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Data

Programmatic Untangled: The Importance of Data Governance in Programmatic

July 27, 2017 — by Michelle Said0

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This month’s Programmatic Untangled features Alice Lincoln, MediaMath’s VP of Data Policy and Governance, who sits down to discuss the way data is used in programmatic advertising. As our lives become increasingly embedded in the online experience, many consumers are becoming more and more wary about the way their data is collected and utilized online. However, as Alice notes, a little education can make a big impact on the way advertisers and consumers interact with one another.

Listen now to hear Alice discuss topics such as:

  • The educational gaps between marketers and consumers regarding data policy and governance
  • The choices consumers have about the data that is collected and how it is used
  • The benefits of data usage in ad tech for both advertisers and consumers
  • The future of data governance

Click here to begin listening.

DataMedia

Here are the Four Metrics Every Marketer Should Demand from Facebook and Other Walled Gardens

July 19, 2017 — by Amarita Bansal0

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This article originally appears in The Drum

Last month, Facebook announced additional metrics to give advertisers more insight into consumer behavior, including how many visitors come to their websites after clicking on ads and whether those individuals are new or returning visitors.

Together, these metrics “help advertisers get a better sense of whether Facebook advertising is having an impact on the business”, said Shane Murphy, vice president of marketing at prospecting and retargeting platform AdRoll, adding it is “certainly relatively small steps toward a true view for the marketers on which ads are having more impact”.

Murphy also said the new metrics are a more impactful measure than what existed before, but not quite as much as a metric that could, say, report down to the customers who actually convert. However, he also said he thinks Facebook is likely working on something like that and noted the platform is positioning this as a journey in which it releases more reporting capabilities “about every month or so”.

Sara Skrmetti, senior director of social at programmatic company MediaMath, agreed this is a step in the right direction for Facebook, but – no surprises here – the platform has “a way to go to satisfy the desire for transparency”.

It’s the latest chapter in Facebook’s data reporting saga after news broke the once omnipotent walled garden had misreported some figures and it has since taken steps to be more transparent. But it’s a delicate balance for these platforms with treasure troves of customer data.
In other words, as marketers have put more pressure on Facebook to enhance its reporting capabilities, insiders say the new measures demonstrate Facebook is taking them seriously. But that’s not to say this marks the beginning of the end of walled gardens.

“The data is what is valuable for Facebook,” Murphy said. “We’ll probably get to a point where the walls are lower – or perforated – but I would be surprised if [we got a] world where [that data was widely available] because it is their IP.”

According to Scott Linzer, vice president of owned media at digital marketing agency iCrossing, walled gardens have always been a perplexing agency challenge because they “have to take Facebook’s data as a source of truth”. Now, however, Facebook is evolving to allow marketers to get a better understanding of that data and to provide more transparency, but it still has a way to go and Linzer said he hopes Facebook will continue down this path.

“I think walled gardens have the propensity to continue to scale back or whatever the phrase is because the reality is in order to buy media, you need the most accurate complete picture to understand behavior,” Linzer said. “If we don’t or if we have gaps, we’re not really servicing the clients correctly and don’t have the full picture we need. I think as we sit here in 2017, it is very important for vendors and agencies to be proactive and understand their data needs to accomplish goals for their clients.”

And as the balance of power between Facebook and advertisers shifts more in favor of the latter, here are the four demands marketers should make in order to glean as much insight as possible:

1. More third party verification.

According to Mike Kisseberth, chief revenue officer at digital publishing and marketplace platform Purch, the key phrase is third party verification. In other words, it’s not that these new metrics aren’t interesting measures marketers will like, but these marketers are still stuck having to simply trust Facebook. Third party verification, on the other hand, would enable marketers to take the data more seriously.
“Third party verification is important because it allows you to trust the numbers there,” Kisseberth said. “As marketers, the responsibility is ultimately on you to know if [ad dollars] are contributing to the revenue of the business…I think there’s a balance point – third party verification is something marketers want, but they also…[need] to figure out if the money they’re spending is actually contributing to sales. You have a third party measuring whether it’s viewable, but did it actually drive performance at a level that is justified given the spend?”

