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DIGITAL MARKETINGMediaPROGRAMMATICUncategorized

Why Clicks Don’t Equal Brand Engagement

May 5, 2016 — by MediaMath

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Our own Parker Noren, Director of Programmatic Strategy and Optimization at MediaMath, takes on brand engagement in a recent article published by Modern Marketing Xchange. Noren suggests it’s time to rethink how advertisers measure their branding efforts — focusing on users’ time on site or the number of pages viewed rather than solely measuring click-through rates. 

Read an excerpt below to learn why it’s time marketers should nix the click:

Advertisers have historically depended on click-through rates and cost-per-click to determine the success of their digital branding efforts. However, these metrics have little relationship with what the brand is ultimately trying to achieve. You won’t find driving clicks in a brand’s strategic roadmap or brand health assessment. It’s time for branding marketers to move beyond the click and onto more accurate measures that align with their overall campaign goals and, in turn, improve brand engagement by optimizing to them.

Let’s first back up. In the past, the focus on clicks was largely due to a lack of alternative metrics. In a 2001 New York Times article, then president of the Interactive Advertising Bureau Robin Webster said that the click-through might have appealed to the early technologists who built the World Wide Web, who were excited at the potential of tying advertising to a direct sale, something previously difficult with TV and print advertising. In addition, Procter & Gamble were basing payments for its online advertising on click-through, setting a benchmark for the wider industry. At the time, there were not an established group of better measurements, though their conception was in development.

The industry has now evolved and added new metrics to the mix. But, just because there are more options now isn’t a strong argument alone to move on from clicks. So, why should marketers nix the click?

According to a comScore analysis, only 8 percent of users actually contribute to the majority of clicks (85 percent). To get to 100 percent of clicks, you only have to expand to 16 percent of users—meaning 84 percent never click. Likewise, in a 2015 MediaMath analysis, only 8 percent of e-commerce purchasers had clicked on a banner ad from any campaign run in the platform (i.e., not just the campaign from which they bought the product). This means advertisers focused solely on optimizing clicks are essentially ignoring the majority of their core audience. And, while mobile specifically garners stronger click-through rates, we all have experienced how easy it is to accidentally click on mobile ads, adding even more scrutiny to clicks as a metric of success.

In addition, click-based metrics are more susceptible to fraud. Over time, fraud perpetrators have become better at making clicking bots look like humans. Measuring success along metrics that are harder for bots to fake is a key step to mitigating fraud, though many advertisers are hesitant to pursue given it can result in lower CTRs and higher CPCs because it strips away the inflated numbers born from bots.

To read the full article via Modern Marketing Xchange, click here.

DIGITAL MARKETINGEducationMediaUncategorized

MediaMath Explains Education: New Marketing Institute

May 4, 2016 — by MediaMath


Joe Zawadzki, Chief Executive Officer and Mike Lamb, President, Commercial sheds light on the importance of empowering marketers through MediaMath’s educational arm — the New Marketing Institute (NMI).

“The way that we are going to show people that you can re-imagine performance and that marketing has been re-engineered is by getting the training stood up such that people are, at least, speaking the same language and all having the baseline level of understanding,” Zawadzki said.

 

 

 

MediaMobileUncategorized

Mobile Apps: How and Why to Target

April 28, 2016 — by MediaMath

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I’m not sure the debate between mobile apps or mobile web advertising dominance will be settled anytime soon. Both platforms provide separate and valuable use cases for advertisers throughout different points of their conversion funnels. Both have significant volumes of users that cannot be ignored but at MediaMath, we see slightly more in-app bid requests than we do mobile web. The numbers do not yet show a major discrepancy one way or another.

The bottom line is that advertisers can no longer chose to ignore in-app opportunities. But how do advertisers successfully target users inside of mobile apps? This presents a fairly unique problem as users are not identified by a cookie inside of an app. Instead, users are identified by a device identifier, which to the dismay of advertisers, does not exist in mobile web. This creates a few unique problems. The first, is that it can be tricky to link users on the same device between their app activity and their mobile web activity. The second, and the problem we’ll discuss here, is how do you go about effectively targeting users in apps?

