Is Bringing Programmatic In-House Right for You?

Modern brands want more control over their advertising — and they deserve it.

In a recent Infectious Media study, 84 percent of surveyed brands expressed a desire to tighten the reins over their programmatic advertising, with 63 percent saying their current situations did not provide adequate data transparency. Combined with brands’ reported struggles with publisher relationships and financial transparency, it makes sense that some would consider bringing their programmatic marketing in-house.

A number of companies have made this shift. For brands entertaining notions to do the same, know that taking control of programmatic marketing isn’t as easy as it sounds. An Interactive Advertising Bureau study revealed that 65 percent of marketers who purchase programmatic ads have moved some or all of those operations in-house. This migration can take up to 18 months to centralize data, set up technology, sign contracts, and hire programmatic experts. Some companies find the challenge to be too steep, with 13 percent of brands moving back to outsourced programmatic tactics after internal tests.

Despite the barriers, brands that transition to in-house programmatic advertising enjoy myriad benefits. To complete that transition successfully, marketers must understand what to expect and how to avoid common pitfalls.

Obstacles to Programmatic Control

Companies outsource programmatic more than any other marketing function by a wide margin. However, as more marketers begin to understand the nuances of programmatic strategy, its momentum within the advertising industry is starting to pick up. In a 2017 study by the Association of National Advertisers, 35 percent of respondents said they had improved their in-house programmatic abilities, more than double the number from the 2016 study.

Still, the challenges to fully integrating in-house programmatic remain daunting for most companies. Some brands don’t anticipate significant savings from the switch. Others see the enhanced control of an in-house approach as a quick path toward profits.

Executed properly, in-house programmatic offers several advantages over outsourcing, but only if the migrating company gives the switch the respect it deserves. These operations require at least a full year to complete, including the time necessary to find and onboard the right talent.

During this switch, the most successful companies tend to focus on getting a firm handle on their data. Disorganization makes it difficult to handle any marketing task, let alone a complex one like programmatic, but proper data management can turn a frustrating struggle into a lucrative opportunity. Better data practices, combined with the right technological tools and partners, solve many of the common problems encountered during the adoption of programmatic responsibilities.

How to Bring Programmatic Home

Brands ready to take control of their programmatic advertising should focus primarily on three areas: data organization, existing marketing environment, and tech stack. 

1. Data Organization

Most marketing initiatives rely on high-quality data to succeed. Programmatic depends more on data than perhaps any other strategy. Bad data management leads to poor programmatic implementation, while great data management yields consistently successful results.

When travel booking site Kayak brought its programmatic in-house in 2016, it needed a way to track the cost-per-click buys and sells it made from competing sites. It eventually settled on MediaAlpha, which handles CPC transactions specific to meta and native search engines and helps clients maintain transparency. With MediaAlpha, Kayak’s campaign ROI jumped 120 percent.

Marketing teams will need access to all the customer and analytics data they can to identify the most profitable tactics for improving programmatic. While third-party programmatic providers can’t access all company data, internal marketers can, which means better insights and faster responses to shifting markets.

2. Existing Environment

A company’s already-in-place infrastructure and priorities provide the foundation for how it should best integrate programmatic advertising. To understand how that infrastructure could influence programmatic adoption, assess how current digital advertising affects marketing goals, then identify opportunities where programmatic could increase effectiveness.

Netflix’s continued push to enhance its library of original content prompted the streaming service to increase its programmatic ad expenditures. Projected to pour $2 billion into its 2018 programmatic budget, Netflix explained to shareholders that its goal was to use programmatic to “do individualized marketing at scale and to deliver the right ad to the right person at the right time.”

Netflix shows that every move has a cost, but it’s important to understand that not every potential gain is worth the price to achieve it. Companies will need to take stock of their current third-party programmatic spends and compare them to the expected gains of moving in-house. This means accounting for downtime, internal resource reallocation, new staff, and other factors pertinent to the move. If the numbers don’t add up, implementation will either need to be adjusted — perhaps made more of a hybrid approach with existing providers — or alternative solutions to programmatic spend will need to be explored.

3. Tech Stack

Programmatic advertising is a highly technical marketing tactic. As such, it requires a tech stack that can handle the load.

One way to “try before you buy” is to test tech stack partners via managed service first. If successful, move toward self-serve and continue testing and refining the approach to measure programmatic gains. Remember that data likely exists in multiple formats on multiple platforms, and you’ll need a vendor (or vendors) to help orchestrate all of it.

Above all, set expectations about the support relationship before entering into a new partnership with a tech provider. When do fees come into play? Where does the company’s responsibility end and the partner’s begin? The clearer the initial expectations, the more successful the relationship will be.

