First Price Auction Is A Step In The Right Direction

Acquire Online believe the move to First Price auctions is a step in the right direction as buyers can now compete for premium inventory which would have otherwise been onsold at a profit or reserved for bigger agencies groups. First price is meant to combat hidden fees being taken from both the buyer and seller but it has its share of problems. e.g. When a publisher is working with multiple Supply Side Platforms (SSP) on different auction dynamics.

New Auction Dynamics

Header bidding was an improvement over the waterfall model in terms of auction dynamics, but it still entails some problems and inefficiencies. These considerations lead to the development of an array of new auction mechanics. Consider this example:

  1. A user accesses a website.

  2. The header-bidding wrapper makes simultaneous requests to Suppy Side Platforms (SSPs)/ad exchanges.

  3. Each SSP/ad exchange broadcasts the bid requests to a number of Demand Side Platforms (DSP) it is connected to.

  4. Each DSP responds with the bid responses and the max CPM price the advertiser of the matched campaign is willing to pay ($10, $5, $8, $6).

  5. SSPs/ad exchanges execute the second-price auctions (e.g. If the SSP received $10 and $5 bids, the winner is paying $5.01 CPM—the clearing price).

  6. The SSPs returns the $5.01 clearing price to the header-bidding wrapper.

  7. The header-bidding wrapper chooses the highest clearing price (first-price auction) and displays the impression.

How can this system be inefficient? Because there was, in fact, another buyer willing to pay more (the $10)

The example above illustrates the inefficiency of the currently used header-bidding auction process. DSP 1’s $10 bid did not winning against DSP 3’s $8 bid because the SSP 1 runs a second-price auction (with a $5.01 clearing price), which loses to SSP 2’s clearing price of $6.01.

However, because there weren’t any high bidders at the SSP level, the clearing price of the second-price auction was just $5.01, less than on the other SSP level. This is a wasted opportunity for profit that publishers want to avoid.

When DSP can pass information like auction type, floor CPM and winning bids, buyers and algorithms can be trained to better understand how the market/SSP is valuing each impression and bid smarter depending on the real supply/demand curve at the time.

*Source: Clearcode

Native Advertising Progression

Native advertising revenue continues to grow each quarter with the latest IABNZ 2017 Q4 figures showing $5.5m, rising to a 13% share of total Display advertising.

Native advertising has come a long way since its inception. Initially native was reduced mostly to content discovery widgets and now we're seeing more and more in-feed native ad units across sites.

Native advertising is a tool to reach a wider audience that do not otherwise response to banner advertising.

What's Native?

A native ad blends seamlessly into the content of a publisher's page or app. Native adopts the style, location (often in-feed or embedded within other editorial or page content itself), and voice of the publisher's content. It encourages user-led "discovery" engagement.

Native ads are cross-environment, serving on mobile app, mobile web, and desktop websites.

Key difference from normal Display Ads:

The key difference between normal display ads and native ads is that the publisher is responsible for the creative layout and the advertisers just provide the components, (eg. Images, text, logos, video and so on). The publisher decides how to arrange these assets, designing them to fit both the available space and the surrounding content, whether it appears on their website or in a mobile app.

Key Benefits of Native:

  • Higher Engagement - Engage users with relevant content. Native ads often see up to 5-8x higher CTRs than banner ads
  • Reduces potential 'banner blindness' - creative is often in-feed & 'Native' to the Platform
  • Increase awareness, leads and sales.
  • Seamless Integration- Native creative is responsive and fits the layout of the sites and devices (mobile and desktop) in which it is served.
  • Creative Flexibility - Options to make shift changes in creatives when penetration and performance starts to drop.
  • Reach amongst Publishers
    o For example, Outbrain has access to Stuff.co.nz and the large audience population the publication commands, Taboola has access to Mediaworks (Newshub) & MSN, Yahoo has the Yahoo platform which commands a very heavy female skewed audience, Stackadpt focuses on Mobile native inventory and Google Native is an extension of DBM and has a very large sitelist.
  • Large variety of Targeting Options - Contextual, 3rd Party, Geo Targeting, Behavioral, Demographic, Device, engagement tracking
  • Native ads are often bought on a CPC basis - whilst you can receive good branding/awareness, you only pay for that Audience who are interested enough in your product or service, to click.

