Meet Intent-Based Content Recommendation

The new personalized approach to B2B content recommendation

Understanding customer intent is one of the most powerful marketing tools available to professionals. The right intent data will allow you to effectively connect potential customers to the content that is going to help them achieve their goal. So where do you find intent data? It exists in the systems you use today. Introducing “behavioral personalization,” a strategy to offer intent-based content recommendation. If you know where to look and you have the ability to mobilize that data you can use it to progress journeys, convert more business and win more often.

Intent: Let your customers tell you

We often talk about website search as being the most common personalized experience. Website visitors identify their need, and if the search engine works well it delivers the content that answers their question. It’s the simplest, most direct method of personalizing the customer experience. Personalization isn’t the only thing that search does. It is also the first inklings of the data you need to drive effective content recommendation.

The search box on any website fulfills not only the search term input function, but also gathers meaningful data about customer intent. This is the real source of search’s power. There are lots of topics that a searcher can query. Many of those also give you insight into why they’re asking those questions. Successfully deciphering intent can not only lead to better search results, but more importantly can lead to more conversions.

Simple Intent: The Keywords

Let’s consider two searches.

“Product X Value”

“Product Pricing”

It’s obvious that these searches will yield different results. If you deliver a results page with relevant content it will help the searcher move forward in their journey. But what’s more important than the topic they’re interested in is what the topic tells you about their intent.

That first term probably indicates someone who is in the interest phase of the process. They’ve gotten beyond the top of funnel messages and are going deeper. Not only are they going to need the right content to answer the question, they may be ready for messages that move them into consideration.

Nosing around pricing content is a clear indicator of someone who is considering a purchase. This is where journey progression becomes even more important. Answer the question effectively and they’re doing business with you.

Both these search terms give actional information about the intent of the visitor. They provide signals about what you should be serving them at this stage of the buying process.

Complex Intent: All that other data

Intentions that are apparent in search term data can also be found elsewhere. One of the most effective places to look for how intent manifests itself in your data is in your web analytics system.

The patterns in visitor journey data can illustrate intent very clearly. If someone is spending a lot of time with content that is in the consideration stage of your journey, that’s an obvious signal. But what if the signal is not readily apparent in the data?

This is where advanced data science tools can be brought to the challenge of understanding what the visitor is trying to achieve. For one of our clients, we’re beginning to use unsupervised machine learning techniques to interrogate tens of thousands of visitor journeys each month.

These methods help us construct models that show patterns of visitor behavior that are associated with intent. Once you can identify the snippets of behavior that are more closely associated with goals, you can understand what behavior signals intent for those goals. Knowing this you can recommend content at just the right moment to help drive visitors to those patterns.

The value of intent-based content recommendation

The value of intent-based content recommendation can be directly measured. Reduced exits and bounces that increase top of funnel progression are the first signals you’re onto something. You also likely have some conversions associated with specific tasks, such as downloads and contact forms, that can be directly measured.

Of course, what you really want to measure are the purchase conversions. In a B2B world making those connections can be difficult, especially if channel or field sales are a big part of your sales engine. However, you’ve been dealing with this challenge for long time. Instrument the tasks and activities that lead to contacts and monitor the activity. All things being equal, if you can reduce the top of funnel abandons (i.e. exits and bounces) you’re going to see more come out at the other end of the funnel.

Want to get started? We might be able to help. Connect with an expert right now.

Steve Zakur

About Steve Zakur

Stephen Zakur is CEO of SoloSegment. SoloSegment provides analytics that improve site search conversion and machine learning technologies that improve content effectiveness.

SearchChat Podcast: Is AI Bigger than the Internet?

In a recent study,  63% of CEOs agreed that AI will have more impact on their business than the internet. Think about that for a minute. The internet. And yet, 23% said they had no plans to do anything about it. Why? Partially, people tend to overestimate how much data they need to get to a reliable result for utilizing AI

Steve and I think it’s possible for most businesses to start implementing machine learning. The new exciting thing is behavioral personalization. Among privacy concerns and the world of GDPR, behavioral personalization is a way to use data that isn’t identifying. Instead, we can match patterns with other user’s patterns. You have more data than you think. You need less data than you think. And adequate new data is more accessible than you think.

What ways can you implement AI using the data you have now, to totally change the visitor journey? It’s about creating patterns and solving problems. Take a listen!

0:00 Intro

1:50 Behavioral personalization changes customer experience

9:30 Are you planning for the AI future, now?

21:35 AI and behavioral personalization combine to create a new visitor journey

27:50 Outro

SearchChat is available on

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About Tim Peter

Will AI Kill Emotion in Marketing?

