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MathCapital: A Venture Capital Fund to Support the Next Wave of Marketing Innovation

January 8, 2018 — by Eric Franchi0

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The year 2018 finds us at the beginning of the next wave of innovation in digital. Billions of connected devices, the emergence of identity-based targeting and measurement, new consumer interfaces such as AR, VR and voice and all forms of Artificial Intelligence will make digital marketing more effective and digital media more engaging than ever.

It’s an exciting time to be a marketer, media creator and consumer. But it’s never been a more exciting time to be a startup focused on addressing the needs of this giant, and growing, market. That’s why we are thrilled to announce the launch of MathCapital, an early-stage venture capital firm focused on the digital transformation of marketing and media. We created MathCapital to help identify and support the startups that will become the next generation of industry leaders.

As long-time entrepreneurs ourselves — Joe Zawadzki is founder and CEO of MediaMath, and founder of [x+1], and I previously co-founded Undertone – our team is often approached for advice and investment by startups. This led to notable personal angel investments in names many are familiar with, such as AppNexus, Moat, Integral Ad Science, BounceX and mParticle.

We’re also excited to have the support of MediaMath. While MathCapital is a separate entity, MediaMath has committed resources via its in-house innovation group that has incubated a number of profitable agencies. This group will serve as an access point to MediaMath’s 4,500+ clients, 350+ partners and 600+ employees, helping to accelerate all facets of our portfolio companies’ businesses out of the gate.

We’re a startup of four partners ourselves, so we can’t wait to roll up our sleeves and get to work. Our first investments will be announced here soon, so watch this space. And if you’re a funder who would like to learn more about working with us, please contact us here.

DataIntelligenceMedia

Incrementality is the Best Way to Prove Your Advertising is Working. Here’s How to Measure It

December 27, 2017 — by Natraj Ramachandran0

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The number one question advertisers are asking now is, “How can I measure incrementality?” In other words, “Did my ad campaign cause the consumer to convert?”

There have been many methods for measuring incrementality, including looking at conversions pre-and post your digital marketing efforts or using a complicated model. But in recent years, marketers have understood that the only true way to measure causality is to use a scientific approach.

In public health, the gold standard in measuring the efficacy of any intervention is a randomized control trial (RCT). For example, if we take two equal populations who have similar behavior and give one population a pill and another a placebo we can then measure the effectiveness of the pill we administered.

The same can be done in marketing. In Incrementality Testing, users are separated into two equal populations. One group (test) receives an ad and another group (control) doesn’t. Once we observe the conversion rates of both these populations we can measure their incremental conversions. More importantly, we can accurately measure the cause and effect of our marketing efforts.

In order to create solid lift measurement tests, you need to follow an experimental design approach:

Setting up your test

Measuring incrementality is simple if you follow these steps:

1) Randomization
Randomization ensures that the populations in test and control are statistically equivalent. This allows us to compare behaviors of the groups.

2) Hypothesis
The key to a strong Lift Measurement test is creating a hypothesis. This can be created by looking at previous observational data. Without a strong hypothesis, the results of your test will be pointless.

3) Primary outcome
Decide the behavior you are trying to observe. For example, if we show an ad for pants, we hope that the desired behavior would be the customer would then buy pants. In your test that would be the conversion event.

4) Length
Select a start and end date of a reporting cycle to ensure that reporting results align to the hypothesis of the test (minimizing distortions due to influences of time).

5) Expected use of outcome
The results of your study should contribute to the advancement of knowledge of your marketing campaign. You will use results to make strategy, budgetary and/or optimizing results.

Understanding your results

1) Lift, incrementality, incremental conversion, confidence, confidence intervals what do they all mean?
A common confusion with any test are the metrics you receive. It is essential that you have defined your results before you receive them.

a) Lift
This will give you the likelihood to convert. For example, let’s say your lift is 30%, this would mean that if we show your ad to a consumer they are 30% more likely to convert.

b) Incrementality

The percentage of conversions you received because of your ad. Let’s say we run our test and get a 20% incrementality, this means we received 20% more conversions because we showed an ad. Or alternatively we would have lost 20% had we not shown this ad.

 

c) Incremental Conversions/Revenue

Generally, a portion of sales would have occurred despite the campaign. Your incremental conversions/revenue are conversions that occurred as a result of your campaign. These conversions wouldn’t have occurred if your ad was not shown.