Speaking of which…

2. More insight into customer spend.

Not surprisingly, Murphy agreed marketers want to see ROI.
“Being able to properly track each ad unit to [put a] value [on] customer spending is critical,” he said. “That’s where you get better at going all the way down to the point of tracking how much each customer is spending, [which allows you] to optimize a campaign based on that.”

This is something AdRoll enables its clients to see and Murphy said it would be valuable to Facebook’s advertisers to have more insight there as well.

3. A more complete audience view.

Linzer said the big mystery is just how big an audience is within Facebook – and how many impressions ads will serve.

“They tend to be very frontline metrics in the Facebook ecosystem,” he added.
And that’s why other metrics, such as videos served or shares of video, would help advertisers get a more complete picture of what their audiences look like and what they are doing.

4. More customization options.

Per Skrmetti, a valuable addition would be the ability to customize data in ways that are relevant to a given brand and/or marketer.

“A lot of our advertisers will work with our data directly in a raw format to get the insights they want…a lot of…our enterprise clients will manipulate their own data for their own insights and I think that’s the holy grail of this – the closer Facebook can get to that without overstepping privacy, the better,” Skrmetti said. “[This is probably] on the roadmap for [Facebook], but, at end of the day, the closer they can get to data that is manipulatable, the better off they’ll be and the more trust they’ll have.”

DataIntelligence

The Power Of Agency-Tech Company Relationships: A MediaMath Case Study

July 11, 2017 — by Amarita Bansal0

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This article originally appears on adotas.com.

Recently, MediaMath, partnered with digital agency PMG to help a global beauty brand address struggles they were experiencing when trying to connect with customers, in real-time.

The brand wanted to deliver its ads to high-affinity audiences in ways that were optimized and personalized. By working together, PMG was able to construct audience profiles in a way that would allow them to target new and dynamic users. The company collected past data into a centralized platform to create current audience profiles built on the history of the customer’s actions. The result was improved ROAS and proof that marketers can improve the user experience of online advertising for greater efficiency for brands.

This represents the potential future of relationships between agencies and tech providers. Lead by CEO Joe Zawadzki (pictured top left), MediaMath believes that by customizing approaches and developing a strategy, a partnership leads to better results for all involved. Agencies must embrace ad technology to stay ahead of their competitors, and by partnering with ad tech providers on a strategic level they can make sure to meet the goals of the brands they are working with. Download the case study, A Triumvirate Approach to Helping a Global Beauty Brand Target New and Dynamic Users in Real-Time.

Adotas explored to subject in a Q&A with Chris Keenan, Director Product Management at MediaMath (pictured left).

Q: This PMG case study, mentions that marketers struggle to connect with customers “due to audience pools that are incomplete, inaccurate, and siloed from activation.” Why does this happen and why is it so commonplace?

A: Incomplete and inaccurate audiences that are siloed from activation are the primary drivers for marketers moving to platforms that have tightly integrated demand side and data management platforms.

We have found that when pushing audience segments from standalone DMPs to DSPs for activation, there is a 10-20% loss in reach due to the cookie sync required. This is particularly troubling when marketers have a granular, niche segmentation strategy. Losing any portion of your low-recency, high-frequency, high-value users results in lower campaign performance.

In addition, cookie-less identity resolution has become a “must-have” as mobile is exploding and the cookie is going away. Three years ago, you could reach 75% of a user with desktop cookies alone. Today, that number is less than 40%. For mobile, you will miss 80% of available RTB impressions without a cookie-less identifier.

A common complaint I hear from friends when I tell them I work in the online advertising industry is “Why am I followed around the internet with ads for a product I purchased days ago?” Another complaint I hear when speaking with marketers is that frequency caps set at the audience level are not respected. This happens when DMPs only update their segments in batch, as opposed to real-time. DMPs that evaluate segment membership in real-time will immediately remove the user who just converted or the user who just received their 8th impression. Not only does real-time segment evaluation drive performance, it also leads to a more positive user experience.

Q: Can you talk more about the pixel resolution/limitations of this client and why they prevented audience targeting?

A: Like many online retailers entering the holiday season, the client had entered a ‘code freeze’ period where they were not able to make any modifications to their existing or place new tags onsite. Code freezes are enforced as a precaution against changes having unintended, negative consequences during historically busy periods that could result in hundreds of thousands of dollars in lost revenue in a very short amount of time.