While these three components are not the final word on in-house programmatic, companies that get these factors right start off on the right foot. Combined with the right team of ad tech talent, the switch to in-house programmatic is not only possible — it’s profitable.

This article was originally published in adotas 9/19/18.

Creating End-to-End Transparency in an Omnichannel Environment

The recent implementation of GDPR suggests that an increasing number of industries — including programmatic advertising — value transparency. MarTech solutions can provide clarity for agencies looking to clean up their end-to-end methodologies, says Kerry Bianchi, CEO, Visto

With a call at the start of 2017 for industrywide transparency, the programmatic ecosystem has made strides in meeting the challenge. But there’s still a long way to go, and the journey isn’t simple. After all, transparency can mean different things to different parties.

There’s contractual transparency, in which advertisers and agencies negotiate a benefit based on established goals. In fact, the World Federation of Advertisers found that 90 percent of advertisers currently review agency contracts to improve transparency and control.

Then, there’s transparency related to data and media execution. Accessing and acting on this information comes with its own challenges, especially when systems aren’t integrated or execution channels don’t share customer data along the sales funnel.

For omnichannel marketers, a holistic, transparent view into the inner workings of their media spend is the holy grail. Bringing those expenditures into focus requires implementing a martech strategy that is transparent and shines a light on every step of the omnichannel journey.

Fully Integrated Transparency

One of the first steps in developing a successful omnichannel approach is to define what transparency means to you. With media buying, the martech and adtech industry is rapidly evolving to meet the demands for transparency, but a lack of standard measures only adds to the confusion.

Tech providers need to work with industry associations such as the Interactive Advertising Bureau, the Association of National Advertisers, and the Media Rating Council to create common standards and classifications of transparency. This will ensure that participants in the programmatic ecosystem use consistent and accurate metrics for measuring performance. With the advent of the European Union’s General Data Protection Regulation, transparency into data sources, permissions, and usage is another highly scrutinized area requiring new levels of clarity.

Transparency will need additional focus around three areas in particular: contracts, impressions, and supply chains.

Contractual Transparency

Agencies should educate clients on their trading practices, with a growing number making their fees transparent to eliminate doubt around whether they are acting in the client’s best interest. If the agency receives any benefits due to usage or spend volume, there should be agreement upfront about whether some, none, or all of those benefits will pass on to the client.

Impression Transparency

Advertisers would prefer not to pay for unviewed impressions. Some programmatic exchanges eat the cost of unviewed impressions to address this concern, but there’s still a need for more high-quality viewable inventory overall. Advertisers that aim to improve transparency can utilize open exchanges and private marketplace deals that prioritize viewability.

However, the issue then becomes how viewability is defined. According to IAB standards, at least half of an ad must be in view for at least one second, which doesn’t fly with everyone on the buy side. Buyers like GroupM require 100% of an ad be in view for at least one second. The industry must agree on not only the definition, but also the value of a viewable impression. On the opposite end of the spectrum, marketers need to determine whether it makes more fiscal sense to incur the often higher premium for a viewable impression or to accept a lower viewability standard in order to achieve a more reasonable cost.

Supply Chain Transparency

Related to cost is the ratio of “working” to “nonworking” ad spend, which is 58% to 42%, according to a survey conducted by the ANA. In other words, for every dollar spent, only 58 cents went to the actual media purchase. The remaining money went to technology and agency fees.

When making a media purchase, it isn’t uncommon for a budget to pass through as many as five parties before reaching the publisher. That’s a lot of fee layers for what seems like a relatively straightforward process. Requiring disclosure of fees along the supply chain from all vendors involved is one way a marketer can assess whether the incurred costs are worth it. Another is to use tools that offer comparative performance metrics to assess the vendor’s success in reaching the marketer’s goals.

The holy grail of media-spend transparency may not exist, but adding some of these tangible tactics to our best practices puts the path to transparency in our sights.

This article was originally published in MarTech Advisors 8/17/18.

The Evolution of Amazon as an Ad Platform

The evolution of Amazon has led to the company holding a unique position. It’s simultaneously one of the biggest advertisers, spending an estimated $3.4bn in the US last year, and one of the largest advertising platforms, expected to rake in $4bn this year. And while many in the industry already consider the company a welcome challenger to the digital-ad duopoly of Google and Facebook, just how big of a piece of the ad spend pie Amazon can take in the future may depend on its willingness to jump outside of the walled garden strategy.

To truly rival the dominance of Google and Facebook and reach its market potential, Amazon needs to gain the same type of power that it has on its marketplace and inventory, off its marketplace – in other words, on other inventory. With their moves to date, they are on the right path to leverage their biggest advantage – customer data – across the ecosystem.