1) Confidence %

Your confidence is the % of distribution that sits above zero. For example, if we get a 90% confidence We are 90% sure that the lift is > 0.  This is a good, positive signal we look for to validate expected results.

2) Confidence Intervals

This provides more detail into the health of a measurement. A confidence interval is a range of values that you can be certain contains the true mean of the population. If for example you use a 95% confidence interval, we are saying that we are 95% certain that interval we calculated has the true lift of the population, the narrow the confidence interval the better. The confidence interval is a measure of how precise your lift/incrementality are in your test.

Guardrails/Mitigating factors

1) Why do my lift/incrementality numbers vary so much?

a) Sensitivity — Lift/Incrementality are calculated based on small conversion rates. In a recent campaign the measured conversion rate for the test group was .03% and .02% for the control group. This would equate to a 33% incrementality.

If the test group conversion rate increased to .04% this equates to a incrementality of 50%.

a) Seasonality – The control group response rate can vary drastically depending on different buying seasons. For example, if we ran a test during Black Friday, the control group response rate may be higher than other times of the year as users are more likely to purchase during this time. This would in turn dampen the influence of your ad campaigns.

b) Brand Awareness – Some products are more familiar to consumers than others. If we ran a test for Snapple and another test for Honest Tea, we should expect larger lift/incrementality for Honest Tea vs Snapple. Snapple is more recognized by consumers than Honest Tea. This means that Snapple consumers are more likely to convert even without the influence of an ad campaign. This means the ad campaign run for an Honest Tea would have a larger impact on consumer behavior.

1) Can you tell me results at the strategy or tactical level?

This is an almost impossible question to answer unless your test is set up appropriately and you have a well-defined hypothesis. Take for example a prospecting and remarketing campaign that are run in tandem. In this scenario, there will be a natural flow of users from the prospecting campaign to the remarketing campaign, causing overlapping populations. As a result, there may be some users who receive two ads then convert (see below).

It becomes almost impossible to understand and attribute the conversion to the appropriate ad. Additionally, this type of behavior isn’t seen on the control side as they are held out to all ads.

 

In order to understand tactic/strategy level results it is best to test each strategy one at a time to avoid overlapping audiences and audience flow.

Conclusion

Incrementality testing can be an incredibly powerful tool if implemented correctly. Many public health organizations continue to use it to measure causality for clinical interventions. Testing in marketing is still nascent and still needs more exploration.

In order to have an effective test you need to:
1. Ensure you determine what you would like to learn from the test before campaign and incremental lift test(s) are set up.
2. Determine the primary goal you are testing (Visits, Checkouts, Signups).
3. Ensure the results of your study contribute to the advancement of knowledge of your marketing campaign.
4. Make sure the results will be used to make strategy, budgetary and/or optimization decisions.

Understand that your results will continue to change because the behaviors of your clients are also constantly changing. In order to make your tests useful have a test plan at the beginning of ever year and continue to iterate on your hypothesis. Running one or two tests won’t provide you with the answers you need. Measurement for measurement’s sake is doesn’t advance learning.  Don’t just measure to say you did it, and then let the results linger in a PPT print out on your desk, measurement should be actioned on.  If you can’t make a decision or take an action based on what you have learned, then that measurement isn’t useful.

Intelligence

In 2018, Marketers Will Discover More AI Applications in Programmatic Advertising

December 14, 2017 — by Amarita Bansal0

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This Q&A with Chris Victory, VP Partnerships at MediaMath, originally appeared on eMarketer.

Even when digital ads are highly targeted down to individual users, it’s hard to get consumers to pay attention to them — let alone interact with and enjoy them. Using artificial intelligence (AI) to further optimize digital ad spending is the next step marketers are taking to make ads a more welcome part of consumers’ digital lives. eMarketer’s Tricia Carr spoke with Chris Victory, vice president of partnerships at demand-side platform MediaMath, about the demand for AI in the programmatic advertising space and the core elements of his company’s partnership with IBM’s Watson.

eMarketer: Are marketers typically aware the benefits of AI in programmatic settings, or are you doing a lot of education on this front?

