Chad Pollitt - RELEVANCE

SEO and 11 Years of Google Algorithm Updates

Chad Pollitt — Thu, 10 Mar 2022 14:03:25 +0000

Over the years, fortunes have been made and lost on Google’s algorithm. Its technology has advanced leaps and bounds since its inception in the late 1990’s. What was once ripe for manipulation is now a search engine results page (SERP) that mostly delivers what Larry Page and Sergey Brin originally envisioned – relevant search results free from spam.

It’s taken Google many years to get its search engine where it is today. SEO is not something you do anymore; it’s what happens when you get everything else right. SERP success is now a trailing indicator of good PR and inbound marketing. Sure, there are some on-page best practices, but that’s not rocket science.

Several weeks ago, Visualsoft, an ecommerce marketing agency based out of the UK, asked if I’d participate in the creation of its interactive infographic Which Google Updates Have Had the Biggest Negative Impact for SEO. I agreed and answered a number of questions related to how updates have impacted my SEO efforts over the years.

They curated my answers and the answers of several other industry insiders and created a beautifully designed parallax scrolling website that takes visitors through 11 years of algorithm updates in an animated way. After reading this article you should definitely click through and give it a scroll. Bravo, Visualsoft!

Here are 16 of the many Google algorithm updates they featured:

Boston – 2003

This update was the very first to be given a name. According to Moz’s Google Algorithm Change History, this update was a combination of algorithm changes and major index refreshes. The announcement of this change took place at SES Boston.

Cassandra – 2003

This was the first update that claimed to target link spam, like mutual links between co-owned websites. It also targeted hidden text and links.

Dominic – 2003

The exact nature of this update was unclear, but the way inbound links were reported or counted seemed to havechanged in a big way. In addition, webmasters were seeing new Google bots on their servers that were bouncing.

Florida – 2003

This update is reported to have ushered in a new era of SEO by completely destroying the value of 1990s tactics. Massive keyword stuffing was severely hit.

Austin – 2004

This was purported to be the update to clean up what Florida missed. It further cracked down on spammy on-page tactics like invisible text and Meta data stuffing.

Bourbon – 2005

Webmasters speculated that this update changed how non-canonical URLs (www vs. non-www) and duplicate content were treated. It was also the first appearance of “GoogleGuy,” the Matt Cutts persona we know today.

Big Daddy – 2005

This update rolled out in December, but continued to rollout until March of 2006. This impacted URL canonicalization, site redirects and other related items.

Buffy – 2007

According to Wikipedia, this update was not a deliberate one, but an accumulation of many smaller changes. Visualsoft reports the update focused heavily on keywords.

Vince – 2009

This was the first update that led webmasters to believe Google was favoring big brands in its SERPs. Cutts called the update minor, but the implications of showing favor to large brands is not a minor change for mid-market companies and SMBs.

Caffeine – 2010

This update directly impacted how and how fast Google indexed the Internet. It was a much needed technological update and laid the infrastructure to handle the vast amount of content being created today. Indexation moved from a stratified methodology to a more dynamic approach. According to Google, it resulted in a 50 percent fresher index.

Panda/Farmer – 2011

SEOs named this update Farmer prior to its official release. However, when announced, Google named it Panda.The update focused on spam, thin content, content farms, scrapers and sites with high ad-to-content ratios. Many updates to this algorithm have occurred since its initial launch and continue today.

Freshness – 2011

This update was an extension of the indexing infrastructure put in place by Caffeine. It sought to reward the newest most relevant content in the SERPs. According to Google it impacted 35 percent of all search queries.

Venice – 2012

According to Moz, this update appeared to have aggressively localized the SERPs by more tightly integrating local search data.

Penguin – 2012

This update looked to identify and diminish websites that were considered over-optimized – sites built for search engines and not people.

EMD – 2012

Exact-Match Domain (EMD) sought to devalue domains containing desirable keyword phrases. Speculation existed that this update was another way for Google to help big brands do better organically.

Hummingbird – 2013

This was a major update by Google. It was reported to have evolved its core algorithm and indexing infrastructure to understand the relationship between words and subsequent queries. It’s also reported to have laid a foundation for the Knowledge Graph and mobile search.

Many other updates are featured and covered by Visualsoft’s animated and interactive parallax scrolling website. Understanding Google’s past through these algorithm updates helps us understand where the search behemoth may be headed tomorrow. It is this writer’s opinion that Google is looking to completely eliminate the field of SEO in the future and to favor brands with the largest and most engaging online audiences.

It used to be that SEO professionals could create enough of the signals Google’s algorithm used to determine relevance. Today, however, it’s become increasingly difficult for an SEO to mimic the signals created by a true brand audience. It’s become easier, in most industries, to do good PR and growth marketing in order to build a real audience than it is to manipulate the link graph and sculpt on-page factors. There’s too much risk for manual or algorithmic penalty to do anything else but build real audiences by creating helpful and engaging content that people want to consume, share, cite and evangelize.

If you would like to connect with an SEO strategist to speak more about implementing innovative strategies, contact one here!

This article was update 3-10-22.

The post SEO and 11 Years of Google Algorithm Updates appeared first on RELEVANCE.

Matching Dollar for Dollar to Help Brands Amplify COVID19 Messaging

Chad Pollitt — Thu, 26 Mar 2020 04:50:34 +0000

SAN FRANCISCO, MARCH 24^TH, 2020 - inPowered, the AI platform for content amplification, announced today that in response to the Coronavirus pandemic, every dollar spent by brands to promote COVID19- related content through their platform will be matched by inPowered at 100% to increase the reach and impact of their campaigns. Consumers have an immediate and urgent desire to seek out helpful information related to COVID19, and brands have an opportunity to drive relevant and resourceful conversations around COVID19 with content marketing.

“COVID19 has created an unprecedented time in history for businesses around the world,” said Peyman Nilforoush, CEO and Co-Founder of inPowered. “Our top priority at inPowered right now is to help amplify important information from brands people can benefit from, effectively cutting through the clutter of online content.”

As an example, Tito’s Vodka & Anheuser-Busch both announced producing and distributing hand sanitizer to address the current shortage. Others such as Starbucks have launched a drive-thru only business model in less than two weeks to help consumers stay safe while still enjoying a cup of coffee. Comcast is now offering free Wi-Fi, waiving data caps and late fees to ensure that no one gets disconnected.

inPowered will also provide a customized call-to-action at no charge for all amplified content related to the Coronavirus. This has already enabled inPowered’s current clients to quickly implement highly strategic COVID19 content campaigns.

“Now, more than ever, content marketing has the opportunity to help people with useful information disseminated directly from the brands that are taking meaningful action,” said Nilforoush. “We want to play a small part in helping these brands amplify their message to the greatest number of consumers who can benefit.”

About inPowered:

inPowered, the AI platform for content marketing, enables brands to deliver business outcomes with content. With inPowered brands are able to increase the ROI of their content investments by optimizing their advertising spend towards post-click engagement metrics and placing call-to-actions at the ideal time to convert engaged audiences. The company was founded in 2014 by Peyman Nilforoush and Pirouz Nilforoush after selling their previous company to Ziff Davis. For more information please visit www.inpwrd.com.

Media Contact:

Chelsea Waite, Director of Communications (415)-968-9859

chelsea.waite@inpwrd.com

The post Matching Dollar for Dollar to Help Brands Amplify COVID19 Messaging appeared first on RELEVANCE.

Before You Do Any Influencer Marketing, Check These Templates

Chad Pollitt — Fri, 14 Sep 2018 06:00:06 +0000

For the many subscribers of Relevance, influencer marketing is a well-known, and perhaps, mature channel. Unfortunately, though, there’s still a significant swath of marketers still not leveraging influencers in their marketing mix. This is ill-advised, because studies show that for every one dollar invested, influencer marketing delivers six dollars of return.

And for many marketers who do influencer marketing already, there seems to be an understanding gap of what exactly it constitutes. Some believe influencer marketing is the run of the mill round-up post featuring industry experts and their insights. Others believe it’s an all-encompassing marketing strategy with a refined goal, and many prudent tactics.

So, which is it? It can be all of them at the same time. However, which path a marketer chooses can have a significant impact on marketing metrics, and ultimately, the bottom line. The most value provided is in an all-encompassing influencer strategy.

