CMOs and CIOs need to be more aligned

A survey of both senior marketing and IT professionals has revealed that there are significant differences between these two core business functions in their perception of organizational priorities and the quality of digital infrastructure. Governance frameworks to ensure better alignment between the CMO and CIO are often lacking.

The Backbone of Digital report, freely available from ClickZ (registration required), has also found that, compared to their colleagues in marketing,  IT professionals have a much rosier view of the customer experience their companies are delivering across digital channels.

Below I have outlined more detail around three key findings from the research which is sponsored by communications infrastructure services company Zayo.

IT pros have exaggerated view of the quality of their companies’ current infrastructure

According to the research, 88% of IT respondents describe their company’s infrastructure as ‘cutting-edge’ or ‘good’, compared to only 61% of marketing-focused respondents, a massive difference of 27 percentage points.

The research also looks at the ability of tech infrastructure to deliver across a range of marketing communications channels, with IT respondents and marketers both asked to rate performance.

Both marketers and IT professionals felt that the best engagement and experience is delivered across desktop, cited as ‘excellent’ or ‘good’ by 71% and 93% of these groups respectively, but trailed by other channels including mobile website, mobile app, desktop display, mobile display, social and push messaging.

Across the board it is evident that those working in IT have a much more optimistic view of how well they are delivering across the full gamut of digital channels compared to their IT counterparts.

It seems likely that those working in more customer-facing departments, i.e. marketers (generally), are much more likely to be aware of deficiencies impacting customer experience which can adversely affect business performance and brand reputation (and often their own bonuses).

A lack of co-operation is undermining excellence in digital delivery

Just 19% of marketers strongly agree with the statement “marketing and IT work closely together to ensure the best possible delivery of product/service”, and only 11% strongly agreed that they “have a clear governance framework to ensure that CIOs/CTOs and CMOs work together effectively”, suggesting a lack of alignment around marketing and IT business objectives.

This compares to 45% of IT professionals who strongly agreed that “marketing and IT work closely together to ensure the best possible network performance”, and a similar percentage (46%) who strongly agreed that they “have a clear governance framework to ensure that front-end business applications and back-end infrastructure work together effectively”.

While there are differing perceptions about the extent of marketing and IT co-operation, the report concludes that business objectives need to be much better aligned to ensure closer harmony across these core business functions.  If a framework to facilitate this is not put in place at the top of the organization, it becomes exponentially more difficult to implement lower down.

Speed of data-processing is crucial – real-time means real-time

Marketers are increasingly aware that the proliferation of data sources at their disposal is only of use to their businesses if they can analyse that information at high speed and transform it into the kind of intelligence that can then manifest itself as the most relevant and personalized messaging or call to action for any given site visitor.

According to Mike Plimsoll, Product and Industry Marketing Director at Adobe:

“A couple of years ago the marketing leaders at our biggest clients typically expected that data could be processed within 24 hours and that was fine.

“Now when we talk to our clients the expectation is that data is processed instantly so that when, for example, a customer engages with them on the website, the offer has been instantly updated based on something they’ve just done on another channel. All of a sudden ‘real-time’ really does mean ‘real-time’.”

The ability to harness ‘big data’ has become a pressing concern for IT departments as their colleagues in marketing departments seek to ensure they can take advantage of both structured and unstructured data and ensure the requisite speeds for real-time optimization of targeting, messaging and pricing.

More than half of IT respondents (56%) said that the ability to manage and optimize for big data was currently a ‘very relevant’ topic for their organization, in addition to 37% who said it was ‘quite relevant’.

According to Chris O’Hara, Head of Global Data Strategy at Krux Digital:

“Today, consumers that are used to perfect product recommendations from Amazon and movie recommendations from Netflix expect their online experiences to be personal, email messages to be relevant, and web experiences customized.

“Delivering good customer experience has the dual effect of increasing sales lift, and also reducing churn by keeping customers happy. Things like latency, performance, and data management are all part and parcel of delivering on that concept.”

Please download our Backbone of Digital research which, as well as a survey of marketing and IT professionals, is also based on in-depth interviews with senior executives at a number of well known organizations.

New Whitepaper: Agencies and DMP!

We’ve just published our latest best practice guide, entitled ‘The Role of the Agency in Data Management.’

The report looks at the challenges and opportunities for agencies that want to become trusted stewards of their clients’ data.

I sat down with the author, Chris O’Hara, to find out more.

Q. It seems like the industry press is continually heralding the decline of media agencies, but they seem to be very much alive. What’s your take on the current landscape?

For a very long time, agencies have been dependent upon using low-cost labor for media planning and other low-value operational tasks.

While there are many highly-skilled digital media practitioners – strategists and the like – agencies still work against “cost-plus” models that don’t necessarily map to the new realities in omnichannel marketing.

Over the last several years as marketers have come to license technology – data management platforms (DMP) in particular – agencies have lost some ground to the managed services arms of ad tech companies, systems integrators, and management consultancies.

Q. How do agencies compete?

Agencies aren’t giving up the fight to win more technical and strategic work.

Over the last several years, we have seen many smaller, data-led agencies pop up to support challenging work – and we have also seen holding companies up-level staff and build practice groups to accommodate marketers that are licensing DMP technology and starting to take programmatic buying “in-house.”

It’s a trend that is only accelerating as more and more marketer clients are hiring Chief Data Officers and fusing the media, analytics, and IT departments into “centers of excellence” and the like.

Not only are agencies starting to build consultative practices, but it looks like traditional consultancies are starting to build out agency-like services as well.

Not long ago you wouldn’t think of names like Accenture, McKinsey, Infinitive, and Boston Consulting Group when you think of digital media, but they are working closely with a lot of Fortune 500 marketers to do things like DMP and DSP (demand-side platform) evaluations, programmatic strategy, and even creative work.

We are also seeing CRM-type agencies like Merkle and Epsilon acquire technologies and partner with big cloud companies as they start to work with more of a marketer’s first-party data.

As services businesses, they would love to take share away from traditional agencies.

Q. Who is winning?

I think it’s early days in the battle for supremacy in data-driven marketing, but I think agencies that are nimble and willing to take some risk upfront are well positioned to be successful.