Chris Victory: Marketers who use programmatic advertising have seen the benefits of implementing machine learning into their decisioning and spend of their marketing dollars. AI is the next step in this journey, and everybody is super excited about it. But the challenge lies in delivering something that goes beyond what machine learning does today and is truly artificial intelligence. Those in the programmatic setting are perfect candidates to use the next generation of AI, because machine learning is core to what they do and it’s much more palatable to them.

eMarketer: How are you helping to propel the use of AI in this sense through your partnership with IBM Watson?

Victory: There are three main areas where we focus on the power of AI. The first is infusing AI into the decisioning process on how to better spend your money. Today that’s based on machine learning, but think about all the additional data that can be ingested, like sentiment and mood of the user, and a deeper contextual analysis of a page. These types of things are driven by AI and can be infused into the bidding process to make better decisions.

The second is AI-driven ad creative — it’s like the next-gen version of dynamic creative optimization — to deliver things like interactive ad units. For example, you can interact with an ad by asking a question, and it will give users different answers based on the question asked.

The third is using AI across customer analytics and marketing insights. Today you can look at analytics through a number of different technologies that give insights into your customer base. But the next generation of that is using AI for more predictive analytics. For example, don’t just tell me who my lapsed customers are, but show me who my potential lapsed customers are based off of behaviors. Then you can create user segments to target with specific messages related to their potential of becoming a lapsed customer.

eMarketer: Are there any roadblocks in the industry preventing marketers from experimenting with AI to optimize their programmatic advertising?

Victory: There’s a lot of noise out there — this doesn’t just apply to AI, but marketing technology in general. AI is the buzzword now, and there are a lot of companies getting money from venture capitalists because they have AI in their strategy. But it’s getting harder for a marketer to cut through the noise and understand exactly what they can do with AI — what’s real and what’s not.

eMarketer: Will consolidation happen in 2018, or is it a long way off?

Victory: I think there will be a proliferation of companies before there is consolidation. Next year will be interesting. You’ll see a lot of companies coming in.

There will be a real need for agencies to play a role here and become experts in the use and the application of AI, because agencies are engaged with their clients. It’s very similar to the evolution of the agency role in the programmatic world.

eMarketer: What else do you expect to happen in this landscape in 2018?

Victory: There will be a lot more marketers and advertisers jumping in headfirst to embrace and adopt AI. Once they start seeing the results of some of the applications of AI, there will be a snowball effect and people will want to do more.

There will also be continued proliferation of companies in this space and in the different applications of AI. Virtual reality has been the hot topic for the last year and a half, but now AI is. It’ll be interesting to see where venture capitalists make their nest here, and where that directs the market.

DataIntelligence

1995-like Media Buying Would Bring 1995-like Outcomes

December 6, 2017 — by Lewis Rothkopf0

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This article, written by Lewis Rothkopf, General Manager of Supply at MediaMath, originally appeared on LinkedIn. 

Like many of you, I read with interest Augustine Fou’s piece, “What a Concept! Buy Media As If It’s 1995,” in which he shares some disturbing examples of middlemen subtracting disproportionate value from media transactions. Also like many of you, I consider Dr. Fou to represent the very best of our industry — he’s a much-needed independent voice in cybersecurity and fraud research and someone whose perspective I hold in the highest regard.

Our opinions differ, however, when it comes to some of the solutions advocated in his latest piece. To be sure, there is no legitimate place for vendors who add no value to the ecosystem — they are a drag on publisher RPMs and do not promote better business outcomes for marketers. Simply put, if you don’t add value to the media transaction, you don’t deserve to be a part of it.

But to paint all intermediaries — “from agencies to ad tech vendors” — with the same brush would be a mistake. When looking at those entities who play a role in the value chain, it is important to separate Black Holes from Shining Stars.

Black Holes:

• Roll up, or aggregate publisher supply without adding a layer of value or differentiation such as data enrichment, geolocation or fraud protection
• Misstate the provenance of the supply or their right to resell it
• Are highly discrepancy-prone
• Are often several layers separated from the underlying publisher inventory

Whereas Shining Stars:

• Offer a layer of protection for buyers by offering assurance — and sometimes a guarantee — that the supply is legitimate, safe and fraud-free
• Add value to otherwise-commoditized supply with the addition of data
• Help identify the most appropriate demand for particular inventory
• Innovate on formats with marketers and publishers
• Normalize disparate pools of supply to make them addressable across channels
• Improve measurement and accountability
• Work to keep costs low

Additionally, though it can be tempting to dispose of baby along with bath water and flee from programmatic and its defense mechanisms (and the associated costs of same), doing so is not without significant risk. Dr. Fou acknowledges this in his piece, in which he says, “Of course, the publishers have to be vetted…” and that marketers shouldn’t simply take their word for it.

