Are Your Benchmarks Up to Date? How Digital Advertising Performance Standards Are Changing
The standards for advertising performance benchmarks are getting higher and higher, forcing many marketers to reconsider how they measure their online efforts.
According to Magna Global’s latest year-end advertising forecast, digital media will account for 44% of all ad spend in 2018, a 13% increase over last year. While all digital ad channels are expected to garner increased investment in 2018, paid search will continue to dominate the space, accounting for $113 billion of the $237 billion global digital advertising tab.
To stand out in this crowded digital landscape, marketers need to reevaluate what constitutes success — and as more companies put more money into digital campaigns, that’s only getting harder to do. New data published in Search Engine Journal suggests that the industry standard for good performance in digital advertising is only getting higher as leading firms get better at reaching internet users, building awareness, and driving conversions.
Digital Advertising Performance Benchmarks on the Rise
The data — which provides benchmarks for search ads purchased through Google AdWords and display ads purchased through the Google Display Network (GDN) — shows a median CTR of 3.17% for search and 0.46% for display across all industry verticals. Companies in the dating and personals and travel and hospitality verticals enjoy the most success on the paid search front, with CTRs of 6.05% and 4.68%, respectively. Unsurprisingly, verticals that benefit from engaging visuals like real estate (1.05%) and dating and personals (0.72%) enjoy the highest CTRs on display advertising.
While these CTRs are significantly higher than in previous years, digital advertisers generally haven’t had to pay a premium to secure these improved results. The median cost-per-click (CPC) currently sits at $2.69 for AdWords and $0.63 for GDN, only slightly higher than the $2.32 and $0.58 benchmarks from two years ago.
Industries like consumer services and legal services, where people tend to do extensive research before making a choice still have fairly high CPCs ($6.40 and $6.75, respectively) but overall, advertisers are getting more bang for their buck than ever before.
In fact, according to the Search Engine Journal research, only the advocacy industry has an AdWords conversion rate (CVR) below 2%, and the cross-vertical median is rapidly approaching 4%. Similarly, the cross-vertical median for display ads sits at 0.77%, driven largely by high CVRs in dating and personals (3.34%), legal services (1.84%), and employment services (1.57%).
The Need for Personalization
Consumers’ growing comfort with online advertising has certainly played a part in this cross-vertical elevation of traditional advertising KPIs. Still, much of the credit must go to the marketers who are simply getting better at their jobs — particularly those working on behalf of industry leaders.
Modern marketing is becoming especially good at personalization, as more and more marketers are realizing that creating “audiences of one” is the most effective way to boost CTR and CVR without inflating CPC.
According to McKinsey, “Personalization can reduce [customer] acquisition costs by as much as 50%, lift revenues by 5 to 15%, and increase the efficiency of marketing spend by 10 to 30%.” What’s more, analyses conducted by HubSpot found that personalized calls-to-action have a 42% higher view-to-submission rate than boilerplate calls-to-action.
For many marketers, delivering 1:1 messaging is a daunting task. Fortunately, cutting-edge tools like Albert™, the world’s first fully-autonomous artificial intelligence marketing platform, offer marketers an easy, intuitive way to achieve the hyper-personalization needed to keep pace with climbing digital advertising performance benchmarks.
Albert offsets the complexities of marketing personalization by handling many of the time-consuming, computationally-intensive tasks that humans are simply unable to execute at the requisite speed and scale. By pairing in-house marketing expertise with Albert’s powerful machine learning algorithms, an organization is well on its way to far surpassing the benchmarks of yesteryear.
Banks Are Using AI to Disrupt Financial Crime
Fraud is getting harder and harder to stop in an increasingly digitized economy, but emerging artificial intelligence technologies may even the playing field for financial institutions.
According to the United Nations Office on Drugs and Crime, between $800 billion and $2 trillion are laundered every year, amounting to roughly 2-5% of the global GDP. Moreover, money laundering and other forms of fraud cost financial institutions an average of 5% of their annual revenues. And to add insult to injury, nearly a fifth (18%) of them are fined by regulators each year for failing to follow proper protocol in the detection of and response to fraudulent activities.
