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Customer Acquisition: Why a Low CPL Isn’t The Only Metric That Matters

Having worked in ad tech for several years, I regularly encounter advertisers who use “the big three” digital ad platforms (Google, Facebook and Amazon) as a strict benchmark for how much they’re willing to spend on advertising to generate a new lead, otherwise known as cost-per-lead (CPL).

That said, CPL isn’t everything. As Kelly Goldston, VP of Marketing at Eloquii, eloquently put it “just looking at acquisition costs by channel can be misleading.” The reason is simple: each channel may generate leads at a different cost, but these leads are also pulled in at different stages of the marketing funnel.



Let’s consider an example: it may cost you $10 to acquire a lead from paid search and $15 to acquire a lead from programmatic display. However, the lead generated from display may be further along in your buyer journey, and thus, of higher value.

Know Your Numbers

So how does one determine which channel is the most effective at driving higher value leads, you ask? It’s all in your customer acquisition costs (CAC). In its simplest form, CAC is a calculation that looks at your acquisition costs (e.g., marketing expenses) divided by the number of customers (not leads) acquired during a specific period of time. To break this down further, if your company spent $100,000 on marketing in a year and acquired 100 customers, your CAC would be $1,000. However, the math isn’t quite this simple.

In his blog post on Customer Acquisition Cost, advertising industry veteran, Neil Patel dives into detail on how to calculate your CAC by marketing channel (i.e., search, social, programmatic). He also explains how to more wisely earmark your media spend so you’re not allocating budget under the wrong pretences by focusing on CPL alone. Patel considers variables such as customer lifetime value (CLV), attribution models, word-of-mouth referrals, as well as meals and entertainment expenses that go into selling, all of which collectively impact your calculation.

The Takeaway

Remember: all leads are not created equal. Is a qualified lead someone looking for more information or someone ready to buy? Some leads require more education and nurturing, whereas others are ready to flip the switch right away. Therefore, even if you acquired a lead at a “cheap” price, you may end up spending more in the long term, driving up your true acquisition costs. This is why it’s important to consider the time, resources and expenses required to move the lead through the buyer’s journey into official customer status. If you start with CAC, you’re already on a better path to effective marketing planning and will have a clearer picture of a lead’s value when it enters your marketing funnel.

4 min read

The Race for Digital Advertising is On

The Midterm Elections are just around the corner – November 6, 2018 to be exact. This handy 2018 Midterm Election Guide provides everything you need to know. And this post will tell you why, as candidate and strategist you need to think outside the (set-top) box to reach key voter groups!

According to eMarketer, as the midterm elections are heating up, ad spending is rising. Political advertising dollars are flowing to publishers at higher rates than had been expected earlier this year, based on statistics from E.W. Scripps – political ad spend will top $112 million in 2018, surpassing not only the previous midterm year but the 2016 presidential election as well, when its political ad spend reached $101 million.

Although the budget forecast is leaning heavily towards traditional channels – broadcast TV being the largest – the battle is also on for digital ad inventory. It will be getting the second-highest share of dollars, based on Borrell Associates media trend tracking, at the tune of $1.8 billion.



Digital advertising is primed to be a sizeable driver in the Midterm elections. With 435 U.S. House seats and 33 U.S. Senate seats at stake, programmatic advertising can easily dominate the race for the digital ad spend. With the power and targeting offered with programmatic advertising technology, campaigns can reach voters with quality, hyper-targeted ads, that unlike other advertising formats, can be adjusted on-the-fly.

MediaPost, in identifying the trends to watch in the 2018 midterm states that, “as we approach one of the most hotly contested midterm elections in memory, we already know one winner: digital advertising has clearly emerged as the preferred medium for both candidates and voters.”

Political advertisers need the right strategy to reach voters who are increasingly mobile and always immersed in content.

Consider Native

Native ads are advertising formats that are consistent with the form, style and voice of the platform they appear on and are considered less intrusive to the reader.

Native ads are consumed the same way people view editorial content which could attribute to consumers looking at native ads 53% more frequently than display ads and why they tend to register higher lift in purchase intent (18%) and brand affinity (9%) than banner ads. They can also register higher lift in voter intent too.

More importantly, native and mobile are dominant partners. Native advertising is even more likely to be mobile than social; more than 90% of native display ad dollars go to mobile placements, thanks to sites and apps designed specifically to include native ads.