Fortunately, the client already had global footer and conversion tags placed. From these tags, we were able to ingest standard retail variables, such as Product SKU, Product Brand, Product Price, Search Phrase, Cart Value, and Order Value. These variables, in conjunction with Page URL, were used to develop a granular audience profiling strategy.

Q: How are sophisticated agencies and brands utilizing data to drive campaign performance?

A: Everyone has heard the phrase ‘the right message, to the right person, at the right time’ before, but how do marketers position themselves to deliver on that principle? I have been fortunate to speak with our clients around the globe and here are two common themes amongst the most sophisticated marketers:

• Eliminate latency
DMPs need to have an incredibly tight feedback loop with their DSP counterparts to remove latency between the two platforms. The propensity for users to convert is highest within a few hours, but if your DMP is only syncing your audiences to your DSP once every 8 hours, or worse, once a day, you are losing the opportunity to reach your audience during the most optimal time. Then, once those users have converted, you will still be wasting your media budget on those users until the next time your segments are re-synced.

By eliminating this latency, tightly integrated DMPs can reach the user with a message for complementary product offers instead of wasting the impression with a creative for the product they just purchased. It also allows for capabilities like accurate global frequency capping and sequential messaging.

• Act quickly and decisively
Today, many marketers are waiting for campaigns to run for at least a few days before looking at any analytics regarding the audiences they are targeting. By storing the raw event, impression and click level data, modern DMPs will be able to tell you how your newly created audiences would have performed against prior campaigns, without spending any media dollars against those audiences. If there is not enough 1st party data available, smart marketers generate similar performance reports to determine which 3rd party audiences will help them accomplish their reach goals in a performant manner. These tactics allows marketers to test their hypotheses without burning through their media budgets; further improving their ROAS.

Another benefit of working with a DMP that stores data at the raw event level allows marketers to define an audience segment that is fully populated and ready for activation immediately. The days of creating a segment, waiting for the audience to populate before activating it in a campaign are over. This is particularly valuable unplanned budget is made available and needs to be spent against while still meeting high performance goals. It also means that you can redefine your segment definition on-the-fly while your campaign is still in-flight with your latest learnings/needs (e.g. drive additional reach vs. improve performance) without having to start from scratch.

Q: Beyond Adaptive Segments, what other tech innovations would you recommend marketers and publishers use to accomplish their goals?

A: One innovation that has had implications for both marketers and publishers, and become a hot topic within the last year, is header bidding. For those not familiar, header bidding allows publishers to run a unified auction across their demand partners (e.g. direct sold vs. RTB) for each impression and the highest bid wins. Header bidding provides a level playing field opposed to the legacy, waterfall method where demand sources would be ranked in priority order and only get the opportunity to win the impression if the partners above them passed on that impression first. This means that the publisher’s ad server would serve the $5 CPM direct sold campaign even when there was an RTB partner willing to pay $15 CPM for that same impression.

Q: What does this mean for publishers and advertisers?

A: For publishers, the primary benefit is an increase in revenue due to the increased the “true value” of each impression being recognized. Publishers commonly report a 30%+ increase in revenue after implementing header bidding. This increase in publisher revenue translates into higher media costs for advertisers. However, advertisers now have access to premium, first look inventory allowing them to scale their campaigns in ways that would not be possible in the waterfall world. While media cost is certainly a metric that marketers should pay attention to, they should ultimately be focused on outcome oriented metrics such as CPA and ROAS. Header bidding has the potential to create a win-win for publishers and marketers alike and is an innovation that I’ll be closely.

DataMedia

Mobile ROI and Cross-Device Advertising Done Right

May 11, 2017 — by Floriana Nicastro0

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Many advertisers see mobile as a black hole. Consumers spend 65% of their time on mobile devices but much of that time seems un-monetizable. While there’s proof that such advertising works, for many that proof isn’t as compelling as on desktop.

As a result, the industry is observing a massive $22 billion gap between user time spent and advertising spend on mobile.

Breaking news! Mobile is actually the primary vehicle to influence consumer purchases. You’re just not able to see it.

When you’re navigating through desktop, mobile and tablets, you’re not only surfing through different devices but also different environments using different user identification methodologies. Building the bridge between all those worlds has been a challenge for the last five years. Only a few actors of the market have managed to gather cross-device data at scale with accuracy to have an accurate user view.

