Blockchain in the Adtech Ecosystem

Posted on February 17, 2018 by Scott Gordon

Advertisers have (seemingly) always been trying to analyze the effectiveness of their advertising dollars. The old adage “I know that 50% of my advertising is working – I just don’t know which 50%” has never been more true. While technology has provided advertisers with tremendous insights into their ad campaigns and audiences, the more advertisers learn, the more questions they have – especially as we move to a multi-channel ad environment where countless ad impressions across several channels (display, mobile, TV, email, etc.) impact a single consumer decision.

Blockchain is being seen as the latest tool to solve this problem. Advertisers hope that by utilizing Blockchain’s unique open ledger structure, they can share ad transaction data anonymously (even amongst competitors) which will help them better understand path to purchase, media spend accuracy, and ultimately the true impact of the campaign.

There are a few challenges with utilizing Blockchain in digital advertising:

Speed. Blockchain can only process a limited number of transactions per second. While the number has improved from 2016, when it was 6 transactions per second, it hasn’t caught up with programmatic advertising which is millions of transactions per second.
The solution may lie in hybrid Blockchain solutions, which eliminate the time intensive elements of Blockchain but keep the key anonymous accounting functions. For example, minimizing the historical look-back window to cut down on the transaction time to generate a new block.
Quality. Blockchain is truly a “garbage in, garbage out” solution. Providers have to be vetted and trusted to transparently supply accurate data, which if they were truly doing in the first place there wouldn’t be a need for a Blockchain solution . Blockchain is a “trust but verify” solution as it pertains to the data providers. And Blockchain still can’t (currently) connect the offline component, where a consumer sees a digital ad but makes a purchase offline.
Reach. In order for Blockchain to be an effective tool for advertising, it will need to process substantial advertising transaction data points across the entire ecosystem from publisher to advertiser. The more data inputs, the more valuable the network is … and conversely the fewer inputs the less valuable it is. Publishers, DSPs, advertisers, etc. all have to adopt the same Blockchain solution to enable accurate tracking.

Fortunately these hurdles are not insurmountable. Blockchain technology will most certainly improve transparency in advertising ecosystem; it just won’t happen overnight. It’ll take influential advertisers leading large publishers and other adtech companies to create a more transparent (and consequently efficent) advertising environment.

Minimum Viable Audiences

Posted on December 11, 2017 by Scott Gordon

In product development we use the term Minimum Viable Product (or MVP) quite often. Techopedia defines Minimum Viable Product as “a development technique in which a new product or website is developed with sufficient features to satisfy early adopters.” In other words, the Minimum Viable Product is quite literally the minimum product features that can be implemented while still allowing the product to be viable. The goal is to launch a product which does a few things extremely well, and is applicable to a broad base of customers, rather than developing and launching a product which has countless half-baked features of interest to only one or two customers.

After years of leading Go-To-Market turnaround strategies for several startups, I can tell you that most product launch failures can be traced back to a deviation from the MVP mindset. Scope / feature creep driven by large marquee clients is the kiss of death for a product launch. A laser focused product launch which utilizes feedback from a finite trusted audience to prioritize current and future feature development is the core of a Minimum Viable Product launch – and most importantly, it works! Which brings us to the topic of Minimum Viable Audience (or MVA).

A Minimum Viable Audience is similar to a Minimum Viable Product, but the focus is on the person (or the audience) instead of the product. Where with a MVP your goal is build a lean and focused product, with the MVA your goal is to build a lean and focused audience.

Following the adage “you can’t be everything to everyone”, the MVA approach starts with achieving relevance with one (or more) small focused groups of current or potential customers. Before you begin to build the product, you build the audience. And then, working closely with the audience, you build the product using a Minimum Viable Product approach. The audience provides guidance on product demand, features, and in some cases even pre-launch funding. While they are not the perfect example of an MVA, crowd funding sites like KickStarter and Indigogo as examples of pre-launches funded by audiences.

The most important thing to recognize about MVAs is that you need to start building your audience well before you start product development. The audience(s) can be made up of brand loyalists, customers, or even sourced from other influencers. Once you’ve built the audience, then your focus is facilitating a dynamic conversion which allows you to glean data from the audience. Email, Online Surveys, Video Conference, and Webinars are all good avenues to engage with your audience.