To this point, I’ve spoken with several publishers in recent months who have a strong understanding about where invalid traffic comes from and who have taken the necessary steps to avoid it. But we’ve interacted with many others who do not yet have such an understanding. One media property whom most would consider to be of the highest quality recently told us that invalid traffic is not preventable by publishers. This is most certainly not an accurate statement, and it was asserted in response to a suspicious traffic inquiry that we raised on behalf of our customers. Of course, when you buy inventory you are also effectively transacting with everyone from whom that publisher has sourced traffic in the past!

Dr. Fou raises several important — and downright horrifying — examples of non-value adding middlemen sucking trust and credibility out of the ecosystem. But turning back the clock to a time when there were no intermediaries facilitating media transactions is not the answer. Lots of things must happen in order to make a transaction possible, safe and effective — including things that can’t be taken for granted, such as data-decorating inventory to make it more addressable, ensuring that the inventory is free of fraud and that the supply source has a right to sell it, and intelligently marrying the right supply with the right demand. These things cost money, and should be considered every bit as much a part of a marketer’s investment in “working media” as the final act of displaying the ad on the web page.

It is in the interest of every legitimate actor in digital media to eliminate Black Holes from the ecosystem. By working together, we can continue to reduce costs and remove points of friction from media transactions, and reach a near future state in which good actor intermediaries are able to compete on their technical capabilities and their ability to drive business outcomes, and not only on their existential legitimacy relative to bad actors.

DataIntelligence

How to Kick Start Your Holiday Campaign this Season

November 30, 2017 — by Laura Carrier0

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This article originally appeared in MarTech Advisor. 

MediaMath’s VP of strategy and measurement Laura Carrier explores how marketers can ensure their advertising campaigns are timed in accordance with the height of consumer holiday spending.

Holiday season is the largest retail season of the year and as the gift giving traditions get underway, now is the time for advertisers to start detailed planning of how they’ll effectively target and reach holiday shoppers. According to eMarketer, Holiday sales will total $923.15 billion, representing 18.4 percent of US retail sales for the year.

To help advertisers make the most of holiday shopping budgets, we looked for trends in the way our best brands and retailers made the most of this season, including the way they think about timing and key dates, budget, media, targeting and more. Consider the following best practices:

Market to Your Audience Based on Deep Understanding

Marketers know it’s important to understand how audiences demonstrate different shopping behaviors – that’s nothing new. But it’s what marketers do with these insights which matters. Ask yourself, does your ad spend correlate to consumer’s shopping behavior? According to our own analysis, 2016 ad spend lagged behind the time frames consumers expected to do most of their online shopping. Over half of consumers plan to start holiday shopping no later than Black Friday, yet marketers had only spent 25% of their campaign budget by that time last year. This year, make sure to pace your holiday budget before customers do their shopping (while they are researching & planning)!

Get Creative Right 

Knowing who you’re targeting on an individual level, as opposed to different segments of customers or audience groups, will help fine tune your creative and targeting strategies this season. Executing true customer-centric marketing with a single view of the customer will allow marketers to optimize against all marketing touch points. Using this approach, dynamic creative optimization, which updates creative elements on the fly without advertisers having to manually build or modify new assets, will allow for more relevancy in the conversations you have with consumers.

When it comes to optimizing campaigns, marketers should take into account differences consumers shopping habits on key holiday dates when deciding on content. For instance, if you’re marketing to someone who is shopping the weekend before Christmas, getting an item to them as quickly as possible is much more important than the price, e.g. offering free shipping or in-store pick up. On the other hand, if you’re marketing to somebody who is shopping on one of the major one-day sales, like Black Friday, Cyber Monday or any retailer’s one-time sales, content around price would take priority, e.g. Buy one, get one free. Knowing the different types of consumption patterns will help advertisers optimize their Holiday campaigns.

M-commerce Market Grows

On the busiest shopping days of the season, customers are reaching for their phones first. Site traffic is just as likely to come from cellular devices as it is desktop site visitors with 47% of mobile share occurring on Black Friday and 49% of mobile share on Cyber Monday out of all total site traffic by device.

