Listen: Podcast with Kim Davis of DM News

ohara_dmnews_podcast

A quick interview about the integration of Krux into Salesforce covering use cases, data science, privacy, and cross-cloud applications for data.

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(Interview) On Beacons and DMPs

how-beacons-might-alter-the-data-balance-between-manufacturers-and-retailersHow Beacons Might Alter The Data Balance Between Manufacturers And Retailers

As Salesforce integrates DMP Krux, Chris O’Hara considers how proximity-based personalization will complement access to first-party data. For one thing, imagine how coffeemakers could form the basis of the greatest OOH ad network.

Data Science is the New Measurement

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

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

Piercing the Retail Veil

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

Overcoming Walled Gardens

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

Understanding the Full Journey

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

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

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

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

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

New Whitepaper: Agencies and DMP!

RoleOfTheAgencyInDataManagementWe’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.

DMPs Go Way Beyond Segmentation

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

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

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

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

Ad Blocking

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

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

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

Header Bidding

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

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

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

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

Personalization

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

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

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

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

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

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

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

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