Remember, its never too early to start building your Minimum Viable Audience.

Why Click Fraud Should Concern You (Even If You’re Not Paying “Per Click”!)

Posted on October 26, 2017 by Scott Gordon

Click Fraud, the process whereby “bad actors” claim traffic that is being sent to a website, has been an industry problem for years. It can impact you, even if you’re not doing Pay Per Click advertising.

Many advertising teams measure digital advertising performance using a “Last Click” method. The Last Click method gives credit of the conversion to the publisher which served the last ad that drove the consumer to the website. This means that when performing campaign analysis, media budgets will be weighted artificially towards the wrong publishers (and the ad networks that support them).

Operationally, Click Fraud is accomplished by utilizing automated software programs (bots) and malicious code which make it appear that website traffic is coming from another source.

Business Insider, via a legal declaration from Elyse Burns of Vista Print, recently exposed how Click Fraud can occur:

“Burns navigated to the VistaPrint site via search and left the browser on overnight. The declaration states she discovered that, without taking any action, the browser had reloaded the webpage on its own. As a result, the visit no longer had tracking code reflecting that she had reached the site by search, but instead reflected the visit occurred as the result of [another advertising network’s] ad, according to the declaration.”

There are several ways to minimize the impact of Click Fraud (if not eliminate it all together):

Don’t count click traffic coming from self-proclaimed bots. Admittedly, true bad actors will mask their bots as human traffic … but discounting bot traffic from your attribution is such an easy process that there is no reason not to do it.
Watch click behavior. While all humans don’t “click alike”, they all have certain similar limitations and behaviors. For example, they likely don’t click on 30 ads an hour. Similar to the first suggestion, this is not a silver bullet. Savvy bad actors can program their bots to mimic human behavior. But it is an important characteristic to watch for.
Stop doing Last Click attribution. It is, quite candidly, a lazy way to measure campaign performance. A proper measurement and attribution program will take into consideration the multiple cross channel touch points a consumer has – both internally and externally – on the path to their ultimate conversion. And ideally that conversion isn’t a click, but something more tangible like an email newsletter sign up or a purchase.

Much like all technological fraud, Click Fraud is made up of a race between good marketers looking to accurately measure campaign performance, and bad actors actively looking to take advantages of technology loopholes.

The best that you can do is pay close attention to the website traffic which is driving your conversions, and watch for irregularities which could indicate fraud. Your website log traffic can provide vital clues as to the source and timing of each web visitor; a valuable tool for all of your campaigns (even email!).

The Future Of Big Data

Posted on October 16, 2017 by Scott Gordon

What Is Big Data?

Big Data defined: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Quite simply, Big Data is the collection of all of the little things we do as humans. For marketers, this means the small actions consumers take and the influences they encounter as they move through the traditional marketing funnel. That marketing funnel in its most basic form is the journey a consumer takes – moving from awareness to interest to consideration to intent, evaluation, and purchase.

Big Data was once only available to select organizations with the resources to collect and analyze it. Big Data is now accessible to everyone, thanks in large part to technological advancements. These technological advancements have made collecting data from disparate online and offline sources, pinning that data to persistent universal IDs, and analyzing that data to better understand consumer behavior, easier than ever.

Why is Big Data Important?

Marketers are ever consumed with efficiently finding the right consumer, at the right time, and delivering the right message. The old adage in advertising used to be “I know that 50% of my advertising is working … I just don’t know which 50%”. Now, knowing which 50% isn’t good enough. Marketers need to know individual level data, such as “Who is the right consumer?”, “When is the right time to reach them?”, and “Which message will best influence them to convert?”.

Up until recently, collecting and analyzing the data necessary to answer those questions has been difficult and expensive. Now, thanks in large part to the ubiquity of Software As A Service providers specializing in data management and execution, it is easy to not only accurately collect greater amounts of consumer data from disparate sources (eg: social, mobile, display, email, direct mail, etc.), but also put that data to use.

