Programmatic Media Buying 101: Why DSPs & DMPs work better together?

Data-driven advertising has been proven to deliver the most effective way of managing an advertiser’s spend as well as the most efficient way to monetize a seller’s digital assets.
The effectiveness of data-driven decisions –planning, selling, buying– make it necessary for both sellers and buyers to take as much control of their data as possible, and for this reason Data Management Platforms (DMPs) are a key technology for media buyers, publishers and marketers. A good DMP should not only be able to collect data from different sources, but also allow for the creation of audiences/ segmentation, consolidated reporting and campaign optimization – the place where people, platforms, partners and processes are brought together to apply audience data that is actionable. For all those marketers, media buyers and advertisers running programmatic advertising the DMP should be the source of truth for activation and analysis purposes.

What Can A DMP Offer?


Web and mobile experiences, speed of ad delivery, relevant ads and smooth and uncluttered paths to purchase have all contributed to the expectation of today’s consumers.  A good versus bad experience will make or break a retailer. The DMP enables advertisers and brands to craft and deliver personalized communications and offers to existing customers, while simultaneously reaching new customers (identified and informed by existing customer data) through digital advertising making sure the experience is seamless and relevant.

So what do advertisers look for when purchasing a DMP? According to John Lockmer, Director of Programmatic and Ad Operations for DuMont Project there are two main factors he considers when shopping for a DMP:

  1. How the platform will connect to his firm’s ad tech stack?
  2. How much it will cost to use?

With costs ranging from a minimum of $15,000 per month for basic usage to up to $500,000 for a license to manage up to 50 million users, advertisers are looking for alternatives which includes an integrated DSP (Demand Side Platform) and DMP solution.

“It makes it easier for us to work with them, as it does not require a yearly contract and commitment like [standalone] DMPs,” Lockmer said.

 

Tight integrations between a DMP and DSP hold a number of advantages for programmatic media buyers:

  • More efficient media activations – which means you can message your known audiences and address them with the right message
  • Advertising Efficiency – when there are clear signs that a user is no longer interested or have already bought your product, you can stop advertising to them
  • You can diversify your data to include first, second and third party sources for maximum advertising impact
  • Better understanding of the impact of your media buys across all online channels with more accurate and robust analytics
  • NO data leakage
  • If you work with Digilant, there is little or no added cost to activating the DMP

In order to execute a data driven programmatic media buy on a DMP, it’s also important to understand the different types of data available to today’s digital advertisers.

First Party Data

Advertisers have numerous potential first party data assets that they already own: CRM, Point of sale (POs), website, search, digital marketing and offline (point of sale, shopper visits, etc) marketing data. This data is frequently referred to as first party
data. The most important evolution for media buyers in first party data is the ability to activate it for programmatic campaigns, in addition to using the data only for email marketing or direct mail campaigns.  An necessary part of using first party data for programmatic is taking out any Personally Identifiable Information, know as PII, by using on-boarding service providers like LiveRamp – used to translate offline or email data signals to a digital user ID, so that DSPs can now translate different kinds of data for programmatic media campaigns.

Second Party Data

Second party data is acquired through exclusive relationships with data providers who do not sell their data in the open market. A DSP platform can help strike a deal with a data provider to enrich an advertiser’s first party data or directly activate second party data. DSPs are in a unique position to provide data intelligence to help advertisers make that data actionable. The DSP data intelligence comes from developing data science models from previous advertising campaigns that can be applied to enrich advertiser data and their campaigns.

Third Party Data

Data Management Platforms (DMPs), collect audience content consumption anonymously through access to publisher sites and sell the information as 3rd party data to advertisers. For sellers, the data that DMPs help collect is leveraged by sell side platforms (SSPs) to understand the value of their content and properties to increase monetization. For buyers, DMPs provide paid access to audience data from many publishers to which they otherwise not have access. DMP platforms that perform this data collection and distribution (“data exchange”) pay a small fee to sellers to be able to collect data and make it available for media buying platforms.
For example, BlueKai, acquired by Oracle, is one such DMP platform, the idea was to be able to monetize the data collected on both sides (sellers and Buyers), making it necessary for it to be anonymous, impartial and independent. This type of data is frequently termed third party data (3P). All DSPs have access to the same third party data for a price. But, additional layers in the media technology stack have implications. One, there is an additional fee to advertisers; and two, there is data loss as the audience universe do not exactly match between platforms. Since these different types of data are housed in different areas, a gap between first party data and third party data has been created for marketers and media buying platforms. these two data sets live in two different companies, in different technology solutions, and in different formats.

