DMP · Media Measurement

Data Science is the New Measurement

tumblr_m9hc4jz_pp_x1qg0ltco1_400It’s a hoary old chestnut, but “understanding the customer journey” in a world of fragmented consumer attention and multiple devices is not just an AdExchanger meme. Attribution is a big problem, and one that marketers pay dearly for. Getting away from last touch models is hard to begin with. Add in the fact that many of the largest marketers have no actual relationship with the customer (such as CPG, where the customer is actually a wholesaler or retailer), and its gets even harder. Big companies are selling big money solutions to marketers for multi-touch attribution (MTA) and media-mix modeling (MMM), but some marketers feel light years away from a true understanding of what actually moves the sales needle.

As marketers are taking more direct ownership of their own customer relationships via data management platforms, “consumer data platforms” and the like, they are starting to obtain the missing pieces of the measurement puzzle: highly granular, user-level data. Now marketers are starting to pull in more than just media exposure data, but also offline data such as beacon pings, point-of-sale data (where they can get it), modeled purchase data from vendors like Datalogix and IRI, weather data and more to build a true picture. When that data can be associated with a person through a cross-device graph, it’s like going from a blunt 8-pack of Crayolas to a full set of Faber Castells.

Piercing the Retail Veil

Think about the company that makes single-serve coffee machines. Some make their money on the coffee they sell, rather than the machine—but they have absolutely no idea what their consumers like to drink. Again, they sell coffee but don’t really have a complete picture of who buys it or why. Same problem for the beer or soda company, where the sale (and customer data relationship) resides with the retailer. The default is to go to panel-based solutions that sample a tiny percentage of consumers for insights, or waiting for complicated and expensive media mix models to reveal what drove sales lift. But what if a company could partner with a retailer and a beacon company to understand how in-store visitation and even things like an offline visit to a store shelf compared with online media exposure? The marketer could use geofencing to understand where else consumers shopped, offer a mobile coupon so the user could authenticate upon redemption, get access to POS data from the retailer to confirm purchase and understand basket contents—and ultimately tie that data back to media exposure. That sounds a lot like closed-loop attribution to me.

Overcoming Walled Gardens

Why do specialty health sites charge so much for media? Like any other walled garden, they are taking advantage of a unique set of data—and their own data science capabilities—to better understand user intent. (There’s nothing wrong with that, by the way). If I’m a maker of allergy medicine, the most common trigger for purchase is probably the onset of an allergy attack, but how am I supposed to know when someone is about to sneeze? It’s an incredibly tough problem, but one that the large health site can solve, largely thanks to people who have searched for “hay fever” online. Combine that with a 7-day weather forecast, pollen indices, and past search intent behavior, and you have a pretty good model for finding allergy sufferers. However, almost all of that data—plus past purchase data—can be ingested and modeled inside a marketer DMP, enabling the allergy medicine manufacturer to segment those users in a similar way—and then use an overlap analysis to find them on sites with $5 CPMs, rather than $20. That’s the power of user modeling. Why don’t site like Facebook give marketers user-level media exposure data? The question answers itself.

Understanding the Full Journey

Building journeys always falls down due to one missing piece of the puzzle or another. Panel-based models continually overemphasize the power of print and linear television. CRM-based models always look at the journey from the e-mail perspective, and value declared user data above all else. Digital journeys can get pretty granular with media exposure data, but miss big pieces of data from social networks, website interactions, and things that are hard to measure (like location data from beacon exposure). What we are starting to see today is, through the ability to ingest highly differentiated signals, marketers are able to combine granular attribute data to complete the picture. Think about the data a marketer can ingest: All addressable media exposure (ad logs), all mobile app data (SDKs), location data (beacon or 3rd party), modeled sales data (IRI or DLX), actual sale data (POS systems), website visitation data (javascript on the site), media performance data (through click and impression trackers), real people data through a CRM (that’s been hashed and anonymized), survey data that been mapped to a user (pixel-enabled online survey), and even addressable TV exposure (think Comscore’s Rentrak data set). Wow.

Why is “data science the new measurement?” Because, when a marketer has all of that data at their fingertips, something close to true attribution becomes possible. Now that marketers have the right tools to draw with, the winners are going to be the ones with the most artists (data scientists).

It’s a really interesting space to watch. More and more data is becoming available to marketers, who are increasingly owning the data and technology to manage it, and the models are growing more powerful and accurate with every byte of data that enters their systems.

It’s a great time to be a data-driven marketer!

[This post originally appeared in AdExchanger on 8/12/16]

DMP

DMPs Go Way Beyond Segmentation

AboveAndBeyondAny AdExchanger reader probably knows more about data management technology than the average Joe, but many probably associate data management platforms (DMPs) with creating audience segments for programmatic media.

