How CRM and a DMP can combine to give a 360-degree view of the customer

360-degree-gif-01For years, marketers have been talking about building a bridge between their existing customers, and the potential or yet-to-be-known customer.

Until recently, the two have rarely been connected. Agencies have separate marketing technology, data and analytics groups. Marketers themselves are often separated organizationally between “CRM” and “media” teams – sometimes even by a separate P&L.

Of course, there is a clearer dividing line between marketing tech and ad tech: personally identifiable information, or PII. Marketers today have two different types of data, from different places, with different rules dictating how it can be used.

In some ways, it has been natural for these two marketing disciplines to be separated, and some vendors have made a solid business from the work necessary to bridge PII data with web identifiers so people can be “onboarded” into cookies.

After all, marketers are interested in people, from the very top of the funnel when they visit a website as an anonymous visitor, all the way down the bottom of the funnel, after they are registered as a customer and we want to make them a brand advocate.

It would be great — magic even — if we could accurately understand our customers all the way through their various journeys (the fabled “360-degree view” of the customer) and give them the right message, at the right place and time. The combination of a strong CRM system and an enterprise data management platform (DMP) brings these two worlds together.

Much of this work is happening today, but it’s challenging with lots of ID matching, onboarding, and trying to connect systems that don’t ordinarily talk to one another. However, when CRM and DMP truly come together, it works.

What are some use cases?

Targeting people who haven’t opened an email

You might be one of those people who don’t open or engage with every promotional email in your inbox, or uses a smart filter to capture all of the marketing messages you receive every month.

To an email marketer, these people represent a big chunk of their database. Email is without a doubt the one of the most effective digital marketing channels, even though as few as 5% of people who engage are active buyers. It’s also relatively fairly straightforward way to predict return on advertising spend, based on historical open and conversion rates.

The connection between CRM and DMP enables the marketer to reach the 95% of their database everywhere else on the web, by connecting that (anonymized) email ID to the larger digital ecosystem: places like Facebook, Google, Twitter, advertising exchanges, and even premium publishers.

Understanding where the non-engaged email users are spending their time on the web, what they like, their behavior, income and buying habits is all now possible. The marketer has the “known” view of this customer from their CRM, but can also utilise vast sets of data to enrich their profile, and better engage them across the web.

Combining commerce and service data for journeys and sequencing

When we think of the customer journey, it gets complicated quickly. A typical ad campaign may feature thousands of websites, multiple creatives, different channels, a variety of different ad sizes and placements, delivery at different times of day and more.

When you map these variables against a few dozen audience segments, the combinatorial values get into numbers with a lot of zeros on the end. In other words, the typical campaign may have hundreds of millions of activities — and tens of millions of different ways a customer goes from an initial brand exposure all the way through to a purchase and the becoming a brand advocate.

How can you automatically discover the top 10 performing journeys?

Understanding which channels go together, and which sequences work best, can add up to tremendous lift for marketers.

For example, a media and entertainment company promoting a new show recently discovered that doing display advertising all week and then targeting the same people with a mobile “watch it tonight” message on the night of it aired produced a 20% lift in tune-in compared to display alone. Channel mix and sequencing work.

And that’s just the tip of the iceberg — we are only talking about web data.

What if you could look at a customer journey and find out that the call-to-action message resonated 20% higher one week after a purchase?

A pizza chain that tracks orders in its CRM system can start to understand the cadence of delivery (e.g. Thursday night is “pizza night” for the Johnson family) and map its display efforts to the right delivery frequency, ensuring the Johnsons receive targeted ads during the week, and a mobile coupon offer on Thursday afternoon, when it’s time to order.

How about a customer that has called and complained about a missed delivery, or a bad product experience? It’s probably a terrible idea to try and deliver a new product message when they have an outstanding customer ticket open. Those people can be suppressed from active campaigns, freeing up funds for attracting net new customers.

There are a lot of obvious use cases that come to mind when CRM data and web behavioral data is aligned at the people level. It’s simple stuff, but it works.

As marketers, we find ourselves seeking more and more precise targeting but, half the time, knowing when not to send a message is the more effective action.