They are the closest to the media budgets of marketers, and those with transparent business models are really strongly trusted partners when it comes to bringing new products to market.

Also, as creative starts to touch data more, this gives them a huge advantage.

You can be as efficient as possible in terms of reaching audiences through technology, but at the end of the day, creative is what drives brand building and ultimately sales.

Q. Why should agencies embrace DMPs? What is in it for them? It seems like yet another platform to operate, and agencies are already managing DSPs, search, direct buys, and things like creative optimization platforms.

Ultimately, agencies must align with the marketer’s strategy, and DMPs are starting to become the single source of “people data” that touch all sorts of execution channels, from email to social.

That being said, DMP implementations can be really tough if an agency isn’t scoped (or paid) to do the additional work that the DMP requires.

Think about it: A marketer licenses a DMP and plops a pretty complicated piece of software on an agency team’s desk and says, “get started!”

That can be a recipe for disaster. Agencies need to be involved in scoping the personnel and work they will be required to do to support new technologies, and marketers are better off involving agencies early on in the process.

Q. So, what do agencies do with DMP technology? How can they succeed?

As you’ll read in the new guide, there are a variety of amazing use cases that come out of the box that agencies can use to immediately make an impact.

Because the DMP can control for the delivery of messages against specific people across all channels, a really low-hanging fruit is frequency management.

Doing it well can eliminate anywhere from, 10-40% of wasteful spending on media that reaches consumers too many times.

Doing analytics around customer journeys is another use case – and one that attribution companies get paid handsomely for.

With this newly discovered data at their fingertips, agencies can start proving value quickly, and build entire practice groups around media efficiency, analytics, data science – even leverage DMP tech to build specialized trading desks. There’s a lot to take advantage of.

Q. You interviewed a lot of senior people in the agency and marketer space. Are they optimistic about the future?

Definitely. It’s sort of a biased sample, since I interviewed a lot of practitioners that do data management on a daily basis.

But I think ultimately everyone sees the need to get a lot better at digital marketing and views technology as the way out of what I consider to be the early and dark ages of addressable marketing.

The pace of change is very rapid, and I think we are seeing that people who really lean into the big problems of the moment like cross-device identity, location-based attribution, and advanced analytics are future-proofing themselves.

DMP 4-5-6

As I’ve previously discussed, there are several basic use cases of the modern data management platform (DMP) for marketers. They include getting “people data” from addressable devices into a single system, controlling how it’s matched with different execution platforms and managing the frequency of messaging across devices.

In a world of ultra-fragmented device identity and multiple addressable media channels, you should be able to tie them together and make sure consumers get the optimal amount of messages. Big marketers use these tactics to save tons of money by chopping off the “long tail” of impressions, such when marketers deliver more than 30 impressions per user each month, and reinvesting to find more deduplicated reach.

There is so much more to the successful application of a DMP, though. The most cutting-edge marketers are taking DMPs to the next level, after investing the time in building consumer identity graphs and getting their match rates with execution platforms as high as possible.

There are several plays you can run when you start to dig in and put the data to work. 

Supercharge The Bidding Strategy

After identifying the long tail of impression frequency and diverting that investment into reach, where users are served up to three impressions per month, the key is driving users down into the sweet spot of frequency. This is where users are more likely to download more coupons, for example, or complete more video views.

If that sweet spot is between four and 20 impressions, marketers can adjust their strategy in biddable environments to ensure they are willing to pay more to “win” users who have only been exposed to three impressions so far. DMPs can match users with fidelity and deliver in near real time these types of targeting sets to multiple execution platforms, including those for display, video and search.

Optimize Partner Investment Through Reach Analysis  

It’s a great start to manage addressable media delivery on a global basis, but what happens after you have identified all of those wasted impressions?

Naturally, the money marketers are spending reaching consumers for the 100th time can be better spent looking for net new consumers. But how do you get them?

For a diaper manufacturer that wants to reach the estimated 6 million new mothers in market every year, it’s critically important to get to 100% reach against that audience. Many marketers start with a single, broad reach partner, such as Yahoo, and see how close they can get to total reach.

It’s fantastic to leverage big spending power to drive down prices and get massive customer service attention to spread a message to as many unique users as possible. But no single partner can get a marketer to 100%. That’s where the DMP comes in.

It’s not just about filling in the missing 25% of an audience that matters; the diaper manufacturer wants to hit those incremental moms across quality, well-lit sites. Determining where you can get a few more million deduplicated moms is the first step. The key is to then decide where to find them more effectively from an investment standpoint, which requires an overlap analysis.

Enhance Partner Selection Through Overlap Analysis 

Say our diapers manufacturer found 4 million new moms on Yahoo at a reasonable CPM. The DMP can then look across all addressable media investments and run a “Where are my people?” type of analysis.

Maybe this advertiser has another 20 partners on the plan after getting the bulk of unique reach from a single partner. How many more unique moms were found on Meredith? Moreover, how about finding moms on classic news and entertainment sites, such as NBC or Turner properties, or even non-endemic sites? Maybe there is an incremental 500,000 first-party “diaper moms” on a particular site, but now the advertiser can decide, based on performance KPIs and price, how valuable those particular moms are.

If those moms on a popular news site can be had for $5 CPM, maybe they are a more valuable reach vehicle than those found on the obvious “Moms.com” site. Without the DMP, they’ll never know.

Plus, marketers are also starting to optimize the way they procure such audiences, by leapfrogging over the existing ad tech ecosystem and doing audience-based programmatic direct buying using their new DMP pipes.

Understand KPIs Drivers Through Journey Building

Marketers that have deduplicated their audience and built an effective reach strategy can now go to the next level and start finding how those diaper moms moved from their first touch point in the customer journey to an actual action, such as downloading a retail coupon or requesting a sample package. When an audience is unified through a DMP, it’s possible to see the channels through which people move across their “customer journey” from awareness to action.

As an example, more large CPG companies are putting more investment into online video and, in fact, one of the world’s largest marketers has embraced a “ban the banner” approach and values engagement more than any other KPI – a metric more easily understood with video. With that in mind, a journey analysis can show marketers if seeing a few search impressions helped drive more completed views on (expensive) video and drive more brand engagement.