While segmentation, audience analytics, lookalike modeling and attribution are currently the primary use cases for DMP tech, there is so much more that can be done with access to all that user data in one place. These platforms sitting at the center of a marketer’s operational stack can make an impact far beyond paid media.

As data platforms mature, both publishers and marketers are starting to think beyond devices and browsers, and putting people in the center of what they do. Increasingly, this means focusing on giving the people what they want. In some cases that means no ads at all, while in others it’s the option to value certain audiences over others and serve them an ad first or deliver the right content – not just ads – based on their preferences.

Beyond personalization, there are DMP plays to be made in the areas of ad blocking and header bidding.

Ad Blocking

DMPs see a lot of browsers and devices on a monthly basis and strive to aggregate those disparate identities into a single user or universal consumer ID. They are also intimately involved in the serving of ads by either ingesting ad logs, deploying pixels or having a server-to-server connection with popular ad servers. This is great for influencing the serving of online ads across channels, but maybe it can help with one of the web’s most perplexing problems: the nonserving of ads.

With reports of consumers using applications to block as many as 10% of ads, wouldn’t it be great to know exactly who is blocking those ads? For publishers, that might mean identifying those users and suppressing them from targeting lists so they can help marketers get a better understanding of how much reach they have in certain audience segments. Once the “blockers” are segmented, publishers can get a fine-grained understanding of their composition, giving them insights about what audiences are more receptive to having ad-free or paid content experiences.

A lot of these issues are being solved today with specialized scripts that either aren’t very well coded, leading to page latency, or are scripted in-house, adding to complexity. Scripts trigger the typical “see ads or pay” notifications, which publishers have seen become more effective over time. The DMP, already installed and residing in the header across the enterprise, can provision this small feature alongside the larger application.

Header Bidding

Speaking of DMP architecture being in the header, I often wonder why publishers who have a DMP installed insist on deploying a different header-bidding solution to manage direct deals. Data management tech essentially operates by placing a control tag within the header of a publisher website, enabling a framework that gives direct and primary access to users entering the page. Through an integration with the ad server, the DMP can easily and quickly decide whether or not to deliver high-value “first looks” at inventory.

Today, the typical large publisher has a number of supply-side platforms (SSPs) set up to handle yield management, along with possibly several pieces of infrastructure to manage that critical programmatic direct sale. Publishers can reduce complexity and latency by simply using the pipes that have already been deployed for the very reason header bidding exists: understanding and managing the serving of premium ads to the right audiences.

Maybe publishers should be thinking about header bidding in a new way. Header-bidding tags are just another tag on the page. Those with tag management-enabled DMPs could have their existing architecture handle that – a salient point made recently by STAQ’s James Curran.

Curran also noted that the DMP has access, through ad log ingestion, to how much dough publishers get from every drop in the waterfall, including from private marketplace, programmatic direct header and the open exchanges. Many global publishers are looking at the DMP inside their stack as a hub that can see the pricing landscape at an audience level and power ad servers and SSPs with the type of intelligent decisioning that supercharges yield management.

Personalization

In ad technology, we talk a lot about the various partners enabling “paid, owned and earned” exposures to consumers, but we usually think of DMPs as essential only for the paid part.

But the composition of a web page, for example, is filled with dozens of little boxes, each capable of serving a display ad, video ad, social widget or content. Just as the DMP can influence the serving of ads into those little boxes, it can also influence the type of content that appears to each user. The big automaker might want to show a muscle car to that NASCAR Dad when he hits the page or a shiny new SUV to the Suburban Mom who shuttles the kids around all day.

Or, a marketer with a lot of its own content (“brands are publishers,” right?) may want to recommend its own articles or videos based on the browsing behavior of an anonymous user. The big global publisher may want to show a subscriber of one magazine a series of interesting articles from its other publications, possibly outperforming the CPA deals it has with third parties for subscription marketing.

This one-to-one personalization is possible because DMPs can capture not only the obvious cookie data but also the other 60% of user interactions and data, including mobile apps, mobile web, beacon data and even modeled propensity data from a marketer or publisher’s data warehouse.

Wouldn’t it be cool to serve an ad for a red car when the user has a statistically significant overlap with 10,000 others who have purchased red cars in the past year? That’s how to apply data science to drive real content personalization, rather than typical retargeting.

These are just some of the possibilities available when you start to think as the DMP as not just a central part of the ad technology “stack” but the brains behind everything that can be done with audiences. This critical infrastructure is where audience data gets ingested in real time, deployed to the right channels at speed and turned into insights about people. In a short period of time, the term “DMP” will likely be shorthand for the simple audience targeting use case inside of the data-driven marketing hub.

It’s a great time to be a data-driven marketer.

Follow Chris O’Hara (@chrisohara) and AdExchanger (@adexchanger) on Twitter. 