Connectivity

Amazon has been fairly proactive when it comes to connecting to properties besides their own with one of its two main advertising offerings, Sponsored Products, which is effectively paid search. They’ve had an API for sellers for quite some time, and more recently they created an API that gives brands self-service, customizable access to platform inventory.

The company’s other ad service, the Amazon Ad Platform (AAP), is where the real opportunity lies. With AAP, Amazon offers advertisers the ability to buy ads on a host of other web and mobile publisher properties. Functioning like a demand-side platform (DSP), AAP enables advertisers to bid on open inventory outside Amazon using the same exchanges through which they usually transact. In the past few months alone, it has progressed very quickly and agencies and marketers are clamouring to get on board.

With a growing list of connections to third-party ad servers, research providers and attribution partners, AAP is gaining traction. If Amazon continues its plans to aggressively go after outside inventory sources for display and video, offer incentives like discounted tech fees, enable integrations and adopt APIs, they’ll be in an optimal position to be the on-ramp for spend and inventory everywhere. Also, I am hopeful that they will build out robust open API access to their advertising offerings. Given Amazon’s pedigree with APIs and microservices as seen on AWS, it is quite natural for them to do so.

Consumers

Amazon’s USP is data, but wouldn’t every platform say the same thing? Google and Facebook both certainly thrive as ad businesses thanks to their user data. But Amazon’s data set is different, and it’s that difference that gives them competitive advantage.

The data that Google and Facebook have still provides insight into users’ “inner lives,” with things like searches and peer group involvement informing the personal profiles that guide ad targeting. For example, based on the behavioral data, Honda can deliver ads to “soccer moms” with a fairly high degree of accuracy. What they can’t do, based on that data, is target by the anticipated purchase intent of those soccer moms. That’s where Amazon has a clear advantage.

Amazon, unlike Facebook and Google has actual records of what people really buy, and not just the things heir habits suggest they might buy. It’s a gold mine of information that no one else can offer. How they can build on this advantage to surpass the power of their rivals is by combining those powers with their own to form a new superpower, where search, social and shopping together provide insights into purchase journeys that can inform a brand’s whole strategy.

The potential is enormous. By charting a consumers’ data all the way from social behavior, through search intent and corroborated by real historical purchase data, platforms could build a value picture in to how audiences really behave. But this is where the connectivity conundrum resurfaces. Because joining together search, social and shopping data is going to require that all three ingredients be put in to the same pot.

The duopoly, as it has been until now, is hardly known for being open and forthcoming with their services. And recent moves in the ecosystem such as GDPR and the end of Facebook’s Partner Categories, through which advertisers could target ads using customer profiles bought from data brokers, suggest further tightening of the internet’s historic openness.

Amazon, with its full funnel access to what consumers are talking about, shopping for and purchasing, is sitting at the precipice of an opportunity to strengthen their offering while being a good guy in a market looking for another trusted partner. At the risk of sounding cliche by quoting Spiderman’s uncle Ben, “with great power comes great responsibility.” I just hope that, in this case, great power brings a revitalized sense of openness that will be necessary for the full possibilities of Amazon as good guy can be realized.

Jaisimha Muthegere, chief technology officer of Visto Hub

Greater Transparency Makes Demand-Side Platforms More Effective

Daryl McNutt, VP of Marketing at Visto discusses the ability to compare side-by-side demand-side platforms (DSPs), arguing the importance of this change from a larger industry perspective.

When analysing the profitability of an assortment of ad channels, there can be a lot of information presented without very much clarity. Some demand-side platforms simplify that data and present it in an easy-to-digest interface, yet those aren’t without their own challenges.

In many cases, advertisers and agencies compare reports manually to review execution platforms, an apples-to-oranges comparison that makes it difficult to assess a report, let alone act on its results. On top of that, there’s a distinct lack of transparency as to where your ad spend actually goes. A recent study by the Association of National Advertisers found that 42% of each programmatic dollar is spent on nonworking media.

Many of the costs aren’t disclosed, which makes it that much harder to know whether money is being spent on media or intermediary fees. Despite these challenges, a multiplatform strategy can be quite useful if you choose the right platform at the right time, but to do that, we need to understand and compare their offerings.

The battle for transparency

In today’s programmatic landscape, the link between transparency and revenue couldn’t be clearer. In the past, digital media buying operated among too many unknowns such as what brands buy; an ad’s cost, quality, and channel; and how an ad performs and is optimised. Side-by-side platform comparisons can facilitate that clarity, helping advertisers see where their ad spend goes and how to reallocate in order to optimise for ROI.