Did consumers download more coupons after viewing two equity (branding) impressions or before seeing the “buy now” (direct-response) message? The ability to understand how messages work together sequentially is the ultimate guide to being able to inform media investment strategy.

These are just a few of the next-level media use cases that can be accomplished once DMP fundamentals are put in place. DMPs are starting to shine a light on the “people data” that will drive the next decade of smart media investment. I think we will look back on the last 15 years of addressable marketing and wonder how we ever made such decisions without a clear view of audience first.

DMPs are starting to shine a light on the effectiveness of marketing, and giving marketers lots of new knobs and levers to pull.

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

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

Is 2015 the Year of Programmatic Branding?

With companies like Kraft and Kellogg’s starting to leverage the programmatic pipes for equity advertising, we are starting to hear a lot of buzz about the potential for “programmatic branding,” or leveraging ad tech pipes to drive upper-funnel consumer engagement. It makes sense. Combine 20 years in online infrastructure investment with rapidly shifting consumer attention from linear to digital channels, and you have the perfect environment to test whether or not digital advertising can create “awareness” and “interest,” the first two pieces of the age old “AIDA” funnel.

The answer, put simply, is yes.

Online reach is considerably less expensive than linear reach, and we are starting to have the ability to reliably measure how that brand engagement is generated. Marketers want an “always-on” stream of equity advertising that comes with measurement—but they also need it. With attention rapidly shifting from traditional channels, investments in linear television are starting to return fewer sales. But most marketers are just starting to gain the digital competency to make programmatic branding a reality.

That competency is called data management—the ability to segment, activate, and analyze consumer audiences in a reliable way at scale. Why is that so?

The most fundamental problem with digital branding is that it is truly a one-to-one marketing exercise. If we dream of the “right message, right person, right time,” then matching a user with her devices is table stakes for programmatic branding. How do I know that Sally Smith on desktop is the same as Sally Smith on tablet? Cross-device identity management (CDIM or, alternatively, CDUI) is the key. Device IDs must be mapped to cookies, other mobile identifiers, and Safari browser signals in order to get a sense of who’s who. Once you unlock user identity, many amazing things become possible.

Global Frequency Capping

One of the reasons programmatic branding has yet to gain serious ground with marketers is because of waste. This is both real (lots of wasted impressions due to invisible ads or robotic traffic) and perceived (how many impressions are ineffective due to frequency issues). The former problem is getting solved by smart technology, and already somewhat mitigated by market pricing. But the latter problem is solvable with data management. Assuming the marketer understands the ideal effective frequency of impressions per channel, or on a global basis, a DMP can manage how many impressions an individual sees by controlling segment membership in various platforms. Let’s say the ideal frequency for cereal advertising aimed at Moms is 30 per day across channels. The advertiser knows less than 30 impressions lessens effectiveness—and over 30 impressions has negligible impact. Advertisers using multiple channels (direct-to-publisher, plus a mobile, video, and display DSPs) are likely over serving impressions in each channel, and maybe underserving in key channels like video. Connecting user identity helps control global frequency, and can save literally millions of dollars, while optimizing the effectiveness of cross-channel advertising.

Sequential Messaging

If “right person” technology is enabled as above, then it makes sense to try and get to “right place and right time.” Data management can enable this Holy Grail of branding, helping marketers create relevance for consumers as they embark on the customer journey. What brand marketers have dreamed of is now possible, and starting to happen. Dad, in the auto-intender bucket, gets exposed to a 15 second pre-roll ad before logging into his newspaper subscription on his tablet in the morning; gets the message reinforced by more equity display ads in the afternoon at work; and, while checking messages on his mobile phone on the way home, gets an offer for $500 off with a qualified test drive. After he hits the dealership and checks in via the CRM system, he receives an e-mail thanking him for his visit and reminding him of the $500 coupon he earned. These tactics are not possible without tying both user identity and systems together. Doing so not only enables sequential messaging, but also the ability to test and measure different approaches (A/B testing).

Cross Channel Attribution

How about attribution? It’s impossible to perform cross-channel attribution without knowing who saw what ad. At the end of the day, it’s really about the insights. Proctor and Gamble is famous for spending millions of dollars every year to understand the “moment of truth,” or why people choose Tide over Surf detergent. Although they know consumer segmentation and behavior better than anyone, even the biggest brand marketers struggle to gain quality insights from digital channels. Data management is starting to make a more reliable view possible. Brand advertising is just another form of investment. Money is the input, and the output is sales and—as important—data on what drove those sales. In the past, brand marketers were reliant upon panel-based measurement to judge campaign effectiveness. Now, data management helps brands understand which channels drove results—and how each contributed. It is early days for truly reliable cross-channel attribution modeling, but we are finally starting to see the death of the “last click” model. Smart marketers are using data to author their own flexible attribution models, making sure all channels involved receive variable credit for driving the final action. In the near future, machine learning will help drive dynamic models, which flex over time as new signals are acquired. We will then start to see just how effective (or not) tactics like standard display advertising are for driving upper funnel engagement.

So, is 2015 the year for programmatic branding? For a select group of marketers that are leveraging data management to enable the best practices outlines above, yes. The more accurately marketers can map online user identity and understand results, the more investment will flow from linear to addressable channels.

What Marketers want from AdTech

If you read AdExchanger regularly, you might think that nearly every global marketer has a programmatic trading strategy. They also seem to be leveraging data management technology to get the fabled “360-degree view” of their customers, to whom they are delivering concise one-to-one omnichannel experiences.

The reality is that most marketers are just starting to figure this out. Their experience ranges from asking, “What’s a DMP?” to “Tell me your current thinking on machine-derived segmentation.”

A small, but significant, number of major global marketers are aggressively leaning into data-driven omnichannel marketing, pioneering a trend that is not going anywhere anytime soon. Over the next five years, nearly every global marketer will have a data-management platform (DMP), programmatic strategy and “chief marketing technologist,” a hybrid chief marketing officer/chief information officer that marries marketing and technology. These are exciting times for people in data-driven marketing.

So, what are marketers looking for from technology today? Although these conversations ultimately become technical in nature, you soon discover that marketers want some pretty basic, “table stakes” type of stuff.