Big Data · Data Management Platform · DMP · Marketing

Big Data (for Marketing) is Real!

MachineLearningWe’ve been hearing about big data driving marketing for a long time, and to be honest, most is purely aspirational.

Using third-party data to target an ad in real time does deploy some back-end big-data architecture for sure. But the real promise of data-driven marketing has always been that computers, which can crunch more data than people and do it in real time, could find the golden needle of insight in the proverbial haystack of information.

This long-heralded capability is finally moving beyond the early adopters and starting to “cross the chasm” into early majority use among major global marketers and publishers. 

Leveraging Machine Learning For Segmentation 

Now that huge global marketers are embracing data management technology, they are finally able to start activating their carefully built offline audience personas in today’s multichannel world.

Big marketers were always good at segmentation. All kinds of consumer-facing companies already segment their customers along behavioral and psychographic dimensions. Big Beer Company knows how different a loyal, light-beer-drinking “fun lover” is from a trendsetting “craft lover” who likes new music and tries new foods frequently. The difference is that now they can find those people online, across all of their devices.

The magic of data management, however, is not just onboarding offline identities to the addressable media space. Think about how those segments were created. Basically, an army of consultants and marketers took loads of panel-based market data and gut instincts and divided their audience into a few dozen broad segments.

There’s nothing wrong with that. Marketers were working with the most, and best, data available. Those concepts around segmentation were taken to market, where loads of media dollars were applied to find those audiences. Performance data was collected and segments refined over time, based on the results.

In the linear world, those segments are applied to demographics, where loose approximations are made based on television and radio audiences. It’s crude, but the awesome reach power of broadcast media and friendly CPMs somewhat obviate the need for precision.

In digital, those segments find closer approximation with third-party data, similar to Nielsen Prizm segments and the like. These approximations are sharper, but in the online world, precision means more data expense and less reach, so the habit has been to translate offline segments into broader demographic and buckets, such as “men who like sports.”

What if, instead of guessing which online attributes approximated the ideal audience and creating segments from a little bit of data and lot of gut instinct, marketers could look at all of the data at once to see what the important attributes were?

No human being can take the entirety of a website’s audience, which probably shares more than 100,000 granular data attributes, and decide what really matters. Does gender matter for the “Mom site?”Obviously. Having kids? Certainly. Those attributes are evident, and they’re probably shared widely across a great portion of the audience of Popular Mom Site.

But what really defines the special “momness” of the site that only an algorithm can see? Maybe there are key clusters of attributes among the most loyal readers that are the things really driving the engagement. Until you deploy a machine to analyze the entirety of the data and find out which specific attributes cluster together, you really can’t claim a full understanding of your audience.

It’s all about correlations. Of course, it’s pretty easy to find a correlation between only two distinct attributes, such as age and income. But think about having to do a multivariable correlation on hundreds of different attributes. Humans can’t do it. It takes a machine-learning algorithm to parse the data and find the unique clusters that form among a huge audience.

Welcome to machine-discovered segmentation.

Machines can quickly look across the entirety of a specific audience and figure out how many people share the same attributes. Any time folks cluster together around more than five or six specific data attributes, you arguably have struck gold.

Say I’m a carmaker that learned that some of my sedan buyers were men who love NASCAR. But I also discovered that those NASCAR dads loved fitness and gaming, and I found a cluster of single guys who just graduated college and work in finance. Now, instead of guessing who is buying my car, I can let an algorithm create segments from the top 20 clusters, and I can start finding people predisposed to buy right away.

This trend is just starting to happen in both publishing and marketing, and it has been made available thanks to the wider adoption of real big-data technologies, such as Hadoop, Map Reduce and Spark.

This also opens up a larger conversation about data. If I can look at all of my data for segmentation, is there really anything off the table?

Using New Kinds Of Data To Drive Addressable Marketing 

That’s an interesting question. Take the company that’s manufacturing coffee machines for home use. Its loyal customer base buys a machine every five years or so and brews many pods every day.

The problem is that the manufacturer has no clue what the consumer is doing with the machine unless that machine is data-enabled. If a small chip enabled it to connect to the Internet and share data about what was brewed and when, the manufacturer would know everything their customers do with the machine.

Would it be helpful to know that a customer drank Folgers in the morning, Starbucks in the afternoon and Twinings Tea at night? I might want to send the family that brews 200 pods of coffee every month a brand-new machine after a few years for free and offer the lighter-category customers a discount on a new machine.

Moreover, now I can tell Folgers exactly who is brewing their coffee, who drinks how much and how often. I’m no longer blind to customers who buy pods at the supermarket – I actually have hugely valuable insights to share with manufacturers whose products create an ecosystem around my company. That’s possible with real big-data technology that collects and stores highly granular device data.

Marketers are embracing big-data technology, both for segmentation and to go beyond the cookie by using real-world data from the Internet of Things to build audiences.