These solutions have become so ubiquitous that even mid-sized companies can afford to collect and store vast amounts of consumer data, analyze that data to create more predictive consumer models, index those models against individual cross channel consumer identities to create audiences, execute those models in the form of targeted cross channel audiences, and then measure the results – both within and outside the click stream.

Does that sound complicated? It is! But now thanks to technology advances in advertising, almost any company can do it. Where companies had to rely on third parties and data collectives to generate data, they’re now able to collect and digest consumer data economically, at scale. The critical component has moved from data collection and storage to data analysis.

This isn’t a fully automated process. It still requires teams of “smart people” to implement. (Data Scientists have reached near celebrity status, commanding larger salaries and greater influence in the corporate structure.) Though those teams are smaller than there were before, and they have greater resources to make more informed decisions.

What Does The Future Hold?

As the accessibility of Big Data (and the solutions that support them) continues to grow, we’ll see several things:

Brands will collect greater amounts of data, as devices become more inter-connected and data storage costs continue to drop.
Brands will demand more access to their data. We’re seeing this now, as Consumer Product Goods (CPG) companies are demanding access to the supermarket shopping data of consumers who purchase their products using loyalty cards, and also access to person level measurement / attribution data from walled garden publishers like Facebook and Google.
As more first party data becomes available, certain third party data attributes will become more commoditized. Brands will realize the value of their consumer data in the open marketplace, and take steps to monetize it. We’re already seeing several adtech companies launch platforms to easily enable Brands to monetize their data.
A continued focus on accurate Identity. Pinning consumers to universal persistent IDs (at the individual and household level) across online and offline channels. This goes hand-in-hand with an increased effort towards identifying and tracking consumers at all stage of the marketing funnel (aka purchase lifecycle).
Leveraging Big Data to reduce the cost of customer acquisition. This is the ultimate goal of marketers … sell more for less. And as Big Data enables us to learn more about consumer behavior, we can expect the traditional marketing funnel to adapt as well.

SEO 3.0 – How to Optimize Your Website For Search Engines

Posted on October 12, 2017 by Scott Gordon

While the importance of ranking highly in relevant search engine queries hasn’t changed in the past 10 years, the process of Search Engine Optimization (aka SEO) has changed dramatically.

One of the foremost authorities on SEO, Moz.com, has published a beginners guide to SEO. This 58 page guide is ideal for business owners who are looking to understand the basics of SEO.

I think that it is one of the most comprehensive guides for SEO beginners that I’ve read in a while, and I highly encourage anyone who utters the word “SEO” in a conversation to read it.

Knowing that we’re all busy, and in the spirit of respecting your time, I’ve created a “cliff notes” version of the Moz.com Search Engine Optimization for Beginners guide (with my notes and comments, having optimized search engines for several years now). This is by no means a substitute for the full guide, which should only take you an hour or so to read. This summary will hopefully make digesting the full guide easier.

Throughout the guide you’ll notice a common theme, which I’ll paraphrase as “don’t try to cheat the system”. Your goal should be to execute a comprehensive digital marketing campaign that makes it easy for search engines to crawl your site and refer visitors to pages on your site that relate to their immediate query. While there are things that you can do to expedite this process, by no means does it happen overnight.

Chapter 1:

Search engines “crawl” your website from page to page, following links. So make sure that the search engines can easily go from page to page by making the link structure simple. They cannot follow search fields, so make sure that you provide links (ideally text links) that they can follow.

Search engines cannot interpret video, flash, nor images (yet!), so make sure that your webpages have lots of text, and use natural language.

Google recomends the following: “Google recommends the following to get better rankings in their search engine: Make pages primarily for users, not for search engines. Don’t deceive your users or present different content to search engines than you display to users, a practice commonly referred to as “cloaking.” Make a site with a clear hierarchy and text links. Create a useful, information-rich site, and write pages that clearly and accurately describe your content. Make sure that your <title> elements and ALT attributes are descriptive and accurate. Use keywords to create descriptive, human-friendly URLs. Provide one version of a URL to reach a document, using 301 redirects or the rel=”canonical” attribute to address duplicate content.”