The potential of hidden costs introduces an inherent lack of transactional transparency into the programmatic ecosystem. In a report by the World Federation of Advertisers, it’s estimated that 60 cents of every programmatic dollar spent goes toward the “technology tax,” which encompasses supply-chain data and transaction fees. These hidden costs create concerns about nondisclosed buying agreements for programmatic media, which limits how closely advertisers can inspect, analyze, or audit a buy’s transactional details.

Then there’s the issue of ad viewability. Many execution platforms can track how long and how much of an ad appears in order to assess its profitability. The Media Rating Council requires 50% to appear for at least one second. Video is a bit different, with the council requiring half a video ad to be visible for at least two seconds to be considered viewable.

Marketers who choose not to invest in fraud-busting are taking a big chance. Ad impressions served to fraudulent sources or clicked by robots instead of humans are all wasted spend. The assumption is that the current marketplace contains significant amounts of falsified inventory, meaning that the integration of anti-fraud and data-disclosure initiatives into platforms can weed out fraudulent inventory and prioritise ads that are aboveboard.

A proliferation of ad-buying channels brings healthy supply-path choices and market competition. More difficult to discern, however, are the multilayered cost structures and trade-offs in performance among vendors. In 2017, Procter & Gamble chief brand and growth officer Marc Pritchard called out programmatic’s seedier ad tech vendors, specifically those that take sizable portions of customers’ media buys before publishers even receive it.

A properly calibrated multiplatform comparison can offer a transparent view into which channels provide the best ROI and the most direct path to an advertiser’s target audience.

Navigating the murky waters

To accurately compare ad channels, it’s first important to create an apples-to-apples situation that quantifies true trade-offs. To do so requires data from each platform to be centrally processed and defined. Once this happens, an ad’s inventory, quality, performance, and price can be used to make sophisticated decisions across platforms. The next challenge is then how to efficiently access each platform’s user interface to enact faster decision-making and execution.

Automation can help reduce repetitive tasks that deal with budget and performance across multiple partners. A centralised ad hub can help with reallocating spend, identifying discrepancies, and adjusting bids. This multiplatform approach maximises campaign performance and leverages every available opportunity, many of which would otherwise be lost. It optimises the return on each of your campaigns and keeps the experience consistent across all channels.

Again, it all boils down to transparency, which is a valuable trait for an effective campaign. Omnichannel comparisons hold platforms accountable for where every penny in the ad-buying process goes and what is bought. Once the entire supply chain is transparent, brands can finally receive the value they expect for their advertising spend. They shouldn’t have to settle for anything less.

Originally published 6/4/18 in PerformanceIN

AI’s Real Moment On The Advertising Stage Is Tomorrow

One of the hottest storylines in ad tech this year has been artificial intelligence (AI). Proffered as potential panacea for effectiveness, brand safety and transparency, AI has grown from niche discussion to industry obsession, a promised key to smarter digital ad targeting and trading. I’m sure you’ve seen the pronouncements: it can determine the bids most likely to succeed, it can use historical performance data to tailor campaigns and it can even swap out creative based on audience data in real-time.

There’s one issue: a lot of what’s being hyped isn’t actually AI. It’s just tools and technologies being marketed as AI in order to differentiate within a complex and competitive arena. With true AI, a machine imitates intelligent, and maybe even sentient, human behavior. And while much of what we see today looks like the computer is thinking for itself, it’s really just following very specific, pre-programmed paths using simple rule-based actions, and/or predictive analytics or machine learning. While all subsets of AI, even together they don’t add up to real AI. They’re more like “artificial AI.”

It may seem like semantics, but there are important differences. With predictive analytics, patterns in existing data are used to predict probable results and trends in the future, typically using statistical models and methods. Then, there’s machine learning, a branch of artificial intelligence where machines learn and adapt through experience, without the need for predetermined rules and human intervention. With machine learning, models and techniques will change themselves over time as more classifiers enter the system and improve the description of the data to be learned. Examples of machine learning classifiers are K Means Clustering, Linear Regression, Logistic Regression and Decision Trees. These techniques are being used in technologies today for things like facial, voice, music and handwriting recognition.

While not using true AI, the market-available technologies for programmatic advertising that we have today are still sophisticated. They effectively use machine learning and data science-based systems to predict the likelihood of desirable outcomes. They’re certainly valuable for advertisers looking to optimize their media budgets. Further, the automation they allow creates huge efficiencies. They do not, however, fulfill the promise of a set-it-and-forget-it system that gets better or more accurate without any human intervention.