It’s creating somewhat of a “cluster” for companies that are stuck in 2015.

CRM · Data Management Platform

How Political Campaigns are Putting DMPs to Work

 

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People like to speculate that social networks impacted the 2016 Presidental election, but Facebook isn’t that influential. I mean, I still don’t like cats very much, and that’s 90% of the content. 

We have all heard about the Democratic Party’s skill with data, and there is no doubt the Obama campaign’s masterful use of first-party registration data to drive online engagement, raise funds and influence political newbies helped put him over the line.

 

Four years later, the dynamics are mostly similar, but we have moved into a world where mobile is dominant, more young new voters are highly engaged and the standard segmentation – at least on the Republican side – might as well be thrown out the window.

In other words, everyone is getting influenced on their mobile phone, especially through news and social channels. There are a ton more mobile-first, new voters out there, and nobody is really sure which voters make up this weird new Trump segment.

To get a handle on this, political advertisers need to properly onboard and analyze their data to identify who they should target, where they live and what they like.

Understand Voter Identity

In politics, a strong “ground game” is key. That means real, old-school retail politics, such as knocking on doors and getting voters in specific precincts out on Election Day. All campaigns have the voter rolls and can do their fill of direct mail, robocalls and door knocking.

But how to influence voters well before Election Day who are tethered to their devices all day and night? It requires a digital strategy that can reach voters across the addressable channels they are on, including display, video, mobile and email. This strategy should leverage an identity graph to ensure the right messaging is hitting the same voter – at the right cadence.

Maybe “Joe the Firefighter,” a disaffected moderate Democrat who has had it with the Clintons, visited the Donald’s website and is ready to “Make America great again.” Before cross-device capabilities were strong, you could only retarget Joe the next time you saw his cookie online.

Today, Joe can get an equity message reinforced on display (“Make America great again!”), a mobile “nudge” to take action when we see Joe on his tablet at night (“Donate now!”) and follow up with an email a few days before the big rally (“Come see the Donald at the Civic Center!”).

Beyond this capability is the incredibly important task of laddering up individual identity into householding, so we can understand the composition of Joe’s family, since households often vote together and contain more than one registered voter.

Nail Geographic Targeting by County and District

Since “all politics is local,” it follows that all digital advertising should be locally targeted. This is table stakes for digital providers that work with campaigns, and targeting down to the ZIP+4 level has brought a level of precision to district-level outreach that approaches direct mail.

But direct mail (household targeting) is the crown jewel and digital is still trying to cross that divide, but is held back by a fragmented ecosystem of identity and, more importantly, privacy considerations.

This has always been a key consideration, given the fact that a small percentage of key districts can flip the presidency to one party or another.

Affiliation Modeling Through Behavior

Sometimes getting an understanding of someone’s party affiliation is super obvious, such as “liking” a specific candidate on social media. But, sometimes, a user’s affinity has to be derived through attributes derived through his or her behavior and the context of content consumed over time.

Data management platforms are bringing more precision to this type of modeling. Functionality, such as algorithmic segmentation, is helping digital analysts go beyond the basics. It’s fairly easy to correlate two or three attributes, such as income and gender, to estimate party affiliation. In this cycle, for example, we have seen a strong bias toward Trump from lower-income males with less than a college degree.

However, it’s hard for humans to correlate eight or more distinct attributes. Maybe those lower-education, low-income, rural males who love NASCAR actually lean toward Bernie Sanders in certain districts. Letting the machines crunch the numbers can give digital campaign managers an unseen advantage, and that capability has just now become available at scale.

“In 2016, relying on TV advertising to sway voters is no longer a solid campaign tactic,” JC Medici, Rocket Fuel’s national director of politics and advocacy, told me via email. “To secure the White House in November, candidates must now add a strong digital media strategy by utilizing best-in-class AI, correlated with strong voter and propensity data assets to ensure they are delivering ads to the right voter, on the right screen, at the right time.”

Social Affinity

One of the hot new areas for political campaign targeting is social affinity, the idea that there is a mutual affinity that can be measured between interests.

Yes, when someone “likes” Hillary, you have an obvious target. But, how about those folks who haven’t stated an obvious choice? Maybe 80% of Hillary fans also liked cat shelters, yellow dresses and Chris Rock.

When strong correlations between deterministic social behavior are shown, it becomes fairly easy to leverage that data for targeting – and make informed choices regarding media. People who liked Hillary also like certain TV shows, actors, causes and websites. Campaign managers can leverage data from Affinity Answers, Affinio and other companies to understand these relationships and exploit them to build support for candidates, while leveraging the ability to geotarget at very granular levels on Facebook.

The Free State Project, an organization committed to getting 20,000 “liberty-loving” people to move to New Hampshire and work toward limited government, just reached its goal – talk about a tough conversion. President Carla Gericke credits the use of data-driven targeting on Facebook for the achievement.