Recommendations for Native Ads:

  • 2-3 images and 3-4 different Ad copy variations per url.
  • Be specific with your title to attract the right audienc
  • A strong call to action
  • Clearly setup user expectations - eg. If you want the user to view your video use the word "watch"
  • Uncluttered and engaging Landing pages

Examples of Native Advertising:

Key Takeouts:

  • Ensure your vendor meets the IABNZ standards - Clearly labels your content as paid advertising
  • Make sure your vendor has the flexibility to run across multiple platforms
  • Create interest not interruption - entertaining and educating content
  • Supply multiple headlines and images so creative can be shifted swiftly and optimised.
  • Ensure that Blacklists are run, so irrelevant and underperforming sites are excluded
  • Each Native Specs Change - different Publishers have different formats
  • Ensure that your landing-page content delivers on the Native Ad's promise.

For further information click here to read 'Going Native' Whitepaper

Native Advertising Ad Specs.

NO ROOM FOR GUESS WORK  - ONLINE ADVERTISING DATA SHARPENS BRAND UNDERSTANDING

How probable is the “opportunity to see” any advertising? How reflective are the media consumption metrics compared to the reality of media consumption? For instance, even if TV ratings shifted to second-by-second statics do we really know if people are paying attention or ignoring the advertising?

Since Acquire Online started tracking online advertising with MOAT from October 2015, we have analyzed 700M+ banner ad impressions across an extensive array of ad (appearance) serving and banner (touch) engagement metrics.

The MOAT system helps advertisers measure whether people see and engage with online ads at a publisher (URL) domain, browser and device level. This is especially pertinent given growing concerns over viewability, fraud and brand safety. It’s efficacy as an advertising measurement tool is reflected on the fact that it works with some big names including Nestle, Uniliever, Facebook, ESPN and Snapchat.

HOW MOAT BUILDS STRONGER ONLINE CAMPAIGNS AND HEALTHIER BRANDS

With data from MOAT, we have completely changed how we value and buy inventory. Through MOAT we are confronting some fundamental questions:

  • Is the viewer a person or a bot?
  • Was the ad placed on a page in time to see?
  • Did the user touch the advertisement with a cursor or finger?
  • How long was that ad viewable?
  • How fast or slow are people scrolling through websites?
  • Are people hovering on the ad but not clicking?

There has been a history of dismissing viewability & engagement tracking in favour of performance (CPL or CPA) campaigns. However, while performance campaigns are optimised to end goal, we know from attribution tracking that better online viewable inventory has a direct influence on future conversion actions being taken.

MOAT delivers quarterly benchmarks for various countries. New Zealand benchmarks should be used by media and creative agencies to set expectations for ad campaign objectives for key metrics such as viewability and engagement...as well as CPM, CTR, CPC & CPA. Having all objectives in mind when organizing a campaign (building whitelists & blacklists, selecting devices & ISPs, choosing creative type etc.) will significantly improve online trading tactics.

There is no better research on publishers than the insights achievable from online viewability and engagement tracking tools like MOAT, as almost every ad impression can be monitored versus a small sample of ads.

All online advertisers should track online advertising to:

  • Ensure that good publishers are rewarded with higher CPMs.
  • Remove the worst publishers with blacklists.
  • Ensure Brands have full visibility on the sites their ads appear.
  • Reduce budget wastage by optimising on good creatives and engaged audiences.
  • Decrease invalid traffic by significantly minimising bot influence (i.e. increase targeting on real people engaging with your ads)
  • Understand how users are engaging with your banner creative through heat mapping.
  • Help publishers to understand the relationship between their audiences and advertising appearing on their pages (vs their competition).