Despite collectively spending more than $8 billion on anti-money laundering (AML) compliance last year, an astounding 87% of financial services organizations believe that their current anti-financial crime systems and processes are, at best, “somewhat efficient.” Luckily, this incredibly costly and complex problem may have a high-tech solution: artificial intelligence.
An Opportunity for Autonomous AI
The prevalence of financial crime today is partially due to anti-fraud professionals’ inability to manage both the explosion of data created by digitization and increasing regulatory pressures. Extensive anecdotal evidence suggests that AML analysts spend three times as many work hours gathering and organizing data as they do actually analyzing the data for suspicious activity.
Processing and interpreting huge sums of data like those that AML analysts deal with on a daily basis is one of AI’s primary applications as it’s rolled out across a wide variety of industries. “The rote nature of evidence gathering combined with the manual intensity of current processes make risk and compliance operations just one of many prime targets for automation,” NICE Actimize Enterprise Risk Case Management VP Chad Hetherington explains. “Automation empowers the knowledge worker to focus on the risk rather than the actual chore, while reducing cost and streamlining operations.”
Many financial institutions see a lot of value in streamlining these processes, so much so that the global FinTech AI market is expected to expand from $1.3 billion in 2017 to $7.3 billion in 2022 — an impressive 40.4% compound annual growth rate.
AI’s Role in Fighting Fraud
Most recently, HSBC announced a long-term partnership with Quantexa, a London-based AI software company. Per Quantexa’s press release, “The technology will allow HSBC to spot potential money laundering activity by analyzing internal, publically available, and transactional data within a customer’s wider network.”
HSBC’s move comes on the heels of its partnership with Ayasdi, an AI startup specializing in streamlining AML investigations. Last summer, HSBC used Ayasdi’s technology to not only reduce the number of investigations flagged for closer scrutiny by 20%, but to do so while dramatically decreasing AML person-hours, freeing analysts to focus on higher-level anti-fraud tasks.
The Financial Times reports that HSBC is just one of many financial institutions exploring AI-based solutions in this vein. Other examples of banks around the world leveraging AI to detect and investigate fraud include the Royal Bank of Scotland, Denmark’s Danske Bank, and the Singapore-based OCBC.
Stepping into the AI Era
Ultimately, AI is gaining traction with financial institutions like HSBC because it enables them to level up their anti-fraud operations with minimal effort. In fact, according to the 2017 NICE Actimize FMC Survey, over 80% of institutions agree that AI-based behavioral analytics software can detect potentially fraudulent activities that typically go unnoticed by even the best analysts.
What’s more, institutions that become familiar with AI tools stand to improve not only their anti-fraud operations, but their general data processing, credit assessment, and trade optimization operations, as well. As summed up by PwC, thanks to cutting-edge technologies like AI, “The financial services industry will be unrecognizable in five years.”
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Less Than Half of Marketers Say Their Programmatic Partners Are Transparent
A new study from the World Federation of Advertisers found that fewer than half of all advertisers are satisfied with the transparency of their programmatic partners. Here’s how that might change in 2018.
A new study from the World Federation of Advertisers (WFA) titled, The Future of Programmatic, reflects on programmatic advertising’s massive impact on the field while offering some predictions as to how the field will evolve in 2018.
The good news, according to the study, is that programmatic is on the rise, as total digital media investment budgets are expected to rise by nearly 10% on a global level. The bad news is that in spite of its increasing importance in the media sphere, programmatic continues to struggle with transparency: fewer than half of the study’s respondents reported that they have a fully transparent relationship with their partners.
The Importance of Transparency
Transparency in programmatic media boils down to trust, communication, and understanding between publishers, brands, media buyers like DSPs, and any other parties involved in programmatic exchanges. Many programmatic platforms offer their services as walled gardens, hiding their inner workings, data usage, and decision trees so that it becomes unclear how they are adding value. As a result, brands are left to wonder what exactly they are being charged for and why they don’t have access to critical data about their customers.
