …


Say what?!

One of the more enjoyable sections of the adelphic patent is its almost rapturous encomium to a concept called Record Linkage. This is not a term encountered often in the digital marketing literature. I mention it here because it turns out to be a rather well-developed method to do exactly what we are trying to do: take two different sets of attributes and figure out if they actually belong to the same person.

The patent points to “A Theory for Record Linkage,” a seminal paper published in 1969. It started a line of development that’s cropping up here. Record Linkage (RL) encompasses both what we’ve called deterministic matching and probabilistic matching.

RL is described like this:

“[It] takes into account a wider range of potential identifiers, computing weights for each identifier based on its estimated ability to correctly identify a match or a non-match, and using these weights to calculate the probability that two given records refer to the same entity.”

In other words, probabilistic matching as described here has two steps:

  • (1) Take all the available attributes and figure out which ones deserve more weight, depending on how well they identify people; and
  • (2) Go through the master ID database full of signatures and figure out whether the particular device matches any of them

That’s a lot of “figuring out.” We can be more explicit. Step (1) here is a classic machine learning problem and can be done either on labeled or unlabeled data (i.e., records that we have already matched to people or ones that we haven’t). The preferred method mentioned is to take the master ID database and look at devices that have already been matched using deterministic methods (e.g., by email or phone no.).

The system can then look at all the various attribute data also captured with those devices and run machine learning algorithms to estimate the weights for different attibutes. (If you’re interested, the specific algorithm mentioned is EM, or Expectation Maximization.)

The output of step (1) is a “rank score function,” or a formula that can take the atributes on the unmatched device and bump them up against the device attributes in the master ID database (that is, for already-known devices) and calculate a score. This score is a number from 0-1, with 0 meaning definitely-no-match and 1 meaning oh-yes-match-baby. A higher number means more probably a match. This is step (2).

The process is described:

“… the system computes the distance of each feature against a subset of candidate matching records in the database. A matching rule takes the distances as input and makes a decision if the features are mapped to an existing entity identity in the database.”

Some of you may be wondering what this “distance” is, exactly. It is a calculation that varies depending on the data type. Numerical data can simply be subtracted and normalized. Strings can be compared to see how many characters match (e.g., OS versions). Other types of data like location require special sub-functions to handle. Obviously, features that match perfectly have no “distance” at all.


I’ll leave you today with an example. Let’s say the RL process has been run in the past and the output of the model was a scoring function. And let’s say it determined that the attributes that are the best predictors of two devices belonging to the same person are:

  • Location (lat/long)
  • Time of Day
  • I.P. address

So a device shows up. It has an AdID but there is no match in the system. It passes its attributes and the system turns them into a “signature” and pings them up against the master ID database signatures, calculating a distance and determining a score against each. If there is one that achieves a high enough score to be considered a match, the AdID and new feature information is added to the existing match record.

And there you have it: a probabilistic match.

Then the fun begins.

To view this article in its entirety, visit Gartner.

The post How Cross-Device Identity Matching Works (part 2) appeared first on Adelphic.

Real-Time Banter: Adelphic and clypd Discuss Programmatic TV

August 30, 2016 in Blog

This edition of Real-Time Banter features a conversation about Programmatic TV with Joshua Summers, Founder and CEO of clypd. Joshua was interviewed by Jennifer Lum, Chief Strategy Officer for Adelphic.

Jennifer: What is clypd?  

Joshua: clypd is a platform aimed at helping TV media owners get the most value they can out of their television inventory. We are focused on delivering software solutions that enable their sales teams to automate the workflow in delivering advertising solutions, but more importantly, to drive the use of advanced data and science into that process. We are operationalizing data sets that enable them to improve the yield of their media and deliver better value back to the brands that are advertising through them.

Can you share the founding story of clypd with us?

My co-founder Doug Hurd and I have been working together for about nine years now. Our last gig was at a company called WHERE. WHERE was a platform in location-based advertising and services. We built up a pretty decent businessjoshua-summers and ultimately sold that business to PayPal back in 2011. It was a fun ride and it really taught us about the need to sometimes pivot to find the right market fit and ultimately, to build a business that was able to produce sizable revenue and find an exit. We went onto PayPal and stuck around there for about 18 months while getting our master’s degree in online and mobile payments.