DSPs and DMPs Work Better Together

How can we bridge this gap? Digilant’s solution lies in its integrated DMP and DSP that bridges data management and media activation.  With all the effort, platforms, people and cost involved in executing data driven programmatic advertising, advertisers need to understand that the recipe for success is not pre-packaged, the right combined DMP and DSP will can help them achieve the right ingredients to create the right programmatic buying strategy that leads to campaigns that scale and perform.

Programmatic Media Buying 101: Why You Should Add Programmatic Audio To Your Media Plan?

Programmatic advertising refers to the buying and selling of digital ad space using special software like DSPs or Ad Networks, built for specifically for digital marketing transactions. It avoids traditional human negotiation and is more efficient, because ad buying in the real world between buyers and sellers can be expensive and unreliable. Programmatic allows for automation in real time satisfying both buyers and sellers.

Today’s marketers need to be savvy in using every resource around them to effectively reach their target audience and as radio becomes mostly digital, it is one of the more important and cost effective media channels that media buyers are paying attention to.


Programmatic radio inventory is beginning to rival TV advertising in terms of reach. According to research by RAJAR (Radio Joint Audience Research) 3.7 million adults listen to podcasts, which is around 6.5% of the adult population. In the today’s world radio seems to be more and more obsolete when trying to reach consumers. However, according to Nielsen, in the U.S. radio surpasses all other platforms when it comes to weekly reach, connecting with 93% of the American population aged between 12 and 54.
Programmatic radio is more than just pure reach. Radio, or audio advertising have opportunities that come from streaming services such as Spotify, SoundCloud, and Pandora. Spotify has over 100 million active users, with over 60% of users opting to use their free service which exposes them to ads. RAJAR Midas Audio Survey states that 51% of time spent with on-demand music services is also a service that features advertising.

“There is a more advanced way to think about advertising budgets. It’s about data and efficiency. As they get better at using data to be more efficient in their advertising spend, they are pushing every media type to be bought that way.”
Mike Dougherty, Jelli CEO

An important thing to note is that programmatic audio ads are unique because you can only hear one ad at a time. In today’s digital atmosphere consumers are bombarded with constant ads almost everywhere they look and usually multiple ads on a page. Consumers have become self-trained to detecting ads and tend to immediately dismiss them. With audio, there is only one ad for the listener to consume and it is not competing with all the other ads the listener would see on a webpage. They can’t listen to anything else other than the ad playing, so engagement becomes a bigger factor when placing audio ads.

Programmatic radio performs best on mobile. In the US, 75.8% of U.S. digital audio listening occurs on mobile vs. 24.2%on desktop. Mobile advertising continues to grow in popularity and effectiveness each day, with programmatic audio advertising you can get in on the action.

There’s no denying that radio – the original broadcast medium – hasn’t lost its appeal. And whether consumers are streaming music, listening to podcasts, or tuning in for the news, they  are going to be all ears.
By adding programmatic audio advertising to your media buying plan you’ll be tapping into new target audiences as well are reaching users in new places.
Contact us to learn more about adding programmatic audio to your digital media buying plan.

Programmatic Media Buying 101: What’s The Difference Between DSPs & Ad Networks

The current programmatic media buying landscape is really just an extension of the traditional two-party system between advertisers and publishers. If you keep in mind what is being sold, who is selling it and who is buying, it should become a little clearer.

So What is the Difference Between DSPs and Ad Networks? 

The acronym DSP stands for demand-side platform. It is a buyer’s side platform for advertisers, it allows advertising buyers to manage multiple ad exchange and data exchange accounts using one interface. An ad network works for the publisher side of the two-party system, connecting advertisers to publishers that have web pages with advertising -matching ad space supply from publishers with advertiser demand.

Let’s Define This Further

Demand-Side Platforms (DSP):   These are used by media buyers at agencies and brands to manage and purchase digital advertising inventory from multiple ad networks through one interface. DSPs allow advertisers to buy ad impressions across a range of publisher sites, but targeted to specific users based on data such as gender, age, location and browsing behavior.
Using a single interface allows marketers to target a very narrowly defined audience segment at scale, without having to manage multiple ad networks or exchanges. The DSPs use the behavioral targeting data which is collected from cookies and data exchanges, to identify the audience segments.  DSPs let the marketers choose audience characteristics and then publishes the ads depending on the target audience.  The main advantage here is that marketers do not have to worry about picking the right websites to advertise on, as the DSPs can do the work for them.