Go Digital and Local

Particularly in midterm elections, localization is key. If your congressional campaign just needs to reach one side of a city or county, geotargeting enables you to zone right in.

Focus the Message

StackAdapt Custom Audience Segments are first-party data sets that target individuals based on the relevant content they’ve recently consumed online, their intent (to purchase or to vote) and their competitor interactions.

To identify users that belong to a segment, StackAdapt uses two main approaches:

  1. Collect users who share similar unique internet browsing patterns – that is users who have recently visited relevant pages that demonstrate a specific interest, such as Republican or Democratic Candidates
  2. Collect users that have recently connected to a specific corporate network such as the University of Georgia, thereby allowing political advertisers to target current college students of a specific campus

These diverse and unique audience pools make targeting your ideal audience fast, easy, and accurate – and gets your message in front of the right set of voters at the right time.

There is a lot of opportunities to reach voters through the various tactics offered only in programmatic advertising, including voter targeting and geotargeting. And it also brings pricing efficiencies and scale that help maximize campaign dollars. And some key elements, such as custom audience segments and programmatic native are only available in StackAdapt.

If you want to learn more about boosting the performance of your political campaigns, reach out to your StackAdapt representative or contact us to get your campaign up and running today!

4 min read

4 Essential Articles To Read This Month: August 2018

What is the team at StackAdapt reading this month? Here are a few essential articles that caught our attention. Enjoy!

1. Required reading for marketplace startups: The 20 best essays

Marketplace businesses are very lucrative but are extremely difficult to get off the ground, mostly because of the “chicken and egg problem” — not be able able to attract demand without supply and vice versa. Read the full article here.

2. When You Need a CMO. And The #1 Reason CMOs Fail

If you don’t read Jason Lemkin’s SaaStr Academy and you are a startup, you need to start immediately. In this article he talks about why so many CMOs fail. A must read for anyone looking to scale their marketing department. Read it here.

3. Why California’s new consumer privacy law won’t be GDPR 2.0

Privacy is arguably the biggest topic in tech in 2018. With GDPR in full effect, more governments look at how to implement their own version of privacy control. Read the article here.

4. Why brands favor a ‘hybrid’ in-house marketing approach

Whether to bring marketing or outsource it to an agency was even covered during one of our previous events. Perhaps, the it’s never going to be black and white, and there is something to be said about finding a happy medium where you can optimize for efficiency and costs, without losing sight of your core business. Read more here.

3 min read

4 Essential Articles To Read This Month: June 2018

Curious what the team at StackAdapt is reading this month? We have asked around and curated a few of the most interesting articles for you to check out.

1. How Food52 Strikes a Winning Balance Between Content and Commerce

Increasingly e-commerce businesses use content to build relationships with their future customers. “It’s further proof of Food52’s core hypothesis: lead with high-quality content — offer value to your readers — and the sales will follow.” Read or listen to the interview here.

2. The IRL channel: Offline to online, Online to offline

While everyone is obsessed with digital channels, some companies leverage their offline distribution to promote their brand. Think of how successful some brands have become simply by being so Instagram-able, like Boby guys or Toronto’s ihalo Krunch, who serve charcoal ice cream. Check out this POV on the “IRL Channel” by Andrew Chen. Read the full article here.

3.ClassPass’ Founder on How Marketplace Startups Can Achieve Product/Market Fit

Marketplace Startups are among the most lucrative business models, and remain one of the hardest models to crack. Learn from the CEO of ClassPass, Payal Kadakia, how they’ve grown to become a household name. Read or listen to the interview here.

4. How To Reach More People With Content Marketing By Changing How You Write

As marketers we’re all guilty of writing jargon content from time to time, as a result, we are missing the mark on creating something that makes an impact. An always insightful view from Tom Tungus on how to be a better writer.  Read it here.


4 min read

From the Founder’s Desk: Vitaly Pecherskiy on The Top 3 Tech Trends in Advertising

Interview with Vitaly Pecherskiy, co-founder and COO. Originally featured on DisruptorDaily. 


 1. What’s the history of StackAdapt? How and where did you begin?

My co-founder, Ildar, was actually my client at a previous job. The first time we met to go over the account was at Starbucks. The conversation very quickly turned into the future of technology, innovation, opportunities, and entrepreneurship. Over the next 5 months our friendship grew and it became apparent that there was a clear gap in the market for another ad tech player. We left our jobs to start a service-based company that introduced organizations to programmatic, but our passion for technology meant we always knew we would eventually develop our own product.