Better Segmentation Through First-Party Data 

Marketers spend a lot of time building customer personas. Once a customer is in their customer relationship management (CRM) database and generates some sales data, it’s pretty easy to understand who they are, what they like to buy and where they generally can be found. From a programmatic perspective, these are the equivalent of a car dealer’s “auto intenders,” neatly packaged up by ad networks and data providers to be targeted in exchanges.

That’s still available today, but the amazing amount of robotic traffic, click fraud and media arbitrage has made marketers realize just how loose some segment definitions may be. Data companies have a great deal of incentive to create and sell lots of auto intenders, so marketers are starting to look deeper at how such segments are actually created. It turns out that some auto intenders are people who brushed past a car picture on the web, which lumped them into a $12 cost per mille (CPM) audience segment.

Those days seem to be coming to an abrupt close as marketers increasingly use their own data to curate such segments and premium publishers, which do have auto intenders among their readerships, use data-management tools to make highly granular segments available directly to the demand side. Marketers are now willing to pay premium prices for premium audiences in a dynamic being driven by more transparency into how audiences are created in the first place. Audiences comprised of first- and second-party data will win every time in a transparent ecosystem. 

Less Waste, More Efficiency

Part and parcel of better audience segmentation is less waste and more media efficiency. The old saw, “I know half of my marketing works, I just don’t know which half,” goes away with good data and better attribution.

As an industry, we promised to eliminate waste 20 years ago. The banner ad was supposed to usher in a brave new world of media accountability, but we ended up creating a hell of a mess. Luckily, venture money backed “solutions” to the problems of click fraud, faulty measurement and endless complexity in digital marketing workflow.

Marketers don’t want to buy more technology problems they need to fix. And they don’t want to spend money chasing the same people around the web. They want to limit how much they spend trying to achieve reach. Data-management technology is starting to rein in wasteful spending, via tactics including global frequency management, more precise segmentation, overlap analysis and search suppression.

Marketers want to use data to be more precise. They are starting to leverage systems that help them understand viewability and get a better sense of attribution by moving away from stale last-click models. The days are numbered for marketers with black-box technology that creates a layer between their segmentation strategies and how performance is achieved against it.

One-To-One Communication Via Cross-Device Identity

Maybe the biggest trend and aspiration among marketers is the ability to truly achieve one-to-one marketing. A few years ago, that meant email, telemarketing and direct mail. Today, if you want to have a one-to-one customer relationship, you must be able to associate the “one” person with as many as five or six connected devices.

That is extremely difficult, mostly because we have been highly dependent on the browser-based “cookie” to determine identity. Cookie-based technologies evolved to ensure different cookies match up in different systems, but it’s a new world today.

Really understanding user identity means being able to reconcile different device signals with a universal ID. That means lots of cookies from different browsers, Safari’s unique browser signature, IDFAs, Android device IDs and even signals from devices like Roku, not to mention reliably “onboarding” anonymized offline data, such as CRM records.

Without device mapping, an individual looks like seven different devices to a marketer, making it impossible to deliver the “right message, right place, right time.” Frequency management is tougher, attribution models start to break and sequential messaging is hard to do. Marketers want a reliable way to reconcile user identity across devices so they can adapt their messages to your situation.

Data-Derived Insights 

Marketers inject tons of dollars into the advertising ecosystem and expect detailed performance reports. Each dollar spent is an investment. Some dollars create sales results, but all dollars spent in addressable channels create some kind of data.

Surprisingly, that data is still mostly siloed, with social data signals not connected to display results. Much of it is delivered in the form of weekly spreadsheets put together by an agency account manager. It seems crazy that marketers can’t fully take advantage of all the data produced by their digital marketing, but that is still very much the reality of 2015.

Thankfully, that dynamic is changing quickly. Data technology is rapidly offering a “people layer” of intelligence across all channels. Data coming into a central system can look at campaign performance across many dimensions, but the key is aggregating that data at the people level. How did a segment of “shopping cart abandoners” perform on display vs. video?

Marketers now operate under the new but valid assumption that they will be able to track performance in this way. They are starting to understand that every addressable media investment can create more than just sales – it can produce data that helps them get smarter about their media investments going forward.

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

[This post originally appeared in AdExchanger on 4.6.15]

CDIM is Table Stakes in the Data Management Wars

A recent analyst report made an astute observation that all marketers should consider: It’s not about “digital marketing” anymore – it’s about marketing in a digital world. The nuance there is subtle, but the underlying truth is huge. The world has changed for marketers, and it’s more complicated than ever.

Most consumers spend more time on web-connected devices than television, creating a fragmented media landscape where attention is divided by multiple devices and thousands of addressable media outlets. For marketers, the old “AIDA” (attention, interest, desire and action) funnel persists, but fails in the face of the connected consumer.

When television, print and radio dominated, moving a consumer from product awareness to purchase had a fairly straightforward playbook. Today’s always-on, connected consumer is on a “customer journey,” interacting with a social media, review sites, pricing guides, blogs and chatting with friends to decide everything from small supermarket purchases to big investments like a new house or car.

Marketers want to be in the stream of the connected consumer and at key touch points on the customer journey. But, in order to understand the journey and be part of it, they must be able to map people across their devices. This is starting to be known as cross-device identity management (CDIM), and it is at the core of data-driven marketing.

In short, identity lies at the heart of successful people data activation.

Until very recently, managing online identity was largely about matching a customer’s online cookie with other cookies and CRM data, in order to ensure the desktop computer user was aligned with her digital footprint. Today, the identity landscape is highly varied, necessitating matching ID signals from several different browsers, device IDs from mobile phones and tablets, IDs from streaming devices and video game consoles and mobile app SDKs.

Matching a single user across their various connected devices is a challenge. Matching millions of users across multiple millions of devices is both a big data and data science challenge.

Real one-to-one marketing is only possible when the second party – the customer – is properly identified. This can be done using deterministic data, or information people volunteer about themselves, in a probabilistic manner, where the marketer guesses who the person is based on certain behavioral patterns and signals. Most digital marketing companies that offer identity management solutions take what data they have and use a proprietary algorithm to try and map device signals to users.

The effectiveness of device identity algorithms depends on two factors: the quality of the underlying deterministic data – the “truth set” – and its scale.