Then one day, we decided that we wanted to do it all over again. So over a couple beers up in Ogunquit, we decided there was an opportunity to leverage some of the advancements that had taken place in the digital advertising world and move them into a market that was ripe for innovation and that had scale beyond anything we’d ever seen (TV), but that also needed technology and data to come in and take it to the next level. That was our birth story: Where could we take some of the understanding we had from our time at WHERE and apply it to the TV marketing opportunity? It took a long time to get from that nugget of a concept to the right product market fit and the right value, but that’s been the fun ride of clypd over the last four years.

What types of companies are leveraging clypd’s platform today?

clypd is pure-play sell-side technology, so we work with the big media owners in television. We focus on linear television, which is the traditional living room experience where you lean back watching live TV or potentially time shifted TV on your television set.

The big media owners really fall under three key categories.

  • MVPDs, which are the multichannel video programming distributors made up of the cable operators like Comcast and Cox, satellite companies like Dish and DirecTV, and telcos like AT&T and Verizon Fios.
  • The local broadcast and station groups. That’s your local broadcaster in-market, and the station groups are the aggregate of a bunch of local markets under a single holding company.
  • The cable networks and national broadcasters. That category includes ESPN, Discovery, Fox Network Group and others. This is where clypd has focused, where we’ve found our beachhead. We focus on national inventory and ultimately delivering value for these types of media owners.

What are the standards that are developing around programmatic TV and are they at all similar to OpenRTB?

When we first started the company, we were very familiar with OpenRTB, coming out of a mobile advertising company. We were hopeful and thought that we could adapt those standards to work in television, which would give us a strong, significant leap forward from where the TV market was. Unfortunately, you can’t take that digital standard and apply it to the linear TV space because everything about linear television is different – the time for delivery, reporting, round-trip delivery of data, the insertion of advertising and the tracking capabilities. Because of all these differences, we needed to start from scratch with redefining how that could work.

We did some early work to develop a spec that we thought might work called “TVonTap.” We released that, working with a number of partners to get to a V1, and we found some early interest. The digital players wanted to participate and be able to help drive it, but it still didn’t have enough industry backing to become a standard. Recently, a group called GABBCON released their own standard called “ABCD,” which, fortunately for us, draws on the TVonTap standard and uses it as a basis for developing a new standard. They’ve got a nice group of participants in the standards body ranging from digital platforms and data platforms, all the way through service providers and into media owners. We are seeing some nice progress there, with the GABBCON ABCD standards, and we’re pretty excited about that.

How has the programmatic TV industry evolved over the past few years? If you think back to when you and Doug were first starting clypd, has your vision remained true? Has the market moved the way you thought it would?

Our vision has remained true in that we believed we could bring value to the media owner, the sell-side of the opportunity, and that we would be pure-play and that we would not rep or arbitrage any inventory. It has also remained true in that those media owners are interested in opportunities to optimize yield and to make more data driven decisions.

Ultimately, I would say the world is vastly different today than it was three or four years ago. When we first started, we found a lot of push back on just the terminology itself. “Programmatic has no place in television.” There were lots of words that we would avoid using in meetings because it would cause different opinions to be raised about challenges.

About 1.5 to 2 years ago, we started to see a pretty impressive shift. People in the industry  started having more conversations, they were more open around the need for automation and driving data into the decision process. Over the last year, we’ve seen the big media owners start to reorganize this and drive innovation directly in the space, bringing on Heads of Data-Driven Advertising, Audience-Based Selling or Programmatic TV.  They’re really starting to think through the concept of “How do you drive value between linear and digital and back, and how you build linear-driven TV campaigns around more advanced audience-based sales?” This has been a very big shift in the last year, and now it’s a very open discussion. Frankly, we are impressed and excited by how much it’s been a part of the Upfront conversation this year, for the first time. I think it’s been a dramatic movement for the industry over the last three to four years.

Five years from now, will the TV industry still be focused on the Upfronts?

Yes, I think the Upfronts are not a phenomenon that is related to the lack of technology, but a phenomenon related to the way the market is structured. In the digital world, supply is a vast, open space. There is almost an infinite level of supply, and demand is the constrained asset. But in the linear TV world, it’s the opposite. The inventory is constrained in its availability. There is only a certain number of TV networks and a certain number of spots available and it is not growing enough to keep up with the demand. Because of that, the demand almost creates a market in itself.