Increasingly, customers are continuing to buy sale items on their phones and check out one-day sales. According to eMarketer, US m-commerce sales will rise by 38% this year, and sales via smart phones will increase by 57.8%. With that in mind, marketers should be adopting an ominchannel approach when making marketing channel decisions.  Consumers are influenced by all of the various different media & channels available to them, so understanding behaviors across devices is becoming even more paramount today than it has historically been.

Online vs. Offline Shopping

The share of eCommerce is growing as 55.6% of US consumers plan on doing most of their holiday shopping online . Marketers will make smarter decisions if they understand the influence of online marketing on offline purchases, without ignoring the fact that offline marketing also influences online purchases.

Online shopping is growing at a faster pace than anything else, now 16.6% in 2017, compared to 3.1% for in-store retail. With Holiday shopping beginning earlier, coupled with the growth of online shopping, it’s important to remember that consumer research and holiday purchase planning is happening a lot earlier, too.To fully market across the customer journey, marketers must speak to consumers online in efforts to influence offline store sales, and measure the impact of those marketing touch points on offline behaviors. This will allow for true customer-level understanding, and ultimately the closing of the loop-optimizing marketing to those consumer behaviors.  As a result, brands and retailers alike will benefit from building out a digital strategy that includes both online and offline presences as one strategy-not as two separate tactics.

Intelligence

51 Artificial Intelligence (AI) Predictions for 2018

November 28, 2017 — by Amarita Bansal0

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This article originally appears in Forbes.

It is somewhat safe to predict that AI will continue to be at the top of the hype cycle in 2018. But the following 51 predictions also envision it becoming more practical and useful, automating some jobs and augmenting many others, combining machine learning and big data for fresh insights, with chatbots proliferating in the enterprise.

“Making smart marketing decisions across all customer touchpoints, using all available data, to drive complex business outcomes is a herculean task — and artificial intelligence is an absolute requirement for making it all work. In 2018, we’ll finally start to see AI deliver on the omnichannel promise to make marketing that consumers — and others in the value chain — love. The technology is there — from players like IBM Watson and others — and now is the time to rally the right processes and people to put it in action.” Dan Rosenberg, Chief Strategy Officer, MediaMath

For the full article, click here!

IntelligenceMedia

Bolstering Brand Safety with Contextual Pre-Bid Segments

October 25, 2017 — by John Van Antwerp0

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Seventy-eight percent of marketers report their brand reputation has been harmed in the past by “unintended” ad placement adjacent to inappropriate content, according to a CMO Council survey. In the contextual pre-bid landscape, our partner Grapeshot has focused on brand safety and their unique take on keyword segments — Predicts, which dynamically adapt to the relevant conversation happening on web pages, social and elsewhere. At a time when one of the largest perils of digital advertising is having your ad appear next to offensive or controversial content, Grapeshot’s brand safety segments are sought after by some of the largest advertisers in the industry. Their Predicts segments are a creative adaptation of keyword segments, along with an interesting application of technology, to create a differentiated and useful product in the market.

Today, we’re proud to announce our newest integration with Grapeshot, unlocking the entire Grapeshot portfolio of contextual pre-bid products in our platform to provide more choice and flexibility to our clients. The launch is the culmination of one of the largest integrations the Grapeshot team has performed in two years. Contextual pre-bid segments are available for both web and in-app including:

  • Brand Safety — make sure your ads only run alongside content that is appropriate for your brand
  • Standard Segments — target content based on a static set of keywords defined by the experts at Grapeshot
  • Standard Predicts — target content based on a dynamic set of keywords determined algorithmically and by following the social conversation across the web, defined by the experts at Grapeshot
  • Language — target based on page languages
  • Custom Segments and Custom Predicts — target content based on a static or dynamic set of keywords (respectively) created by you, tailored to your unique marketing needs

We’re excited to have Grapeshot available within our platform and look forward to hearing how it’s improved your targeting experience.

Intelligence

The Crawl, Walk, Run Guide To Audience Suppression

October 19, 2017 — by Amarita Bansal0

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This article originally appeared in AdExchanger. 

Brands often have a long list of people they don’t want to show ads to: customers who just bought a product, current subscribers, non-subscribers or a group of people in their CRM database they know won’t qualify or be interested in a product.