As we start to see more seamless connections between CRM (existing customers) and DMPs (potential new customers), we imagine a world in which artificial intelligence can manage the cadence and sequence of messages based on all of the data — not just a subset of cookies, or email open rate.

As the organizational and technological barriers between CRM and DMP break down, we are seeing the next phase of what Gartner says is the “marketing hub” of interconnected systems or “stacks” where all of the different signals from current and potential customers come together to provide that 360-degree customer view.

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

Chris O’Hara is the head of global marketing for Krux, the Salesforce data management platform.

(Coverage) Salesforce Bolsters Einstein AI With Heavy-Duty Data Management

Through its acquisition of Krux, Salesforce is combining its artificial intelligence (AI) layer with deeper data management in Salesforce Marketing Cloud.
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Customer Relationship Management and Data Management come together in a delicious way.

Today at its Salesforce World Tour stop in New York, the company began to roll back the curtain on how its AI and data layers will work together. Salesforce announced new AI, audience segmentation, and targeting features for Marketing Cloud based on its recent acquisition of data management platform Krux. The company’s new Marketing Cloud features, available today, add more data-driven advertising tools and an Einstein Journey Insights dashboard for monitoring end-to-end customer engagement in everything from e-commerce to email marketing.

Salesforce unveiled its Einstein AI platform this year, baking predictive algorithms, machine and deep learning, as well as other data analysis features throughout its Software-as-a-Service (SaaS) cloud. Einstein is essentially an AI layer between the data infrastructure underneath and the Salesforce apps and services on top. The CRM giant is no stranger to big money acquisitions, most recently scooping up Demandware for $2.8 billion and making a play for LinkedIn before Microsoft acquired it. The Krux acquisition gives Salesforce a new, data-driven customer engagement vector.

“We’re working to apply AI to all our applications,” said Eric Stahl, Senior Vice President of Marketing Cloud. “In Marketing Cloud, Krux now gives us the ability to do things like predictive journeys to help the marketer figure out which products to recommend. We can do complex segmentation, inject audiences into various ad networks, and do large-scale advertising informed by Sales Cloud and Service Cloud data.”

As Salesforce and Krux representatives demonstrated Krux and how it fits into the Marketing Cloud, the data management platform acted more like a business intelligence (BI) or data visualization tool than a CRM or marketing platform. Chris O’Hara, head of Global Data Strategy at Krux, talked about the massive quantities of data the platform manages, including an on-demand analytics environment of 20 petabytes (PB)—the entire internet archive is only 15 PB.

526951-krux-data-pattern-analysis“This is our idea of democratizing data for business users who don’t have a PhD in data science,” said O’Hara. You can use Krux machine-learned segments to find out something you don’t know about your audience, or do a pattern analysis [screenshot above] to understand the attributes of those users that correlate greatly. We’re hoping to use those kinds of signals to power Einstein and do things like user scoring and propensity modeling.

The Einstein Journey Insights feature is designed to analyze “hundreds of millions of data points” to identify an optimal customer conversion path. In addition to its Krux-powered Marketing Cloud features, Salesforce also announced a new conversational messaging service called LiveMessage this week for its Salesforce Service Cloud. LiveMessage integrates SMS text and Facebook Messenger with the Service Cloud console for interactions between customers and a company’s helpdesk bots.

The more intriguing implications here are what Salesforce might do with massively scaled data infrastructure like Krux beyond the initial integration. According to O’Hara, in addition to its analytics environment, Krux also processes more than more than 5 billion monthly CRM records and 4.5 million data capture events every minute, and maintains a native device graph of more than 3.5 billion active devices and browsers per month. Without getting into specifics, Salesforce’s Stahl said there will be far more cross-over between Krux data management and Einstein AI to come. In the data plus AI equation, the potential here is exponential scale.

 

Dynamic Real Time Segmentation

What-is-Real-Time-MarketingThe term “real time” is bandied about in the ad technology space almost as heavily as the word “programmatic.”

Years later, the meaning of programmatic is finally starting to be realized, but we are still a few years away from delivering truly real-time experiences. Let me explain.

Real-Time Programmatic

The real-time delivery of targeted ads basically comes down to user matching. Here is a common use case: A consumer visits an auto site, browses a particular type of minivan, leaves the site and automatically sees an ad on the very next site he or she visits. That’s about as “real-time” as it gets.