Data Quality Matters

There is data, and then there is data. The old software axiom of “garbage in, garbage out” certainly applies to cross-device user identity. Truly valuable deterministic data include things like age, gender and income data. In order to get such data, web publishers must offer their visitors a great deal of value and be trusted to hold such information securely. Therefore, large, trusted publishers – often with subscription paywalls – are able to collect highly valuable first-party user data.

Part of the quality equation also relates to the data’s ability to unlock cross-device signals. Does the site have users that are logged in across desktop, mobile phone and tablet? If so, those signals can be aggregated to determine that Sally Smith is the same person using several different devices. Publishers like The Wall Street Journal and The New York Times meet these criteria.

Scale Is Critical

In order to drive the best probabilistic user matches, algorithms need huge sets of data to learn from. In large data sets, even small statistical variances can yield surprising insights when tested repeatedly. The larger the set of deterministic data –the “truth” of identity – the better the machine is able to establish probability. A platform seeing several million unique users and their behavioral and technographic signatures may find similarities, but seeing billions of users will yield the minuscule differences that unlock the identity puzzle. Scale breeds precision, and precision counts when it comes to user identity.

As digital lives evolve beyond a few devices into more connected “things,” having a connected view of an individual is a top priority for marketers that want to enable the one-to-one relationship with consumers. Reliably mapping identity across devices opens up several possibilities.

Global Frequency Management: Marketers that leverage multiple execution platforms, including search, email, display, video and mobile, have the ability to limit frequency in each platform. That same user, however, looks like five different people without centralized identity management.

Many marketers don’t understand what ideal message frequency looks like at the start of a campaign, and most are serving ads far above the optimal effective frequency, resulting in large scale waste. Data management platforms can control segment membership across many different execution platforms and effectively cap user views at a “global” level, ensuring the user isn’t over-served in one channel and underserved in another.

Sequential Messaging: Another benefit of cross-device identity is that a user can be targeted with different ads based on where they are in the consumer journey. Knowing where a consumer is in an established conversion path or funnel is a critical part of creative decisioning. Optimizing the delivery of cross-channel messages at scale is what separates tactical digital marketers and enterprise-class digital companies that put people data at the heart of everything they do.

Customer Journey Modeling: Without connecting user identity in a centralized platform, understanding how disparate channels drive purchase intent is impossible. Today’s models bear the legacy of desktop performance metrics, such as last click, or have been engineered to favor display tactics, including first view. The true view of performance must involve all addressable channels, and even consider linear media investment that lacks deterministic data. This is challenging but all but impossible without cross-device identity management in place.

The ubiquity of personal technology has transformed today’s consumers into “digital natives” who seamlessly switch between devices, controlling the way they transmit and receive information. Marketers and publishers alike must adapt to a new reality that puts them in control of how editorial and advertising content is accessed. Delivering the right consumer experience is the new battleground for CMOs. Unlocking identity is the first step in winning the war.

[This post originally appeared in AdExchanger on 3.16.15]

2015 is the Year of Programmatic Branding

With companies like Kraft and Kellogg’s starting to leverage the programmatic pipes for equity advertising, we are starting to hear a lot of buzz about the potential for “programmatic branding,” or the use of ad tech pipes to drive upper-funnel consumer engagement.

It makes sense. Combine 20 years in online infrastructure investment with rapidly shifting consumer attention from linear to digital channels, and you have the perfect environment to test whether or not digital advertising can create “awareness” and “interest,” the first two pieces of the age old “AIDA” funnel.

The answer, put simply, is yes.

Online reach is considerably less expensive than linear reach, and we are starting to have the ability to reliably measure how that brand engagement is generated. Marketers don’t just want an “always-on” stream of brand advertising that comes with measurement – they also need it. With attention rapidly shifting from traditional channels, investments in linear television are starting to return fewer sales.

But most marketers are just starting to gain the digital competency to make programmatic branding a reality. That competency is called data management – the ability to segment, activate and analyze consumer audiences in a reliable way at scale.

The most fundamental problem with digital branding is that it is truly a one-to-one marketing exercise. If we dream of the “right message, right person, right time,” then matching a user with her devices is table stakes for programmatic branding. How do I know that Sally Smith on desktop is the same as Sally Smith on tablet?

Cross-device identity management is the key. Device IDs must be mapped to cookies, other mobile identifiers and Safari browser signals to get a sense of who’s who. Once you unlock user identity, many amazing things become possible.

Global Frequency Capping

One of the reasons programmatic branding has yet to gain serious ground with marketers is because of waste. This is both real, including all those wasted impressions due to invisible ads or robotic traffic, and perceived, such as impressions that are ineffective due to frequency issues.

Smart technology and market pricing solves the first problem, while data management solves the second. Assuming the marketer understands the ideal effective frequency of impressions per channel, or on a global basis, a DMP can manage how many impressions an individual sees by controlling segment membership in various platforms. Let’s say, for example, the ideal frequency for cereal advertising aimed at moms is 30 per day across channels. The advertiser knows showing fewer than 30 impressions reduces effectiveness, while more than 30 impressions has a negligible impact. Advertisers using multiple channels, such as direct-to-publisher, plus mobile, video and display DSPs, are likely overserving impressions in each channel and possibly underserving in key channels like video. Connecting user identity helps control global frequency and can save literally millions of dollars, while optimizing the effectiveness of cross-channel advertising.

Sequential Messaging

If “right person” technology is enabled as above, the next logical step is to try and get to “right place and right time.” Data management can enable this holy grail of branding, helping marketers create relevance for consumers as they embark on the customer journey. What brand marketers have dreamed of is now possible and starting to happen.

Dad, in the auto-intender bucket, is exposed to a 15-second pre-roll ad before logging into his newspaper subscription on his tablet in the morning. The message is reinforced by more equity display ads he sees in the afternoon at work. And while checking messages on his mobile phone on the way home, he receives an offer for $500 off with a qualified test drive. After Dad hits the dealership and checks in through the CRM system, he receives an email thanking him for his visit and reminding him of the $500 coupon he earned.

These tactics are not possible without tying user identity and systems together. Doing so not only enables sequential messaging, but also the ability to test and measure different approaches through A/B testing.

Cross-Channel Attribution

How about attribution? It’s impossible to perform cross-channel attribution without knowing who saw what ad. At the end of the day, it’s really about the insights.