Chapter 2:

Moz.com likes to say “Build for users, not for search engines”. I couldn’t agree with this more. You need to build an online marketing strategy that is natural, and as a bi-product is search engine friendly. Trying to engineer to only support search engines will only lead to your website being de-listed for SEO Fraud.

A search engine’s only goal is to deliver the most relevant answer to the person searching.

You should focus your content on answering questions. Searches fall into three categories: “Do” (I want to do something), “Know” (I want to know something), or Go (“I want to go somewhere – either an event or a webpage).

Search engines still drives a LOT of traffic to websites, and a majority of that traffic goes to the top listings in a search engine query … so it is worthwhile to spend the time / resources to ensure that your website ranks high in search engines.

Chapter 3:

Things that get in the way of search engines include online forms, duplicate content, and other items previously mentioned. Use common terms, make sure that your language is region specific (the Brits are known for spelling words with an S intead of a Z), and make sure that the language aligns with your primary target audience.

Remember that SEO is always evolving, which is another reason that you want to follow “white hat” (aka honest) approaches. You can check how a search engine reads your website by using SEO-Browser.com or Moz.com. Or you can look at Google’s text cache of the page.

The text in the link (eg: <a href = “website.com”>seo advice</a>) is important for SEO.

Use specific, and relevant, keywords. But don’t over abuse them. Make sure that the language is natural.

Chapter 4:

Moz.com recommends for keyword(s) / key phrases:

Use the keyword(s) in the title tag. Use them at least once. You can use them more than once, but remember to keep the language natural.
Use the keyword(s) / key phrases once prominently near the top of the page. For example, as the bolded header at the top of the page. Remember that search engines cannot read images, so make sure that it is “pure text”.
Several times (with different variations) throughout the rest of the webpage.
As part of the alt tag of an image.
Once in the URL of the webpage.
And as part of the Meta Description for the page. Search engines will pull from the Meta Description when creating the “sneak peak” which shows beneath the listing for your site in the query. Not only should the Meta Description include the keyword, but it should be written as a compelling call to action that drives the reader to go to the website!

Chapter 5:

Choosing the right keyword / key phrase is an artform. You need to choose keywords which are not only relevant to the search, but also aren’t so general that you won’t rank highly in the search query. There is an old saying “The riches are in the niches”, and you should think about keyword the same way.

Moz.com’s Keyword Analysis tool can help you understand the competition for a specific keyword or key phrase, and consequently how difficult it will be to rank highly for them.

Chapter 6:

As mentioned earlier, search engines are trying to give searchers the best possible results for their query. Generally, search engines like websites that are:

Easy to navigate and understand.
Provide clear information relevant to the query.
Designed for modern browsers, across numerous platforms (mobile, desktop, etc. ).
Deliver high quality, credible, and unique content.

Chapter 7:

Who links to you, and how they link to you, is important.

Trusted sites like Wikipedia (where there are a lot of communal editors who ensure the quality and accuracy of the content and links) carry a lot of value. Also .gov, .edu, etc. sites.
Related sites (sites that discuss a similar topic) carry value as well.
The Anchor Text is important.
The freshness and frequency of linking is important. This is not a “set it and forget it” activity.
And more recently social sharing is important.

The guide also discusses techniques you can use to build a strong, legitimate linking strategy.

Chapter 8:

This chapter discusses behind the scenes tools like Robots.txt and SiteMaps. These are things your webmaster should setup for your site to make it easier for search engines to crawl the site.

Chapter 9:

Chapter 9 discusses the “black hat” techniques that are likely to negatively impact your listing, like keyword stuffing, etc.

Chapter 10:

Chapter 10 covers the tools you should use to measure the success of your SEO activity. They include:

The search engine share of referring visitors (how many visitors are finding your site via search engines)
The terms and phrases that consumers use when finding your website.
The ultimate conversion ratio of search engine traffic, by keyword or key phrase.
And how many pages benefit from search engine referred traffic.

The chapter also covers which tools you can use to measure these benchmarks, and how to use the tools.

What Do The New Communication Privacy Rules Mean To You?

Posted on April 14, 2017 by Scott Gordon

A lot is being made about the recent moves to unwind the FCC privacy rules, which made it difficult for Internet Service Providers (ISPs) to share consumer web and app behavior data without permission.