The good news is that measurement is improving and advertisers have more insights today to better understand their customer path to conversion and how mobile is impacting their business. Consider these steps to help build successful planning:

  • Understand how your customer is interacting with your brand

While having a complete view of user cross-device activity may be challenging, such activity is a good indicator of determining: Which device has your customer used when first interacting with your brand? How many of your website visitors are coming from desktop and how many are coming from mobile/tablet? Which device does your customer use when converting?

  • Build a unique online identity

Since consumers split their time between mobile, desktop and TV, knowing the consumer’s cross-device identity allows for a stronger consistency in advertiser messaging along the customer journey and better accuracy in advertising performance view. A cross-device solution is vital but it’s important to get it right.

The ad tech industry has developed a few solutions, especially in the past year. I believe the most accurate method to determine cross-device identity is to gather deterministic data. Deterministic data affords 100 percent accuracy and when there’s scale, it offers a robust cross-device solution because deterministic data includes a user ID, or a login. The user will be logging in across their devices (desktop, mobile, tablet) and across the environments they are surfing in (website, app).

The second method for establishing cross-device identity is to use the proprietaries of the device itself to determine user identification. Advanced algorithms will use this data to determine the probability of two devices owned by the same person. Probabilistic data can be based on user agent, font, networks, IP address and browser, among other factors. The accuracy of identification cross-device has shown some limits when it comes to gathering probabilistic data at a large scale.

  • Think further and build the bridge to offline

In the last year, unified identification has become even more important for brick-and-mortar businesses, since they want to understand the impact of their online advertising over their in-store revenues.

Again, when impact of mobile in-store is easy to understand, using observed behavior such as ROPO (Research Online, Purchase Offline), it’s a real challenge for advertisers to attach their mobile advertising investment to in-store revenue to determine real ROI.

Today advertisers have three solutions to unify online and offline activity by finding common ground to user identification:

In-Store Traffic

Location data allows advertisers to improve their advertising relevance according to where the user is, where they’ve been and when, all based on smartphone data.

In-store traffic lift allows advertisers to estimate the incremental lift online advertising brings to their in-store traffic by overlapping the user ID of the customers reached through the campaign and customers who have been in-store.

Couponing

Coupons can provide unique IDs that can be used to link online ID and offline conversion. By using coupons, the user will generate an ID where advertisers can link to a cookie ID/device ID. When converting in-store, the coupon ID used for the purchase will be stored and then mapped to online IDs to attribute the conversion to your campaign.

CRM data onboarding

CRM data mapping provides highly valuable information to in-store user conversion. By being able to bridge CRM data through third-party ID mapping, for instance, using a loyalty card as common ground for internal mapping, an advertiser can then easily calculate the overall ROI from its advertising activity.

Data

One Tough Question: Is Poor Data Quality Derailing Your Campaign’s Results?

May 10, 2017 — by Lauren Fritsky0

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DMNews recently issued an eBook titled “One Tough Question” on data quality. In it, they ask specifically what steps marketers should take to ensure first-party data is reliable, non-duplicative, up-to-date and has the probabilistic data needed to enrich it. Philipp Tsipman, VP, Audience Identity for MediaMath, provided the below answer:

“High-quality data is critical to high-impact direct marketing. Whether clients have a number of existing data vendors or are assessing a new one, we recommend they start with an assessment RFI ensuring that the data vendor is clear about the accuracy and scale of its data and quality practices and that its data is right for the client’s use case. For cross-device solutions, the Data and Marketing Association’s XDID RFI is a good place to start.

If you have an existing known set of shopper data, we recommend that you also run a live two-part quality assessment pilot. Request your quantitative analytics or data science team to compare the data samples from various data vendors against each other. Second, run a marketing campaign using data from each vendor to compare performance. Stepping back to invest in internal data quality best practices and working with vendors who see data accuracy and quality as key differentiators is well worth the investment.”

Download the full eBook here.