Procter & Gamble is famous for spending millions of dollars every year to understand the “moment of truth,” or why people choose Tide over another detergent. Although they know consumer segmentation and behavior better than anyone, even the biggest brand marketers struggle to gain quality insights from digital channels.

Data management is starting to make a more reliable view possible. Brand advertising is just another form of investment. Money is the input. The output is sales and, just as important, the data on what drove those sales. In the past, brand marketers relied on panel-based measurement to judge campaign effectiveness. Now, data management helps brands understand which channels drove results and how each contributed.

It is early days for truly reliable cross-channel attribution modeling, but we are finally starting to see the death of the “last-click” model. Smart marketers use data to author their own flexible attribution models, making sure all channels involved receive variable credit for driving the final action. In the near future, machine learning will help drive dynamic models, which flex over time as new signals are acquired. We will then start to see just how effective – or not – tactics like standard display advertising are for driving upper-funnel engagement.

Is 2015 the year for programmatic branding? For marketers that are leveraging data management to enable the best practices outlined above, the answer is yes. The more accurately marketers can map online user identity and understand results, the more investment will flow from linear to addressable channels.

[This post originally appeared on 1.4.2015 in AdExchanger]

How Can Advertisers Bypass The Industry’s Walled Gardens?

In this increasingly cross-device world, marketers have been steadily losing the ability to connect with consumers in meaningful ways. Being a marketer has gone from three-martini lunches where you commit to a year’s worth of advertising in November to a constant hunt for new and existing customers along a multifaceted “customer journey” where the message is no longer controlled.

Consumers’ attention migrates from device to device, where they spread their limited attention among multiple applications. It’s become a technology game to try and track them down, and starting to become a big data game to serve them the “right message, at the right place, at the right time.”

Modern ad tech is supposed to be the marketer’s savior, helping him sort out how to migrate budgets from traditional media, such as TV, radio and print, to the addressable channels where people now spend all of their time. Marketers and their agencies need a technology “stack,” but they end up with a hot mess of different solutions, including various DSPs for multiple channels, content marketing software and ad servers.

Operating and managing all of them is possible, but laborious and difficult to do right. Worse still, these systems are nearly impossible to connect. Am I targeting the same consumer over and over through various channels? How to manage messaging, frequency and sequencing of ads?

Since all of these systems purport to connect marketers to customers on the audience level, the coin of the realm is data. It’s not just “audience data” but actual data on the individuals the marketer wants to target.

Marketing is now a people game.

Yet, in the cross-channel, evolving world of addressable media, connecting people to their various devices is difficult. You need to see a lot of user data, and you have to not only collect web-based event data, but also mobile data where cookies don’t exist. Deterministic data, such as a website’s registration data, can lay the foundation for identity. When blended with probabilistic data and modeled from user behavior and other signals, it becomes possible to find an individual.

Right now, the overlords of the people marketing game are platforms like Google, where people are happy to stay logged in to their email application on desktop, mobile and tablet, or Facebook, which knows everything because we are nice enough to tell them. Regular publishers may be lucky enough to have subscription users that log in to desktop and mobile devices, but most publishers don’t collect such data. Their ability to deliver true one-to-one marketing to their advertisers is limited to their ability to identify users.

This dynamic rapidly makes the big “walled gardens” of the Internet the only place big marketers can go to unlock the customer journey. That might work for Google and Facebook shareholders and employees, but it’s not good for anyone else. In our increasingly data-dependent world, not all marketers are comfortable borrowing the keys to user identity from platforms that sell their customers advertising. Soon, everyone will have to either pay a stiff toll to access such user data, or come up with innovation that enables a different way to unlock people-centric marketing.

What is needed is an independent “truth set” that advertisers can leverage to match their anonymous traffic with rich customer profiles, so they can actually start to unlock the coveted “360-degree view of the user.” Not only does a large truth set of users create better match rates with first-party data to improve targeting, but it also holds the key to making things like lookalike modeling and algorithmic optimization work. Put simply, the more data the machine has to work with, the more patterns it finds and the better it learns. In the case of user identity, the probabilistic models most DMPs deploy today are very similar. Their individual effectiveness depends on the underlying data they can leverage to do their jobs.

In the new cross-device reality: If you can’t leverage a huge data set to target users, it’s time to take your toys and go home. Little Johnny doesn’t use his desktop anymore.

Think about the three principle assets most companies have: their brand, their intellectual property and products and their customer data. Why should a company make a third of their internal value dependent upon a third party, whether or not they pledge “no evil?” Those that offer a “triple play” of mobile, cable television and phone services are also part of the few companies that can match a user across various devices. The problem? They all sell, or facilitate the sale of, lots of advertising. Marketers are not sure they want to depend on them for unlocking the puzzle of user identity.

Some of the greatest providers of audience data are independent publishers who, banded together, can create great scale and assemble a truth set as great as Facebook and Google. Maybe it’s time to create a data alliance that breaks the existing paradigm. The “give to get” proposition would be simple: Publishers contribute anonymized audience identity data to a central platform and get access to identity services as a participant. This syndicate could enable the deployment of a universal ID that helps marketers match consumers to their devices and create an alternative to the large walled gardens.

The real truth is that, without banding together, even great premium publishers will have a hard time unlocking the enigma of cross-device identity for marketers. Why not build a garden with your neighbors, rather than play in somebody else’s?

[This post was originally published in AdExchanger on 12.11.14]

Understanding Social Data

A Conversation with Sree Nagarajan, CEO Colligent

Marketers are increasingly turning to social platform data to understand their customers, and how to tap into their social graphs to reach more of them. Facebook “likes” and Twitter “follows” are religiously captured and analyzed, and audience models are created—all in the service of trying to scale the most powerful type of marketing of all: word-of-mouth.  With CRM players (like Salesforce, who recently acquired Buddy Media and Radian6) jumping into the game, digitally-derived social data is now an established part of traditional marketing. I recently sat down with Sree Nagarajan, the CEO of Colligent, a social data platform that measures “social affinity” to find out more about what marketers should be measuring, and how important the social graph really is when it comes to audience targeting.

 The relatively recent availability of social data has given marketers the ability to enrich their customer data like never before, so they can understand their audience composition. How important is having access to social data for the brand marketer?