Influencer marketing is a process of identifying and engaging with persons with the ability to influence others into buying your product or ordering your services. With their sheer influencing powers they can create a higher level of engagement and drive conversions.

For many people in the limelight, influencer marketing has become a key source of income and even a career path. Recent statistics reveal some interesting data:

Influencers are more trusted than celebrities by over 70 percent of people in their teens
Influencers are seen as better than friends by a large percentage of online shoppers
More people are willing to trust influencers’ advice to make buying decisions
There is a marked shift towards influencers when it comes to seeking advice on buying a brand. People are trusting influencers more than other recommenders.
A large number of purchases are now made based on influencer recommendations

With ROI in influencer marketing doing much better than other forms of advertising and marketing, more and more businesses and organizations are now leveraging this opportunity to sell their brands more and earn handsome profits.

To make influencer marketing more easily understood, executed and rewarding for both the newbie and experienced, I spent many hours interviewing and researching a successful company that featured influencer marketing as its marketing strategy (not a one-off tactic) and tracked its performance all the way to revenue.

The results were astounding. YoY revenue was up 35% in 2017 and up 20% the year prior. This company’s unique approach to influencer marketing was highlighted as the reason for this growth. The case study is featured on Convince and Convert and should be considered the roadmap on how to implement an influencer marketing strategy that will positively impact revenue.

This was merely step one. Step two was to turn the most impactful tactics into templates any marketer can use to implement and execute influencer marketing. For the newbie or the experienced, the case study and the templates should be valuable tools to efficiently deploy tactics, gain more insight, get buy-in and understand the value of influencer marketing.

Want to start simple with a round-up post? There’s a template for that. Want to bring in influencers to contribute to a blog? There’s a template for that. There are five templates in total that not only help in the execution of the owned media side of influencer marketing, but give the steps to use earned and paid media promotion, too.

Using earned and paid media to promote influencer content helps influencers feel more confident in the value of their work. This added validation can keep them coming back to contribute more.

Influencer Marketing Template Orientation

The X axis of each template consists of the following: each step’s deliverable, tasks associated with each step’s deliverable, who owns it, and due date. The Y axis consists of each individual deliverable required to do that specific influencer marketing tactic.

The templates produced from the successful case study above have three major value propositions for marketers, among others.

The duplication of a proven recipe for success – the results for the above-mentioned case study are too impressive to ignore. Implementation of the tactical mix has demonstrated success, not just for them, but for many others.
Speed to proficiency of tactic(s) – rather than starting from scratch and learning by making mistakes over time, marketers can get a substantial head start.
A complete checklist – keep track of all activities to maximize opportunities.

It’s also important to note what these templates are not – a one size fits all proposition 100% of the time. They’re meant to be the starting point for success across most industries. In addition, each deliverable and task associated with them have had their own blog posts and guides written on precise execution, for example, “how to construct an influencer outreach email.”

The purpose of these templates is not to do a deep-dive into the minutia of each step, deliverable or task, but rather, it’s to know they need to be done, when they’re due, and who’s responsible for them – remember, the goal is to replicate proven models and constructs of success in influencer marketing.

In order to keep the blog post reasonable in size and scope, I’m not going to go through all five templates. Instead, I’ve chosen two of the most valuable. The influencer round-up post template is often-times the quickest win in the tactical quiver.

Soliciting influential blog contributors is a sustained and constant campaign that over time produces some of the biggest value in influencer marketing. It’s also the core of the sustained strategy in the case study mentioned above. The entire bundle of templates is available here or from the last slide on the SlideShare embeds below.

Influencer Round-up Post

Let’s start off with the tried and true round-up post, since it represents the tactic, that often times, drives the quickest win. The concept is simple: target and feature several industry influencers to share their thought leadership on a blog (or some other form of content delivery system) to drive earned media traffic and brand awareness.

Influencer Marketing Templates – The Incomplete Bundle from Chad Pollitt

The below steps mirror the ones taken by the example mentioned in the above case study. It’s a shining example of what success looks like in a campaign. Following these steps gives the best chance for high-levels of campaign success.

Build an Influencer Outreach List

Is influencer marketing about a celebrity endorsing your brand or a reputed personality recommending the product to a targeted community? There is a fine line between the two and that’s what influencer marketing is all about.

Influencer marketing is certainly not easy but if you push the right buttons, it can create an amazing win-win situation for your business, the audience, and of course, the influencer as well. The audience also benefits by remaining current with the best and latest trends in the industry they are keen to know more about.

If you are planning to be a part of the influencer marketing success story, the trick lies in connecting with the right influencers. One thing that you must focus on sharply is that your brand and the influencer values must be absolute synchrony.

A targeted outreach campaign is where it all begins from. You must know how to create an effective as well as scalable influencer outreach strategy.

Determine Blog Post Topic – Determine the “what” by figuring out what the roundup post is going to be about.
Identify Influencers – When it comes to finding influencers to work with there are three main options:
1. Use software like Buzzsumo, Littlebird, DemandJump, Cision or the software de jour at the time to uncover industry influencers
2. Explore Twitter, YouTube, Instagram and/or LinkedIn (other social sites may be prudent, too) to identify influencers by hand
3. Recruit influencers from conferences, networking events, and even your customer base. Don’t be afraid to pick up the phone.

Send Outreach Emails

Draft Outreach Emails – The goal of outreach emails is to make it as simple as possible for influencers to contribute. A few tips include:
1. Write a very short email
2. Tell them who you are, why you’re emailing them, what’s in it for them, and give them clear instructions
3. Include link to Google doc or Google form where they can put content, links, headshot, etc.
4. Don’t forget a list (or partial list) of the other influencers that will be in the round-up post (if it’s available yet)
5. If you can’t get their email address look on their website for their contact form. Social media can be used, but it’s less likely to be followed up on by some.
Send Emails – Once influencers are identified and the outreach email is drafted it’s time to send out personalized one-off emails (do not use a marketing ESP or automation to do this) and gauge the response.

Create the Blog Post

Blog Post Copy – Once the influencer content is collected it’s time to put it all together. Write an intro for the post and be sure to include a thought leader from your brand in the round-up.
Blog Post Design – Create any graphics needed to support the blog content.
Publish Blog Post – Give the post one last review and push it live (don’t forget to check the influencer copy for edits, too).

Follow-up with Influencers

Draft Social Copy – To make social sharing easy for influencers, draft a Tweet, LinkedIn post, Facebook post (or whatever their social-site fancy is) for them to share with their audience. Include appropriate hashtag(s).
Draft Follow-Up Email – Send a note to your influencers thanking them for their contribution and share the article with them. Include the social copy drafted for easy sharing.
Send Follow-Up Emails – Send emails to all the influencers featured in your round-up post. Make sure these are personalized one-off emails and not volume or automated emails.

Use Social to Promote the Post

Draft Social Copy – Write a series of social posts to share over time promoting the post. Tag 3-4 of your featured influencers in each post and include prudent hashtag(s).
Schedule Social Posts – Influencers are more likely to reshare content multiple times if posts are spaced out appropriately over time. Keep in mind that sharing frequency is dependent on the social platform and prudent audiences.

Measure Performance

I am keenly aware that marketers use several different methods to measure performance online. However, since the stated goal above is to drive earned media traffic and brand awareness, traffic and social shares were chosen as performance indicators for this particular tactic. That doesn’t mean marketers can’t include their own performance indicators in the template.

Track Blog Traffic and/or Social Shares
1. Measure traffic and/or social shares to the round-up post. To gauge success, compare round-up post traffic and/or social shares with that of non-round-up posts. If it doesn’t outperform an average blog post, it isn’t a win.

Blog Contributor Campaign

Here’s something many content marketers have been exposed to – blogs that are more like industry communities. Many of their content contributors generally possess some level of industry influence. This approach can be quite fruitful for a brand because each contributor has an audience of their own and a vested interest in their content being successful. In addition, such authors can bring prestige and credibility to a brand’s blog or publication.

The award-winning publication, Relevance, which I helped co-found, was founded on these ideas. The below template outlines exactly how to recruit industry influencers to write original content for a publication or blog.

Influencer Marketing Templates – The Incomplete Bundle from Chad Pollitt

Build an Influencer Outreach List

This deliverable has some tasks that are identical or nearly identical to the previous template/campaign. However, make sure the influencers identified show a proclivity to produce the type of content needed for the blog. If the blog features videos, then try and identify influencers that are using video, etc.