How did that happen? The site updated the user segment to include “minivan intender,” processed the segment immediately and sent that data into a demand-side platform (DSP) where the marketer’s ID was matched with the DSP’s ID and delivered with instructions to bid on that user. That is a dramatic oversimplification of the process but clearly many things must happen very quickly – within milliseconds – and perfectly for this scenario to occur.

Rocket Fuel, Turn and other big combo platforms have an advantage here because they don’t need to match users across an integrated data-management platform (DMP) and DSP. As long as marketers put their tags on their pages and stay within the confines of a single execution system, this type of retargeting gets close to real time.

However, as soon as the marketer wants to target that user through another DSP or in another channel, user matching comes back into play. That means pushing the “minivan intender” ID into a separate system, but the “real-time” nature of marketing starts to break down. That’s a big problem because today’s users move quickly between channels and devices and are not constrained by the desktop-dominated world of 10 years ago.

User matching has its own set of challenges, from a marketer’s ability to match users across their devices to how platforms like DMPs match their unique IDs to those of execution platforms like DSPs. Assuming the marketer has mapped the user to all of his or her device IDs, which is a daunting challenge, the marketer’s DMP has to match that user as quickly as possible to the execution platform where the ads are going to be targeted and run.

Let’s think about how that works for a second. Let’s say the marketer has DMP architecture in the header of the website, which enables a mom to be placed in the “minivan” segment as soon as the page loads. After processing the segment, it must be immediately sent to the DSP. Now the DSP has to add that user (or bunch of users) to their “minivan moms” segment. If you picture the internet ID space as a big spreadsheet, what is happening is that all the new minivan moms are added to the DSP’s big existing table of minivan moms so they are part of the new targeting list.

Some DSPs, such as The Trade Desk, TubeMogul and Google’s DBM, do this within hours or minutes. Others manage this updating process nightly by opening up a “window” where they accept new data and process it in “batches.” Doesn’t sound very “real-time” at all, does it?

While many DMPs can push segments in real time, the practical issue remains the ability of all the addressable channels a marketer wants to target to “catch” that data and make it available. The good news is that the speed at which execution channels are starting to process data is increasing every day as older ad stacks are re-engineered with real-time back-end infrastructure. The bad news is that until that happens, things like global delivery management and message sequencing across channels will remain overly dependent upon how marketers choose to provision their “stacks.”

The Future Is Dynamic

Despite the challenges in the real-life execution of real-time marketing, there are things happening that will put the simple notion of retargeting to shame. Everything we just discussed depends on a user being part of a segment. I probably exist as a “suburban middle-aged male sports lover with three kids” in a variety of different systems. Sometimes I’m an auto intender and sometimes I’m a unicorn lover, depending on who is using the family desktop, but my identity largely remains static. I’m going to be middle aged for a long time, and I’m always going to be a dad.

But marketers care about a lot more than that. The beer company wants to understand why sometimes I buy an ice-cold case of light beer (I’m about to watch a football game, and I might drink three or four of them with friends) and when I buy a six-pack of their craft-style ale (I’m going to have one or two at the family dinner table).

The soda company is competing for my “share of thirst” with everything from coffee to the water fountain. They want to know what my entry points are for a particular brand they sell. Is it their sports drink because I’m heading to the basketball court on a hot day, or is it a diet cola because I’m at the baseball game? The coffee chain wants to know whether I want a large hot coffee (before work) or an iced latte macchiato (my afternoon break).

This brings up the idea of dynamic segmentation: Although I am always part of a static segment, the world changes around me in real time. The weather changes, my location changes, the time changes and the people around me change constantly. What if all of that dynamic data could be constantly processed in the background and appended to static segments at the moment of truth?

In a perfect world, where the machines all talked to each other in real time and spoke the same language, this might be called real-time dynamic segmentation.

This is the future of “programmatic,” whatever that means.

[This originally appeared in AdExchanger on 8/31/2016]

CX: The CFO’s Best Friend!

BFFS

When CFO’s embrace data and use it to drive customer experience, good times ensue.