DataMedia

Programmatic Hasn’t Lived Up To Its Promise, Yet

May 2, 2017 — by Amarita Bansal0

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In a commentary piece from MediaPost written by Tobi Elkin, the article speaks to the recent issues plaguing adtech, specifically programmatic and the issues of brand safety and transparency. Now that programmatic is turning 10, Elkin sits down with our CMO Joanna O’Connell, who says ad tech is still trying to overcome legacy attitudes on what programmatic is. Read an excerpt of the article below: 

In the last few months, ad tech overall — and programmatic, in particular — has taken a beating over issues related to brand safety, transparency, and more. In all fairness, these issues extend to the digital media and advertising worlds at large, not only to the sub-sectors of ad tech/martech and programmatic media.

Reflecting on these concerns, Joanna O’Connell, CMO, MediaMath, said ad tech is still trying to overcome legacy attitudes on what programmatic is. So how does the industry create an environment that adheres to principles of transparency? “Marketers care about being in control of their own data and destiny, and about inventory quality,” she said.

O’Connell reflected: “Ten years ago, you had ad exchanges and then the DSP [demand-side platform] was about letting the buy side get more direct access to the inventory and the users you could find in that inventory.” But things didn’t evolve the way they should have. Programmatic hasn’t “totally paid off on that promise.”

She said marketers are looking to gain access to audiences within the context of programmatic. And not just any audiences, the best audiences. Private marketplaces (PMPs) are one example where programmatic hasn’t quite lived up to its promise. PMPs were supposed to offer a more direct relationship to publishers. “But they didn’t solve the problem of getting to the audiences. PMP deals don’t really solve the publishers’ problems. PMPs were a step in the right direction, but they didn’t go far enough,” O’Connell opined.

“The way in which these deals came to life — a traditional buy through programmatic pipes — doesn’t accomplish everything. You have to find the people you really care about in those buys with publishers. The deals need to be about an audience-first approach in premium environments,” she said.

Against that backdrop, O’Connell cited what she believes are the biggest challenges facing programmatic media:

–Measurement and attribution, which are interrelated. “Attribution should be something that becomes an input into the actual buying and optimization process; it’s not a report. Attribution vendors need to be integrated into the decisioning. We need start measuring true incrementality and the causal relationship between a business’ revenues and the media channels that directly contributed to that revenue growth. Return on ad spend can’t be faked.”

–Ensuring the advertising supply chain is brand-safe and fraud-free. “We need a clean, well-lit supply chain that people can understand. I still feel like there’s so much room for education. The more marketers understand what’s happening with their dollars, the more effective they’ll be in spending them.”

–Marketers are still buying individual media channels. “That’s not a good way to manage the consumer’s experience,” since it doesn’t really reflect the consumer’s purchase path.  In order to deliver a positive consumer experience, the lens through which the industry should focus, an integrated media approach is best. Programmatic should be an enabler of good marketing, not the other way around.”

To read the full article via MediaPost, click here.

DataMediaTrends

With The Right Message: Bringing More Creative Exploration to Digital

April 19, 2017 — by Parker Noren0

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This byline originally appeared on MarTech Advisor. 

Programmatic has a creative problem. It’s not that programmatic and creativity can’t co-exist. It’s that the approach by which organizations develop creative appears to be a big culprit in limiting creative exploration in digital and programmatic. Across many brands, creative development cycle time and, as a result, overall content output are common complaints. We expect the frustration is caused by organizations largely following the same processes that they’ve long used for linear creative development.

These processes are meant to de-risk what is otherwise a risky proposition. TV creative development, in particular, is laden with gating processes and pre-testing, and for good reason: 1) production costs are high, on average — around $350,000 for a 30-second spot and 2) how spots are purchased and lag time to understand performance means hundreds of thousands could be out the door by the time the marketer has an early in-market read. Neither factor plays such a pronounced role in digital programmatic — especially the latter because of real-time performance feedback.

Digital creative development processes should be built from the ground up rather than lifting practices historically used for other channels. This applies to everything from ideation to development to approvals. We gain more opportunities to find stronger performance by reducing timelines to final creative and increasing the size of the creative content space included in launch and refreshes. Examples abound of the big benefits from achieving better creative. In one recent case, a client lowered acquisition cost by ~25% by testing into more emotionally-oriented messaging. In another case, moving from similar creative across the campaign to a sophisticated decisioning tree based on both browsed products and other behavioral indicators improved conversion metrics by over 200%.