This is why, as the study reports, a full 41% of respondents say that greater transparency with programmatic partners is a major priority for 2018. “In terms of its public relations with the wider marketing community, it’s fair to say that 2017 hasn’t been a great year for programmatic, but there’s too much momentum now,” says Matt Green, Global Lead of Media and Digital Marketing at the WFA. “In spite of the issues, clients do generally see the benefits — brands will spend a growing share of their digital ad budgets in programmatic in 2018.”
Change on the Horizon
Since programmatic isn’t going away anytime soon, optimists believe that transparency advocates will have be more empowered to pressure programmatic partners to address long-standing issues with walled gardens and data access. Support for addressing segment mark-ups and data arbitrage rose by nearly 50 points over the past year, with 62% of respondents marking it as a major priority. With such strong support, positive developments in data transparency are sure to come.
Moreover, the concern with data transparency extends beyond the industry. As Green points out, new regulation like the EU’s General Data Protection Regulation is already working to put much stricter controls over how customer data may be used. Similar rules are sure to follow in other countries as well, leaving the clock ticking for programmatic media companies.
While some brands continue to demand greater transparency from their programmatic partners, others are turning to alternatives like those provided by Albert, the first autonomous marketing platform built from the ground up on AI. Albert allows companies to bring media buying in-house, and his Inside Albert feature allows him to show and report on his work. Inside Albert offers a comprehensive campaign narrative based on quantitative data from all active channels, giving his fellow marketers a look into his real-time decision-making.
Brands no longer have to put up with opaque exchanges and secretive partners — with Albert, you can understand exactly what your data is doing for you.
Blockchain Promises to Put an End to Ad Fraud
The latest MadTech craze is focused on blockchain technology, which some say has the potential to end ad fraud for good.
If you’ve watched the news or perused the internet in the last year, chances are you’ve heard about Bitcoin, a volatile cryptocurrency that some say is the future of commerce. While Bitcoin is an intriguing development for digital prospectors, blockchain — the technological foundation that Bitcoin is built on — is what has the tech world truly excited.
“Blockchain has proved itself robust and adaptable to dozens of high-impact use cases,” says Alex Tapscott, coauthor of Blockchain Revolution. “Companies need to develop compelling enough applications that it can make a real impact, [and] this is already happening.”
Security Through Decentralization
The power and adaptability of blockchain technology stems from its decentralized architecture. Put simply, a blockchain is a way of connecting transactions between different devices and parties to form a single, unchangeable record.
Conventional databases are hosted on a single server, and devices trying to access the authoritative “master copy” must ping the server each time they want the most up-to-date version. While this is an effective approach to ensuring data consistency, it makes things easier for hackers and other bad actors by providing them with a single, location-specific target. If they manage to hack the server on which the master copy is hosted, they can steal and/or manipulate data without immediately raising suspicions among other users or devices.
That’s precisely the problem that the blockchain was designed to solve. Since every device participating in a particular blockchain holds a complete copy of the ledger at all times, a hacker would need to deceive thousands of unrelated devices simultaneously in order to corrupt the data “blocks” in the chain. This decentralized architecture not only makes a blockchain nearly impervious to hackers, but it also prevents users from changing or erasing any information in the chain’s ledger. As such, a blockchain can bring transparency and security to practically any kind of transaction.
Ad Fraud Still a Major Problem
Combatting ad fraud is one of the more exciting potential use cases for blockchain technology. Though there are many ways to defraud digital advertisers, bot traffic continues to be one of the advertising industry’s most intractable problems. By setting up “dummy” sites featuring everything programmatic digital media buying software looks for and flooding these sites with bot traffic, cybercriminals are able to trick advertisers into paying huge sums of money for ad space that only robots are clicking on.
It’s not hard to see why blockchain technology is attracting so much attention from digital advertisers, even at this early stage of its development. For instance, in June, MetaX and the Data & Marketing Association launched adChain, an open protocol blockchain tool that enables advertisers to “follow” their creative materials across the web by embedding a tracker in the xml of each ad.