To avoid showing ads to people – a technique called audience suppression – brands need tech, talent and a strategy to back up their choices. Audience suppression done right cuts down on media costs and makes customers happy. Done wrong, it can annoy them and send KPIs plummeting.

Here’s what agency and tech experts have learned along the way about how to do audience suppression right.

How has this tactic been used historically?

Direct mail marketers use audience suppression all the time to make sure the same person doesn’t receive too many offers, or to avoid exposing them to a better offer right after they convert to an okay offer. Email marketers have continued this tradition, withholding certain messages from subscribers unlikely to be interested in them. But in other mediums, like TV, marketers haven’t been able to use the tactic – although that’s changing.

My brand is just getting started. What’s the basic way to use audience suppression?

Make sure you aren’t showing ads to users who just bought something online.

Before advancing on to anything else, brands need to use this simple, high-value technique. “Suppressing converted customers from your retargeting campaign is critical to optimizing,” said Serge Del Grosso, SVP of media services at Merkle.

Still, many brands have a hard time even doing that right, continuing to show ads long after an online purchase. “That is the most common holiday complaint from family and friends,” said MediaMath Chief Product Officer Jacob Ross.

I’m crawling. How do I walk?

Brands can also suppress customers who don’t have the right purchase profile to qualify for the prospect pool, advised Del Grosso. And they can suppress prospects that have already been exposed to an ad multiple times – though figuring out just when to give up requires detailed analysis.

Or, instead of not showing ads to users who converted, brands can mix things up by showing users a different offer – like a camping backpack to go with their hiking boots.

Good audience suppression also needs to work across devices.

“We have one customer that sells subscriptions for a TV service,” said LiveRamp CMO Jeff Smith. “Similar to a direct mail marketer, they are always tweaking their offers to acquire the customer. The last thing they want to do is offer that customer they just hit on a web browser with the basic offer [who converted] and give them the advanced offer on the mobile device.”

How do I run?

Smart advertisers will understand the customer life cycle for their products and use that knowledge to inform audience suppression strategies.

“You should not be suppressing audiences in perpetuity until you have built up a healthy picture of how your customer historically interacts with your brands,” said Essence’s Jon Taylor, global director of data strategy. “I certainly wouldn’t suggest suppressing audiences from day one if you are uncertain about when you should bring them back into the targeting mix.”

Brands make audience suppression more effective by bringing together offline and online purchase data. “Offline transactions are as important as ecommerce transactions in looking at what customers to suppress, ” Del Grosso said.

A bank, for example, needs to bring together online and offline databases in order to smartly suppress audiences.

“If you just use online information about your customers, you aren’t taking into consideration all of your customers that already have credit cards, for example, in an offline database,” said Bryan Simkins, technology solutions partner at Transparent Media Partners. “You may be wasting acquisition media dollars on customers who already have a credit card because your systems aren’t connected.”

What will mess up my campaign?

“One of the most common mistakes is the time lag between the conversion and removing them from the prospecting pool and retargeting pool,” Del Grosso said. Campaigns require active management to ensure customers are moving in and out of the right suppression buckets.

Second, brands shouldn’t start suppressing audiences right out of the gate. It’s better to let a campaign run across a broad audience and then start making decisions. That way, traders can look at the average ad frequency before conversion, for example. Otherwise, brands risk removing people who were still on the path to purchase or incorrectly excluding audiences.

Data quality is also a biggie. If brands want to suppress ads to women, and the data is only 50% accurate, they’re wasting money on media and on data, Ross said. And brands might be making incorrect assumptions about their audiences. For example, men may use the product, but maybe women are buying the product as a gift. 

How do I measure the ROI?

Audience suppression can move the needle on almost every brand metric, from CTR to sales. Focusing on the proxy metrics, like clicks, can be an easy way to get a feel for efficacy. But it’s even better if the brand or agency can connect new audience suppression strategies to sales or a cost per acquisition.

“We have a number of clients looking at a CPA [cost per acquisition] on the financial services side,” Del Grosso said. “When the operator starts seeing the cost per order creeping up, that’s a trigger that tells us our costs are going up and we need to refresh our audiences.” The agency will then suppress users that haven’t converted after a high frequency or bring back others that haven’t been served ads in a while.

