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