Speaking of social, it is also highly important to get the context right.

“Programmatic has introduced two new challenges: bots (who don’t vote) and brand safety,” Trust Metrics CRO Marc Goldberg told me. “In the age of immediate and shocking news, it has become more important that a political ad does not end up next to porn, hate or issues that are contradictory to the politician’s beliefs. One screen shot and bam, you are on Twitter.”

Onboarding And Offboarding 

Perhaps the most critical functionality for digital political campaigns continues to be the ability to “onboard” offline data, such as phone numbers, email addresses and party affiliation, and match it to an online ID for targeting purposes. This is essentially table stakes, considering the years of political investment in collecting offline records for phone banks and direct mail campaigns.

Previously, the onboarding of such data was limited to associating it with an active cookie for retargeting use. But with the emergence of real cross-channel device graphs, this data can now be tied to a universal consumer ID that is persistent and collects attributes over time.

Simply put, that onboarded email – now a UID – can be mapped to a number of identities, including Apple and Android mobile identifiers, third-party IDs from Experian and the like and device IDs from Roku and other OTT devices. In other words, the device graph enables that email to be associated with the voter’s omnichannel footprint, giving campaigns the ability to sequentially target messages, map creative to execution channels and truly understand attribution.

What’s even more exciting is the idea of offboarding some digital data back into the CRM. How valuable would it be to know that a potential voter watched an entire YouTube video on a candidate after being reached by the phone bank? Certain types of behavioral data, depending on compliance with privacy policies, can be brought back into the CRM to impact the effectiveness of offline voter outreach.

It is fair to say that 2016 is the most exciting campaign season we’ve had in a generation – and it’s only the primary season. As data-driven marketers, we will see campaigns push the limit in applying big marketing dollars to digital channels, trying to unlock new, mobile-first millennial voters, while persuading independents through more addressable advertising then ever.

It’s a great time to be a data-driven marketer.

Data Management Platform

CPG goes DMP

 

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I often wonder how Wayne Gretzky feels when he looks at this photograph. Maybe he’s like, “man, that blazer was totally boss.”

If you think about the companies with perhaps least amount of consumer data, you would automatically think about consumer packaged goods (CPG) manufacturers. Hardly anybody registers for their website or joins their loyalty clubs; moms don’t flock to their branded diaper sites; and they are at arms-length from any valuable transaction data (store sales) until well after the fact. So, with little registration, website, or offline sales data, why are so many large CPG firms licensing an expensive first-party data management platform?

 

While CPG companies will never have the vast amounts of point-of-sale, loyalty-card, app, and website data that a big box retailer might have, they do spend a ton of dough on media. And, as we all know, with large media expenditures come tons of waste. Combine this with the increasingly large investment and influence that activist investors and private equity companies have in CPG, and you can see where this leads. PE companies have installed zero-based budgeting that forces CPG concerns to rationalize every penny of the marketing budget—which, until lately, has been subject to the Wannamaker Rule (“I know half of my budget is working, but not which half”). Enter the DMP for measurement and global frequency control, cutting off and reallocating potentially millions of dollars in “long tail” spending. Now, the data that the CPG marketer actually has in abundance (media exposure data), can be leveraged to the hilt.

This first and most obvious CPG use case has been discussed extensively in past articles. But there is much more to data management for CPG companies. Here are just a few tactics big consumer marketers have written into their data-driven playbooks:

 

The Move to Purchase-Based Targeting (PBT)

Marketers have come a long way from demographic targeting. Yes, gender, age and income are all reliable proxies for finding those “household CEOs,” but we live in complicated times and “woman, aged 25-54, with 2 children in household” is still a fairly broad way to target media in 2016. Today, men are increasingly as likely to go grocery shopping on a Thursday night. Marketers saw this and shifted more budget to behavioral, psychographic, and contextual targeting—but finding cereal buyers using proxies such as site visitation sharpened the targeting arrow only slightly more than demography.

Packaged goods marketers have long understood the value of past purchases (loyalty cards and coupons), but until the emergence of data management technologies, have struggled to activate audiences based on such data. Now, big marketers can look at online coupon redemption or build special store purchase segments (Datalogix, Nieslen Catalina, News America Marketing) and create high value purchase-based segments. The problem? Such seed segments are small, and must be modeled to achieve scale. Also, by the time the store sales data comes in, it’s often far too late to optimize a media plan. That said, CPG marketers are finding that product purchasers share key data attributes that reveal much about their household composition, behavior, and—most interestingly—affinity for a company’s other products. It may not seem obvious that a shopping basket contains diapers and beer—until you understand that Mom sent Dad out to the store to pick up some Huggies, and he took the opportunity to grab a cold six-pack of Bud Light. These insights are shaping modern digital audience segmentation strategy, and those tactics are becoming more and more automated through the use of algorithmic modeling and machine-learning. CPG has seen the future, and it is using PBT to increase relevant reach.