At the same time, we met our third co-founder, Yang, who recently moved back to Toronto having spent a few years in New York building equity trading platforms. The fit was immediate – we had complementary skills, we bonded over how we envisioned the company and the product, and we had similar risk tolerance. We started building early proof of concepts that got picked up by a large automotive brand about 9 months into the journey. The rest is history.


2. What specific problem does StackAdapt solve? Who do you solve it for?

Nowadays consumers build relationships with brands on a value level and content marketing is a powerful vehicle to build that trust. Clients that use our platform come to us with a clear problem: they struggle with getting attention in today’s crowded marketplace. They’ve noticed that what worked in the past doesn’t work as well today – social channels are getting increasingly saturated and organic reach is dropping, and search engine optimization takes too long to yield results. Our customers are looking for ways to increase exposure to new target audiences on demand. Our native advertising platform lets them break through the noise and reach potential customers with content-driven ads. More importantly, it helps them understand how their media dollars actually drive their business forward beyond surface level metrics like impressions and clicks.


3. What is your solution to their problem? 

As marketers ourselves, we felt the pain of using complex programmatic platforms. Our vision was to build something different than the traditional media-trading platform, something intelligent and intuitive. We didn’t want another complex “switchboard” type of product. We wanted something that makes complex things simple. An enterprise product that solves problems and that people would love logging into every day.

Because StackAdapt operates in many ways as a data-company, we collect proprietary browsing behavior data. Then we use machine learning to predict which products people are interested in. Buying native ads is easy. Making them actually drive your business forward is hard, so that’s where we focus our energy: How can we find people online who are actively looking for a product just like our customers’ and nudge them in their favor? Our proprietary data engine does just that.


4. What are the top 3 tech trends you’re seeing in the advertising industry?  

Trend #1: Data transparency. I think more marketers have started to ask questions related to how data is actually aggregated and who is in the audience segments that they target on a trading platform. I see more and more people question whether the price they pay for 3rd party data is justified and what sort of performance lift it actually gives. It’s a very overlooked topic especially in the context of awareness campaigns where tracking ROI isn’t as straightforward as with conversion-based campaigns, but overall marketers are becoming more data savvy.

Trend #2: Video advertising. It boggles my mind how many dollars are flowing into TV advertising and how little accountability there is. Obviously, I am biased because I work in digital. There are challenges in tracking in digital too, but when I hear that in many places people are asked to use pen and paper to self-declare their TV watching habits which are then used to gauge the success of TV campaigns, it makes me laugh. We’ll see more brand managers wake up and demand more transparency around TV dollars.

Trend #3: Content experiences. We are starting to see marketers evolve beyond text-based blogs to building content experiences where they ask consumers to engage with their brands in a more interactive, engaging way. It’s no longer a wall of text that people are expected to read, it’s interactive quizzes, it’s user-generated photo galleries, it’s visual storytelling that makes brands stand out from the rest. When you have nailed that, your content distribution and paid media strategy is like adding fuel to the fire.


 5. What’s the future of native advertising? 

All digital advertising will eventually become native. It has taken longer than we anticipated in 2013 when we started StackAdapt, but it’s already apparent that this trend is unstoppable. As more publishers become responsive and cross-device, native advertising is the only route that makes sense for them as a monetization channel. Native ads are a powerful way to deliver branded content that’s less interruptive and more engaging than traditional ads. Once we accept that native is going to be the default way to communicate brands’ messages online, the questions of attribution and ROI remain. Are these native ads reaching the right audience? Are the native ads moving the needle for our brand? We think most marketing channels will evolve to become performance channels and native advertising will play an integral role in this change.

4 min read

Will AI Eliminate Digital Marketing Roles? CTO Yang Han Doesn’t Think So

AI is making a tremendous impact on digital marketing. We may not always be aware of it, but many of the tools and applications we use today leverage some sort of AI. Understandably, marketers are worried that the same technology that makes their lives easier may land them out of a job. Should they be worried? Let’s take a look at exactly what AI is capable of… and what it isn’t.

Natural Language Processing

Let’s start with Natural Language Processing (NLP). The proliferation of online articles and conversations has allowed machines to crack the code of language and its meaning. This has spawned many new innovative applications, the most popular of which are chatbots that help automate conversation.