Determine Blog Post Topic – Determine the “what” by figuring out what the roundup post is going to be about.
Identify Influencers – When it comes to finding influencers to work with there are three main options:
1. Use software like Buzzsumo, Littlebird, DemandJump, Cision or the software de jour at the time to uncover industry influencers
2. Explore Twitter, YouTube, Instagram and/or LinkedIn (other social sites may be prudent, too) to identify influencers by hand
3. Recruit influencers from conferences, networking events, and even your customer base. Don’t be afraid to pick up the phone.
Set Budget – Consider paying top influencers for content. This can be just the jumpstart needed to more easily recruit later. Set a budget before starting outreach.

Send Outreach Emails

This deliverable is very similar to its counterpart in the above template/campaign. However, there are some slight, but important differences. Take note below:

Draft Outreach Emails – The goal of outreach emails are to make it as simple as possible for influencers to contribute. A few tips include:
1. Write a very short email
2. Tell them who you are, why you’re emailing them, what’s in it for them, and give them clear instructions
3. If an honorarium is being offered to cover their time mention it, but don’t share a dollar amount yet.
4. Include link to Google doc or Google form where they can put content, links, headshot, etc.
5. Don’t forget a list (or partial list) of the other influencers that will contributing to the blog (if it’s available yet)
6. If you can’t get their email address look on their website for their contact form. Social media can be used, but it’s less likely to be followed up on by some.
Send Emails – Once influencers are identified and the outreach email is drafted it’s time to send out personalized one-off emails (do not use a marketing ESP or automation to do this) and gauge the response.

Create the Blog Post

Again, this deliverable is similar to the above version, but with slight differences of importance.

Review Blog Content – Review the influencer’s piece for grammar, clarity, and alignment with your content strategy.
Publish Blog Post – Give the post one last review and push it live.

Follow-Up with Influencers

It’s important to make sure it’s as easy as possible to get contributors to share their content with their audience. That’s why follow-up is important. It also helps the brand steer the contributor’s message to their audience.

Draft Social Copy – To make social sharing easy for influencers, draft a Tweet, LinkedIn post, Facebook post (or whatever their social-site fancy is) for them to share with their audience. Include appropriate hashtag(s).
Draft Follow-Up Email and Send – Send a note to your influencers thanking them for their contribution and share the article with them. Include the social copy drafted for easy sharing.

Use Social to Promote Posts

These two tasks should be pretty familiar to most content marketers. Note: Paid social promotion is certainly recommended, especially for well known influencers. However, that process is in its very own template included in the bundle available for download.

Draft Social Copy – Write a series of social posts to share over time promoting the post. Use the contributors social handle on each post and include prudent hashtag(s).
Schedule Social Posts – Influencers are more likely to reshare content multiple times if posts are spaced out appropriately over time. Keep in mind that sharing frequency is dependent on the social platform and prudent audiences.

Measure Performance

Marketers can certainly include additional performance indicators in this template if needed.

Track Blog Traffic and/or Social Shares
1. Measure traffic and/or social shares to the contributor’s post. To gauge success, compare contributor post traffic and/or social shares with that of non-contributor posts. If it doesn’t outperform an average non-contributor blog post, it isn’t a win.

The above merely represents two of five different templates in the bundle. It’s my sincere hope that the case study and this post, with the template bundle it represents, can serve as important references for those marketers new to influencer marketing and those with experience. This project was designed to help with influencer marketing across the spectrum – from one-off tactic to all-encompassing marketing strategy – for some time to come. Use the data in these resources to understand the value of influencer marketing and get buy-in, if it’s not gotten already. Use the templates to help with flawless execution.

A variation of this article was originally published on CMI.

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Everything to Know About AI-Powered Content Intelligence

Chad Pollitt — Thu, 12 Jul 2018 14:00:28 +0000

Content intelligence has been getting a lot of buzz lately, and rightfully so. Content marketers are in need of some real strategic help on a big data scale. This is no secret, either. So much so, in fact, there are now at least 15 martech vendors that specialize in just this one aspect of AI-driven analytics—content intelligence.

The Content Marketing Institute’s own studies have shown that budgets for content marketing have been on the decline over the last few years. Additionally, they’ve shown that perceived performance has been declining, too. This is reflected in both B2B and B2C brands. Content intelligence can help fix these problems.

What is content intelligence? There are many definitions floating around out there, but Curata’s seems to be one of the best.

“It’s the systems and software that transforms data into actionable insights for content strategy and tactics. Content intelligence means having the full context of an individual piece of content.”

While this definition is pretty good, it focuses exclusively on the “individual piece of content.” In fact, a well-rounded content intelligence system can also take a macro view of an entire blog, website, and ecosystem, as well as each individual article.

This macro view helps uncover underserved content queries (prudent topics) and overserved content queries (topics to avoid) across an entire industry and represents valuable information that can inform a strategy. It also can uncover competitors’ content weaknesses and strengths.

Unstructured Data for Content Intelligence

Most content marketers have access to technology that helps parse structured big data and have so for many years—the tried-and-true, one-dimensional analytics solutions like Google Analytics. However, the problem with these first-generation solutions is that they mostly focus on structured data and output-based insights, which limits how much intelligence they can provide. They also dwell only one degree away from the websites they connect to. Meaning, that insights derived are only from sites driving traffic directly to the website.

This is because the foundation of these solutions was built on technology from the last decade. Today, we have a second-generation of analytics solutions powered by AI that can tackle both structured and unstructured big data to provide real content intelligence.

In addition, these solutions explore nearly 100% of the topical sphere of potential influence brands desire, not just the current 20% most are used to through Google Analytics or its clones.

This is significant because the level of insights derived is multiples greater than they’ve ever been. This second generation of analytics will be critical for content marketers who want to improve their performance and grow their budgets.

The below screenshot is a good example of what content intelligence can do. It starts off at the macro level and works all the way down to the individual blog post. At the macro level, the AI-driven software goes out and maps a brand’s relevant digital ecosystem on the internet.

Unfortunately, most content intelligence solutions require the manual input of competitor websites or keywords and can’t actually figure it out on their own. For the solutions that can figure it out on their own, the macro results would include websites that link to the brands, websites that link to those, and websites that link to those. It would include competitors, blogs, publications, affiliates, and many other different types of websites and apps.

From there, microdata such as social shares, links, author, date, type of article, traffic, etc. can overlay the macro data. When sorted and organized through charts and lists, deep insights can reveal themselves like never before.

Predictive and Prescriptive AI-Driven Content Intelligence

A key differentiator for successful and unsuccessful brands executing content marketing in the near term will be their use of AI-driven content intelligence solutions. They can help determine what content to create to drive potential action at each stage of the buyer’s journey—which topics to focus on, and which topics to avoid (among other things).

Of the content intelligence solutions, there are three types: predictive, prescriptive, and both. Predictive solutions predict future outcomes based on big data. Marketers still must make decisions based on these predictions. Prescriptive solutions parse structured and unstructured big data in order to recommend what to do next. Marketers don’t need to interpret the data as the best course of action in this scenario.

The complete solution does both predictive and prescriptive reporting. Content intelligence solutions that are only predictive in nature hearken back to the one-dimensional analytics we’re all used to—report information to the marketer who decides the best course of action. While better than the old-school analytics, it still requires interpretation of the data.

It’s an exciting time to be a content marketer. AI is bringing a whole new level of insight into our world. It’s easy to feel overwhelmed at times considering there are nearly 6,000 martech vendors to date. However, I hope that this post serves to shed some light on the content intelligence category.

This is one of many topics covered in my newest book, “Everything You Need to Know About Marketing Analytics and Artificial Intelligence.” It covers AI’s impact on owned, earned, and paid media and is available free for download.

This article originally appeared in the Marketing Artificial Intelligence Institute.

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The Great Debate: Defining What Online (Content) Engagement Is

Chad Pollitt — Wed, 27 Jun 2018 12:00:06 +0000

The term “engagement” or “content engagement” is tossed around the blogosphere and in publisher circles around the world. Many of the stakeholders can’t agree on what really defines online engagement. Is it a click, a conversion, a purchase, etc.? Ultimately, brands must decide what online engagement means to them based off defined goals and strategies. However, some measures are more meaningful than others.

