PPC changes 2018 and their impact on advertisers in 2019

Major changes in SEM made waves throughout 2018 and are redefining nearly every aspect of paid search marketing. There were a number of momentous shifts, nearly all with a common thread of more automation and machine learning.

Google Ads: New brand name, new UI – The name change from AdWords to Google Ads is indicative of the fact that keyword selection plays a lesser role in paid search marketing than even a year ago, but more broadly the name change reflects the platform’s growth from one created for text ads to one that now includes dozens of ad formats across Search, YouTube, Gmail, Maps and a network of partner sites and apps. With the new interface, we lost some things such as Display Planner and gained a YouTube reach planner, notes and the ability to make changes from the Overview page in addition to other new features.

Responsive Search Ads – RSA are part of the continuum of letting machine learning models do the work of ad creative optimization. Some of the initiatives that have come before it: dynamic search ads, automated ad suggestions and Google efforts over the past years to get advertisers to give up manual A/B testing and add at least three ads per ad group. An ad strength indicator and somewhat more extensive reporting for RSA were introduced in August. Same month, Google said it would soon roll out RSA to more languages and in the meantime extend the extra character benefits of RSA to text ads for everyone. This fall, Bing added support for the third headline and second description in text ads, including the ability to import the longer ads from Google.

Exact Match – As with ads more machine learning was injected into keyword-to-query matching in 2018 with the inclusion of same meaning words in close variants of exact match keywords. The match type lost its literal meaning and forced marketers to rethink how they use match types altogether.

AI-powered insights – Google and Bing have dedicated significant resources to developing much more robust recommendation engines in their interfaces. Bing introduced a competition tab, performance insights and location recommendations that highlight performance changes and competitive pressures, all delivered with machine learning. Google also continues to iterate on the data visualizations available from the overview page. The goal is to spend less time downloading and analyzing spreadsheets and more time focused on strategy and creative tactics.

AI-powered bidding – The manual bidding option is now buried below a growing list of machine learning driven bidding strategies, including ECPC. On the smart bidding front, Google introduced Target Impressions share, Pay for Conversions in Display Campaigns when Target CPA is the bidding strategy and rolled out Smart Bidding for search partners. Bing introduced Target CPA and Maximize Conversions bidding strategies.

Audiences: LinkedIn data for Bing, MSAN and more – Bing launched the Microsoft Audience Network (MSAN) which encompasses native ad inventory on MSN.com, Microsoft Outlook and the Microsoft Edge browser, as well as syndication partner for what are now called Microsoft Audience Ads. It uses AI for ad delivery optimization and uses data from the Microsoft Graph for audience targeting, including web and search activity, demographic and consumer behavior activity and select LinkedIn profile dimensions. In October, Bing also made LinkedIn categories of company, job function and industry available for targeting in search and shopping campaigns in the US.

Universal Automation vs. Hybrid Management – Google introduced goal-optimized Shopping campaigns, Smart Campaigns for small businesses and Local campaigns for driving in-store traffic. With Smart Campaigns, everything from ad creation, audience targeting, ad delivery across Google channels and soon landing page creation are automated based on the advertiser’s goal. Goal-optimized Shopping campaigns employ machine learning to automatically optimize ad delivery to achieve the defined conversion goal value, such as revenue or return on ad spend (ROAS). It also combines dynamic remarketing and standard Shopping in one campaign to deliver an ad across Google properties and the Google Display Network. For Local campaigns, advertisers set a budget, and the ads are generated automatically based on ad creative elements from the advertiser and their location extensions. Google automatically optimizes ad delivery across Search, YouTube, Maps and websites and apps in its ad networks.

Based on the trajectory of automation in ads and exact match, this hybrid approach has the potential to spell the end of manual control of match types (except negatives), bid modifiers, geo-targeting and other tools we use for campaign management. This doesn’t mean people will be erased from the equations and certainly not in 2019 but it will require SEM specialists to adopt a very different mindset. We have to embrace machine learning as a tool that helps us find opportunities that are imperceptible through traditional techniques.

New Inventory Locations and Surfaces – There were a number of new surfaces for ad formats that opened up in 2018. Bing’s native ads extending across the Microsoft Audience Network is just one example. In November, Google made AMP Story ads available to all publishers, and there are now more than 100 ad tech vendors with AMP integrations. Hotel deals from Google Search are now featured on the Benefits tab of the new Google One cloud storage app (for Android only at this point). Expect to see more promotional opportunities. Google said more promotions from the Google Store and Google Express benefits and more will eventually be featured on the tab. Google has also been testing native ads in the Discover Feed (originally Google Now) on the front page of the Google app. It’s a limited whitelist test for now, but could open up more native inventory in a prominent location.

Connected TV advertising is growing rapidly, and YouTube says more people are streaming it on their televisions. This fall, “TV screens” became the latest device type for video and display campaigns in Google Ads. Eligible display and video campaigns are running on TV screens by default now, and can be managed with bid modifiers. Expect to continue hearing much more about connected TV advertising in 2019.