Optimizing Category Reach

CPG marketers are constantly thinking about how to grow the amount of product they sell, and those thoughts typically vary between focusing on folks who are immensely loyal (“heavy” category buyers) versus those who infrequently purchase (“light” or “medium” category buyers). Who to target? It’s an interesting question, and one answered more decisively with purchase-based sales data.

Take the large global soda company as an example. Their average amount of colas their customer consumes is 15 a year, but that is an immensely deceptive number. The truth is that the company has a good amount of “power users” who drink 900 colas a year (two and a half per day), and a lot of people who may only drink 2-3 colas during the entire year. Using the age-old “80/20 Rule” as a guideline, you would perhaps be inclined to focus most of the marketing budget on the 20% of users who supposedly make up 80% of sales volume. However, closer examination reveals that heavy category buyers may only be driving as little as 50% of total purchase volume. So, the marketer’s quandary is, “Do I try and sell the heavy buyer his 901st cola, or do I try and get the light buyer to double his purchase from two to four colas a year?”

Leveraging data helps CPG companies not have to decide. Increasingly, companies are adopting frequency approaches that identify the right amount of messaging to nurture the heavy users (maybe 2-3 messages per user, per month) and bring light buyers to higher levels of purchase consideration (up to 20 messages per month). Moreover, by using DMP technology to segment these buyers based on their category membership, creatives can be adjusted based on the audience. Heavy buyers get messages that reinforce why the love the brand (“share the love”), and light buyers can receive more convincing messages (“tastes better”).

Increasing Lift through Cross-Channel Messaging

CPG marketers have some highly evolved models that show just how much lift a working media dollar has on sales, and they use this guide to decision on media investment by both channel and partner. With the power of DMPs for cross-channel measurement, CPG companies are finally able to apply even small insights they can to tweak sales lift.

What if the data reveal that a 50% mixture of equity and direct response ad creatives lifts coupon downloads by 200%? In other words, instead of just showing “Corn Flakes are Yummy” ads, you mixed in a few “Buy Flakes now at Kroger and save!” creatives afterwards, and you saw a huge impact on your display performance? Sadly, this simple insight was not available before data management platforms corralled cross-channel spending and associated it with an individual, but now these small insights are adding up to appreciable sales lift.

In another example, a large CPG company sees massive lift in in-store coupon redemptions by running branded display ads on desktop all throughout the week—but giving a “mobile nudge” on the smartphone on Friday night when it’s time to fill the pantry. This cross-channel call-to-action has seen real results, and only involves grabbing a brand-favorable consumer’s attention on another device to create a big impact. Again, a simple tactic—but also impossible without the power of a DMP.

CPG marketers have been able to achieve a ton of progress by working with relatively sparse amounts of data. What can you do with yours?

 

 

 

Data Management Platform

Data Triangulation: How Second-Party Data Will Eat The Digital World

 

bearshark
This is the most bad ass illustration I have ever seen. Who painted this? I mean, whoa. 

Marketers are getting frustrated with spending up to 60% of their working media dollars to fund intermediaries between themselves and their publishing partners. By the time a marketer pays his agency, trading desk, exchange, third-party data provider, and subsidizes the publisher’s ad serving stack, dollars turn into dimes. Marketers want less fraud, more people, less ad tech, and to put more media dollars to work to drive performance. Quality publishers, who for so long sacrificed control for access to an always-on stream of programmatic cash, are now seeing balance return, as shady sources of inventory leave the ecosystem and start to create scarcity for premium supply.

 

Publishers with desired audiences are starting to leverage hacks like “header bidding” and private marketplaces to get more control and capture more revenue from transactions. But they are also starting to look at data-only transactions among trusted demand-side partners. Now that marketers are catching up with DMP technology, securely sharing audiences becomes possible, opening up a new era where “second party” data is poised to reign supreme. Before we talk about how that happens, let’s first define some data terms:

A Primer

First-party data is proprietary data that marketers and publishers have collected – with permission, of course – and, therefore, own. It can be cookies collected from a site visit, offline data onboarded into addressable IDs and even data from marketing campaigns. Second-party data is simply someone else’s first-party data. Second-party data gets created any time two companies strike up a deal for data that is not publicly available. The most common use case is that of a marketer – say a big airline –getting access to data for a publisher’s frequent travelers. Big Airline might say to Huge News Site with business travelers, “Let’s user match, so every time I see one of my frequent flyers on your site, I can serve him an ad.” Huge News Site may decide to allow Big Airline to target its users wherever they are found (a “bring your own data” deal) or make such a deal incumbent upon buying media. Either way, Big Airline now has tons of really valuable Huge News Site reader data available in its data-management platform (DMP) for modeling, analysis and targeting.