Data collection and sharing comes down to Notice & Choice. Was the consumer aware that you were collecting data, did they know how you intend to use that data at the time the data was collected, and do they currently have the option to Opt Out of the collection and subsequent use of that data at any time.

The rules around notice and choice vary from channel to channel and region to region. If you have ever asked yourself why many European websites have begun posting obnoxiously prominent privacy notices (in some cases requiring you to actually accept the privacy policy before continuing), now you know.

As I mentioned, how this is managed varies from channel to channel, in large part because the technology from channel to channel varies. In a web browser, a specific no-follow cookie is (somewhat ironically) use to indicate to a web browser that the consumer doesn’t want to be tracked by cookies, or other means. If the consumer deletes that no-follow cookie, then a website assumes it is alright to follow and track that consumer.

But cookies don’t work in every channel. Addressable TV doesn’t accept cookies. Most mobile webs don’t either. Given the many to many relationship between the numerous potential online and offline channels, the countless signals that can be pulled from a consumer device (cookie ID, latitude/longitude, mobile ID a.k.a. MAID, cookie, IP address, device type, etc.), and the different ways in which a consumer can opt out (DMA, IAB, no-follow cookies, etc.) – the challenges of properly managing consumer opt-outs is arduous.

Understandably there was concern when it was announced that there might be changes in the policy which governs how ISPs collect and share your viewing and browsing behavior. Questions like: what data would be collected and shared? how would consumers be notified of this data collection (through a pop up screen? in the terms and conditions of their contract?) and how could they opt out ?

Unlike other “walled gardens” such as Google and Facebook, who each maintain massive databases on digital consumer behavior, ISPs are unique in that they see all of the Internet activity for their customers. They are the “last mile” ending at the home, so in the case of wifi they see all activity coming from a specific modem or mobile device, and are able to tie that to the identity of the person paying the internet bill. Google and Facebook only see that activity for registered users which occurs within their own sites, or sites which use their targeting and analytics services.

At this point it is unknown what the specific changes will be, but it’s certain that they will impact both consumers and marketers alike.

Marketers need to closely monitor how data is collected not only internally but also by their marketing partners (including media publishers) to ensure that it is done so compliantly.

Technology is Making the Billboard Sexy (Again?)

Posted on March 14, 2017 by Scott Gordon

You can trace the modern day billboard back to the 1790’s, when the advent of lithography made it easy to mass produce signage. In the 1900’s, billboards followed the explosion of the automobile and moved from the sides of buildings to highways. Close to 100 years later in 2012, the digital billboard was born – allowing content to change interactively.

Thanks to the proliferation of location tracking through mobile devices, digital billboards are now advancing even further.

Clear Channel just launched a program called Radar, which uses technology and location data from partners like AT&T, Place IQ, and Placed, to build audience profiles based upon the people that pass by the billboard. They’re able to tell an advertiser not only the exposure of the ad but the aggregated demographics of the consumers who likely drove by the billboard. This is a vast improvement over the old method, which relied on a person actually counting the cars that drove by a particular ad.

Synapse Labs is using cameras to identify the make, model, and year of a car passing by a billboard – and changing the ad content in realtime. So BMW can target the drivers of late model Audi’s differently than early model Mercedes Benz owners.

Technology has vastly improved two of the three legs of addressable advertising for outdoor signage – modeling and targeting. All that is left is accurate post-campaign measurement, tying those who are exposed to an advertisement to their future purchases.

These are exciting advancements, and this type of technology isn’t limited to billboards and cars; it can be applied to any outdoor signage. For example, targeted advertising in a shopping mall based upon the gender of the person standing in front of the sign. And I’m certain that this isn’t the last advancement we’ll see to billboards and outdoor signage. As cross channel hyper-targeting becomes more accurate through technological advancements, you’ll see better uses of this “classic” media.

Sources: History of Billboards, Wikipedia

The Rise of the Chief Privacy Officer

Posted on February 28, 2017 by Scott Gordon

Your company’s responsibility as a steward of consumer data is greater than ever. Advances in technology have made it extremely easy to access and collect sensitive consumer data from disparate first, second, and third party sources, analyze that data, and then use that data to build sophisticated consumer profiles which could impact how you engage with that consumer.