Sree Nagarajan (SN): It is very important for the following reasons. Firstly, it means having access to a large virtual panel: Instead of doing research on few thousand consumers, they get access to tens of thousands, even millions, of consumers. Such research using traditional methods would cost them a lot of time and money to obtain. Secondly, it is Specific to their brand; traditional syndicated research or datasets were usually media specific, not brand specific. In contrast, social data can be highly specific to their brand.

Are today’s current “social signals” strong enough to base large enterprise decisions on? In other words, are “likes” driving valid marketing activity, or are many brands just buying “likes” for their own sake?

SN: In any social network’s lifecycle, likes (or “follows” or friends) start out as genuine signals of brand affinity. However as more and more like the page their audience gets increasingly diluted, making likes less of an indicator of brand’s true audience. True engagement as measured by comments, photo posts, re-tweets, hashes, etc. became much better indicators of brand affinity and engagement.

Is understanding a person’s friends interests (or “social graph”) important?

SN: Yes. Such an “interest” or “affinity” graph is complementary to a social graph. Each have their own strengths and weaknesses. While the social graph facilitates viral marketing, in practice it’s been harder to pull this off on a large scale consistently. This is because most  social graphs are very heterogeneous, making it noisy and less actionable (think about the diversity of your friends and their interests). In contrast, an interest graph, when properly assembled, can be very actionable since it targets a virtual group of people that come together around their shared interests. A relevant analogy may be fans in a Lady Gaga concert. They are not connected by social graph but, rather, connected by a common interest. Interest graphs can provide much larger reach than social graph in many instances, because they can be media agnostic. For example, if Pepsi fans are interested in Nicki Minaj, Pepsi can get access to the Nicki Minaj audience by buying ads in digital, TV or doing sponsorships. A social graph is only actionable within the social network it’s part of, providing less opportunity for reach expansion.

Many 3^rd party data providers use contextual or behavioral “signals” as a proxy for understanding consumer intent. What is the best data for really getting to a consumer’s purchase intent? Search? Membership in a social group/page?

SN: The best digital data sets for understanding purchase behaviors are search and social engagement. The value of search is already proven in bottom of funnel. However, social engagement drives the mid-funnel audience who are highly representative of your buyers. A correlation study done between album sales and engaged fans of hundreds of music artists in social networks revealed a 90% or better correlation in most cases.

With hundreds of millions of rich, deep customer profiles, how relevant are 3^rd party segment providers anymore? Why shouldn’t marketers just “one stop shop” on Facebook for audience targeting (or use FB profile IDs to cookie target on the exchanges)?

SN: This is not practical for the following reasons. First off, you need to filter casual affinity (likes or follows) from true engagement. While engagement is predictive of purchase, “likes” may not be. Third parties can provide such filters.  Secondly, each of the social networks capture only a subset of consumer’s engagement dimensions. Think about your own behavior: When you want to engage with your friends or some brands you probably go to Facebook. However, when you want to be up to date on favorite sports personality or celebrity, you do that much more on Twitter. Thus, no single network has all the contexts. Integrating different contexts from multiple networks is the value a 3rd party segment provider can offer. Thus, you can understand what celebrities capture Pepsi fans’ imagination in Twitter, and armed with that intelligence, can better target those celebrities’ fans in Facebook to expand your audience in a targeted way.

What is social “affinity” data? Why is this better than “likes”?

SN: Social affinity data measures true engagement. Such engagement is defined by the social network. For Facebook, it’s the people making comments, posting photos or videos, liking comments, or sharing them. On Twitter, it’s re-tweets, hashes, replies and talks beyond simple following of the brand. In order for the engagement to be truly effective, it needs to be measured from both sides (mutual engagement). The parallel is a real-world relationship. It’s not enough for me to like you, but you have to like me for us to have a relationship. Mapped to the brand affinity world, it’s not enough for Pepsi fans to engage with Nicki Minaj; enough Nicki fans have to engage with Pepsi (more than the population average on both sides) to make this relationship truly meaningful and thus actionable. When true engagement is married with such mutual engagement, the result is intelligence that filters the noise in social networks to surface meaningful relationships.

How far can you take social affinity data (beyond digital)?

SN: Because there so many people participating in social networks, and the measurement of their engagement is done passively by measurement companies, this data set represents the true behavior of brand’s consumers. In other words, social becomes a “looking glass” for mapping real world affinities. Think about your Facebook profile: How many of the likes you have there are driven by real world influences that you had from outside any social network (e.g., favorite artists, TV shows, celebs, etc.?) Thus it can be applied beyond digital. Affinity data can be used for traditional television media planning (i.e., buying the shows, networks, and radio stations that my fans are engaged with); public relations (focusing on the media outlets my fans are engaged with); and sponsorships (using affinity data to determine which celebrity should be a brand’s spokesperson, for example).

You mentioned a distinction between “brand” vs. “media” audiences Talk a little bit about your concept of and why its important to be aware of.

SN: Much of the audience selection options available in the market today are media based. Nielsen defines TV audience, Arbitron radio, ComScore digital sites, MRI magazines, etc. Brand marketers are forced to define their audiences in the way media measures audience: by demographics (e.g., 18-49 male). Now, for the first time, social data allows marketers to define audiences based on their own brand and category terms. Now, they can say “I want to buy TV shows watched by Pepsi and more generally, Carbonated Soft Drinks audience.” This will  truly make marketing brand-centric instead of media-centric. Imagine a world where brand and category GRPs can be purchased across media, rather than GRPs in a specific media.

This interview, among many others, appears in EConsultancy’s recently published Best Practices in Data Management by Chris O’Hara. Chris is an ad technology executive, the author of Best Practices in Digital Display Media, a frequent contributor to a number of trade publications, and a blogger.

Managing Data in [real] Real-Time

A Conversation with Srini Srinivasan, Founder and VP Operations of Aerospike

Even today, the notion that a consumer can go to a website, be identified, trigger a live auction involving as many as a dozen or more advertisers, and be served an ad in real-time, seems like a marvel of technology. It takes a tremendous amount of hardware and, even more than ever, a tremendous amount of lightning-fast software to accomplish. What has been driving the trend towards ever faster computing within ad technology are new no-SQL database technologies, specifically designed to read and write data in millisecond frameworks. We talked with one of the creators of this evolving type of database software, who has been quietly powering companies including BlueKai, AppNexus, and [x+1], and got his perspective on data science, what “real time” really means, and “the cloud.”