App ID Targeting

The most obvious form of targeting users in apps would be to target a specific set of apps, identified by the app/bundle ID. This works well, provided you know what apps you’d like to serve on. This is a very similar approach to how many advertisers today selectively define the websites they’d like to display ads on or conversely, which sites they would like to not display ads on. This concept of white/blacklisting app IDs is a widely applied tactic in targeting mobile app users.

The obvious benefit with app ID targeting is the granularity and control it provides marketers to make sure that they are only messaging users inside desired apps that are perhaps on brand, or represent similar intent or demographic profiles to users they want to reach. One of the downsides of this mechanism is that it can be difficult to curate a wide enough list of apps, gather the individual app IDs across multiple platforms (iOS, Android, etc.) to achieve the desired scale of a campaign. Leveraging a contextual targeting solution here for apps may help alleviate this issue.

All things considered, App ID targeting is an excellent way to target a group of apps, but it should not be considered the be-all and end-all of app targeting.

Contextual App Targeting

Contextual targeting has proved to be an incredibly effective mechanism for display based advertising as it provides marketers with the ability to target certain categories or classifications of content. In theory, this allowed all sites to be considered eligible to have an ad served, provided they meet the content or classification criteria originally defined. MediaMath is excited to expand its contextual targeting offering to mobile apps. This allows marketers to target apps by any of the following:

  • Content Advisory Rating
  • App Category
  • Popularity
  • Price
  • Top Ranked Apps
  • User Rating

For marketers who understand the types of apps their intended audience uses, this provides a much simpler and perhaps “turn key” approach to targeting apps (and the users who engage with them).

Obviously, the downside here is the lack of granularity you have over specific apps. However, by combining contextual app targeting with a blacklist of app IDs, marketers begin to have fine grained control of app targeting, while leveraging an entire category or classification of mobile apps. Both app ID targeting and contextual app targeting provide marketers — who want to target all users who engage with certain apps — powerful tools to reach users. But what about reaching mobile in-app users based on profiles, personas or demographic data?

Mobile App Audiences

How much can you tell about an individual by the apps installed on their phone? Turns out — a lot. MediaMath has partnered with PushSpring to provide targetable segments of users based on the apps installed on a device. For example, say if you are targeting a fitness enthusiast, this can result in targeting a user who has workout apps, gym apps and calorie tracking apps installed on their device. As a result, marketers have a more holistic understanding of users and who they want to target.

If a marketer wants to target frequent travelers, they could target the entire category of travel apps, however that does not infer anything about the user except they may be currently traveling and consequently engaging with a specific travel app. However, if a user has several travel apps installed on their phone, regardless of what app they may be currently using (travel or not), PushSpring targets the device identifier and not the app itself, so a user can be served an ad in any app they engage with.

PushSpring provides targeting by the following:

  • Apps Owned by Genre
  • Demographic
  • Intent
  • Interest and Activity
  • Life Stage

PushSpring can also create custom segments based on apps, installed to allow marketers even more powerful and custom segments to identify and reach users who may fit a conquesting model, or a non standard user profile/persona.

This type of audience targeting provides another mechanism leveraging app targeting. While the PushSpring offering is technically an audience (i.e. a pool of device IDs) the audiences are created using information about apps installed on a single device, which provide strong intent and often demographic signals.

Putting It All Together

There is no sure fire, one size fits all mechanism for app targeting. But there are certainly several different tactics to leverage apps to reach consumers. Marketers can layer several of these tactics on top of each other to provide an incredibly custom and powerful solution, or run with any single one of these tactics on their own.

DIGITAL MARKETINGMediaUncategorized

MediaMath Explains Competitive Advantage

April 27, 2016 — by MediaMath2

Mike Lamb, President, Commercial talks about what sets MediaMath apart from its competitors, including the company’s commitment to the demand-side and to true outcomes for marketers.

“We have built a system and a business on the proposition that marketing can and should be accountable to true business outcomes, not to marketing outcomes — cost per click, cost per thousand — but to true business outcomes.”

Press play to learn more!