Amazon Ads – The Google - Amazon face off started with where consumers start their product searches. Now it’s extending to search and other advertising. Amazon was declared the third-largest digital ad seller in the US by eMarketer in September, (far) behind Google and Facebook. Its ad revenue is expected to increase by 50 percent per year through 2020, which would put its market share at 7.0 percent, up from 4 percent currently. In our Amazon Advertising Forecast 2019, 80 percent of Amazon advertisers said they plan to increase spends in 2019, with 30 percent saying they’ll shift some budget from search.

Google’s strategy to counter Amazon’s encroachment into product search is to partner with retailers. It debuted Shopping Actions in March to address three key challenges on the e-commerce front: (1) how to make mobile shopping from its properties like Search faster; (2) how to maintain market share for product search in a splintering mobile landscape of apps and digital assistants; and (3) how to compete against Amazon. Shopping Actions runs across Search, any Google Assistant-enabled devices, Google Express (which includes Shopping ads that now feature blue shopping tags instead of parachutes), and features a universal shopping cart and a Google-hosted checkout when users save their payment information in their Google accounts.

2018 is kind of a turning point in terms of SEM I think. “Keywords aren’t gone, but they’ve been greatly depreciated. SEM in 2019 is going to be much closer to modern programmatic than it is to the ‘enhanced’ era.

Implementing Cohort Analysis Modeling

When reporting paid search results, marketers often field a few recurring questions “How does search contribute to retention?” However, the ability to strategically and correctly answer these questions about search campaign effectiveness over time requires both deep reporting capabilities and a strong grasp of your organization attribution model.

Adoption of Cohort analysis as part of paid search reporting can be a powerful means to assess trends, retention and path to purchase. It also allows for greater accuracy when analyzing campaign results over a dedicated window of time based on the time it takes users to move through the funnel. In marketing, the term “cohort” describes segments of users who share specific events or experiences within a specific time frame. Cohorts include purchasers, email subscribers, trial and/or demo downloads or any other conversion action in the funnel.

Shifting to a Cohort model requires diligent up-front assessment and work, it’s crucial to ensure accurate data is being collected. The most important spreadsheet columns in this instance are the date and time stamps, such as “Original created date for the lead” and “Date when the lead transformed into its next stage” and so on. The date allows measurement of the time it takes for users to move through the funnel and application of that knowledge to paid search reporting and insights.

Once the right data is flowing and a statistically significant lookback window of results to review is available, it’s time to analyze the time it takes our users to pass through the sales funnel from paid search. To set up a Cohort analysis with ample data, shoot for a 6 – to – 12 month window of data. It’s vital to have a large enough date range so we don’t misinform paid search contribution to the marketing platform.

The Cohort model can be used to make faster and smarter search optimizations. It’s not practical to wait for 100% of our leads to move through the funnel before making decisions. Choose the right percentile to use instead. For example, taking the 75^th percentile will help determine how many days it takes for the fastest 75% of our paid search leads to move through the funnel. This may significantly reduce the days between stages from previous analysis.

The key to developing accurate reporting is to ensure prospects; opportunities and customers aren’t being reported outside their time windows. This means if a customer window is 30 days, we’re not viewing any customer results unless they’re 30 days old and have had that time to mature. To get an accurate cost per customer in this instance, we also want to exclude spend from the most recent 30 days. We should only view spends in the maturity window for our customers or opportunities.

Cohort Analysis application for Paid Search

Forecasting - Understanding the flow and evolution of paid search cohorts in correlation to pipeline or revenue makes it much easier to forecast the behavior of a new subset of customers.

Retention strategy – Comparing cohorts by day, week or month of acquisition by revenue generated from that group over the next 6 – to – 12 months will shine a light on purchase and engagement habit changes. If repeat purchases don’t increase, it may be best to implement a retention or re-engagement strategy to guide users back to the sales journey.

Seasonality - Assessing date of first customer/purchase against repeat purchase will highlight users who fall off after a holiday or busy season. Using this data can help inform marketers whether they should double down post-season.

Geo-specific purchase behaviors – If employing international or geo-focused paid search initiatives, measuring revenue incurred month over month by location will make it clear where LTV thrives or dives by region.

Analysis models vary greatly, and shifting to a cohort analysis or model can be a big decision. For many marketers, such a move is necessary for working with lead-gen campaigns. Implementing cohort analysis into paid search reporting is often a powerful means of charting true long-term trends for retention, churn and attribution at a more granular level — and more importantly, bringing to light opportunities within paid search programs.

Vertical Search Optimization

Vertical search engines, mobile and search trends seem to be reshaping the search landscape. Whether spoken, typed or tapped, search queries are the medium through which consumers discover information and make decisions. Search is all around us; it is embedded into smartphone devices and is the fulcrum of Artificial Intelligence (AI) powered digital assistants. Moreover, recent research shows that mobile now accounts as much as 57% of total search traffic. Search has become more powerful, dynamic and fragmented. This brings some challenges along with opportunities.

