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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

How TouchBistro is Using Content Marketing to Grow its Business

In today’s crowded digital landscape, the question driving every marketing campaign is how to get noticed? With people’s shifting priorities now favouring the natural discovery of products, marketing has become an area where companies aim to have an increasingly strategic competitive advantage for growing their business.

In this series on BetaKit, you’ll join COO Vitaly Pecherskiy as he meets with top minds in the marketing and advertising industry to uncover how Canadian companies use forward-thinking strategies and cutting-edge software to break through the noise.

 

To kick things off, Vitaly Pecherskiy had the pleasure of sitting down with Tiffany Regaudie, content marketing manager at TouchBistro, where he learned more about how the company successfully uses content marketing to grow their business.

Can you talk about how your team is structured and what is the culture of the marketing team like at TouchBistro?

Our marketing team is made up of two main pillars: demand generation and brand. Content marketing sits within a communications team on the brand side. I report into a senior communications manager, and I have a content marketing specialist that reports into me.

Our marketing team culture is one of my favourite parts about working at TouchBistro! We live by autonomy and accountability, which means we are given the freedom to succeed and fail. It’s an exciting time to be at TouchBistro – we’re at a high growth stage, and luckily our leadership know that growth requires being bold and trying new things. I’ve never been so well-resourced to do my best work.

TouchBistro's POS system

TouchBistro’s POS system.

How does your team judge its success?

My KPIs as content marketing manager are based on increasing traffic to our blog, subscribers to our blog, and views on our brand videos. I live and die by these metrics every quarter! I also personally judge our success using other metrics I think are important, such as increasing time on site, decreasing bounce rate, and getting more video completions under our belt so we can retarget that audience with campaigns that tell a larger narrative about our brand.

But I do also have an “agency” function to my work, which is really to support generating leads on the demand generation side of the marketing team. I’m the creative lead on many of the projects that come from our demand gen team, so I am still tangentially accountable for generating leads that way.

How do you distinguish between must-have content and fluff? Or are you a believer that any content can have legs?

Our audience is made up of independent restaurateurs who are very much expected to be the jack-of-all-trades of their business. They’re people who just want to make great food and deliver an amazing guest experience, but they actually need to be accountants, marketers, and human resources experts on top of being great chefs.

So our must-have content is the “how-to meat” for restaurant owners: how to know enough about accounting to make sound business decisions, how to rock a social media campaign to get noticed, how to design a menu in such a way that’s going to increase sales – this is the content that does very well for us, because it’s geared toward making restaurateurs’ lives easier.

Producing content takes up so much time and so many resources that it can be easy to overlook a thoughtful distribution strategy.

I come from a book publishing background, so I’ve spent a lot of time editing whole manuscripts and content that goes very deep on a subject. I’m pretty grateful for this experience, as I like to think it’s kept me devoted to quality content over fluff. I do believe that any piece of content that speaks to your audience can have legs – it’s just a case of how long you’re willing to wait for those legs to start moving and gain speed. You have to be timely and solve a problem to get noticed quickly.

Have you seen patterns in the type of content you produce and its success?

Video as a medium is, of course, a type of content that does well for TouchBistro. We just released our first brand video, which garnered more than 3 million impressions and a higher-than-average completion rate across several channels (through StackAdapt and YouTube, among others). While it’s definitely easier said than done, we are shifting toward a video-first strategy, as video is obviously now the most consumable form of content across all audiences.

How do you determine ROI for content?

We measure ROI in several ways, from both a demand generation perspective and a brand perspective. On the demand gen side, we measure ROI by the amount of leads we can move down our funnel, from engaging with a piece of gated content to requesting a quote and being passed to our sales team as an opportunity. We have some strict economics at TouchBistro that govern how much we are willing to pay for a lead that books a demo vs. downloads a piece of gated content or subscribes to our blog.

But on the brand side, we’re much more focused on establishing our brand presence in new markets, given that we have just entered London, UK, Bogota, Colombia, and Mexico City. So as we are still establishing our brand in these markets, we measure ROI via impressions, website traffic, and how many people engage with our SEM ads.

Creating content can be expensive – how do you decide if it needs to be amplified through paid channels and how do you decide how much to pay for distribution?

I base our paid distribution decisions on past organic search traffic performance, so I know the content is in demand and has legs. From this foundation, I’ve developed a solid bank of data that tell me which types of content I should amplify and for how much.

Think about how much money you’re willing to put behind a piece of content … because organic traffic just isn’t what it used to be.

We already know we’ll be putting a larger paid push behind video campaigns, but for other types of content, I base my budget on topic relevance. For example, this year 18 states and 20 cities in the US raised minimum wage, which has been a hot topic among restaurant owners. I developed a significant repertoire of content on rising minimum wage, which I’ll continue to slow drip throughout the year with several paid campaign pushes behind them – the topic is timely and people want to read about it, so it deserves the amplification.

What advice would you give to an organization that’s new to content marketing?

Think as much about content distribution as you do about content production. Producing content takes up so much time and so many resources that it can be easy to overlook a thoughtful distribution strategy. So don’t just think about the content itself and what it will look like, but where it should live and how users should consume it. Think about how much money you’re willing to put behind a piece of content … because organic traffic just isn’t what it used to be.

Rapid-fire bonus questions!

Number one metric every marketer should care about is…Time on site. Visitors don’t mean much if they’re not consuming and therefore remembering your content.
One thing most marketers don’t spend enough time on is…Thinking big picture. Marketers are doers, which is great, but you need to think about how your campaigns relate and build on each other.
To become a better marketer one must… Read, read, and read some more. Marketing is a moving target. If you don’t keep up with best practices, you’ll become obsolete before you know what hit you!

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

StackAdapt Releases Smart Motion, Whitelist Me, and StackBot

Twelve months ago today we released game-changing Astrological Targeting. Since then we have been working on products that we thought were key to redefining how modern marketers will experience enterprise advertising software. We wanted to take the best parts of user experience from consumer products and bring that onto our StackAdapt platform.

Today, we would like to introduce three of our latest products

StackAdapt Smart Motion

With Smart Motion, we read input from your phone’s accelerometer and translate that into actions on the StackAdapt platform. Shake your phone to stop a campaign, or toss it in the air to start one.

StackAdapt Whitelist Me 

For the modern market on the go, the big challenge is being able to understand where the ads are running. The biggest challenge marketers face is that you have to navigate through these domains manually. We built an app that allows you to swipe left or right to remove or add domains depending on whether you think they are suited for your campaign or not.

Personal Virtual Assistant: StackBot

We already leverage a lot of AI to build our targeting segments. We wanted to take it a step further with our new personal virtual assistant: StackBot. StackBot can understand, interpret, and take action on complex commands that you give it. This includes a number of commands such as increasing budgets by a certain dollar value, or pausing a campaign during certain dates.

By the way, April Fools’. 

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.

Personalization

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.

Transparency

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