Welcome to the age of the Chief Privacy Officer.

As market capabilities have grown, so too has the breadth of what could be considered sensitive consumer data. No longer is it just name, address, birthdate, and social security number. Collecting personal information such as transaction data at the product SKU level, digital IDs, location, viewing history, etc. is not only very easy, but also quite common.

As is the ability to tie that information directly to an individual – across both identifiable and (seemingly) anonymous online and offline activities. The technology exists to build more sophisticated Device Graphs, which connect disparate signals to a single persistent offline ID which can ultimately can be associated with an person. Anonymous cookie ID ‘123’ can be connected to mobile device ID ‘ABC’ which is tied to IP address 123.456.789.123. Once you’re able to associate any of those signals to a individual “offline” signal (for example, a name, postal address, email address, etc.), you’re able to align all of the behavioral data through each of those channels back to that same individual.

And the number of signals that can be used for identification continue to grown. Recent studies have shown that a phone’s battery behavior as well as the characteristics of a phone’s audio hardware are unique enough to create an anonymous identity for the device.

The responsibility of how this data is collected, the notice and choice that is provided to the consumer, how consumers can access and alter this data, and how your organization ultimately uses that data falls directly on the shoulders of the Chief Privacy Officer.

Let’s use the example of Uber’s recent study on the price elasticity of Uber rides, which showed that consumers are more likely to pay a surge fee if their battery level is low. In this case, Uber is clearly collecting through its app (among other things) the battery level of your phone. The obvious concern would be the use of that data in determining the price of a ride. Uber could theoretically increase fares of consumers based upon their then current battery level, giving consumers with lower batter levels higher prices (because, theoretically, they would pay it). This could have negative PR and legal consequences.

What is the consumer’s responsibility?

The right to collect, associate, and use this data is buried within each provider’s privacy policy. It’s up to the consumer to understand what data is being collected, and how it is being used. It is also the consumer’s responsibility to proactively opt out of marketing channels.

Unfortunately, technology has not made this process simple. What was once a relatively easy offline process of opting out of receiving direct mail either through the brand or the Direct Marketing Association, has become more challenging as the number of media channels has grown. Each channel has its own opt out process. For example, in digital display on a browser it is – ironically – an opt out cookie that alerts brands to not track the consumer, while other channels that don’t support cookies must offer different alternatives.

What is your company’s responsibility?

This isn’t just a legal question. A key function of the Chief Privacy Officer role is to understand not only the legal implications of actions your company takes, but also the moral and branding implications. Ideally, actions are decided based upon what is best for the consumer, best for your company, and in compliance with local and federal laws and guidelines.

While the Chief Privacy Officer function won’t achieve the rockstar status that Chief Data Scientist has, it is certainly just as important as your company’s role as a data steward evolves.

The Problem With Cookies Isn’t The Cookies …

Posted on February 14, 2017 by Scott Gordon

There is quite a bit of debate going on about people based matching versus cookie based matching. I thought that I’d take a minute to set the record straight.

Before I start, it’s important to have a basic knowledge of how the Internet and cookies work together.

WHAT IS A COOKIE?

A cookie is actually just a small text file, which sits on your device (like a laptop, a tablet, or a mobile phone). Your device can store countless cookies from the various websites you’ve visited, and each is unique to your device and in some cases your web browser.

Cookies are used to store information about your web browsing. This information could be your name (so that the webpage can greet you … eg: “Welcome back Scott”, address, your username / password (to give you access to a specific website), the web pages that you’ve visited before, etc.

Now to understand the importance of a cookie, you need to understand how a web server works.

THE WEB SERVERS AND COOKIES IN BASIC TERMS

Imagine you’re at a hotel bar, and you’re charging drinks to your room. Each time you go up to the bar for a refill, you have to repeat your room number because the bartender has a really bad memory. Now imagine that the bartender can look up your room number using your room key.

This is how the Web Servers and cookies work together.