Opportunity – The sheer quantity and variety of online content is necessitating this change. Almost 45% of people watch more than an hour of online video online each week on either Facebook or YouTube, Snapchat users share over 500,000 every minute, and according to Internet Live stats, Google processes over 3.5 Billion queries every day. In order to sift through the information at their fingertips and arrive at the right result as quickly as possible, an increasing number of consumers prefer the specialized nature of a vertical search engine.

Vertical Search Engine focuses on one specific industry or type of content. Common examples would include a travel search engine like Kayak or the image based interface of Pinterest. The term “vertical” applies to both the indexation and serving of content, which is neatly organized by category. Product searches may take place on Amazon or a consumer may go to a site like Indeed to look for a new job.

For marketers with one specific type of product or service to sell, the lure of vertical search can be clear too. They can meet their audience when their search intent is overt and can focus their energies on a platform that they know will deliver results. Google universal search, which indexes and ranks image results alongside video and local listings, is an aggregation of verticals into what appears to be more conventional, horizontal search engine. Recent moves into the jobs market, along with a revamped flights search engine, show Google’s ambitions to develop specific new technologies to gain market share in profitable verticals.

If we analyze recent data, we can see that vertical search is still taking off, outside of Google. Google web search has merged with Google Image and Google Maps, and the likes of YouTube, Pinterest and Amazon are in the ascendancy while still remaining minor players in the grand scheme. The innately commercial nature of Amazon searches will be of interest of retailers, while Pinterest reports that 97% of its searches are non-branded. Both of these platforms are improving their paid search offering at a rapid rate, which is again a sign of their increasing prominence in the search landscape.

The competition for consumer attention spans grows ever fiercer, and SEO is no longer just about getting Google right. Google itself is more complex than ever before and marketers could also conceivably focus their attention on vertical search engines rather than the global search giant. For marketers to consider is the nature of consumer behavior on the relevant vertical search engine for their brand. Consumer demands and expectations will differ based on the search engine, and they will have started query there for specific reasons.

There are some best practices we can apply for any vertical search optimization campaign:-

1.      Research your audience behaviors across different search engines

2.      Maintain a cohesive brand presence across all major social networks

3.   Use structured data and Open graph tags to help search engines locate and understand your content

4.    Access behaviors across your websites and mobile apps, focus on unblocking any challenges users have in accessing content

5.   Master the fundamental elements of site experience that will benefit performance on any search engine, such as page load speed

6.      Adapt your content for every search engine

7.     Use specific integrations with vertical search engines that can allow your website content to be served within their results

SEO is not just about trying to rank on Google anymore. Search behaviors are changing and new content opportunities arrive constantly. We need to evolve our strategies to make the most of this set of circumstances. The industry is reaching a point now where deep learning allows search engines to understand both content and context with accuracy levels we could barely have imagined just a few years ago.

Impact of Blockchain on Search Marketing

If you’ve heard of Bitcoin then you most likely have heard of Blockchain, the technology that enables Bitcoin and other crypto currencies to exist and function. The technology is forecast to disrupt many industries as it allows users to conduct transactions without a middleman in a secure and transparent format.

Some of the industries that can potentially be disrupted are car sales, voting, ridesharing, real estate, insurance, sports management and loyalty cards. If Bitcoin is adopted by large companies such as Amazon or Walmart, it will certainly have an impact on the future of payments between search marketing agencies, website owners, advertisers and others. Contract agreements will also be impacted, as the Blockchain could be leveraged for more transparency and accuracy.

Blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value. It’s like a Google Doc spreadsheet that is shared with the public which displays transactions and is tamperproof. Many are considering Blockchain to be as impactful as the Internet was in the 90s.

In the digital marketing world, many central authorities, such as Google and Facebook, connect advertisers with website owners. For Example, Google is a central authority in programmatic ads, where it helps advertisers run ads on websites via the Google Display Network. Google essentially is the middleman that helps advertisers and website owners trust each other. If they already trusted each other, they would not need Google as an intermediary taking a cut of the profits.

Enter Blockchain, which can verify that every user is genuine with 100 percent accuracy and that the website owner is only charging the advertiser for genuine clicks through to their site. Then the website owner and the advertiser don’t need a middleman to arbitrate their agreement, which would save them money. Blockchain presents a big threat to Google’s Display Network revenue.

Blockchain being the unhackable distributed ledger is going to also help reduce online fraud. It will provide transparency for persons involved in a transaction without giving away their personal details, essentially proving they are a real person. Ad fraud is a big problem. It cost advertisers over $7 Billion in 2016. A number of players including Microsoft, the Interactive Advertising Bureau and DMA are already working on Blockchain based digital identification system.

As companies start to adopt Blockchain, they will need to integrate it with their websites. This involves the web developers as well as the SEOs, if they are trying to gain organic search benefits as well as display the information from the Blockchain transactions. This will present both technical issues and opportunities in which SEOs will have to work alongside developers to resolve compatibility issues with different content management systems and website platforms. As new Blockchains are developed and it is more widely adopted, it will certainly disrupt the search marketing industry in many other ways. For now, search marketers should pay close attention to Blockchain as it grows.