Brands should also keep qualitative metrics in mind. “The softer aspect is not wanting to make your brand seem creepy because you are following them around with an irrelevant message. That’s part of the ROI too,” said LiveRamp’s Smith.

“You have to think about what the true lifetime value is of exposing users to ads over and over again,” MediaMath’s Ross said, himself a veteran of retargeter Criteo. But making determinations about lifetime value is difficult, and one he thinks is best solved by machine learning algorithms. 

What technology do I need?

Brands definitely need a DSP to optimize quickly. If brands need to activate in multiple places, they may need a separate DMP. If they’re bringing in CRM lists or offline data, a data onboarder may also be in order.

Do I need to hire anyone with special training?

Mastery of DSPs and DMPs is a must, said Essence’s Taylor. “Historically, segmentation skills have existed further back in the campaign planning process, in strategic planning. We need to bring more of that thinking forward into campaign management and data management.” That said, most media planners are adept at doing audience suppression – but analytics experts will help refine strategies and provide more insight.

What’s the future of audience suppression?

Better identification will enable omnichannel audience suppression. Suppressing audiences across multiple channels, like television, out-of-home and digital, is the oasis that agencies and tech companies are plodding toward. There is steady progress in cross-device and cross-channel identification of audiences, which will pave the way for omnichannel audience suppression.

Also, brands want to start doing audience suppression using people-based identifiers, not cookies, which will make it easier for brands to tie their decisions to business outcomes like sales.

Intelligence

Social Advertising is Getting Less Antisocial

October 16, 2017 — by Sara Skrmetti0

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Marketers have woken up to the fact that their communication with consumers needs to be consistent across every touch point. There’s just one problem: Some touch points remain resistant to becoming part of an omnichannel solution.

Take social networks. While video and mobile advertising have mostly folded into a 360-degree view of the consumer, social as a channel remains siloed. Mainly, that’s because the largest social networks are walled gardens, meaning they don’t make their data available or their auctions open. This makes everything about campaign execution hard – separate platforms, discontinuous and manual optimization and incomplete pictures of performance and attribution.

This situation won’t last. The market demands that social becomes more of a standard component of an omnichannel solution. In time, the market will win.

Forrester’s view

The latest proof that social is not exceptional came in the Forrester Wave Social Technology Q3 2017 report in August. The report portrays a market divided by small players with DSPs and marketing cloud solution providers waiting in the wings.

Forrester counts 144 vendors, which range from agencies to pure-play tech companies. As the report notes, “most are small fish managing microscopic media budgets.” Most reported less than $80 million in social ad spending running through their platforms in 2016.

Forrester predicts that social adtech won’t be its own category for long. There is already consolidation and although U.S. social ad spending will increase from $4.1 billion in 2016 to a projected $21 billion this year, the amount of ad spend managed by the top players in the space didn’t even double. Where’s the rest of that money going? To the social networks themselves.

Social ad buying has remained immature because the tech involved has remained immature for a long time, aside from the tech provided by the social networks directly. Many social ad companies are managing campaigns on behalf of advertisers, while the rest of the adtech ecosystem has leaned toward self-service. That indicates that many marketers are still figuring social advertising out. As the report notes, DSPs including MediaMath are beginning to offer social ad inventory alongside their RTB inventory. Eventually, social ad buying will be standard in DSPs, and ultimately absorbed into marketing cloud solutions, Forrester predicts.

My take

I couldn’t agree with Forrester more on this subject: Social can’t remain its own category for much longer. Currently, consumers spend about 50 minutes a day on Facebook properties alone out of a total of roughly 12 hours of daily media exposure. Marketers now realize that they need to coordinate their messaging during all of those 12 hours.

That said, the social networks won’t make it easy. It is in their best interest to remain walled gardens for a number of reasons, but this desire to stay separate won’t withstand the market’s demand for an omnichannel solution that includes social from activation to optimization, all the way through to measurement.

Market demand caused Facebook to partner with Visual IQ and the Neustar-owned MarketShare last year to provide third-party attribution. Similarly, they have partnered with a number of firms on third-party viewability measurement. I predict we will see more of this – the market will push walled gardens further toward transparency and independent verification.

I don’t expect the social networks to abandon their walled garden status any time soon. The siloed nature of social advertising will remain a challenge for marketers a bit longer, but I am confident the market will work out a solution that allows social to be one component of a 360-degree view of the customer.