 

DIGITAL MARKETINGMediaUncategorized

MediaMath Explains Goal-based Marketing

April 20, 2016 — by MediaMath

Erich Wasserman, Chief Revenue Officer, speaks on the importance of goal-based marketing and how this enables marketers to control their entire marketing ecosystem.

“As a primary input to our systems today, we asked our clients to think deeply about how it is that they make money so that we can translate that into outcomes, into goals, that we then optimize to,” Wasserman said. “When we partner with a client in a transparent way, they have all of the inclination to give us the information about their business, such that we can translate that into actual outcome-based goals.”

 

MediaMobileUncategorized

Unlocking the Mobile Advertising Opportunity: Getting Attribution Right

April 12, 2016 — by MediaMath

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In a commentary piece from MediaPost written by Loren Hillberg, the article speaks to the challenges marketers face when measuring mobile advertising ROI. So what’s the best option? Consider using mobile location data to measure foot traffic. Read an excerpt of the article below to learn what attribution solution is best for you: 

Today, there are two main approaches to measure foot traffic: auction-based and mobile consumer panels. And, while they measure the same thing, their results may vary widely based based on methodology used. Marketers must understand which method is being used, what data is actually being collected, and how accurate that data is.

Auction-Based Approach

When a person interacts with an app and an ad request is sent, an app publisher also sends location data. Auction-based measurement providers typically “listen” to these ad requests to identify whether a user is at a targeted location when the ad request is sent.

If they are, a visit to that location is counted as “positive” foot traffic. Solution providers then perform a match to see which of these mobile users were exposed to an ad that is part of the campaign they are measuring. If they have been, they attribute that foot traffic to that ad being seen.

With this approach, the number of potential mobile consumers is larger than is available via panels. Thus, the potential audience can mirror the specific demographics selected for a campaign. However, not all of the user’s real-world activity is recorded.

This approach only captures location data when a user has an app open, and when that app sends out an ad request. Also, measurement providers do not have a direct relationship with mobile publishers and do not have control over the accuracy of the data being sent in an ad request. They are at the mercy of a publisher’s own accuracy thresholds.

Further, the true location of a user can vary significantly from the stated location in the ad request, which means visits to a store might be recorded when, in fact, there was no visit or equally misleading, the reverse.

Consumer Panel Approach

Alternatively, consumer panels for foot traffic work somewhat like the Nielsen Set Top Box panel. An attribution measurement provider creates a large opted-in audience—the “panel”—of mobile consumers who agree to have their location tracked continuously.

The measurement provider sees who does and does not visit a specific store. Users can then be tracked back to a mobile ad campaign to determine who did or didn’t see a specific ad.

Since data collection is “always on,” data for each user on the panel is more complete and includes both coverage and duration of visit metrics. Data accuracy is superior and data quality is higher because the measurement provider is able to control how the location data is gathered.

Panels typically have less scale and their demographics may not be representative of the campaign’s target audience. Certain panels may incentivize panelists, contributing to a potential demographic skewing of data.

To read the full article via MediaPost, click here.

MediaMobileUncategorized

Three Mobile Trends For A Smartphone-Centered World

April 5, 2016 — by MediaMath2

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Mobile was big last year, with more than 1.4 billion smartphones sold globally. But it’s set to be even bigger this year. Now more than ever, smartphones are becoming a major computing device for consumers and according to a Forbes article there are three key mobile trends to look out for, including:

  • The use of “invisible” apps and services. More mobile users are looking for apps that can process information in the background and on a moments notice; it can automatically bring up intelligence when actionable. For example, when Google can re-route your journey as driving conditions change in real-time – it’s this experience that consumers are craving for.
  • 5G network. You thought 4G was quick? Imagine a world where your wireless connection could be 50x to 100x faster than 4G, surpassing the speed of most wired internet connections. Some companies are already planning to test the use of 5G technology in the next year or so, including AT&T and Verizon – improving the user’s experience with faster connectivity.
  • Location. With issues such as ad blocking, ad fraud and bots within the digital ecosystem, attribution is becoming increasingly significant. Advertisers seek to understand a consumer’s journey and by using location data, they are able to get a glimpse of a customer’s actions, rather than trying to connect with them based on assumption.

Read the full Forbes article here to learn more on this year’s mobile trends.