According to a study sponsored by Pitney Bowes and reported on by eMarketer, the number one business objective marketing professionals in North America are trying to accomplish when enhancing the online customer experience is increased engagement – at 58% surveyed.

It’s likely that these marketers run the gamut as to which metrics they use to measure engagement. However, they know it’s important. Ultimately, there’s two types of engagement that can be measured – tactical and strategic engagement. Tactical engagement is where most of the confusion lies. Let’s explore each below.

Tactical Engagement

Many of the below are considered merely vanity metrics by many. That said, they can still have some usefulness. Most of them, by themselves, don’t do a very good job at giving a clear macro engagement picture. However, once away from the vanity metrics the picture starts to become much clearer.

Views/traffic – Back in the day we used to call this “hits.” In fact, it was quite common for websites to have a hit counter at the bottom of its home page. This screams vanity metric. Keeping track of views and/or traffic is important, but it’s far from an engagement metric. Besides, bots and click-fraudsters can really skew this measurement.

Clicks – This can be a very valuable thing to track when used to measure the health of the on-site buyer’s journey. Are users clicking on the appropriate links in order to move from one stage to another? Is the information architecture flat enough? Click-through rate is also important to track because it informs marketers what messaging and/or calls to action work best to drive users to a desired action.

However, just like views, clicks can be skewed from bots and other nepharious online activity. It also doesn’t really tell marketers if someone actually consumed the content on the other side of the click. While a valuable metric to track, when it comes to engagement it’s mostly just vanity.

Social Acknowledgement – This includes shares, likes, retweets, social comments, follows, etc. This area of measurement has a black eye on the marketing playground and has been sufficiently beat up by bloggers over the years. As mentioned in the other categories above, bots play a role with these numbers, too.

Some of these numbers can indeed inform the editorial of a content team. However, studies show little to no correlation between number of shares and actual engagement/consumption. Most of these metrics inform the content team as to the quality of their headlines or the earned trust their content has gained over time. This category is mostly just vanity, though.

Blog Comments – This is indeed a form of engagement. Some argue that it’s the most important measure of content engagement. The Moz blog can get dozens of comments on one post, while others rarely get a comment. It is my belief that this has to do with the culture a brand develops with its audience.

Some audiences weren’t built to have a commenting culture, while others were. Anecdotally, it seems the more mature (older) a blog is the more likely it is to have a commenting culture with its audience. Comments are valuable because they provide direct feedback to the content team. However, in the grand scheme of things, the data points they provide tend to be miniscule compared to other engagement metrics. This is not a vanity metric.

Links – Naturally earned links are great for SEO and should absolutely be tracked. These citations mean that the person linking actually read some of the content. That’s definitely a form of engagement, too. However, like blog comments, the data set is too small to make macro conclusions about online content engagement.

Conversions – The ultimate form of online engagement that helps drive business outcomes. Whether it’s a product or an ebook, marketers love getting conversions. When conversions happen, the audience grows. This is one of the most important metrics to track and measure. Unfortunately, it doesn’t really inform marketers as to the quality of their overall editorial. This is one of the most important metrics to track, but it’s not much of an overall macro engagement measure.

Subscribers – I, like many, subscribe (pun intended) to the idea that content marketing’s first purpose is to build an audience that can later be harvested as enthusiastic customers. When someone subscribes to a blog or another type of website, they’re showing an intent to engage with content. Intent doesn’t equal macro content engagement.

Scroll Depth – This is a very good way to look at a website’s overall engagement level. While not perfect, there’s a very good chance that if someone took the time to scroll to the end of a post they read and engaged with at least a portion of it. Unfortunately, very few, if any, analytics platforms report this information. However, the technology from Crazy Egg offers a “Scrollmap report” using a heat map layover on every website page.

Dwell Time – Like scroll depth, this is a very good way to track overall site and page engagement. Chartbeat Analytics found that users who spend 15 seconds or longer on a page consume 80% or more of the content. Also, like scroll depth, most analytics de jour aren’t built to accurately measure this.

Google Analytics’ (GA) time on site measurement doesn’t include bounces. A bounce is a single page session on a site. Session duration is calculated once a user clicks on another page. Without advancing to another page GA can’t calculate dwell time.

To overcome this, analytics software like Moat can track real dwell time without needing another session to calculate it.

Strategic Engagement

This is a more strategic senior management view of what online engagement is. It represents the total engagement over time of an individual IP address, person or subscriber and is generally an indicator of the propensity of that content consumer to become a customer. It’s usually assigned a scoring scheme. Engagement scoring is also known as “lead scoring” – assigning points based off tactical engagements with online content and/or in-app. Apps, through geo-fencing, can also track brick and mortar touches.

Engagement Scoring – According to Google’s 2011 report on ZMOT, the average customer becomes one after 11 touches. These touches can happen on a website, blog, social media, email, native advertising, brick and mortar, etc. When these touches add up to seven or more hours the likelihood of the user becoming a customer is optimized – this is where longer form content becomes handy within the buyer’s journey. This could include longer videos, demos, ebooks, etc. According to Marcus Sheridan in his book “They Ask, You Answer,” a prospect’s close rate soars from less than 5% to 80% if they consume 30 pages of content or more.

However, for those products and services that require very specific targeting characteristics (i.e. annual revenue thresholds, specific title, decision maker, industry, etc.) measured through propensity scoring, touches don’t matter because the prospect is not qualified. That said, it doesn’t mean they won’t become qualified some day in the future.

Conclusion

Engagement scoring, especially in a B2B setting, is an absolute must to track potential strategic business outcomes. Without question, this is a bottom line way to properly measure online engagement over the digital half-life of a prospective customer or repeat customer.

That said, from a tactical point of view, everything mentioned above has some value associated with engagement. The question is – how much value? The vanity metrics – not so much. However, the two most important engagement identifiers on the tactical list include scroll depth and dwell time.

In a perfect world, marketers would have access to both. Realistically, due to the limitations of most analytics software, this may not be the case. However, either one on their own is a sufficient measure of content engagement.

This post originally appeared on inPowered's blog.

The post The Great Debate: Defining What Online (Content) Engagement Is appeared first on RELEVANCE.

How AI is Helping to Reduce Waste in Digital Advertising

Chad Pollitt — Thu, 21 Jun 2018 15:59:17 +0000

According to Chartbeat analytics, two out of three clicks on native advertising bounces in 15 seconds or less. From Facebook to Outbrain, it costs three times the cost of a click for a content marketer to get any meaningful engagement with their work.

According to that same study, users who spend 15 seconds or more on a webpage will read 80% of the content or more.

Paying $0.50 for that click? For every $1.50 spent on native advertising, $1.00 of it is wasted.

John Wanamaker, known as the father of advertising, famously said:

Half the money I spend on advertising is wasted; the trouble is I don’t know which half.

In this case, it’s two-thirds.

With native advertising spend projected to surpass $85.5 billion globally by 2020 (according to Adyoulike) this is a critical concern - this means that $57 billion of this will be from users that engage with less than 80% of the content. Saving calls to action for the end of the blog post or video? That might not be the best idea knowing the above.

It’s even worse for those content marketers paying on an impression-based (CPM) scheme. According to Nectar, 91% of total ad spend is viewed for less than a second. That means, last year alone in the US, $38 billion was wasted on CPMs.

Marketers are getting tired of digital paid media waste

Proctor & Gamble cut $200 million from its digital ad spend in the last 12 months, citing brand safety, fraud and waste as the primary reasons. Ironically, its reach was shown to have grown by 10% after the cut.

It's not the only brand making moves either, as others follow suit.

That said, digital paid media is growing by double digits year-over-year, and is projected to do so until 2022, according to eMarketer. This means that P&G’s level of frustration isn’t necessarily felt by most of the marketplace - however, this doesn’t change the fact that’s there’s plenty of waste in programmatic advertising.

There must be a better way (AI to the rescue)

Artificial intelligence isn’t some monolithic technology that can cure all woes. As a matter of fact, many marketing and ad tech companies already use some form of AI, but they don’t really use it to provide much value.

For example, AI embedded in a programmatic technology solution which bases its rates on clicks and impressions may have slightly better targeting and real-time bidding, but it'll still produce most of the same waste mentioned above.

AI and machine learning works best when it has access to very large quantities of data - the bigger the database, the more, and faster, it can learn.