In theory, when an advertiser purchases an ad impression, adChain will encrypt this transaction in a data block and transmit it to each participant in the service’s chain. Once the media publisher provides evidence that the ad has been served in a legitimate fashion, they verify the data block and append it to the adChain ledger as an unimpeachable record of the transaction.
A platform like adChain would allow advertisers to know whether their ad was seen, who saw it, and where it ran, all but eliminating the possibility of unknowingly pouring thousands of dollars of ad spend into fraudulent sites and bot traffic.
Ad Spend Optimization with AI
As promising as blockchain-based anti-fraud tools like adChain are, they’re still at least several years away from wide availability. What’s more, limiting waste through problems like ad fraud will always only represent one side of the digital advertising coin. The other — ad spend optimization — is just as important.
Fortunately, another strain of cutting-edge technology — artificial intelligence — is already driving real results for companies in numerous market niches. With Albert™, the world’s first fully autonomous AI marketing platform, companies get a partner capable of gathering, aggregating, and analyzing huge volumes of cross-channel, cross-device data at superhuman speeds, ensuring that every ad dollar drives the best possible results.
As the MadTech revolution continues to unfold, companies smart enough to pair an AI tool like Albert with a blockchain tool like adChain will be perfectly positioned to establish themselves as true leaders in their market vertical.
Why Do We Need to Know What AI Is Thinking?
People may never be able to fully understand how AI thinks, but transparency is still needed to build trust with users and optimize the productivity of these platforms.
How important is it to understand the technology we use? Practically everybody uses a computer on a daily basis, whether at work or at home, but relatively few of us know how that computer stores files or connects to the internet. The fact of the matter is that nearly all devices today operate at such a high level of complexity that understanding their inner workings is completely impractical. What really matters is that we produce results in partnership with that technology.
But artificial intelligence is different. A huge reason behind lagging adoption of the technology is the simple fact that it is misunderstood, engulfed by numerous myths and misconceptions that cause many people to worry that it will radically change their day-to-day work processes or even take their job.
Combatting those myths will take a degree of comprehension on the part of users, despite how complex the technology may be. Though AI may be too complex for anyone to fully grasp, even a rudimentary understanding will build trust with a skeptical public.
Can Humans Really Understand Machines?
A growing school of thinkers, including technologist David Weinberger and Facebook’s head AI scientist Yann LeCun, argue that understanding an AI’s thought process is not only unnecessary, but actually detrimental to the efficacy of the technology.
The main strength of AI, Weinberger argues, is that it makes decisions based on more variables than humans are capable of considering at any given time. Since the complexity, speed, and nuance of these decisions are beyond the scope of human understanding, rendering the technology understandable or explainable to the average user would require simplifying the process in such a way that would limit its efficacy.
Optimization Over Interpretation
But of course, this doesn’t mean that AI-enabled tools should be black boxes, completely illegible to their users. Though marketers may not be able to fully understand the technical details of how AI works, it’s clear the opacity of AI makes them worry about control — our recent customer survey revealed 25.5% of marketing AI users felt they had little control over their platform’s activity. Instead, measures of transparency should be introduced that allow users to at least understand the AI’s end goal in making a decision.
No technology is perfect, and at one point or another, any system will need to be calibrated. By giving their operators a certain degree of transparency into what inspires its decisions, AI algorithms can facilitate active collaboration with humans, which will only help build trust and dispel common myths about the technology. 58% of agencies responding to our survey said they discovered new audiences with their AI, but those kinds of insights can’t happen if marketers don’t trust the information they’re getting from their autonomous partners.
That’s the motivation behind tools like Inside Albert, which we created to grant users transparency into the world’s first marketing platform built from the ground up on AI. Instead of limiting the scope of the platform, Inside Albert gives marketers the information they need to calibrate and recalibrate the way they use Albert. Any understanding of how the platform works, even if it’s limited, gives professionals the opportunity to see AI as a tool that augments their work, rather than a threat that replaces it altogether.
Before AI adoption can really surge, some degree of transparency needs to be established. The real value of AI isn’t in its algorithm or its computing power — it’s in the relationship between that power and the human operator that understands how to leverage it. But that relationship has to be based on trust, and that trust has to be based in transparency.