WHAT DO WE KNOW? (Desktop Benchmarks Q2 New Zealand from MOAT)

The MOAT benchmark from Q2 can give us a NZ perspective on performance for banner advertising on desktop devices. These metrics and an explanation on each follows:

  • 97.8% In-View Measurable Rate. The percentage of impressions where viewability-related metrics were measured. It is calculated as the number of In-View Measurable Impressions divided by the number of Impressions Analyzed.
  • 61.9% On-Screen Rate. The percentage of impressions where at least one pixel of the ad was in-view with focus.
  • 53.3% In-View Rate. Percentage of impressions where at least 50% of an ad was In-View for at least one continuous second. If the ad is as large or larger in area than 970x250 (eg. 300x1050 or 970x418), then it only needs to have 30% of its area In-View.

But, what happened to the impressions not in-view?

  • 2.8% Universal Interaction Rate. Percentage of impressions where a user entered the frame of the ad and remained active for at least 0.5 seconds.
  • 28.7s In-View Time. The length of time an ad has been active and In-View.
  • 6.9% Screen Real Estate. The average percentage of pixels that the ad fills on the user's screen. This is calculated by taking the ratio of ad pixels to device screen pixels for all measurable impressions.
  • 15.1s 50% On-Screen Time. The average length of time that at least 50% of an ad has been on-screen.
  • 58.2% 50% On-Screen Rate. The percentage of impressions where the ad surface was at least 50% on-screen for any period of time. For ads that are 242,500 square pixels or more, the ad only needs to have 30% of its area on-screen.
  • 48.9% 80% On-Screen for 1 Sec Rate. The percentage of impressions where the ad surface was at least 80% on-screen for one continuous second.
  • 44.0% 1 Sec Fully On-Screen Rate. Percentage of impressions where the ad surface was 100% on-screen for at least one second continuously.
  • 50.3% Fully On-Screen Rate (No Time Minimum). Percentage of impressions where the ad surface was 100% on-screen for any period of time.
  • 37.0% Human and 2 Sec Fully On-Screen Rate. The percentage of measurable impressions where the ad surface was 100% on-screen for at least two seconds continuously.
  • 6.95% Out of Focus Rate. Impressions served into a backgrounded or minimized tab.
  • 36.68% Out of Sight Rate. Impressions had no pixels visible on screen.
  • 5.31% missed opportunity (area) rate. Impressions partially visible on screen but did not meet the 50% pixels requirement.
  • 5.40% Missed Opportunity (Time) Rate. Impressions had 50% of their pixels visible on screen, but not for a full second.
  • 53.3% Human and Viewable Rate. The percentage of measurable impressions that were viewable under the MRC standard and were delivered to humans.
  • 52.6% Human and Fully On-Screen or Large Ad Rate. The percent of impressions where the ad surface was 100% on-screen for any period of time or was as large or larger than 970x250 (eg. 300x1050 or 970x418) and was delivered to a human.
  • 9.4s Universal Interaction Time. Average length of time the user interacted with the ad.
  • 9.4% Hover Rate. The percentage of impressions resulting in a user hovering on an ad.
  • 67.1% Scroll Rate. Percentage of impressions where the user scrolled.
  • 29.0% Attention Quality. Ratio of users that converted from hovering to interacting.
  • 54.8s Active Page Dwell Time. Average length of time the user was on the page with the window in-focus.
  • 0.8% IVT Rate. The percentage of total unfiltered impressions that were determined to be delivered to a non-human end point. This includes General IVT (Spiders, Excessive Activity, and/or Data Center Traffic categories) and Sophisticated IVT (Invalid Proxy, Automated Browser, and/or Incongruous Browser Traffic categories).

DIGITAL AD MEASUREMENT IN THE RESEARCH AGENCY REALM

There is no escaping that digital advertising is growing. According to Dentsu Aegis Network’s (DAN) Ad Spend Forecast (June 2017), digital’s share of ad spend is set to surpass television’s for the first time (37.6% against 35.9%), totaling NZD$313.8 billion. Not only that, but within digital, programmatic (automated ad buying) is set to grow by 25.4%. As DAN CEO Jeery Buhlmann says brands must be ready to embrace advertising innovation as new technologies become available and “ensure that they remain relevant by creating new value for their consumers.”