Benefits:

  • Access to multiple inventory sources — they connect to several ad exchanges and SSPs and offer several channels
  • Media buyer can choose which sites to buy on
  • You can set the price at you think each individual impression is worth
  • Added Data segments — use third-party or first-party audience data to enhance buy

Challenges:

  • There are many different DSPs in the marketplace and you need to set up a contract with each one to have access to their platforms
  • Steep learning curve — it takes time to master the nuances of buying on each platform, unless you work with a partner like Digilant

Ad Networks: An advertising network aggregates, categorizes and sells a range of publisher inventory in a way that can be easily understood and purchased by advertisers on a fixed CPM basis, connecting advertisers to web sites that want to host advertisements. . By aggregating inventory, Ad Networks offer advertisers the ability to better reach their target audience while allowing publishers to sell their inventory more effectively. There are many types of Ad Networks and they focus on delivering different objectives.  Some focus on delivering reach and price while others focus on audience demographics and quality.

There are three main types of ad networks:

  • Platform for buying audience segments and data
  • Platform for buying media
  • Platform for creative optimization

Ad networks are often used by media companies to sell out their online display inventory. However, unlike DSPs, not all ad networks support real-time bidding. They will have to incorporate a DSP, in order to facilitate real-time bidding.

Benefits

  • Centralized source for inventory for media buyers and advertisers
  • No need to buy from individual publisher sites

Limitations:

  • Lack of transparency — site reporting often masked
  • Fixed CPM — all impressions cost the same regardless of value
  • No automation — you need to contract each buy with a separate IO

What’s the Takeaway?

Technology creates efficiencies between advertisers and publishers. A DSP enables media buyers to incorporate automation using machine learning into the media buying process, giving advertisers access to more sophisticated targeting tools, data and analytics to improve their advertising performance.  DSPs consolidate purchasing needs in one platform.  But in today’s world of data privacy regulations and walled garden most advertisers can’t afford to use only one DSP.   Each DSP like Google, Facebook, Amazon, MediaMath and others all offer their own unique audiences, data and targeting capabilities.  Not only that but if there are buys or a platform goes down you don’t have options.  You can’t be overly reliant on the infrastructure of one partner because if they decide to change something that has implications for your business you can’t afford the lag time that might cause.
That’s why Digilant partners with all of the best platforms, giving media buyers a holistic view of their ad buys across multiple DSPs so that advertisers can measure results and get value from their ad spend.

Programmatic Media Buying 101: What is a Data Lake?

A data lake is a centralized place, like a lake, that allows you to hold a lot of raw data in its native format, structured and unstructured, at any scale. You can store your data as-is, without having to first structure the data or define it until its needed.  It can then be used for creating reporting dashboards and visualizations, real-time analytics, and machine learning to guide better programmatic advertising decisions.

As data grows and diversifies, many marketing and especially digital strategy teams are finding that traditional methods of collecting data are becoming outdated and are pushing for something more centralized like a data lake.  According to Aberdeen research done in September 2017, the average company is seeing the volume of their data grow at a rate that exceeds 50% per year. Additionally, these companies are managing an average of 33 unique data sources, according to the research study. With data split into silos by team, like search, social or direct marketing, CMOs are being challenged with how to efficiently manage the analysis for their media campaigns.  If they don’t consolidate their data, they risk targeting the same consumer more than once or even exposing them to the wrong message.

Why Do You Need a Data Lake?

Most data platforms will only store data if it’s been formatted to fit a particular structure, like rows and columns.  So unstructured data like log files, data from click-streams, social media, and internet connected devices typically can’t be uploaded into a data platform until the data has been defined.  A Data Lake allows you to import all marketing data in real-time, from multiple sources and in its original format. It also allows you to scale data of any size.  Then you can figure out how to use it in an automatic yet personalized way to attract and retain customers through digital advertising.  Companies like Digilant can help you set up a Data Lake and use it for media activation.

What is the difference between a data lake and a Demand Management Platform (DMP)?

If you are a digital marketer, a Data Lake allows companies to collect PII data (Personally Identifiable Information), which DMPs do not.  A DMP’s is main function is the collection of cookie data for media audience activation where a Data Lake is often the first step used by data scientists to expand the knowledge of the DMP.  The DMP often connects directly to the media activation tool which for programmatic is most likely a DSP (Demand Side Platform).  A DMP will establish connections between several external data providers, and the data lake then supplements it with new internal data like social media feeds or connected device data.

Four Main Advantages to Having a Data Lake

1. DATA INDEXING
Data Lakes allow you to store relational data (a collection of data items organized as a set of formally-described tables from which data can be accessed or reassembled in many different ways without having to reorganize the database tables.) —operational databases (data collected in real-time), and data from line of business applications, and non-relational data like mobile apps, connected devices, and social media. They also give you the ability to understand what data is in the lake through crawling, cataloging, and indexing of data.