Brands can benefit from infusing new best practices into their creative process, namely by:

  • Taking an outsider’s view the next time you are developing creative for an upcoming digital campaign to understand where things are either 1) slowing down or 2) nonsensical for creative development in digital.
  • Challenging every step as to whether it’s required for digital development or an artifact of legacy processes and gating. For example, timelines may be getting sidetracked by misalignment among your cross-functional team, or because of laborious creative approval requirements.
  • Maximizing the creative space (i.e., number of variables/variations) to allow algorithm-driven optimization to find what works best. Leverage a dynamic creative technology partner, who can help you serve meaningful one-to-one messaging in real-time by using data to define the best mix of elements such as background image and text. In doing so, make sure to 1) have full data and optimization alignment between dynamic creative and advertising platform, and 2) inform creative messaging and imagery with a full view of consumer behavioral indicators rather than just last product viewed.
  • Conducting purposeful iterative testing as part of every activation strategy to develop a playbook for what best drives attention and persuasion for your brand.

Once complete, your creative output will increase along with performance opportunities from your campaigns.

DataTrends

Beet Retreat Recap: What Makes Great Marketing, By MediaMath’s O’Connell

March 31, 2017 — by Lauren Fritsky0

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Push marketing, pull marketing, paid, owned, earned media—none of it matters if it’s not for the sake of brokering a conversation with a consumer.

Take it from someone who knows, our CMO Joanna O’Connell, who has been on many sides of the advertising equation (she started as a media buyer, built a trade desk and worked as an analyst before coming to MediaMath). She attended the Beet Retreat last week in Vieques, Puerto Rico, where she was asked about her recipe for effective marketing.

“How do you help a marketer or an agency create conversations at scale with their best prospects and customers?” she asks. “That’s what great marketing should be about…great marketing should enable a marketer and a consumer to connect in a way that’s providing value to both of them.

“The way in which you do that is a really strong understanding of data, an enrichment of data sets with additional sets of data that have proven value. You need to do that at a core.”

For more on what Joanna had to say, watch the video below.

Data

Don’t Be Like Biff in Your Approach to Data Management

March 28, 2017 — by John Slocum0

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What if I said you could know the future, just like the fictional character Biff Tannen from Back to the Future? When Biff stole a time machine and brought future knowledge back to his younger self, he was unknowingly flirting with “a time paradox, the result of which could cause a chain reaction that would unravel the very fabric of the space-time continuum and destroy the entire universe!”

It’s 2017, and now you can anticipate events and outcomes without time machines or unraveling the space-time continuum. In fact, with the state of audience management tools today, it’ll soon be irresponsible to spend your (or particularly your clients’) ad budget without already knowing the outcome. Balderdash, you say? Raving, frothing-at-the-mouth ramblings of a madman, you say?

Thanks to massive device and addressability proliferation coupled with user-level identification and the technology to manage said data, we can now ask and answer questions that predict audience response—before spending a dime of new budget.

Sound amazing? Just nod. I’m sure it sounds amazing. Instead of waving my hand and telling you, “These aren’t the droids you’re looking for,” let’s see what’s behind that statement.

Staying one step ahead of your customers

Workstations, laptops, phones, tablets, smart TVs, offline data—you name it, a modern data management platform can track it or ingest it. But it goes beyond device. To anticipate what your customers and prospects might consider next, you need user-level identification. You need access to human behaviors and outcomes. If you’re looking for performance across audiences, for example, you need to be able to sample those audience behaviors to predict what they’ll do next. Unlike the stock market, past audience performance IS indicative of future results!

This is a simple concept with less simple implementation. We’re talking about identifying the humans behind all those devices to analyze and predict what they’ll do next. Tablets don’t buy new shoes and handbags—my wife does. Piloting the technology to ask and answer the critical questions that predict audience response is the fun part, in my opinion.

There are many different approaches to predicting response. A couple key questions before we go shopping for ‘big data’ solutions—who is doing the predicting? Are you predicting results for your budget today? Are you predicting results for someone else’s budget? Is someone else predicting results for your budget? How are predictions measured? How far off do they need to be before budget, tools or even partners are reconsidered? Is the transparency available to confirm predictions? Can you predict performance for an audience and, if so, can you verify it?