MediaMobileUncategorized

Bridging the Gap: Changing the Way We Think About Mobile eCommerce

March 18, 2016 — by MediaMath

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It finally happened — 2015 was the year that mobile ad spend was greater than desktop. Chew on these numbers for a minute:

  • Mobile became the highest percentage of digital ad spend to date at 52.4 percent
  • $30.45 billion was spent on mobile advertising
  • It’s projected that mobile advertising will account for 50.2 percent of all internet advertising by in 2018

The numbers show mobile is here to stay. But how can we, as marketers, fully use it to its full capacity?

Mind the Gap

Mobile ecommerce accounts for just 30 percent of all US ecommerce, generating $104.05 billion. Nevertheless, by 2020 consumer behavior is projected to catch up to the ad dollars being spent with 45 percent of total ecommerce expected to transact on mobile, generating roughly $284 billion in sales.

It would make sense that if the majority of ad dollars are being spent on mobile, then mobile should be generating the most conversions and/or revenue. But that’s simply not the case for commerce — at least not yet. Yes, some percentage of the discrepancy will diminish organically, as consumer confidence increases and mobile continues to dominate the digital landscape. However, sales (or conversions) do not exist in a vacuum.

A mobile device is now the primary touch point used by consumers, but desktop and laptop computers still play a role — just not as much. High-consideration conversions are a much harder sale to make on a mobile phone than a desktop. The importance and the seeming complexity of the checkout process deters mobile conversions. However, consumers might research a product on mobile — perhaps initiated by viewing a mobile ad — but without a cross-device tracking solution the true value of mobile cannot be understood. And mobile should get the credit it deserves for the role it plays in the transaction.

Measuring the Impact

As users flow between multiple devices, they become influenced by different ads and, in fact, use different devices for different tasks. In 2012, Google released a report stating that while 65 percent of online shopping started on a mobile device, 61 percent of that traffic was later continued on a desktop or laptop computer. While that report is surely outdated, and the number of sessions continued on a desktop/laptop computer has definitely been reduced, the trend remains. While desktop/laptop traffic is shifting to mobile, that traffic still converts at a higher level, as consumers often have researched and understood exactly what they’re looking for when they switch to a personal computer for conversion.

MediaMath’s own identity management product allows marketers to understand behavior across devices and, subsequently, optimize their own advertising and conversion processes from the insights gained. So perhaps, while the conversion is taking place on desktop, the process was started on mobile as a result of an ad viewed. Mobile is the new hub of a persona’s digital identity and, as such, must be linkable to other device activity.

Mobile Behavior

Mobile has completely different usage patterns than a desktop/laptop computer. The average American adult cell phone owner interacts with their mobile around 150 times per day. Further, it’s projected US smartphone and tablet users spend four hours and six minutes per day on “non-voice” activity. That means each interaction on a device has an average span of 1:38 — yes, there are obviously longer interactions with more attention placed on the device and content, and the “check-in” where users are simply looking for notifications.

According to a Quantcast presentation at Mobile Media Summit 2015, mobile users are between 30 to 50 percent more likely to convert during morning and evening commutes than at other parts of the day. So what does this mean? We need to place more emphasis on the impact of mobile checkout flows, be it native or in-app. These optimized and user-centric conversion funnels will provide a simple, clean and friendly way for users to convert. Marketers must adapt to the tendencies of mobile users, their behavioral patterns and intent when on a mobile device. This not only includes the time of day a user is interacting with mobile, but what that time of day means to them. Location, no doubt, plays a major role in this as well.

Optimize for Mobile

Marketers must start thinking about the mobile conversion or checkout process in a dramatically different way than they do the desktop checkout process. When mobile checkout requires too much work, it becomes discouraging to the user. Speaking to Nick Kroetz, a senior UX designer at Prolific Interactive, he explains, “You never want users to think about the checkout process, and the moment they do is when you lose their trust.” The checkout process should be organic, as some mobile retailers begin to leverage technologies like Apple Pay and simplified native checkout flows, the conversion rate increases, as friction to checkout decreases. Nick continues, “I often see users trying to pinch to zoom in on form fields, or being let down about an expectation that their information should have been saved for them.”