With the right amount of data, AI can enable two very groundbreaking things in programmatic paid media – a new pricing model [cost per engagement (CPE)] and dynamic pricing per engagement (dCPE).

CPE – Cost per engagement is only paying post-click for content that’s had 15 seconds or more of engagement. This is generally a fixed cost (i.e. $1, $1.12, etc.).
dCPE – Dynamic cost per engagement is similar to CPE. However, as the AI get’s smarter over time the cost per engagement continues to decline. In some cases, up to 80% (i.e. $0.20, $0.28, etc.)

P&G and the other brands which have followed suit in budget cutting could have cured their ails with this technology - it eliminates waste by not charging per impression or click, but rather by engagement of 15 seconds or more. That means that they would only pay for content that was consumed, on average, at least 80%.

This model doesn’t just eliminate waste, it eliminates fraud and brand safety concerns, too, by reducing the importance of the impression and click in favor of engagement.

inPowered was the first native advertising company to introduce CPE, however just this week it introduced its new dCPE model for the first time. The technology continually reduces CPEs over time, then reinvests the savings into even more engagement.

That means a brand may pay $X amount for 10,000 engagements the first month, but by the third month it could be paying the same $X amount for 19,000 engagements. The process does the complete opposite of waste - it creates efficiencies in programmatic native advertising.

It’s able to achieve this where others can’t because inPowered taps into over 35 native and social channels to distribute content at scale. The amount of big data collected by its AI technology are multiples greater than using just one network like Facebook or Taboola.

As previously mentioned, the more data fed to AI technology the better and faster it learns, thus allowing for the world’s first dynamically priced CPE.

Get Chad Pollitt's latest book, "Everything You Need To Know About Marketing Analytics and AI" for free.

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This article originally appeared on Social Media Today

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The Marketer’s Guide to EU’s General Data Protection Regulation

Chad Pollitt — Tue, 22 May 2018 14:00:27 +0000

The majority of Relevance readers live and work in the US. However, the EU’s General Data Protection Regulation (GDPR) has the potential to impact any business with a website no matter its geography. There’s potential to be fined even if you don’t have customers in the EU. Businesses around the world are working on or have finished their compliance to this new regulation. It goes into effect May 25, 2018.

Two primary groups must comply with the General Data Protection Regulation:

Businesses located in the EU
Businesses not located in the EU, if they offer free or paid goods or services to EU residents or monitor their behavior (Ebook downloads?)

It’s designed to harmonize data privacy laws across Europe, to protect and empower all EU citizens data privacy and to reshape the way organizations across the region approach data privacy.

It seeks to apply these three updates to the existing 1995 law:

A new transparency framework
A new compliance journey
A new punishment regime

Here's some important information to know regarding the change and its penalties:

Companies have 72 hours after learning of a data breach to send a notification to your regulators
Fines can reach 4% of annual global revenue (or €20 million, whichever is greater)
Consumers now have the right to have data deleted, the right to move data (data portability), and the right to object to data processing, among others

The below is a truncated list of a person’s “data bill of rights.”

Breach Notification – companies must notify individuals if their data has potentially been accessed.

Right to Access – companies must provide individuals with electronic access to their data.

Right to be Forgotten – businesses must delete and cancel all third-party processing of an individual’s data if requested.

Data Portability – individuals can request their data in a commonly used digital format and move it to another data controller.

Privacy by Design – calls for the inclusion of data protection from the onset of the designing of systems.

My biggest concern with this new law is a potential wave of ambulance-chasing European lawyers making lists of US and other foreign companies out of compliance this year to sue. It might even cause some small online businesses to pull out of Europe entirely.

My advice to companies that do NO business in Europe and never intend to is to simply block all European IP addresses. There’s no need to be compliant with the regulation or be harassed by ambulance chasers.

The post The Marketer’s Guide to EU’s General Data Protection Regulation appeared first on RELEVANCE.

What Content Marketers Should Know About AI

Chad Pollitt — Wed, 02 May 2018 16:23:07 +0000

Like many people around the world, I made a couple of professional resolutions for the new year. I wanted to learn as much as I possibly could about artificial intelligence (AI) and blockchain technology. The year is a quarter of the way over and my quest to learn about AI is complete. So complete, in fact, that it culminated into my latest free ebook, “Everything You Need to Know About Marketing Analytics and Artificial Intelligence.”

(Editors Note: Chad will be co-hosting a Twitter chat on this topic with Social Media Today on May 8th. RSVP to attend the chat here: "What You Need to Know About AI and Marketing")

In this article, I will cover everything I learned about AI and its impact on marketing. We’re just getting started down a new and disruptive path in marketing and it’s very exciting.

There’re only three things that can be guaranteed in life - death, taxes, and the disruption of an industry. Disruption is happening all around us, every day. Look what Amazon has done to retail, Uber to taxis, LinkedIn to job boards, the Internet to the media, cryptocurrencies to finance, and so on, and so on.

AI alone is predicted to disrupt forecasting, customer service, education, finance, food service, personalized healthcare, medical, logistics, loyalty programs, marketing, procurement, public relations, search, and security.

According to Salesforce Solutions CTO, Brett Colbert, blockchain is set to disrupt land use, identity, global logistics and shipping, automotive, aviation, manufacturing, prescription drugs, finance, government, banking, and many more.

It used to be that major disruption across an entire industry occurred maybe once a decade or less. However, in today’s environment, we’re beginning to see whole swaths of industries being disrupted at the same time.

Today, we’re beginning to see the very first signs of disruption to come in digital marketing and advertising. Many of us aren’t even aware of what that will look like.

ADYOULIKE has integrated with IBM Watson. Salesforce has launched its Einstein technology. HubSpot acquired a machine learning company, Kemi. Blockchain companies like adChain, Bitteaser, Native Video Box, and AdHive have all entered the ad tech space.

This new technology will make it easier for brands to not only manage their’ big data and get actionable insights but will drive marketing key performance indicators (KPIs) to new heights. They’ll also usher in a new level of transparency never seen before in ad tech.

As marketers, we all need to ask ourselves, “Are we prepared for marketing to be fundamentally disrupted?”

No matter the answer, it’s likely the vast majority of us have little clue how technology is poised to change our industry forever.

The above-mentioned tech companies are usually the examples given when describing the use of AI and other disruptive marketing technologies. However, email, lead scoring, and online ad optimization are merely scratching the surface of what the future holds for the technology that powers our marketing.

The future use of these technologies will impact all aspects of owned, earned, and paid media. It’s not just their future use, either. The technology exists today to fundamentally disrupt all the marketing and media channels.

Artificial Intelligence in Marketing

Many people before me have written volumes on AI. Many still, have written about marketing AI. In fact, my friend, Paul Roetzer, launched the Marketing AI Institute. The site is full of good articles and musings about marketing AI. This is important to note because it signifies that marketing AI is not some trend that’s going away any time soon. So much so, in fact, there’s a hub of marketing AI thought leadership online.

Roetzer has some valuable things to say about marketing AI that today’s modern marketer needs to know. For example, he says:

“Marketers who can harness the power of artificial intelligence will be able to do more with less, run personalized campaigns of unprecedented complexity, and transform business as usual through new methods of machine-intelligent marketing. The opportunities are endless for marketers and entrepreneurs with the will and vision to transform the industry.”

According to a Forrester survey, 86% of more than 700 marketers in decision-making roles at companies across the US, Europe, and Australia agreed that AI would make their marketing teams more effective and efficient.

One of the major problems with marketing AI solutions is one of marketing versus reality. While many solutions tout their use of AI, only a percentage of them are actually providing value using it. The others are using the phrase merely to market the product and drive sales. How AI is actually applied within the solution is what determines its value.

AI used to process images and personality types has long been available. They’re considered elementary at this stage and provide minimal value. Solutions focused on interpreting many different structured and unstructured data streams to help scale, report on, predict results and improve accuracy are the ones providing value with AI.

Many of today’s current marketing AI solutions strictly cater to the person (who) in the seven P’s of Marketing (more on this later). That’s OK. We, marketers, need those solutions, too. Most of them are merely predictive, as opposed to prescriptive) in nature and still require a marketer to digest the information and decide on which actions to take.

For example, using AI to send personalized emails at the best time to individual recipients, automating lead scoring and intelligence, and scoring on-page content. These are all examples of marketing AI being used to cater to the person (who) and are predictive in nature using past data.