When real AI is finally applied to advertising, it will be transformational. It will intelligently enable desired outcomes to be produced by calling on not one, but a collection of interrelated sciences, techniques and data processing. One day, a truly intelligent AI-based advertising system will enable buyers to seamlessly construct their entire campaign, complete with optimized buys and evolving tactics, just by specifying their goal(s) and budget. Once the algorithms take over, the system will leverage historical data about similar campaigns to make predictions and changes on the fly.

This is all achievable. But supporting it requires highly complex systems to come together, and we still have a long way to go based on today’s fragmented, disconnected assortment of pseudo-systems that look fantastic in isolation, but, in aggregate, don’t add up to the holistic system that buyers need.

Eventually, AI will evolve to where it can improve programmatic media and create a better user experience. And we will eventually get to the point where technology can drive ad campaigns that, without human interaction, achieve campaign KPIs through a virtuous circle of measuring, analyzing and acting on campaign spend, allocation and outcome variables.

In the meantime, platform vendors intent on presenting today’s “artificial AI” will accomplish more by being open about the realistic expectations of their products and the fact that their capabilities are that of early-stage, partial expressions of AI. At the same time, we as an industry should raise our level of thinking and education, so we gain an accurate understanding of AI — not just what it is, but what it can do for us. It is with that knowledge that we can begin to see the true potential of real AI.

Originally in MediaPost – MarketingDaily.

The Case For Connectivity

Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media. Today’s column is written by Ashley Herzog, vice president of product at Visto.

Like a good mass transit system, interconnectivity is the key differentiator for advertising technology these days. But for any brand embarking on a self-serve programmatic strategy – for many an agency, too – it’s not as straightforward as simply plugging services together. Challenges and delays remain in vetting potential programmatic partners, reviewing commercial terms and poring over contracts – time-consuming work that can all be for naught if the systems can’t be integrated.

One big hurdle: vendor vetting friction. Considering the current size of the Lumascape, just assessing hundreds of providers for initial fit could take an eternity. Seemingly every day a new partner enters the ad tech ecosystem, offering new or sometimes duplicative services to brands and agencies. The most persistent or notable often push their way to the front, whether or not their technology truly warrants the recognition.

What is more, marketers need sign-off from multiple people within their organization. A customer wants to assemble a group of diverse and complementary partners with experience and market equity, but putting together the puzzle can be like solving a Rubik’s cube.
Another challenge is the commercial negotiation. There is a steep adoption curve to embracing programmatic platforms’ business terms. Most will require a 12-month upfront contract with minimum spend guarantees, which have now risen to upward of 20%. Some providers require a strict and steep monthly minimum, which for a brand manager can add up to a lot of early pressure. In many cases it may take two to four months to ramp up a team to the kind of understanding or visibility to drive significant results.

Finally, there is the contract review. Legal due diligence is important – and time-consuming. I recently learned of one big publisher that took more than six months trying to get its legal team to review a programmatic ad tech contract. And I heard about one major brand that already had a data management platform and programmatic director in place, but just didn’t know where to start to build out its programmatic stack. Just getting one demand-side platform in place took it more than eight months.

These points of inertia are not just road bumps slowing down brands’ inevitable adoption of programmatic tools – they are actually suppressants discouraging many from jumping in at all. I have seen many advertisers and agencies, put off by these challenges and complexities, actually shy away from the prospect.

Programmatic buying is still a mystery to most marketers, according to the ANA, with only 23% claiming to understand how to effectively leverage programmatic strategies. This simply isn’t good enough – when brands are looking at investing millions of dollars in media spend, any mistakes are costly. It’s on the vendors to add in a layer of product onboarding and training to help alleviate this gap.

When I think about how my peers in engineering can smooth out the disconnection and inefficiencies between the core executional actions of advertising platforms from a technical standpoint, I enviously wish there were an equivalent magic wand we could wave over on the business side, to reduce the friction and uncertainty around vetting, negotiating and clearing supplier contracts.

That is why I think everyone in the industry needs to make a concerted effort to reduce the inertia and increase the simplicity with which customers can onboard themselves to a programmatic stack.

Too often, people leading brand marketing efforts are overwhelmed at the first step on the on-ramp. That can limit spending against tactics that may be extremely valuable to a brand, but are deemed too daunting to explore. So, vendors should take it upon themselves to make the best in programmatic solutions as accessible as possible.

Building the first programmatic stack is about contracts and fine print as much as it is about APIs and data. The industry needs to make these business matters as plug-and-play as the technology in the platforms it sells, reducing the time it takes to assess partners, negotiate terms and review contracts.

Follow Visto (@vistosays) and AdExchanger (@adexchanger) on Twitter.

Originally by AdExchanger