That’s what the Upfronts are – an opportunity to pre-negotiate and buy in-bulk portions of your media spend over the yearto lock it in and feel comfortable that you’re going to be able to use those dollars. The scatter market complements that with buying throughout the remainder of the year, but the Upfronts are, in my opinion, a product of that scarcity, not because of the lack of technology. I think in five years, the Upfronts will not only still be here, but will have all kinds of new technologies and data strategies that support them.

It’s interesting to think about how inventory being sold at the Upfronts may not be bucketed by show, but may be more focused on audience segments.

It’s certainly a direction we see happening. Now I wouldn’t say we believe all dollars will be sold that way, either. Like any good advertising or marketing plan, you want to spread the way that you’re purchasing your media. That means you could be going after different goals: content, breadth of distribution, more reach, specific audiences or specific targets. A well-rounded media plan will still be a well-rounded media plan five years from now.

There’s been a lot of progress with the industry adopting automation, data, and programmatic technologies. What else is needed in order for programmatic TV to reach meaningful scale?

We are at that point where we are seeing the tip of the iceberg. But there are always things that need to happen for adoption to continue to grow. Moving to more industry standards will absolutely help. Industry standards around how data comes into and leaves the system – both from a segmenting and targeting standpoint and from a reporting and verification standpoint. Being able to reach scale is important on both the buy-side and the sell-side. In the national market, we are seeing great progress. I think we’ll continue to see that progress also in the local broadcast, local station group and MVPD markets. Ultimately standards will help ensure that we don’t have a lot of point solutions or one-offs that create fragmentation of experience.

When we get to a point where everything is truly automated – and I would say we have just started to really see the benefits of automation, but we’ve got a long way to go – it opens up the floodgate to really reach that scale. Automation is more than just connecting to buyers and being able to source demand effectively, it’s connecting all the way through to the  delivery systems, traffic and billing systems, into video content delivery systems and ultimately reporting and data systems. There are a lot of disparate technologies using different integration capabilities and different ways of talking to each other and that just takes time for the industry to really connect.

What are some interesting new data sets and measurement solutions that are being applied to programmatic TV?

The TV world is quite different than the digital world. The ability to take a cookie and attach data to that cookie and dress up impressions is not the same. First, we have to talk about two types of data around television. There is currency data and then there is the advanced audience data you can attach to that. The vast majority of everything we see in terms of U.S. currency is Neilsen, which is based off of age and gender.

Once you have that currency set, you have to look at where to go beyond that, and that’s into advanced audiences. There are lots of data sets that have rich, advanced targeting capabilities and are in some ways becoming potentially interesting currencies. Some of the obvious ones that are getting a lot of talk are folks like Rentrak, now part of comScore, and TiVO TRA. There are other data sets that have been fused into primary currencies like a Neilsen, such as MRI Fusion, or NBI, which is credit card data, or Catalina, which is shopper data. clypd takes an agnostic view to the data, on both the currency and on the advanced audience data. We work with whatever our media owner customers and their buying partners want to use. Our job is to apply a gold standard to the way that it is used. clypd’s role is to ensure quality of use in data and how to operationalize it, but not to decide on the currency or the data set itself.

Now the final piece that’s starting to become pretty interesting is where we see digital data sets start to work their way into television. We are at the early days of this, but where it gets exciting is when customers can bring first-party data that they’ve used to deploy campaigns in non-linear digital video and bring those targets into television. I wish that were a straightforward process, but it’s certainly not a process outside our ability to deliver a solution on.

When you think about platforms that are interesting there, you have to think about things like the Oracle data initiatives – the Oracle cloud – and the Neilsen marketing cloud and their eXelate solution, and how they’ve brought data in and ultimately their ability to influence targeting and segmentation through digital data sets into linear TV, which is a very interesting and an emerging opportunity.

Absolutely. They are allowing marketers to get closer to the true cross-device dream.

That’s right.

sony-1000A final, fun question for you. What was your first mobile phone?