We’re also seeing marketing AI enter into the influencer advertising space, too, or promotion in the seven Ps. These solutions are using AI to efficiently and optimally match influencers and brands, all while facilitating a financial transaction between them. It also exists in the non-paid influencer marketing space, too, for better influencer identification, among other things.

In many ways, prescriptive AI can dramatically boost a marketer’s ability to be successful, overachieve, and get more done. In the past, this would require whole teams of analytics professionals or data scientists to accomplish the same tasks prescriptive AI can perform today.

Mike Kaput, Director of the Marketing Artificial Intelligence Institute, shares that marketers will be enhanced in one or more of three functions by AI. It’s rare for a marketing AI solution to enhance all three. The implication of this is that most marketers aren’t in danger of losing their jobs anytime soon. These functions are featured below:

Assessment: Marketers analyze data, past performance, and best practices to learn what works. They communicate this to stakeholders and colleagues.

Recommendation: They use human creativity to recommend new actions that may be successful. These recommendations rely on data from the assessment phase. But they also include healthy doses of intuition, guesswork, and bias.

Implementation: Marketers create assets and execute campaigns. They may do this with or without the help of machine systems like automation software.

Forrester also discovered that 46% of companies said that their marketing and sales teams are leading the investment in AI technology – the highest of any department. It’s safe to say that the AI genie has been let out of the bottle and marketers need to be ready to embrace it.

Artificial Intelligence & Analytics

There are some parts to marketing that fundamentally never change. Take, for example, the seven P’s of marketing – product, price, promotion, place, packaging, positioning, and people. With online marketing, these seven P’s still exist. However, one is consistently forgotten and has been since the first analytics programs were integrated into websites – place (where).

What makes the place so powerful in the offline world is pretty simple to understand. Where a brick and mortar business exists in relation to other businesses, households, government, and infrastructure can impact the visibility and viability of a company in profound ways.

This P, as it relates to the online world, is largely ignored by today’s digital marketers. Where a website exists in relation to other websites is important. The infrastructure of connections via links from these websites massively contributes to the visibility and viability of a website, and ultimately, the business itself in many cases.

One Degree of Separation

Our analytics that we rely on to report on the health of our websites and the impact of our campaigns only provides information for websites that link to us one degree away. And in most cases, attribution is only provided for the last touch. This means that marketers have no clue what succession of websites ultimately drove the visitor to the site, that in turn, drove them to the marketer’s website. This is how our analytics reports information:

Now let’s bring this back around to one of the seven P’s – Place. This is the equivalent of an offline brick and mortar only being aware of the businesses and places directly next door and across the street. In reality, the business could be in a bustling downtown area filled with dozens of shops, restaurants, parks, a courthouse and condominiums. It’s likely this business’s customers stop by some of these other places before visiting it.

With most of today’s analytics, a marketer would only see patrons that left the places next door or directly across the street. What if the business knew the exact cadence of places visited by each patron just prior to them making a purchase? This would be very detailed attribution. What if they knew this information about every business in the area?

Maybe the business discovers that many of its competitor’s patrons reside in the condominiums two blocks away and they only make a purchase after they run in the park. That would be important information to know. Now the business can inquire about sponsoring the condominium’s newsletter with a coupon or sending an employee to the park dressed up in a goofy costume promoting a sale.

An Overreliance on Person (Who) Reporting

Most of today’s analytics programs don’t report the important data mentioned above. Why? Quite simply, because they can’t gather it. It’s too much big data to parse through for the technology that powers much of today’s most adopted analytics platforms. They were built on top of a 15-year-old technology framework.

Quite frankly, they don’t have much incentive to change either. Why? It’s simple – most of the frequently used analytics programs today are owned by one of the big four or so online advertising networks. If marketers had the level of visibility discussed above, they could spend less and get even better results.

Instead, these platforms have convinced most marketers to ignore place (where) in our online marketing. They’ve been able to do this by being really good at reporting on the person (who) in the seven P’s of marketing.

While a person is certainly important, and so are the rest of the P’s, we’ve never been given the complete picture online. As marketers, our view of the Internet is nearsighted with tunnel vision. This means that the decisions we make are made with only a small fraction of the information that exists. In this way, today’s analytics platforms are only one-dimensional.

Three Degrees of Separation

What if AI could take our current one-dimensional view of the Internet and transform it into a truly three-dimensional view? We’d be able to see all of the connections within our potential topical sphere of influence up to three websites away. We’d know which ads and influencers were the most successful at driving clicks and engagement for all the websites that were relevant in our industries. Also, we’d know which affiliates were the most successful within our industry.

What I’m describing would be a new analytics system, powered by AI, that would report and track data up to three degrees away from a website. In other words, we’d see data about all the websites that link to ours, the data about websites that link to them, and the data about websites linking to them.

To do that with the most adopted analytics programs today would require unfettered access to thousands and in some cases millions, of websites’ analytics. The amount of data generated would be on a massive level and likely unusable at any scale for marketers.

This model of analytics and reporting paints a fuller picture for marketers that includes full attribution and prospective customers’ online journey. Not so much how we typically describe the customer journey—only one degree of separation on owned, earned, and paid channels—but, a three-dimensional picture.

Having access to this kind of data can have a huge impact on the performance and cost of paid media by informing marketers which sites to stay away from and which ones to double down on. It can also be a boon for earned media, influencer marketing, and media outreach.

From an owned media perspective, data like this can help inform marketers about what content in their industry is the most popular or underserved – true content intelligence. The paid and organic social media insights would be much richer and more thorough in this model, too.

The visual representation of this new analytics system is shown below:

Data-Rich. Insight Poor

We live in a world that is totally consumed by data. But what is data really? At the end of the day, it’s nothing more than a commodity. A resource. What should matter to marketers right now is what they can do with it. The question every marketer should be asking themselves about data is, “What insights are being surfaced that can drive my campaigns to the next level?”

There has been increasingly more attention paid to the idea of a Customer Data Platform (CDP). Defined as unified, integrated storage of all your customer data, this idea was first introduced in 2013. A CDP is critically important in understanding prospects and customers in a holistic way. This includes both pre-acquisition and post-acquisition.

Unfortunately, very few marketers have a true CDP. Instead, most have a disparate array of systems and tools that don’t share data or insights very well. Those marketers who have access to CDP technology have a massive advantage over those that don’t.

The best marketing analytics and attribution technologies have CDP built into them. The real value for marketers lies in what the technology can do with the data and the recommendations they provide. AI plays a critical role in this feature.

Marketing Attribution

As with most of today’s analytics, marketing attribution is very one-dimensional in nature. This is simply a reflection of the data being delivered by many of the current analytics technologies. The below infographic from Bizible does a good job at explaining the different types of modeling, marketing channels and activities, channel mapping, and metrics associated with marketing attribution.

For example, referral traffic is listed as a marketing channel. We can easily go into our analytics du jour and find the amount of traffic that was delivered by a referral source. Some of that traffic could have become customers, too.

Depending on our attribution model (last touch for example) we could give the converting referral traffic credit for delivering revenue. It would look like this with the website being ours and the conversion (sale) being on our website:

This is fine in a one-dimensional analytics world. However, in reality, it’s more likely something drove traffic to the referral source and something else drove traffic to it. That’s data that the vast majority of marketers don’t have access to today. Here’s what that model would look like:

This is an example of attribution modeling three degrees away. The other websites listed could be other referral sources, social media, email, search engines, etc. As a marketer, this is extremely valuable information to have.

If we knew what website two was, we could go directly to them and partner on a sponsorship. Maybe it’s a popular blog or online publication. Going the earned media route and pitching them a story to write is a possibility, too. Maybe website one is a popular influencer in the industry. That’s a possible marketing partnership.

The above is an extremely simplified example of three-dimensional attribution modeling and is fairly easy to understand. Now imagine a model that showed everything that linked to all traffic sources and everything linked to those. Pretty hard to imagine, right? It would look something like the graphic below.

The above model can inform marketers on which sites to stop advertising on or double down on while identifying new and better-performing sites in which a marketer should invest. It does the same thing for earned media, as well, but in this case, it’s where to spend time, as opposed to budget. It also enables marketers to gain true content intelligence on which topics are resonating or are being underserved in their contemporary sphere of influence online.

Each website represented in the above on its own might not be very important. However, their relationships and connections with the rest of the websites are. Discovering and identifying relevant clusters are, too.