2. ANALYTICS

Data Lakes allow data scientists, data developers, and operations analysts to access data with their choice of analytic tools and frameworks. This also includes open source data frameworks such as Apache Hadoop, Presto, and Apache Spark, and commercial offerings from data warehouse and business intelligence vendors. Data Lakes allow you to run Analytics without the need to move your data from one system to another.
3. MACHINE LEARNING

Data Lakes will allow organizations to generate different types of marketing and operational insights including reporting on historical data, and doing machine learning where financial models are built to forecast likely outcomes, and suggest a range of actions, if taken, have the ability to achieve optimal results.

4. IMPROVED CUSTOMER INTERACTIONS

A Data Lake can combine customer data from a CRM platform with social media data analytics, as well as a marketing platform that includes buying history to empower the business to understand the most profitable audiences, the root of customer churn, and what promotions or rewards could increase loyalty.

In Summary

Marketers and Media Buyers would want to implement a data lake for three main reasons. First, they want to take advantage of more advanced and sophisticated analytical tools and dashboards, using a more complex and diverse foundation of information. Secondly, they also want to make traditional activities — like data access and speed of retrieval — more efficient and easier to accomplish. The third reason is they want to bring all the data from the different parts of the organization into one place creating efficiencies of time as well as cost savings.  While not every company succeeds at achieving all three objectives simultaneously, the most effective ones will able to see positive results on their ability to make better programmatic media buying decisions.

Programmatic Media Buying 101: Programmatic Creative is the Future for Display Advertising

Digital advertising that includes both high quality creative and relevant messaging is increasingly a high priority for media buyers and marketers.  Advertisers see no reason why creative, rich media, and programmatic should be mutually exclusive –­ it’s the combination that achieves engagement and results with consumers. The combination of programmatic and engaging creative offers a wide range of new opportunities – using data to precisely tailor messages.

Marketing teams are moving away from click-centric strategies as the only way to measure engagement. With all the new high-touch, high-impact ad formats and the growing popularity of native ad placements, there is a whole new world opening up to advertisers in display ads, to provide a more robust user experience while still reaping the benefits of programmatic buying.

Creative has never been more crucial to display ads as it is today and agencies and marketing teams are paying attention because they realize that a display ad’s message or creative is just as important as the channel or medium through which it’s served.

What is Programmatic Creative?

Programmatic creative has the ability to use the data collected from a programmatic display campaign to create a more personalized experiences for consumers. Rather than displaying one generic creative, new technologies, like Dynamic Creative Optimization (DCO), mean that the ad creative can be tailored to the viewer in real-time, across multiple devices, according to their location, what they are doing, and the time of day – improving the overall user experience.
Where programmatic advertising matches users to ads on a one-to-one basis in real-time, DCO supports the matching of the best creative for that user during the programmatic advertising process.

Instead of marketers and advertisers having to figure out a one-size-fits-all, mass-market approach to their creative for a campaign, now they can create hyper-relevant ads that are relevant to individual users, while reaching a larger audience.  Using the sizable amount of data that is collected from each campaign, programmatic creative can enable automatically generated ads relevant to products or services that customers are viewing, helping to move customers towards the conversion path, and returning customers into repeat purchasers – building long-term loyalty and increasing returns for those campaigns.

Programmatic Advertising has Changed the Role of Display Ads


With programmatic taking the largest share of digital marketing budgets, the role of display advertising has been reborn and redefined.  More than four in five US digital display ad dollars, or $45.72 billion, will flow via programmatic means by 2019.
It’s no secret that different formats accomplish vastly different goals for marketers and media buyers. As the role of display advertising is redefined, and programmatic has dramatically changed the landscape, marketers need their display options to emphasize relevance for each consumer and define their experience as unique rather than obtrusive.
If campaigns are to remain relevant, marketers should be considering themselves not solely as advertisers, but as storytellers.  Marketers and publishers alike are turning to programmatic creative to enhance user experience and keep the customer at the center.

Programmatic Media Buying 101: The Difference Between First and Second Price Auctions in RTB

If you are buying advertising programmatically then you are most likely using either a first or second price auction bidding process.  Most recently there has been more talk of moving towards first price auctions because of the popularity of header bidding.  DSPs (Demand Side Platforms) have traditionally been set up to use second-price auctions and for most DSPs adapting and changing strategies to first price auctions is expensive because they have to invest in technology that will specifically adapt to the rules of every auction and allow them to bid effectively.