Despite the much heralded death or merely diminution of third-party data, it is still a staple of addressable media buying. This is data that is syndicated and made available for anyone to buy. This data could describe user behavior (Polk “auto intenders” of various stripes) or bucket people into interesting addressable segments based on their life circumstances (Nieslen “Suburban Strivers”), describe a user’s income level (Acxiom or Experian) or tell you where a user likes to go via location data (PlaceIQ or Foursquare). Most demand-side platforms (DSPs) make a wide variety of this data available within their platforms for targeting, and DMPs enable users to leverage third-party data for segment creation – usually allowing free usage for analytics and modeling purposes and getting paid upon successful activation. Data Quality And Scale So, which kind of data is the best? When asked that question by a marketer, the right question is inevitably, “all of it.” But, since that’s an annoying answer, let’s talk about the relative scale and value of each type of data. It’s easily visualized by this wonderfully over-simplified triangle:

TRIANGLE OF DATA

First-party data is the most limited in scope, yet the most powerful. For marketers –especially big CPG marketers who don’t get a lot of site traffic – first-party data is incredibly sparse but is still the absolute most valuable signal to use for modeling. Marketers can analyze first-party data attributes to understand what traits and behaviors consumers have in common and expand their reach using second- or third-party data. Retail and ecommerce players are more fortunate. A Big Box Store has first-party data out the wazoo: loyalty card data, point-of-sale system data, app data, website registration data, site visit data and maybe even credit card data if it owns and operates a finance arm. It can leverage a DMP to understand how media exposure drove a store visit, where customers were in the store (beacons!), what was purchased, how many coupons were remitted and whether or not they researched their purchase on the site. Talk about getting “closed loop” sales attribution. The power of first-party data is truly amazing.

The biggest problem with third-party data is that all of my competitors have it. In programmatic marketing, that means both Ford and Chevy are likely bidding on the same “auto-intender” and driving prices up. The other problem is that I don’t know how the data was created. What attributes went into deciding whether or not this “auto intender” is truly in-market for a car? There are no real rules about this stuff. A guy who read the word “car” in an article might be an “auto-intender” just as someone who looked a four-door sedans three times in the last 30 days on reputable auto sites. Quality varies. That being said, there is huge value in having third-party data at your disposal. Ginormous Music App, for example, has built a service that is essentially a DMP for music; it knows how to break down every song, assign very granular attributes to it and delivers highly customized listening experiences for free and paid users. Those users are highly engaged, have demonstrated a willingness to buy premium services and are, by virtue of their mobile device, easily found at precise geolocations. Yet, for all of that, the value to a marketer of a Maroon Five segment is rather small. Everyone likes Maroon Five, from grandmothers to tweens to Dads. A Maroon Five segment provides little value to an advertiser. Yet, if Ginormous Music App could push its app-based user data (IDFAs) into the cookie space and find a user match, it could effectively use third-party data to understand the income, behavior and general profile of many Maroon Five fans. And that’s what their advertisers like to buy. That’s pretty damn valuable.

So, how about “second-party” data? These are the “frequent business travelers” on Huge News Site and the “Mitsubishi intenders” on Large Auto Site. These are real users, with true demonstrated intent and behavior that has been validated on real properties. One of the most valuable things about audiences built on second-party data is that there is usually transparency regarding how those users found their way into a segment.

The ironic and kind of beautiful thing about the emergence of second-party data is that it is most often merely a connection to a premium publisher’s users. However, it can be uncoupled from a publisher’s media sales practice. Marketers, increasingly sick of all the fraud and junk in the programmatic ecosystem are turning toward second-party data to access the same audiences they bought heavily in print 30 years ago. This time, however, they are starting to get both the quality – and the quantitative results – they were looking for. On the flip side, quality publishers are starting to understand that, when offered in a strict, policy-controlled environment that protects their largest asset – audience data – they can make way more money with data deals than media deals.

Put simply, second-party data is heralding a return to the good old days when big marketers depended on relationships with big publishers as the stewards of audiences, and they created deep, direct relationships to ensure an ongoing value exchange. Today, that exchange increasingly happens through web-based software rather than martini lunches.

[This article originally appeared in AdExchanger on 1/25/16]

Advertising Agencies

Classic Wrap Up Article with Typical Next-Year Guru Predictions

 

Guru-Ram-Das-picture
Everything I predicted came true, but I still cannot grow a manly beard. 

2015 was a fantastic year for many data-driven marketers, with data management platforms (DMPs), consultancies and marketers getting something nice under their trees.

 

Unfortunately, 2015 also saw legacy networks, supply-side platforms (SSPs) and some less nimble agencies receive coal in their respective stockings for failing to keep up with the rapidly changing paradigm as marketing and ad technology merge.