Data is growing exponentially, and becoming easier and cheaper to store and access. Does more data always equal more results for marketers?

Srini Srinivasan: Big Data is data that cannot be managed by traditional relational databases because it is unstructured or semi-structured and the most important big data is hot data, data you can act on it in real-time. It’s not so much the size of the data but rather the rate at which data is changing. It is about the ability to adapt applications to react to the fast changes in large amounts of data that are happening constantly on the Web.

Let’s consider a consumer who is visiting a Web page, or buying something online, or viewing an ad. The data associated with each of these interactions is small. However, when these interactions are multiplied by the millions of people online at any moment, they generate a huge amount of data. AppNexus, which uses our Aerospike NoSQL database to power its real-time bidding platform, handles more than 30 billion transactions per day.

The other aspect is that real-time online consumer data has a very short half life. It is extremely valuable the moment it arrives, but as the consumer continues to move around the Web it quickly loses relevance. In short, if you can’t act on it in real-time, it’s not that useful. That is why our customers demand a database that handles reads and writes in milliseconds with sub-millisecond latency.

Let me give you a couple examples. [x+1] uses our database to analyze thousands of attributes and return a response within 4 milliseconds. LiveRail uses our database to reliably handle 200,000 transactions per second (TPS) while making data accessible within 5 milliseconds at least 99% of the time.

This leads into the last dimension, which is predictable high performance. Because so much of consumer-driven big data loses value almost immediately, downtime is not an option. Moreover, a 5-millisecond response has to be consistent, whether a marketing platform is processing 50,000 TPS or 300,000 TPS.

What are some of the meta-trends you see that is making data management easier (standardization around a platform such as Hadoop? The emergence of No-SQL systems? The accessibility of cloud-hosting?

SS: Today, with consumers engaged more with Web applications, social media sites like Facebook, and mobile devices, marketers need to do a tremendous amount of analysis against data to make sure that they are drawing the right conclusions. They need data management platforms that can absorb terabytes of data—structured and unstructured—while enabling more flexible queries on flexible schema.

In my opinion, classical data systems have completely failed to meet these needs over the last 10 years. That is why we are seeing an explosion of new products, so called NoSQL databases that work on individual use cases. Going forward, I think we’ll see a consolidation as databases and other data management platforms extend their capabilities to handle multiple use cases. There will still be batch analysis platforms like Hadoop, real-time transactional systems, and some databases like Aerospike that combine the two. Additionally, there will be a role for a few special-purpose platforms, just like in the old days we had OLTP, OLAP and special purpose platforms like IBM IMS. However, you won’t see 10 different types of systems trying to solve different pieces of the puzzle.

The fact is we are beginning to see the creation of a whole new market to address the question, “How do you produce insights and do so at scale?”

One of the biggest challenges for marketers has been that useful data is often in silos and not shared. What are some of the new techniques and technologies making data collection and integration easier and more accessible for today’s marketer?

SS: Many of our customers are in the ad-tech space, which is generally at the front-end of technology trends adopted by the broader marketing sector. We are just beginning to see a new trend among some of these customers, who are using Aerospike as a streaming database. They are eliminating the ETL (extract, transformation, load) process. By removing the multi-stage processing pipeline, these companies are making big data usable, faster than ever.

The ability to achieve real-time speed at Web-scale, is making it possible to rethink how companies approach processing their data. Traditional relational databases haven’t provided this speed at scale. However, new technology developments in clustering and SSD optimization are enabling much greater amounts of data to be stored in a cluster—and for that data to be processed in milliseconds.

This is just one new way that real-time is changing how marketers capitalize on their big data. I think we’ll continue to see other innovative new approaches that we wouldn’t have imagined just a couple years ago.

Storing lots of data and making it accessible quickly requires lots of expensive hardware and database software. The trend has been rapidly shifting from legacy models (hosted Oracle or Neteeza solutions) to cloud-based hosting through Rackspace or Amazon, among others. Open source database software solutions such as Hadoop are also shifting the paradigm. Where does this end up? What are the advantages of cloud vs. hosted solutions? How should companies be thinking about storing their marketing-specific data for the next 5-10 years?

SS: A couple years ago nearly everyone was looking at the cloud. While some applications are well suited for the cloud, those built around real-time responses require bare metal performance. Fundamentally it depends on the SLA of the applications. If you need response times in the milliseconds, you can’t afford the cloud’s lack of predictable performance. The demand for efficient scalability is also driving more people back from the cloud. We’re even seeing this with implementations of Hadoop, which is used for batch processing. If a company can run a 100-server cluster locally versus having to depend on a 1,000-server cluster in the cloud, the local 100-server option will win out because efficiency and predictability matter in performance.

What are top companies doing right now to leverage disparate data sets? Are the hardware and software technology available today adequate to build global, integrated marketing “stacks?”

SS: Many of the companies we work with today have two, four, sometimes more data centers in order to get as close to their customers as possible. Ad-tech companies in particular tell us they have about 100 milliseconds—just one-tenth of a second—to receive data, analyze it, and deliver a response. Shortening the physical distance to the customer helps to minimize the time that information travels the network.

Many of these firms take advantage of cross data center replication to include partial or full copies of their data at each location. This gives marketers more information on which to make decisions. It also addresses the demand for their systems to deliver 100% uptime. Our live link approach to replication makes it possible to copy data from one data center to another with no impact on performance and ensures high availability.

Over the last year, we’ve have had customers experience a power failure at one data center due to severe weather, but with one or more data centers available to immediately pick up the workload, they were able to continue business as usual. It comes back to the earlier discussion. Data has the highest value when marketers can act on it in real-time, 100% of the time.

This interview, among many others, appears in EConsultancy’s recently published Best Practices in Data Management by Chris O’Hara. Chris is an ad technology executive, the author of Best Practices in Digital Display Media, a frequent contributor to a number of trade publications, and a blogger.

This post also appeared on the iMediaConnection Blog 1/11/12.