So what are the differences between the two types of auctions?  And why should media buyers care which one gets used?

First Price Auctions

The programmatic buying model where if your bid wins, you pay exactly what you bid. This type of auction maximizes revenue potential for the seller.

In the first price auction model the bidders pay exactly what they bid. This type of auction can lead to unnaturally high prices because buyers are forced to guess how much their competition will bid.  This auction mechanism gives publishers the highest eCPMs for their inventory but can lead to the advertisers overpaying which can then lead to a lower demand for that publisher’s inventory.

The first-price auction allows both buyers and sellers to see the actual cost of the impression and the fees taken by the SSP/ad exchange will at least be known. The winning price is exactly what the advertiser agreed on, but there is a risk of overpaying for impressions.

The workings of the first-price auctions make sense economically only when the buyer knows the fair market value of the impressions they are bidding on, and understands the mechanics of hard- and soft- price floor mechanisms. The Price Floor, is the minimum price a publisher will accept for its inventory, which technically means they will ignore all bids below that price. This can turn a second-price auction into a type of first-price auction.

Second Price Auctions

The programmatic buying model where if your bid wins, you pay $0.01 above the second highest bid in the auction. In this type of auction, it is in your best interest to bid the highest amount you are willing to pay to win that impression, knowing that you will most likely end up paying less than that amount.
The second price auction is preferable to first price auctions for advertisers because it gives the winner a chance to pay a little less for the ad impression than their original submitted offer — instead of paying the full price, the winning bidder pays the price offered by the second-highest bidder, plus a bit more, usually $0.01. The final and winning price of the impression is known as the clearing price.

So What About Header Bidding?

Header bidding has become a popular type of first price auction where publishers place a piece of code on their webpage headers that allows a limited number of advertisers to bid on inventory outside of their primary ad server. This lets advertisers compete for premium or reserved inventory before or instead of the second-price auction.

Header bidding creates an auction prior to the final auction in a publisher’s ad server. Because of that inefficiency, SSPs (Supply Side Platforms) who run a fair second-price auction in the header, will have less competitive bids for that final auction, and find themselves with low win rates.  Being less competitive in the auction has terrible implications for SSPs as more competitive bids from header bidding can steal their market share.

Media Buyers Are Asking for Transparency in the Bidding Process

Programmatic ad buying exchanges have a mostly obscure bidding process, making it unclear for the buyers whether they are dealing with first or second price auction. If you want complete transparency, then first-price auction seems to be the better option (there are no floor mechanisms or hidden fees), but it offers few real benefits for the advertiser. Truthful bidding in this model (i.e. bidding the real value of the impression, which means if an impression has a value for you of $1.00, you should also bid $1.00) is not only more challenging but it’s also more expensive. A transparent first-price auction will squeeze the margins of the many ad tech players in the middle, and deliver more actual working media to the publisher. But if programmatic media buyers think they are still playing according to second-price auction rules, they will end up overpaying for inventory. Advertisers don’t like the feeling that they are being manipulated into bidding higher than they need to, which is exactly why DSPs use algorithms to predict the price floors and bid accordingly.

Many programmatic traders are left in the dark when it comes to the setup of the auctions they are bidding in. Since media buyers can only audit the vendors they are working with directly on the demand side, they have no way to verify if other programmatic platforms in the ad supply chain are altering their auction structures to make more margin. Which means a buyer might think they are buying based on second price auction but really be in a first price auction. That can get expensive, since the bid strategies are drastically different.

The industry will likely be in a transition period for much of 2018 as DSPs adjust their algorithms to allow for some Bid Shading to minimize the chance of overpaying.  It’s important for media buyers to clarify the auction type (first or second) whenever negotiating a deal and floor price with a publisher. To combat price increases, some buyers have already started Bid Shading, or reducing bid prices. But that strategy comes with risks because buyers can lose out on inventory they want if they submit too low a bid.  So until trust or transparency in auction type and fee structure is available in the open exchange, some media buyers will either try to work with adjusted algorithms or push towards more private exchange tactics so that they can trust the contracts and pricing models.

Programmatic Media Buying 101: Why Are Marketers Talking About Blockchain Technology?

Will Blockchain be the technology that solves the programmatic industry woes, or is it just another buzzword that we need to add to our vernacular in case someone brings it up in a conversation?
Either way it helps to know why people are talking about blockchain technology and how it will help or change the programmatic buying industry.  The problem that most people are hoping that blockchain has the potential to solve is transparency throughout the advertising supply chain – which means advertisers having a better understanding of cost and the visibility of their ads.

What is Blockchain Technology?