In the great tradition of end-of-year prediction articles, here’s my take on the year’s biggest developments and what we’ll see in 2016, including a rapid technology adoption from big marketers, a continuing evolution of the agency model and an outright revolution in how media is procured.

Agency Ascendant?

I thought 2015 was supposed to herald the “death of the digital agency model.” As agencies struggled to define their value proposition to big marketers that were increasingly bringing “programmatic in house,” agencies were reputed to be on the ropes. Massive accounts with billions of dollars in marketing spend were reviewed, while agencies churned through cash pitching to win new business – or at least trying keep old business.

The result? Agencies swapped a ton of money, but were abandoned by no serious marketers. Agencies got a lot smarter, and starting spinning new digital strategies and DMP practices to combat the likes of system integrators and traditional consultancies. And the band played on.

In 2016, we will continue to see agencies strengthen their digital strategy bench, start moving “marketing automation” practices into the DMP world and offer integration services to help marketers build bespoke “stack” solutions. Trading desks will continue to aggressively pursue unique relationships with big publishers and start to embrace new media procurement methodologies that emphasize their skillset, rather than the bidded approach in open exchanges (more on that below).

Marketers Hug Big Data

Marketers started to “cross the chasm” in 2015 and more widely embrace DMPs. It’s no longer just “early adopters” such as Kellogg’s that are making the market. Massive top-100 firms have fully embraced DMP tech and are starting to treat online data as fuel for growth.

Private equity and activist investors continue to put the squeeze on CPG companies, which have turned to their own first-party data to find media efficiency as they try to control the one line item in the P&L usually immune to risk management: marketing spend.

Media and entertainment companies are wrangling their consumer data to fuel over-the-top initiatives, which put a true first-party relationship with their viewers front and center. Travel companies are starting to marry their invaluable CRM data to the anonymous online world to put “butts in seats” and “heads in beds.”

If 2015 saw 15% of the Fortune 500 engage with DMPs, 2016 is when the early majority will surge and start to make the embrace of DMP tech commonplace. The land grab for 24-month SAAS contracts is on.

Busy Consultants

It used to be a that a senior-level digital guy would get sick of his job and leave it (or his job would leave him), leading to a happy consultant walking around advising three or four clients on programmatic strategy. In 2015, that still exists but we’ve seen a rise in scale to meet the needs of a rapidly changing digital landscape.

Marketers and publishers are hiring boutique consultancies left and right to get on track (see this excellent, if not comprehensive, list). Also, big boys, including Accenture, Boston Consulting Group and McKinsey, are in the game, as are large, media-centric firms, such as MediaLink.

These shops are advising on data strategy, programmatic media, organizational change management and privacy. They are helping evaluate expensive SAAS technology, including DMPs and yield management solutions, and also doing large systems integrations required to marry traditional databases with DMPs.

Match Rates (Ugh)

Perhaps unpublicized, with the exception of a few nerdy industry pieces, we saw in 2015 a huge focus on “match rates,” or the ability for marketers to find matches for their first-party data in other execution systems.

Marketers want to activate their entire CRM databases in the dot-com space, but are finding only 40% to 50% of cookies that map to their valuable lists. When they try to map those cookies to a DSP, more disappointment ensues. As discussed in an earlier article, match rates are hard to get right, and require a relentless focus on user matching, great “onboarding services,” strong server-to-server connections between DMPs and DSPs (and other platforms) and a high frequency of user matching.

This was the year that marketers got disappointed in match rates and started to force the industry to find better solutions. Next year, huge marketers will take bold steps to actually share data and create an available identity map of consumers. I think we will see the first real data consortium emerge for the purposes of creating an open identity graph. That’s my big prediction – and hope – for 2016.

Head For The Headers

2015 was the year of “header bidding,” the catch-all phrase for a software appliance that gives publishers the chance to offer favored demand-side partners a “first look” at valuable inventory. I am not sure if “header bidding” will ultimately become the de facto standard for “workflow automation,” but we seem to be relentlessly marching back to a world in which publishers and marketers take control of inventory procurement and get away from the gamesmanship inherent in exchange-based buying.

Big SSPs and networks that have layered bidding tech onto open exchanges are struggling. Demand-side platforms are scrambling to add all sorts of bells and whistles to their “private marketplaces,” but the industry evolves.

Next year, we will see the pace of innovation increase, and we have already seen big trade desks make deals with DMPs to access premium publisher inventory. It’s nice to see premium publisher inventory increase in value – and I believe it will only continue to do so.

2016 will be the year of “second-party data” and the winners will be the ones with the technology installed to easily transact on it.

2015 was a great year for data-driven marketing, and 2016 will be even more fun. Stay safe out there.

This post originally appeared in AdExchanger on 12/17/2015