Websites are hosted on Web Servers. When you surf the web, each webpage is delivered to your device (phone, ipad, etc.) from the Web Server independently. The Web Server is a “session state” environment, meaning that it cannot natively associate one request from a device to another. In other words, it only “sees” (and remembers) each webpage request separately. This is where cookies come in. They help carry information to the Web Server, from one webpage request to another.

In the example above, you are the web browser. The bartender is the Web Server. The drink you order is the web page. And your room key is the Cookie.

Cookies can also be used to target advertising. An Ad Server (like a Web Server for advertisements) can be programmed to serve specific ads to specific cookies. This is the technology used to deliver ads to specific people.

THE PROBLEM WITH COOKIES

A cookie, in and of itself, isn’t the problem. The problem is the linkage. How was a cookie associated with the person to whom the ad is being served. As marketers, we need to make sure that we are reaching the right people with the right ad … and more importantly not reaching those people who have opted out.

This is especially true in the world of regulated data, where you need to know who you are targeting. And cookie-based linkage is controlled by a handful of companies who 1) are walled gardens and don’t share how they link offline people to online cookies 2) they don’t collect this information directly. They rely on other websites to gather PII, and connect it to their cookies. In some cases, the data is very accurate (especially with transaction data). In some cases, it is not (think websites that collect PII when giving surveys, offering coupons, etc.).

In short, in order for you to use cookies based targeting accurately, you need to have insight into the source of the base linkage data that was used to connect the offline consumer record to the online cookie. And until that happens, the debate will continue to rage on.

Translating the new language of TV

Posted on January 29, 2017 by Scott Gordon

TV – it’s a whole new world. With new ways that live and prerecorded video are being distributed, so too does the list of new terms that define this medium. Here’s a list of the most common terms being used in Television today.

Addressable Advertising: TV audiences which can be segmented, usually at the household level, based upon attributes such as geography, demographics, and / or behavior.

Cable Operators: Companies that provide television content via a cable in the ground – for example, Cox communications.

Connected TV: A television that supports the delivery of OTT content.

Digital Video Recorder: A device which records linear TV digitally, for viewing at a later date.

Gross Rating Point (GRP): The common method in which television viewership is rated. Also known as “TV Ratings”, it shows the percentage of households which watch a particular program.

Internet Protocol Television (IPTV): Also known as Internet TV, it is the streaming of video content to any media device (such as a personal computer, game console, etc.).

Linear: Television service where the programs are delivered on a set schedule, as opposed to Video On Demand.

Mobile TV: This is real-time video content which is broadcast over a mobile network.

Multiple System Operators (MSO): An operator of multiple cable or satellite transmission systems. MSOs include AT&T, Comcast, Charter, Dish, Verizon, Cox Communications, Altice, Frontier, Mediacom, WOW!, Cable One, TPG, Windstream, Century Link, Midcontent Communications, Atlantic Broadband Group, Amstrong Cable Services, Service Electric Cable TV, Metrocast, Blue Ridge Communications, Google Fiber, etc. They are also known as Multichannel Video Program Distributors (MVPDs).

Over The Air (OTA): Television which is broadcasted using radio waves to a TV receiver. OTA is typically not addressable at the household level, meaning that it cannot be segmented by household.

Over The Top (OTT): This refers to the delivery of video content (TV, movies, etc.) using an Internet protocol – without requiring a television subscription to a cable or satellite provider.

Satellite Providers: Companies that provide television content via Satellite transmissions.

Set Top Box: This is the device which decodes the signal transmitted by your cable or satellite TV provider. It likely was named because it was a box which, in early days, sat on top of your TV set. The Set Top Box is registered and is unique to a household.

Streaming: Streaming is the real-time distribution of video and audio content over an Internet protocol, as opposed to the content which is downloaded. Streaming content can be stored for a short period of time (commonly referred to as a buffer, for obvious reasons) to ensure a consistent user experience in case of an interruption in the Internet connectivity between the device and the server.

Time Shifting: The process by which a viewer watches content at a different time than the scheduled broadcast time. They can do so using various technologies and services, such as DVRs, VOD, OTT, and Mobile TV.

Video On Demand (VOD): Television and video content which can be accessed by the viewer at any time.