The moral here is that marketers must invest in the research and technologies in order to entice users to convert on mobile. Failure to do that is a huge opportunity lost. At the very least, marketers must truly measure the impact mobile has across devices. It’s no longer trivial. Following suit will bridge the divide between the ad dollars spent on mobile and the actual revenue mobile generates.

MediaSocial MediaTechnologyUncategorized

5 Questions with AddThis

March 16, 2016 — by MediaMath

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MediaMath has maintained a close data partnership with AddThis, having seen success both with their standard and custom offerings, especially given their monthly coverage of 2 billion unique visitors from 15 million mobile and desktop websites. Now a part of the Oracle Data Cloud family, alongside BlueKai and Datalogix, we look forward to working with them even closer, especially given their scale in international markets. I recently sat down with Hal Muchnick, CIO of AddThis, on his recent visit to our offices to hear his thoughts on what differentiates their data and what lies ahead in 2016.

1. AddThis has a really unique behavioral dataset. Can you talk about your methodology and why it’s so differentiated in the space?

Sure. Our dataset is generated by the anonymous activity of 2 billion unique users who visit any one of the 15 million domains that have chosen to install AddThis JavaScript on their pages. This massive scale gives us unique insight into the interests, intent and activity of consumers across the web, and we update all of this data in real-time. This helps us equip brands with a truly current and holistic window into their target audiences, and we do this at a global scale.

2. What are your best tips for using behavioral data in campaigns?

Behavioral data is extremely versatile. It works as a standalone for awareness campaigns, or in concert with other data types. We’ve seen a lot of success with creating custom audiences in combination with first-party data. For example: you’re an online retailer that owns and understands an incredible amount of data about your active customers, and how they behave on your properties. But what you don’t know is how that user behaves when they leave your site. AddThis behavioral data can help complete that picture, allowing you to target your users on a much broader scale.

3. What do you think will be the biggest challenge for data providers in 2016?

I think data providers need to realize that the days of big brands and agencies buying black box data are numbered! More and more marketers want to know the source, quality and relevance of their audience data; providers who can’t give a clear and transparent answer will struggle.

4. You were founded in 2004 before programmatic was even a word. What have been the most surprising evolutions in the intersection of martech and adtech?

The sheer speed at which the two industries have collided is surprising in itself. We’ve come a long way from the days where advertising tech and marketing tech were two very different practice areas. We’re now in a place where we really need to streamline efficiencies between all the disparate tech available, and I think we’re going to see a focus on that in the year to come.

5. What do you think is on the horizon for the rest of the year when it comes to martech and adtech?

Standard audiences have been a great way to get brands and agencies using programmatic and testing the water. The more standard audiences are activated against, however, the clearer it is that brands are all targeting the same users. Our standard audiences are a little different, because we create them from looking at our first-party-permissioned data set, but folks who are repackaging other people’s data don’t have the ability to create really targeted standard audiences. I think we’re going to see the use of more custom audiences. Marketers are growing weary of standard, off-the-shelf audiences due to the lack of transparency into the composition of the audience and the risk of wasting budget on fraudulent impressions. Programmatic audience buying will continue to evolve from the use of standard data to the need for custom data to plug into and create distinct audiences.

In addition—international growth! There is so much opportunity in EMEA and APAC, and I think this is the year where we’ll really see market penetration in these regions.

Click here to read the other “5 Questions” posts.

DataMediaUncategorizedVerticals

Download Our “Optimizing for Easter” eBook

March 15, 2016 — by MediaMath

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Easter marks the start of the spring gift-giving season and also grabs attention from consumers looking to switch out their wardrobes, spring clean and get a start on their backyard gardens. But unlike the Q4 gift-giving holidays, there’s not as much of a lead-up to Easter (who knows, maybe there will be a Black Friday for Easter in years to come).

Given that Easter marks a more condensed gift-buying period, we decided to look back on data from our 2015 Easter campaigns to analyze fluctuations in CPM, CPA and conversions for this spring holiday and offer some tips for optimizing campaigns this year. Download the “Optimizing for Easter: A Quick Analysis of our 2015 Campaigns” eBook here to learn more.