Another thing that a three-dimensional map of swaths of the Internet can do is triangulation. Meaning, that previously unknown traffic sources (direct traffic) can be revealed in many cases. This is very significant because some websites can have huge percentages of their traffic labeled as direct.

Imagine making marketing decisions based on one-dimensional attribution modeling and missing large percentages of data because it’s hidden as direct traffic. Marketers don’t need to imagine it because we’ve been doing it for well over a decade already and it’s been accepted. Most of us only see 20% of the data in a relevant digital ecosystem. It’s the other 80% we’re not tapping into to inform our marketing decisions.

The yellow above represents the 20% of the digital sphere of industry influence a marketer can see. This has been accepted since the early 2000s. Today’s AI-driven analytics makes it possible, for the very first time, to see the remaining 80%.

I know what many readers are thinking, how can we get our hands on this data, and once we get it how do we make sense of it? What’s outlined above is truly big data. There’s a reason this article started off talking about marketing AI and analytics. When the two are married it makes this possible.

The technology currently exists to do this and it’s not just predictive – it’s prescriptive AI. This means, that it looks at copious amounts of structured and unstructured data to explicitly tell the marketer what to do next. In this way, analytics as we know it has changed forever.

The Future is Now

Every tactic associated with earned, owned and paid media described in the ebook offers stand-alone solutions and is not part of an integrated AI-driven analytics platform. This is helping drive the glutton of Martech software marketers is confronted with every year. This is not ideal.

In 2017 alone, the martech software ecosystem was over 5,000 vendors. When these categories are combined under one AI-driven analytics platform it makes marketing-wide integration with sales and service software much simpler and the learning curve much easier to navigate. Not to mention, the procurement process for one all-encompassing solution is much more palatable than the alternative.

Otherwise, marketers are looking at adopting 12 unique AI-driven software solutions to power their marketing. This is why marketers have adopted marketing automation software this decade. Sure, it’s possible to enlist eight different software vendors to do what one marketing automation vendor can accomplish, but it’s not ideal, for obvious reasons.

Adopting multiple marketing technologies, in general, produces ununified data streams stored in separate places. These systems are disconnected from one another and offer incomplete data as a result. Imagine having to look at 12 unique dashboards to decipher strategy and tactics in marketing. That’s what many are doing.

That said, now is the future because all-encompassing AI-driven analytics solutions exist that can unify the customer view, make big data actionable, parse structured and unstructured data, predict outcomes, prescribe actions, provide full marketing attribution, and open up 80% more of the web to marketer’s eyes.

For over a decade, marketers have been beholden to just a few analytics vendors that also sell online advertising. They really have no incentive to help marketers spend less and still get better results. Data independence is the only way to avoid this inherent conflict of interest.

The alternative would be to stop doing online advertising altogether or to slash budgets. Proctor & Gamble announced last year that it would be slashing its online media spend by up to $140 million because of brand safety concerns, bots and objectionable content.

This problem could have been easily solved with a comparatively conservative investment in AI-driven analytics. This technology would empower Proctor & Gamble to focus on Place (where) in the seven P’s of marketing – that’s at the core of their problem.

It’s not just paid media that benefits; paid, earned, and owned media can be positively impacted by this new technology – from influencer marketing and SEO, to display advertising and social media.

If you want to do a deeper dive into AI and these tactics, check out my full ebook is available for free. I set off to learn everything I could about AI and marketing and it’s my sincerest hope that you learned something new, too, from this journey.

(Note: You have until May 15th to register for the Rutgers Business School's "Complete Content Marketing: The Art and Science of Making Content Matter." It's an online course taught by my friends Ian Cleary and Mark Schaefer. Your's truly lectures, too. Use our discount code, "grow," for 10% off.)

A version of this article was first published on Social Media Today.

The post What Content Marketers Should Know About AI appeared first on RELEVANCE.

Everything You Need to Know About Artificial Intelligence and Its Impact on Paid Media

Chad Pollitt — Tue, 24 Apr 2018 14:00:16 +0000

This year I took on a couple of ambitious tasks. One was part of my professional development, to learn everything I could about artificial intelligence (AI) and marketing, and the other focused on annual native ad tech research, similar to what was presented here last year – the 2017 Native Advertising Technology Landscape.

Little did I know at the time, but an entire ebook came out of the subsequent AI research, “Everything You Need to Know About Marketing Analytics and Artificial Intelligence.” It literally is everything you need to know about marketing and AI today and its impact on analytics, earned, owned and paid media. As a result, I’d like to share what I learned conducting all of this recent research.

Before we can start on the impact of AI on paid media we must first look at its impact on analytics. That, perhaps, above anything else has the most direct impact on paid media.

Analytics

Most of us are used to using one of the big three or so analytics platforms. They shall remain nameless. These platforms also own some of the largest online advertising marketplaces in the world. They don’t have much of an incentive to help us spend less and achieve more.

As a result, they only focus on data up to one degree away from our websites. Here’s what that looks like:

Most of us have grown used to looking at our analytics in this attribution model. However, this model only represents up to 20% of the data available within our topical sphere of influence online. If we want to view the other 80% the model would need to focus on data three degrees away from our websites. Here’s what that looks like:

Using AI to pull in many disparate structured and unstructured data streams, analytics can actually see nearly 100% of a website’s topical sphere of influence online, opening up the 80% we can’t see using one of the big three analytics platforms. It’s the equivalent of looking at the Internet like this:

As opposed to just this view that the big three give us:

Having this view has a very significant impact on earned owned and paid media and I explore each and their subcategories in my new ebook. However, for this article let’s now look at its impact on paid media specifically.

Display

The phrases “programmatic” and “real-time bidding” (RTB) have been all the buzz the last several years in and around display, and paid media in general. Occasionally, these phrases are discussed alongside AI, machine learning and natural language processing. While both programmatic and RTB systems have a tinge of AI, they really represent a bridge technology that’s moving display advertising from its current state of mediocre-transparency, to a fully attributed and transparent future.

Two technologies will have the biggest impact on this transition – AI and blockchain. The display space struggles with both transparency and attribution. There are many third parties out there that stick their hands in the candy bowl and grab pennies at a time of our precious budgets spent. Add to that a glutton of spam bots committing click-fraud and you have a system rife with problems.

On average, display advertising has a 0.05% click-through rate. Of those click-throughs only 30 to 40% of them don’t bounce immediately. The inefficiency of this channel is astounding. The first display ad was from AT&T back in 1994 and featured a 44% click-through rate. By 1998 click-through rates fell dramatically – closer to what we see today.

The good news is that technology is helping to fix these problems with inefficiency. In an AI-driven analytics environment that boasts three degrees of attribution away from the website, brands will not only be able to see the most efficient display channels driving traffic to them, but all of the channels efficiently driving traffic to all of the prudent website in and around their industry.

Through AI-driven analytics, brands will know exactly where they need to double down and where they need to pull budget. This level of insight is helping double, and even triple click-through rates and the overall post-click performance for display advertising.

Pay Per Click

AI-driven analytics solutions can surface the most impactful keyword phrases for a brand using many different unstructured data sources. PPC isn’t just for advertising on Google. It identifies gaps and prescribes new keywords, bid adjustments and ad groups. It helps marketers more efficiently manage their budgets.

The possible combinations of keyword phrases, ad groups, targeting, etc. are nearly infinite for a brand. Allowing this big data to be analyzed using AI-driven analytics is the most efficient way to ensure a brand is investing in the best possible combinations and permutations.

Using machine learning the optimization only gets better over time. It’s constantly improving to drive revenue or whatever goals are established for PPC. With its real-time nature, AI-driven analytics used to power account management, is especially critical for brands sensitive to fast-acting seasonal, market or consumer shifts.

While AI has made many end roads in PPC, it still is not at a level where account management can be completely automated without a marketer behind the wheel. However, future iterations built on top of neural networks with deep learning capability will get there. Just like AI can be taught to play a game better than a human, so too will it be able to run a PPC campaign by itself one day.

Native Advertising

AI is having a significant impact on native advertising already. On the ad tech side, the use of machine learning is creating cost per engagement models (CPE), as opposed to traditional CPC, CPM or CPA. This is ideal for marketers wishing to distribute their top-funnel content at scale. Content marketers want their content engaged with.