 

Although it’s starting to become a well-worn aphorism, “data is the new oil” resonates more than ever. Like oil, data is an abundant resource, but it doesn’t become useful until it is refined for use and turned into fuel.

Without the proper refinement, big data may be worthless. The stock of big data unicorn Palantir, for example, sunk on news that it lost key client relationships due to a lack of perceived value. The company collected abundant data from CPG companies but was unable to apply it to practical use cases, according to a recent article.

Marketers are starting to turn away from using abundant, yet commoditized, third-party data sources in exchanges and move toward creating peer-to-peer data relationships and leveraging second-party data for targeting. This speaks to the refinement of targeting data: Better quality in the raw materials always yields more potent fuel for performance. Not all data is the same, and not every technology platform can spin data straw into gold.

Marketers have been using available data for addressable marketing for years, but now are starting to mine their own data and get value from the information they collect from registrations, mobile applications, media performance and site visitation. Data management platforms (DMPs) are helping them collect, refine, normalize and associate their disparate first-party data with actual people for targeting.

This is a beautiful thing. Technology is enabling marketers to mine their own data and own it. Yet many marketers are still just scraping the surface of what they can do, and using data primarily for the targeting of addressable media.

Some, however, are starting to deliver customer experiences that go beyond targeting display advertising by using data to shape the way consumers interact with their brands beyond media.

The case for personalization – customer experience management, or CX – is palpable. When the Watermark Group studied [PDF] the cumulative stock performance of Forrester Research-rated “leaders” or “laggards” in customer experience, the results were staggering. During a period in which the S&P 500 grew by 72%, those focused on personalized experiences outperformed the market by 35%, and the laggards underperformed by 45% on average. That’s a delta of nearly 80% in stock price performance between the winners and losers.

Moreover, 89% of customers who have a, unsatisfactory experience will leave a brand, according to a recent study; the cost of reacquiring a churned customer can run up to seven times the amount it took to win a new customer.

The stakes could not be higher for marketers and publishers looking to drive bottom-line performance. For many companies, whether they are marketing print or online subscriptions, promoting their content or selling products off the shelf, it’s hard to justify the heavy costs associated with licensing platforms to gather the right data and use that data to drive relevant customer experiences to their CFOs. Yet, when looking at big company priorities on multiple surveys, the desire to “create more relevant customer experiences” is right up there with “earn more revenue” and “increase profits.” Why?

The simple answer is that customer experience has an enormous impact on both revenue and profitability. Giving new customers the right experience provides a higher probability of winning them, and giving existing customers relevant experiences reduces churn – and creates opportunities to sell them more products, more often. When both top-line revenue and profitability can be driven through a single initiative, most CFOs start to invest and will continue to invest as results confirm the initial thesis.

Take the “heavy user” of a quick-service restaurant who dines several times a week and consistently transacts an over-average per-visit receipt. QSRs understand the impact these valued customers have on the bottom line. These users provide a strong baseline of predicable revenue, are usually the first to try new product offerings and respond to market-facing initiatives, such as discounting and couponing, which can strategically increase short-term receipts. Smart marketers should not be content to sit back and let this valuable segment remain stagnant or find new offerings with a competitive restaurant. They must show these users that they are valued, ensure they retain or increase store visits and keep them away from the hamburger next door.

That can be as simple as offering a coupon for a regular’s favorite order. Or it can be as complex as developing a mobile application that enables the customer to order his food in advance and pick it as soon as it’s ready.

Since the restaurant collects point-of-sale data and has authenticated user registration data from the mobile app, it can now personalize the customer’s order screen with his most popular orders to shorten the mobile ordering experience. Perhaps the app can offer special discounts to frequent diners for trying – and rating – new menu items. When on the road, the app can recommend other locations and direct him right to the drive-in window through popular map APIs. The possibilities are endless when you start to imagine how data can drive your next customer interaction.

Marketers and publishers are quickly embracing their first-party data and aligning it with powerful applications that drive customer experience, increase profits, reduce customer churn and boost lifetime value.

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

[This post originally appeared in AdExchanger on 5/23/16]

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.

How Political Campaigns are Putting DMPs to Work

 

Spacey

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.