In 1997, I had a Sony Ericsson. I think it was called the D1000. It had a pull up antenna, was about three inches thick by about three inches wide, and probably eight inches long. I would stick it in my pocket and it would stick out so far – it looked absolutely ridiculous. It had a leather wrapper around it. And my first mobile plan was thirty minutes of talk time but I had free incoming first minute so I would have everyone call me and I wouldn’t talk for more than a minute.

You have a great memory.

It was a meaningful phone for me – it was very exciting.
Thank you for speaking with us today Josh!


The post Real-Time Banter: Adelphic and clypd Discuss Programmatic TV appeared first on Adelphic.

Adelphic Is Proud to Announce a New Approach to Cross-Channel Advertising

August 17, 2016 in Blog

Adelphic’s VP of Product, Yael Avidan, provides an overview of Adelphic’s new cross-channel solution. 

Understanding consumers’ device usage is beneficial, but understanding consumers’ behaviors across multiple channels, in addition to the devices used, provides much richer data to power campaigns. This is what comprises our proprietary data set, which we refer to as our Behavior Graph™–which powers real-time optimization based on a complete, cross-channel user profile.

Having the Behavior Graph™ integrated into Adelphic’s decisioning layer enables marketers to transition to a real user-centric approach to campaign planning, management, optimization and reporting–and drive superior performance and efficiencies for campaigns targeting consumers on the move.

Cross-channel sits at the heart of Adelphic’s platform.



Adelphic’s cross-channel solution is comprised of:

  • Adelphic’s Device Graph:  Adelphic’s proprietary device graph leverages non-PII behavioral signals in addition to contextual and location signals in order to identify and link devices.  The device graph includes intra-device as well as cross-device, cross-channel links and makes use of deterministic data when possible, but scales using a probabilistic algorithm.   Over trillion data points serve as starting point and then filtered.  Between 50-100 features are used to identify user pairs. The graph is based on roughly 40% in-app requests and 60% web requests and is continuously updated based on real-time data collection.   Adelphic holds a patent for audience recognition across multiple devices.
  • Adelphic’s Behavior Graph™: Behavioral signals, captured in real-time, are mapped back to the device graph to form a richer dataset that joins identity and behavior.  Behavioral signals range from performance (e.g. clicks and conversions) to behavior patterns (e.g. time of day, location, browsing behavior).
  • Adelphic’s  ^tag (“a-tag”):  A persistent identifier that overcomes the lack of a standard user identifier for mobile and desktop (and future channels).  Using Adelphic’s Behavior Graph™,  multiple devices and their associated behaviors can be assigned to single ^tag, creating user profiles that incorporate data from multiple channels
  • Adelphic’s Predictive Performance Engine:  User behavior and ad performance history across devices and channels are used in real-time to predict the value of a user for a specific ad  (e.g. did this user see this ad on a different channel? Does this user convert more on mobile or desktop?).  The engine leverages cross-device user models as well as contextual models (combined via a combination model) to drive bidding decisions and superior performance.

Our solution enables flexible onboarding of audience segments (1st party, 3rd party, campaign data, pixel-based), forecasting available inventory pools and targeting them across channels.  Adelphic’s Predictive Performance Engine sits at the heart of the solution by leveraging users’ complete profile to drive performance and efficiency.   

Key strategies that are supported via Adelphic’s solution:

  • Cross channel targeting across Display, Mobile,  Video and cTV.
  • Extending the reach of advertiser’s retargeting audiences
  • Extending the reach of lookalike segments
  • Extending the reach of behavior-based segments
  • Creative sequencing across multiple channels
  • Frequency management across channels 

More information:

  • How Cross-device identity matching works – Here
  • Looking for a Cross-Device Solution? 3 Questions Ad Buyers Should Ask – Here
  • Adelphic Launches First Behavior-Centric Cross-Channel Programmatic Ad Solution – Here

Central to our cross-channel solution is, of course, privacy.  Adelphic is fiercely focused on consumer privacy and takes specific steps to ensure that our user profiles are anonymous. Neither we nor the clients who partner with us can glean personally identifiable information from the consumers we engage, even by accident. Adelphic is a member of the Digital Advertising Alliance and complies fully with the DAA’s Self-Regulatory Principles for Online Behavioral Advertising and Self-Regulatory Principles to the Mobile Environment. Adelphic also allows opt out from behavioral tracking through Ghostery.

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