From an analytics perspective, all of the same benefits AI provides for display advertising are realized, too – knowing which sites are most efficient at delivering actionable traffic up to three degrees away. This data allows for budgets to be moved around only to those sites that perform and allows brands to pull budget back from those sites that don’t. This level of visibility helps marketers avoid almost all of the waste, fraud and abuse associated with online paid media.

It also gives a very accurate competitive view. This is useful for other less obvious reasons. Collecting an inventory of competitor’s creative assets in native advertising for those units that perform well can help give brands a competitive edge in their creative. In addition, the content intelligence built into AI-driven analytics lets the marketer know which content will likely perform the best when using native advertising solutions to scale distribution.

Paid Social Media

Over time, organic social media visibility for brands has drastically diminished. This forced many to invest in the multitude of in-feed paid solutions on social channels. In fact, 60% of total global programmatic ad spend on native advertising will be on Facebook by 2020.

Paid social media marketers realize the same benefits as described in the above programmatic native advertising section. However, one major benefit it provides with paid social media marketing is data independence. Marketers don’t need to rely exclusively on the Twitter or Facebook dashboards to monitor performance. Data normalization and benchmarking across all social media channels is an advantage, too.

Also, with the three-degrees view, marketers will be able to identify where the user was prior to visiting the social media network. This information could prove to be highly valuable for identifying new places to advertise or to pitch a story idea to.

The bottom line on how AI impacts paid media is simple – better performance and less cost. Waste, fraud and abuse are better identified, and we have a better view of our industry’s corner of the Internet. Join us again next week as we take a deep dive into the entire native advertising technology landscape. For more on how AI impacts earned and owned media, and their sub-categories, feel free to download my latest ebook.

A variation of this article first appeared on Martech.Zone.

The post Everything You Need to Know About Artificial Intelligence and Its Impact on Paid Media appeared first on RELEVANCE.

The 2018 Native Advertising Technology Landscape is Still Growing

Chad Pollitt — Tue, 17 Apr 2018 14:00:54 +0000

I spent the last several months conducting copious amounts of research in the areas of native ad tech and artificial intelligence which culminated into the publication of two free ebooks. The first, “Everything You Need to Know About Marketing Analytics and Artificial Intelligence,” was shared via email with the Relevance audience. The second rolls out what I uncovered cataloging the 2018 native advertising technology landscape – “The Global Guide to Native Advertising Technology 2018.”

From 2017 to 2018 there was nearly 50% growth in the number of native ad tech vendors. It went from 272 to 402. Definitions and the methodology of the categories and the project at large can be found here.

The 2018 Native Advertising Technology Landscape

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2018 Native Advertising Technology Landscape (PDF)

Since we’re already in Q2, I thought I’d take a look at the growth of the native advertising technology landscape in just Q1 of this year. In the last three months, the landscape has grown by 48 vendors for a total of 450. That’s nearly a 12% increase since the beginning of 2018.

At that rate of growth, the 2019 vendor landscape will have annual growth of 48%, the exact growth from 2017 to 2018. It’s growing at the same speed in Q1 as in all of 2017. Now let’s explore the vendors that didn’t make the original 2018 native ad tech landscape and the categories they are in. Note: Just because these vendors didn’t make the original 2018 landscape doesn’t mean they didn’t exist in 2017. It just means I discovered them Q1 of 2018.

Programmatic/Networks/SSPs/Exchanges/Platforms

Adiant – Native and non-native advertising units
Adknowledge – Programmatic native and non-native advertising units
Adzerk – Technology platform and APIs for buiding a native ad server
Content Ignite – White label solution to take very own native platform to market. For publishers, advertisers and agencies
DeepIntent – AI-driven native and non-native advertising units. Platform and exchange.
Improve Digital – All-in-one advertising platform for publishers, content providers and broadcasters
IndustryBrains – Premium native ad serving suite for publishers and advertisers
OptiServe – Platform that facilitates the buying and selling of native and non-native inventory from Adiant
RythmMax – Omni-channal exchange for RhythmOne
Revenee – Programmatic native advertising SSP
Solve Media – Native advertising through CAPTCHA
Ströer – Programmatic ad server with both native and non-native capability – Germany, Austria, Switzerland
Yieldlab – Premium native ad serving suite for publishers and advertisers – Native and non-native

Mobile Programmatic/Networks/SSPs/Exchanges/SDKs

Apple News App – Native ad units in the Apple News app
Cheetah Media Link – App monetization and customer acquisition using native units
Glispa – Mobile only programmatic exchange, content discovery and playable creatives
Madgic – Predictive programmatic mobile advertising technology. Connects supply and demand.
Opera Media Works – Acquired by AdColony
Pokkt – Mobile only exchange focused mostly on video and gaming

DSPs/Managed Services Technology

BizzClick – A full-stack programmable advertising platform for native, display, mobile and video
Maximus – Cross-platform native advertising campaign management
Oath – Platform that taps into Gemini and BrightRoll
SoMo Audience – A Self Service DSP created to help set up and run Internet advertising programs. Swipe thru technology.

Mobile DSP

LiquidM – A mobile only self-service DSP

Video Only

Biites – A video marketplace for connecting content, brands and publishers
GothamAds – Video only SSP and DSP

Augmented/Virtual Reality

io – Native ad units and formats with call-to-action and advanced interactivity features support across all major platforms and devices

Social Media

ShareChat – India's fastest growing social network

Social Media Native Ad Management

Connectio – Facebook advertising platform

Messaging Apps

Facebook Messenger – In-feed sponsored content units

Attribution and Analytics

Tune – Tracking and attribution for native, non-native advertising and marketing

Influencer Advertising

Buzzanova – An influencer driven platform with paid distribution on networks and social media
Crowdtap – Connects brands with micro-influencers. Pay using rewards - products or services. Gamified.
Ad – Connects influencers and content creators with advertisers

E-tail/Ecommerce

Amplio – Amazon seller software with white-glove service teams
Ecommerce Search Ad Insight (Kantar Media) – Competitive intelligence tool for monitoring sponsored ads on Amazon
Sellics – It’s an Amazon PPC management software that tracks, analyzes and optimizes for performance
Teikametrics – Software that optimizes sponsored product campaigns on Amazon
Viral Launch – Amazon intelligence and seller platform for startups and enterprises

Blockchain for Native

adToken – The token that powers adChain's technology
ClearCoin – A technology company that powers the real-time buying and selling of media on decentralized applications and the broader digital environment
DATx – Blockchain-powered digital advertising terminal to build native and non-native networks on
IVEP – Blockchain-Based Cross-Platform and Cross-Network Protocol For Audio and Video Content
Market – Connects bloggers and social media influencers with advertisers
WildSpark – Blockchain driven content curation platform that connects creators and advertisers

In-Game

HQ Trivia (Intermedia Labs) – Native advertising in a trivia game app
Playtem – Native advertising in video games

According to ADYOULIKE, global native advertising spend will be $85.5 billion by 2020 and make up 30% of all advertising spending globally. Juxtaposing Q1’s numbers with all of numbers from last year make it clear that the native advertising technology landscape growth isn’t slowing down. The growth is consistent with the growth of last year’s numbers – in fact, it’s exactly the same. Entrepreneurs are jockeying for their piece of the native advertising investment growth.

For more details of the native advertising technology landscape visit the Native Advertising Institute. The free ebook download is also available there. The native advertising and artificial intelligence ebook is available here.

A variation of this article first appeared on Martech.Zone.

The post The 2018 Native Advertising Technology Landscape is Still Growing appeared first on RELEVANCE.

Chad Pollitt - RELEVANCE

SEO and 11 Years of Google Algorithm Updates

Boston – 2003

Cassandra – 2003

Dominic – 2003

Florida – 2003

Austin – 2004

Bourbon – 2005

Big Daddy – 2005

Buffy – 2007

Vince – 2009

Caffeine – 2010

Panda/Farmer – 2011

Freshness – 2011

Venice – 2012

Penguin – 2012

EMD – 2012

Hummingbird – 2013

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The 2018 Native Advertising Technology Landscape is Still Growing

Programmatic/Networks/SSPs/Exchanges/Platforms

Mobile Programmatic/Networks/SSPs/Exchanges/SDKs

DSPs/Managed Services Technology

Mobile DSP

Video Only

Augmented/Virtual Reality

Social Media

Social Media Native Ad Management

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In-Game