First of all Blockchain isn’t a new technology, and it wasn’t developed specifically for the advertising industry.   It was originally created for managing cryptocurrencies like Bitcoin.  Blockchain is a continuous series of records – blocks – linked by encryption, that sit across a distributed database and are stored on computers all around the world. Each time a transaction is made, a message is sent to the network to agree (or disagree) that the transaction is legitimate before giving the approval.

Why Are Marketers Interested in Blockchain for Programmatic Buying?

Blockchain has the ability to create a highly secure trading network for advertisers, by publicly storing data to create a permanent audit trail with an unchangeable record of all transactions that occur within the programmatic buying marketplace. This provides marketers with full visibility into their ad buy, to better track all transactions that are taking place automatically and ensure their budget is actually being used effectively. Using blockchain technology, a record of all transactions taking place throughout the ad-buying and selling process is made and in the future marketers can use this knowledge to reduce, or even eradicate, hidden costs or fees from multiple intermediaries within the ad-buying supply chain.

The main benefits of blockchain for advertisers include:

  • Keeping track of each point where that ad shows up effectively, so that the advertiser can control the process and get more working dollars in front of users/ clients
  • It can provide more transparency with relation to ad fraud and brand safety by allowing advertisers to record exactly where their ad campaign is being delivered and whom it is reaching

For those companies who are thinking of bringing their programmatic in-house there will be some benefits from the direct line of communication that blockchain offers with data providers and other vendors.  This means more transparency on how data is collected and sourced.  So if the advertiser doesn’t have to worry about security or fraud and is able to leverage transparency they can focus on improving their targeting strategies and invest in creative and an overall better experience for their audience.

So When Should We Expect Blockchain to go Mainstream?

If blockchain is so powerful, why has it not being used more widely?  After all, it’s not a new technology, what’s holding the ad-tech business back from implementing it?
First of all, it’s really bad for the environment!  Blockchain inherently uses an immense amount of energy.  It’s by nature a space-hungry technology because the series of blocks become very large very quickly and become hundreds of gigabytes in size.  And as the chains get bigger you need more storage and capacity is limited.  Then that data needs to load every time you make a transaction, which is not practical for any type of programmatic buying, which involves millions of transaction per second.  That’s a lot of blocks.
So this probably not going to be the year of blockchain for Real-Time Bidding (RTB) but it doesn’t mean it can’t be implemented in other parts of the ecosystem.  For instance, it can be used to authenticate the publishers advertisers are working with when they set up private marketplace deals.  Even though PMPs are meant to be safer or fraud free they are still subject to domain spoofing. Using blockchain to set these deals up could give advertisers another layer of verification.  So blockchain still has some possibilities.  We are keeping an eye on it but haven’t seen it move the needle in any direction as of yet.

Programmatic Media Buying 101: What is the Difference Between AI, Machine Learning & Programmatic?

The world of digital advertising and programmatic advertising has developed its own language in the last couple of years, full of terms that are commonly heard and used everywhere but mean something very specific when attached to the word advertising. Most recently it’s almost impossible to read an article or even talk about media buying without bringing up the terms Artificial Intelligence (AI) or Machine Learning. The terms AI and machine learning are often used interchangeably but they are different. What is the difference between the two and what should they mean to us or me as a marketer or CMO?

AI for Programmatic Buying

Artificial intelligence is the concept of reproducing human intelligence in machines so they can execute on activities that normally would require a human brain to be involved in, such as making data-based decisions.  By using AI-powered systems brands and advertisers case save money and time by completing tasks faster than us mere humans and make less mistakes.  When you apply this to the programmatic media buying industry, you bring efficiency to the media buying process, freeing people who’s job it is buy media from the more tedious and allowing them to focus on the strategic and creative elements of their jobs.

The reason digital media executives keep talking about AI technologies is that they allow us to have algorithms that analyze a user’s behavior, allowing for real time programmatic campaign optimizations towards consumers who are more likely to convert. Advertisers then have the ability to gather all this rich audience data to then use it to be more accurate with their media buys and overall targeting tactics – ultimately spending less money and time and bringing in a higher ROI.

Will Machine Learning Replace Media Buyers?

The words Machine Learning can conjure up images of old sci-fi movies in which someone develops an intelligent robot that then dominates its creator or destroys a large city… leading to many questions about how this technology could affect the digital media industry.
Machine learning is a type of Artificial Intelligence that provides computers or robots with the ability to learn things by being programmed specifically to take certain actions, improving their knowledge over time, much in the same way our brains do.
Computers using machine learning focus on imitating our own decision-making logic by training a machine to use data to learn more about how to perform a task.