Specific examples include Q&A systems, virtual assistants, automated content summarization, content extraction, sentiment analysis, language detection/translation, and even automated content creation. In these scenarios where content can be analyzed or predicted, a machine can completely replace a human.

Image Recognition

Another key innovation in AI is image recognition. As the internet stores billions of images, machines are now able to detect patterns allowing them to understand the meaning behind a given image. Understanding what an image means is a powerful tool for many systems. It can be used to automatically find and recommend relevant images, detect sentiment in images, and help classify content.

Voice Recognition

Similarly, we have seen tremendous progress in the area of voice recognition, which is used in voice-activated AI. The most popular examples are Google Home, Amazon Echo, and Apple’s Siri Home. The potential in this area is incredible, however, the technology is still new and there are limitations to what the machine can understand.


Companies are also storing billions of user-related data points, such as online activity and browsing behaviour. This has allowed machines to gain deep insights and understand people in distinct ways. Researchers have found that Facebook probably knows more about you than your friends and family. In advertising, this data is valuable in targeting the ideal profile of users at the right time and the right place.

Researchers have found that Facebook probably knows more about you than your friends and family. In advertising, this data is valuable in targeting the ideal profile of users at the right time and the right place.

This data can then be combined with NLP and image recognition to create a highly personalized ad, where AI can form variations of headlines and images and automatically A/B test to find the best combination.
As a result, AI has allowed various industries, especially marketing, to focus more on unique and personalized experiences.

The combination of understanding users and automating conversations & content can be applied in many ways. More and more companies are innovating by building software that allows users to apply AI more easily. As a result, we gain powerful tools that help us automate and do a lot of the heavy lifting for us, and ultimately make our life easier as a marketer.

So Where Does That Leave Us?

The technology is there to handle the execution, from reaching specific audiences, personalizing content, running automated A/B tests, and much more. But AI is not nearly advanced enough to create high-level creative execution — at least not yet.

AI is not set to replace jobs anytime soon. As we head into the future, manual and operational tasks will slowly get replaced by new AI applications that appear on the market each year. But this is good news! It will leave marketers to focus their attention on what they do best — coming up with creative ways to connect with their audience in a human way.


4 min read

Data Science in Advertising: How Thompson Sampling is Revolutionizing Performance

As a Data Scientist at StackAdapt, my role is targeted primarily at two things: improving the user experience and improving the performance of our ad campaigns. My team’s objective is to reach users who want to engage with the ad content (creating an non-disruptive viewing experience) and to ensure our clients hit their targeting metrics such as desired click-through rates (CTRs), low effective cost per click (eCPC), placement on premium domains, high user engagement rates, and more. We meet these objectives through a variety of innovative techniques, but one, in particular, stands out from the pack. Here is how Thompson Sampling is revolutionizing campaign performance.

Predicting CTRs

Before we get into any specific techniques, it is important to note that the key to achieving the two objectives listed above is the ability to predict CTRs. One of the most difficult things in data science is determining the “features” or pieces of information required to make this prediction. The information can be broken down into several sources: information about the advertisement itself, information about the publisher or website where the advertisement is displayed, and information about the interests of the user.

One of the most difficult things in data science is determining the “features” or pieces of information required to make this prediction.

For example, information about the advertisement itself might include the words in the title of the advertisement, or information about the image in the advertisement. Information about the publisher or website may include a categorization of the website’s content using StackAdapt’s custom content categorization engine, and information about the user may include their past behaviour when engaging with StackAdapt’s advertisements.

Thompson Sampling

In addition to predicting click-through rates, StackAdapt’s data science team regularly creates new methods of executing campaigns, such as alternate ways of bidding and placing advertisements for an advertiser. Bidding for advertisements is closely tied to click-through rate prediction because, intuitively, one wants to bid higher on user-publisher-ad combinations that produce high click-through rates.

Bidding for advertisements is closely tied to click-through rate prediction because, intuitively, one wants to bid higher on user-publisher-ad combinations that produce high click-through rates.

Recently, StackAdapt’s data science team has applied some novel implementations of Thompson Sampling, a method of automatically selecting bidding strategies, to improve the performance of campaigns on StackAdapt’s platform.

For example, suppose that there are two ways of executing a campaign, method A and method B. Ideally, we would want to know which method produces better metrics for a specific campaign and use only that method for that campaign. However, it takes some time to learn which method has better performance.