So, what does this mean for a research agency?

At the analysis and reporting side, it means the agency can have a greater understanding into consumer behavior and interaction with a brand. Are rural customers not able to load ads because the creative is too complex for their internet speed? And is that why their awareness of a new product line so low? Are urban commuters scrolling past an ad because it’s too text heavy as they are scanning through the news during their morning commute? Could that be why they find the ads annoying and feel more negatively towards a brand? Understanding these factors along with the insights gained from market research will give a deeper level of knowledge into a brand’s ecosystem and in turn allow them to deliver stronger and more refined recommendations to clients in an advertising world that growing ever more complex, fast-pace and evolving.

Data from:

Grapeshot Predict is a live audience targeting solution that predicts intent – what, when and where your audience is going to search, read, and watch, up to 72 hours in advance!

Predict spots trending content within news channels and social networks allowing advertisers to reach large relevant and interested audiences. Brand messaging is targeted directly into content topics people are engaging with most at any given moment.

Predicting intent is found to deliver higher click through rates and conversion rates.

Powerful when added to Grapeshot’s other targeting options:

  • Keyword targeting ensures your ads are be displayed next to the most relevant content, reaching consumers that are interested in similar or complementary subject matter.
  • Re-targeting users based on their browser history. For example, target users who have previously consumed business related online content. This is indicated by a list of keywords we upload into the grapeshot platform.
Understand where your data comes from - Are you running highly successful campaigns?

It’s a simple truth that the quality of data can directly affect the efficient deployment of marketing budgets and campaign success. So, it’s all the more baffling that many online advertisers don’t question the origin, accuracy or scale of the audiences they buy programmatically.

Right next to fraud and viewability, measurement and audience validation should be at the foundation of marketing success metrics for 2017.

Defining Data

Firstly, it’s important to understand some basic truths about data in order to understand it’s role in programmatic advertising.

  • Offline data
    This tends to be derived from PII (personally identifiable information)

  • Online data
    Anonymous data even if it was derived from PII originally

  • 1st Party Data
    Data which marketers collect and own e.g. site registrations, surveys. This data is often deterministic meaning it has one-to-one correspondence with the actual attributes and behaviours of individuals and their devices. This data has greater accuracy but can often lack scale.

  • 3rd Party Data
    Data owned by another source who then sells it to marketers. This data can be deterministic or probabilistic (based on inferred or modelled attributes rather than one-to-one correspondence). When probabilistic, this data is easy to scale but can be less accurate

What are the data dilemmas?

Accuracy Drops

Many companies strive to move their offline customer personas and models online. However, this process typically causes data segments to retain only 20-50% of their accuracy. To compensate, companies then over-model, leading to poor online targeting performance.

Time Sensitivity

On-boarded data (and most 3rd party) is time sensitive. The time needed to integrate data into a DMP/DSP usually causes additional data decay. Using this stale data causes results to suffer.

Weak Models

The more attributes you have on consumers the better you can predict their behaviour. However, if using 1st party data – which can suffer from a lack of scale - modelling platforms will not have enough data to provide accurate models

Applying the Wrong Measurement Methodology

Data measurement is not a one size fits all process. Programmatic systems use either panel or predictive measurement. Panel measurement relies on small deterministic data to infer coverage while predictive measurement scores the entire internet with a probability score for greater granularity. While panel measurement works well for targeting large samples, accuracy drops when measurement requires more granularity.

The data you need

Online advertisers need to constantly optimise their campaigns to drive the success of their campaigns. This means not only using quality data but also quality measurement methodologies. Advertisers should seek to leverage data that is:

  • Fresh – data that was recently collected
  • Massive – smaller data can make modelling difficult and inaccurate
  • Accurate at scale – to target large samples, it should not loose accuracy when scaled

This means that as a marketer you should:

  • Never assume your programmatic partner’s data meets your standards
  • Ask lots of questions
  • Know the methodology used and the source of all the data that powers your targeting and measurement

Contact Anthony Ord from Acquire Online to see how to use data most effectively to maximum ROI. Ph 027 649 9198

Source: Huffingtonpost