Imagine you ride your bike to work every day. Over time, after trying different ways to get to work, you will learn which route is faster or maybe which road or path is better according to the day of the week or based on the weather outside. This is exactly how machine learning works. You feed the computer or algorithm with large amounts of data so it will analyze information from the past and learn from it to apply the learnings to any new data it receives in the future.

When applied to programmatic advertising, machine learning algorithms can analyze large volumes of data from difference sources and draw conclusions from it. It means you can almost replicate the brain of an experienced media buyer in a machine or algorithm so it becomes capable of  predicting, planning and optimizing media. Almost…. but not yet, though the machines can certainly make programmatic advertising more efficient, faster and easier to implement, there remain many factors which need human brains to input link the machine learning to an overall media buying strategy.

So How are AI and Machine Learning Connected to Programmatic Advertising?

Programmatic advertising is the automated process of buying and selling ad inventory through an exchange, connecting advertisers to publishers rather than having to make individual deals with each publisher. This process uses artificial intelligence technologies to improve efficiency and make better decisions for the advertisers with their budgets.
There is a lot of investment being made in marketing and ad buying technologies to leverage AI.  Companies like Xaxis, are betting heavy on AI for improving their future Programmatic Buying Platforms.  Fo right now marketers are using AI to stitch massive amounts of their data together, but it still hasn’t replaced human analysis.  For media agencies, Artificial Intelligence is still more a buzzword or a catchphrase to get peoples attention.

David Lee, programmatic lead at ad agency The Richards Group, said that he regularly gets pitches for AI-enabled products but the AI part of the products usually “doesn’t seem to affect performance outside of being a buzzword.”

You need Machine Learning to feed AI but you don’t need AI for Machine Learning. What that means is that machine learning is the technique — using algorithms to process data, learn from insights and make predictions for future programmatic campaigns which then trains the AI.
Both Machine Learning and AI are here to stay.  If you are a marketer or a media buyer, get familiar with these terms as they will continue to occupy the press and blogs like ours.  But for now they are not taking over for humans, that’s still in the sci-fi section of the video library.

Programmatic Media Buying 101: Amazon Invests in Ad Tech with its DSP AAP (Amazon Advertising Platform)

Interested in learning about Amazon’s DSP capabilities and how it can add value to your media plan? Reach out to us here and learn about Digilant’s unique partnership with Amazon’s AAP (proprietary ad platform).

Amazon is now everywhere, seemingly moving into every industry and recently making great strides in ad tech with its growing DSP business, opening up self-service programmatic ad products, and offering training programs to make direct connections with ad buyers. Its Transparent Ad Marketplace is the most popular server-to-server wrapper in the ad industry.

According to eMarketer‘s latest report, Amazon’s advertising revenues will total $1.65 billion in 2017 —far below that of Google or Facebook, but above brands like Twitter and Snapchat.
By investing in it’s demand-side platform (DSP), which is now one of the largest in the US, Amazon has a larger share of the US digital display ad market. With 3.0% of net US digital display ad revenues, Amazon takes 4th place for display ad buying in 2017 and is keeping it’s eyes on 3rd place. By offering Headline search ads, Amazon can compete with Google and Facebook for ad dollars.  Amazon is the most popular site for customers to search for consumer products online and by offering headline search ads, they are now dipping into Google’s search engine market share.

Amazon is Changing Digital Advertising as we Know it!

The one thing marketers hate is spending media budget to buy ads and then having to prove that they are converting with attribution methods.  Amazon is promising its programmatic ad buyers that if you buy ads on their DSP platform, you’ll know that they work and they will show you data to prove it. Because marketers not only want to be able to place ads in the right place and at right time, but they also want the right relevance.  Amazon offers measurement metrics from impressions and clicks to deeper data on sales information, full shopping journeys and things like a customer’s worth over a lifetime, giving media buyers what they need to prove their ads are contributing to conversions.  Amazon has a gigantic pool of real-time data, not just likes and habits, but actual purchases – what people are buying and how they are doing it -, you will know what ads work in actually driving people to make purchases — and then be best positioned to target those ads.

Their timing couldn’t be better, as Amazon’s DSP is growing in popularity, ad buyers are cutting back the number of DSPs they use. Media buyers and CMOs are choosing to use less DSPs and self-service platforms are on the rise in the ad tech industry, specifically for brands who are bringing all of their digital media buying in-house, with the goal to trim fees and have more control over their overall go-to-market strategy.  Amazon has greatly benefited from the programmatic in-housing trend. It offers agencies and brands a programmatic self-service model, and its DSP fees are among the lowest in the market.