Thompson Sampling is a methodology for acquiring that knowledge. Specifically, it employs method A for some time, then method B, then switches between the methods probabilistically, based on their performance. This method is highly optimized and even though it is learning as it goes, it is able to achieve performance that is similar to having apriori knowledge of the better performing method.

This method is highly optimized and even though it is learning as it goes, it is able to achieve performance that is similar to having apriori knowledge of the better performing method.

Soon all campaigns on StackAdapt’s platform will have access to multiple bidding strategies and be employing Thompson Sampling to automatically optimize their performance. This will allow StackAdapt to deliver better click-through rates, lower effective costs per click, and higher user engagement rates than in the past.

The central feature of this optimization is that it is on a per-campaign basis. In other words, we are able to automatically do what is best for each campaign based on that specific campaign’s goals and performance.

Using click-through rate prediction and Thompson Sampling opens the doors to creating more custom bidding strategies and leveraging our vast amounts of historical data to automatically optimize performance.

Using click-through rate prediction and Thompson Sampling opens the doors to creating more custom bidding strategies and leveraging our vast amounts of historical data to automatically optimize performance.

In the future, we have plans to completely reinvent our core bidding methodology on a per-campaign basis, so that each campaign bid is based on the campaign’s goals while at the same time producing engaging, unintrusive advertisements that a user wants to engage with.

4 min read

Performance Marketers Are Increasingly Relying on Intent-Based Targeting

In 2016 StackAdapt released proprietary technology we call Custom Segment. Named for the intent-based audience segments it generates through machine learning and natural language processing, these segments are becoming increasingly popular with our clients.

Made up of users who have actively shown intent to purchase on a rolling seven-day basis, relevant users are constantly dropping into a brand’s audience pool while old data is removed, ensuring marketers reach interested audiences in a timely manner. As the popularity of this targeting technique increases, it is clear that performance marketers are increasingly relying on intent-based targeting to reach the right person at the right time.

Popularity of Custom Segments by the Numbers

In the month of February 2018, the number of advertisers actively using StackAdapt’s Custom Segment technology to target unique audience pools in their campaigns rose by 35%. Advertisers who run conversion campaigns have the highest adoption rate of custom audience segments at 56%. Overall, 30% of all impressions being served on the platform are now reaching users through StackAdapt’s custom audience segments. What are the reasons behind the increasing popularity of intent-based targeting?

Advertisers who run conversion campaigns have the highest adoption rate of custom audience segments at 56%

Why Are Marketers Relying on Intent-Based Targeting?

More Relevant Audiences

From my perspective as a Business Intelligence Analyst, I believe intent-based targeting is becoming more popular among marketers and advertisers because it allows them to collect more relevant audiences than with third-party or contextual targeting. Often, clients cannot find their narrow audience with third-party segments, or the third-party segments available are too broad to reach bottom-of-the-funnel intent (something they need to capture in order to acquire more customers).

I believe intent-based targeting is becoming more popular among marketers and advertisers because it allows them to collect more relevant audiences than with third-party or contextual targeting.

An example of this would be an airline company who wants to target travellers going to Florida. The most relevant third-party segment available might be “Domestic Travel”, “Frequent Travel”, or “Vacation Travel” but these people could be going anywhere. With intent-based technology we can home in on people specifically reading about Florida vacations, browsing for hotels & lodging in Florida, and searching up flights to airports in Florida.


Another reason for the increased popularity of our intent-based targeting is that we provide complete transparency into the topics or pages that the users are visiting. Marketers no longer have to guess where these users are coming from or why exactly they were targeted: it’s right there in the Custom Segment dashboard.

Increased Engagement & Conversions

Intent-based techniques like custom audience segments often have a lower than average click-through rate (CTR) when compared to campaigns using other targeting methods, but this hasn’t deterred performance marketers. This is because users who do click are more likely to be engaged, spending at least 15 seconds or more on the site, and ultimately, are more likely to convert. Just as JPMorgan Chase slashed its programmatic scale from 40,000 to 5,000 sites, many performance-driven marketers are choosing to bid smarter. In recent experiments by clients who have A/B tested custom audience segments against third-party segments in conversion campaigns, custom segments have generated 20-75% lower cost-per-conversion.

As intent-based targeting moves into the mainstream, I am interested to see how agencies and brands get creative in leveraging the increasingly intelligent technology at their fingertips.

4 min read