Enter the modern DMP

Modern DMPs should be able to answer most of these questions for you, particularly around telling you how an audience will perform and which audiences will perform (or not). A modern DMP should be able to take your recent golf equipment campaign data, per human, and tell you how any group of humans in your addressable population actually performed in that campaign. If you have defined audiences, be they modeled, first-party site audiences or third-party audiences, you can sample overlaps of the campaign audience and your available audiences, calculating overlap performance (test group) versus the rest of the campaign audience (control group). Starting to sound a little like your Intro to Stats class? It should, these are basic concepts. We can calculate significance on these samples as well to determine how likely your sample represents the true population. Find audiences that are popping without spending on ‘em!

A modern DMP can also test performance of single audiences in real-time, upon creation, without new spending! This can be more of an ad-hoc, iterative approach—not testing means you are leaving insights on the table. This capability requires user-level event data, campaign data and a dynamic audience platform to make it sing; the one thing it doesn’t need is SQL! Not to worry, (fellow) data geeks, user-level campaign and audience data in a modern DMP is liberated, so you can grab that and get your hands dirty.  Many platforms are also offering user-level data in a self-service analytics platform to support your own ad-hoc queries, reports and even scheduled jobs.

You should know if your audience can deliver desired outcomes, before you spend. You should also be able to see exactly how, where and why it did, as it happens. Time travel is no longer required. Don’t be like Biff.

DataTrends

Five Questions to Viktor Zawadzki, Regional Manager DACH, Nordics, Central / Eastern Europe, MediaMath

March 20, 2017 — by Lauren Fritsky0

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This interview has been translated from an article on ExchangeWire.

1. Identity Management has become one of the new industry buzzwords, but what does that term exactly mean?

Identity Management is, in principle, a technology to effectively assign consumer identities to several devices and thus to get a holistic view of each individual consumer.

2. How can companies successfully use Identity Management for their marketing?

Identity Management is used by advertisers to provide consumers with a seamless, personalized brand experience across each contact point, maximizing their marketing impact.

This goal is not easy to achieve and requires a central technical platform in which an advertising company can cover the full marketing spectrum. This includes a media strategy, reporting, data processing, workflow and media and data acquisition, a smart identity management solution to ensure that campaigns are effective in every marketing channel and on any terminal.

3. Since Omni-Channel communication has now become the norm, how can a continuous data stream be created?

The basic prerequisite for efficient identity management is the existence of comprehensive insights about budget allocation, as well as the effect of each advertising ad. Particularly in the programmatic environment, where advertising is traded and displayed in real-time, within milliseconds, measurement and optimization are very complex, so making decisions about marketing budgets without a clear view of campaign success is a very difficult task.

Traditional metrics such as last-click attribution are not suitable for the use of Identity Management. Advertisers need a dynamic attribution model that takes into account different points of contact in the evaluation and allows for Omni-Channel marketing mix optimization. By deploying a unified technology that provides an integrated Data Management Platform (DMP) and a Demand Side Platform (DSP), advertisers can ensure that they can target 100 percent of their target audience, avoiding any data loss between different technology platforms.

4. And how can the advertising effect be maximized?

By analyzing existing data, advertisers can identify where and how advertisements need to be displayed for best results. With this knowledge, marketers can develop a setup in which all elements of a campaign work together. Each individual advertising contact should be part of an advertising sequence, which enables consumers to effortlessly connect channels and end devices to a catchy experience to provide a solid advertising message and to create an emotional storytelling tailored to the individual consumer.

5. What can a company achieve by using Identity Management?

That is the million-dollar question. With Identity Management, organizations can track data on responses to each purchased placement from all digital channels and merge them into their internal systems. Based on this, companies can use these data to predict future points of the customer journey and define them. The selection of the right platform is the prerequisite for the realization of this setup. This platform must not only work on a global infrastructure, offer open APIs (application programming interface), use smart machine learning, but also large volumes of media, as well as complex audience segmentation and ad media management,

Identity Management reduces the reliance on cookies and enables accurate Omni-Channel marketing. It must be taken into account, however, that identity management does not automatically trigger a revolution. In order to provide relevant, timely and responsive communication with consumers through marketing, companies are required to create a technical system that consolidates their Omni-Channel marketing requirements and makes the insights provided by Identity Management accessible to them. In addition, all relevant data must be made available in real-time for campaigns in order to control communication with consumers based on their interaction with advertising.