If you want to know more about Amazon’s DSP capabilities and how it can add value to your media plan, or ask questions about Amazon’s AAP (proprietary ad platform) Digilant can help.  Reach out to us here.

Programmatic Media Buying 101: Why Marketers Are Talking About Attribution?

Digital media buyers started using attribution measurement as a way to understand which aspects of their programmatic, social and search campaigns are contributing the most to campaign performance and/ or lead generation. In digital advertising, attribution measurement can now be done at a user-specific level, what this means is the that most ad-tech platforms apply technology in order to assign a consistent user identifier across all advertising related events. This is opposed to traditional digital media performance analysis, where ROI is generally calculated per user event or group of users because there is no consistent user identifier available.

Attribution Defined

Attribution is defined by a couple of different things.  First, it’s the cause or the origin of an event like a conversion or a download.  Second, how much of a customer’s decision can be attributed from exposure to an ad on a certain channel? For example, Jessica bought a pair of shoes because she downloaded a coupon from a specific publisher’s page.

Multi-touch attribution quantifies the influence each advertising impression has on a consumer’s decision to convert by assigning a credit value to each touchpoint.  For example Jessica bought her shoes after visiting the retailer’s website several times and then downloading the coupon. Each marketing touchpoint gets x% credit or attribution towards the sale of the shoes.

So Why do Marketers Find Attribution Critical to Their Marketing Efforts?

Today’s Internet users are no longer browsing on a single device but switch from laptops to tablets to desktops to phones and, depending on the device, also interact with different browsers. Another attribution complexity is that users are no longer stuck in a single marketing channel— but are regularly exposed to TV ads, emails, Facebook ads, radio and more. In order for marketers to address a multi-device and multi-channel world they need attribution measurement.  Through attribution they can look at users through the lens of the least common denominator—the person, the individual engaging with a brand across all channels, devices and browsers.

Other reasons cited by marketers according to AdRoll’s Annual State of MarTech Industry Report included a “full-funnel approach” that can help brands better assess where a user is in the customer journey and what event led to their conversion. The survey revealed that marketers in 2016 allocated 72% of their budgets to prospecting for new customers, with the most successful channel being paid-for social media according to half of the participants.  32% of the 1,000 US-based marketers surveyed said programmatic display ads were the most effective channel for them.

If you’re not measuring the impact of your marketing efforts—especially in today’s world of fragmented devices and touch points—you are likely missing out on ROI opportunities and wasting spend on channels, strategies and audiences that aren’t performing well. Plus, getting attribution right helps you maximize your learnings to make better business decisions over time. This guide will provide an overview of the what, why and how of attribution in today’s marketing landscape.

Using Attribution Models is Next Step in the Programmatic Media Buying Evolution

An important next step in the evolution of programmatic media buying for marketers, is to improve algorithms based on attribution models. Attribution models can and should be used to improve programmatic media advertising algorithms.

Using an attribution model, the behavior of individual customers can be tracked both cross-platform and cross-device.   Individual purchase paths across the customer journey can be observed and used to continuously improve the programmatic media buying algorithm.  Using an attribution model marketers can collect more customer data and then they can show relevant ads in ever better places at ever better times.
When attribution models are used to inform programmatic algorithms, marketers gain a more realistic view of ad effectiveness. Plus, with full transparency they can decide how much more relevant it was.

Tips for Moving to Multi-Touch Attribution

Make no mistake—multi-touch attribution requires an investment in cost, time and expertise. However, you can take steps toward shifting your marketing organization’s mindset about attribution.

  1. Run a data assessment. The results of any attribution exercise are only as useful as the data is accurate, completed and connected across silos.
  2. The attribution model and product you use should allow you to make ad buying decisions by using the variables that are influential in your conversion goals.
  3. Have internal meetings to help qualify budget, anticipated goals and expected ROI, infrastructural and technological elements to consider and how multiple systems might need to hook together.
  4. Choose the right data matching partners, which often comes down to the best match for the type of CRM data (email vs phone numbers, for example) and best practices around data hygiene, including ongoing data cleansing. The more conversion data you have to look at, the fuller the picture of attribution across channels and online-to-in-store sales.
  5. Prepare to activate and optimize in media. The attribution model and product you use should allow you to decision on the variables that are influential in your conversion goals. Some technology companies offer real-time integration with multi-touch attribution vendors that makes this process simpler and faster to execute on in media.
  6. Rely on your technology partners, like Digilant, for support in implementing the right solution.

 

Like what you see? Join the 500+ clients that have partnered with Digilant.