Artificial intelligence is a huge buzzword across marketing right now, with over 60% of CEOs saying AI will have a larger impact on their businesses than the internet. Let that soak in for a moment. A larger impact than the internet. ‘Cause, y’know, that’s been a non-event over the last couple decades.

Of course, I’ve talked with a number of marketers who worry that this shift will make them obsolete — that once the machines are in charge, their creativity and passion and emotion will take a back seat to algorithms, to math, to machines.

But is this true? Will AI kill emotion in marketing?

Let’s get the obvious answer out of the way. No, AI does not kill emotion in marketing. Not even close. Suggesting that artificial intelligence kills emotion in marketing is like claiming email, or the internet, or television or whatever technology came before or will emerge in the future kills emotion in marketing. Because marketing is about connecting with customers. And customers, in pretty much every case that I’ve run into across my career, are, y’know, people. And people are emotional. Always.

In fact, I’d argue most marketing, most sales, depends on emotion. IBM famously used to close sales with technology leaders by reminding them, “No one ever got fired for buying IBM.” If that’s not an emotional sell, I don’t know what is.

Instead, here’s what artificial intelligence will do — and in many cases, is doing already. AI does a great job of content recognition and recommendation. My friends at SoloSegment have worked with one client to expose the right content to around 20,000 additional customers every month. These are customers who knew what they wanted, were well along the way on their customer journey, and still were failing to find the information they were looking for on the client site. Even if each of those customers only convert about 1% of the time, that’s two hundred additional conversions — 200 additional sales opportunities — every single month. That’s incredibly powerful.

That effect is even more powerful when combined with the kinds of emotionally-resonant content that great marketers know how to produce.

AI can also help marketers process huge amounts of data. In fact, artificial intelligence often requires large data sets to learn how to provide the best value to marketers. The upside is that it makes understanding that data quite a bit easier. You know why you haven’t heard folks talking as much about “big data” over the last couple years? Because, as a friend of mine always says, AI makes big data little. And better still, most marketers don’t like spending their time digging into data. They’d rather spend their time focused on customers. That’s a Good Thing™. But the algorithms can process the data about what your customers do, what they care about, what motivates them to guide you to deeper understanding of the people you’re trying to connect with. Let the machines do what they’re good at. And that will let you focus on what you’re good at: Emotion. Passion. People.

Let’s be clear, AI isn’t going away anytime soon. But neither are people. The marketers who will achieve the greatest success in the coming years are those who know how to harness the power of artificial intelligence and pair it with a deep appreciation for people, for emotion, for passion. It’s not “AI or emotion.” It’s “AI and emotion.” It’s technology and people. It’s logic and passion.

AI continues to dramatically shape marketing. But so will the creative choices you make as a marketing professional. Use it to better understand your customers, to better connect customers with the content that matters to them, and to continue to deliver emotionally-resonant, customer-focused messages. Who knows? You might just learn to love it. And that’s an emotion we can all use more of, today and every day.

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AI and … Pizza?

The Italians first invented pizza roughly 1,000 years ago. We can only assume the first developer meeting was scheduled for ten minutes later. Otherwise, whatever did they need the pizza for?

Now, seriously, it’s fair of you to ask what in the world pizza has to do with AI and digital strategy. A lot more than you might think. Here’s why.

Pizza was one step into the future, a dish that would last a thousand years. AI is another step into the future. Just the far future. In fact, it’s a reality right now. One of my favorite quotes says “The future is already here. It’s just not evenly distributed.” It took centuries for the chewy, wonderful goodness of pizza to make its way around the world. It will take time before AI is “everywhere.” But don’t think it’s not around just because you don’t see it every day.

Google, YouTube, and Facebook use AI in the algorithms that determine which websites, videos, and shares you see on their respective platforms. The Associated Press, Washington Post, and other media outlets routinely use AI to develop content and create rough drafts — and not so rough drafts — of articles for publication. And one of these days, you can bet someone’s going to teach an AI to develop the world’s perfect pizza.

The point is that it’s time for you to start thinking about how you plan to use AI to improve your business. And the best way to do that is to order a couple of pizzas.

No. Seriously.

Jeff Bezos at Amazon popularized the idea that to get something done effectively and efficiently, think in terms of “one pizza teams” and “two pizza teams.” By which he meant that the best teams — where best is defined by quick and effective — were teams that you could feed with no more than two pizzas. Any more than that and you’ve got too much overhead, too much cross-talk to truly be effective. There’s a bunch of well-understood math that explains why two pizza teams make sense. (BTW, Fred Brooks’ classic project management text, “The Mythical Man-Month: Essays on Software Engineering,” said the same thing almost 45 years ago. He just didn’t use the terms “one pizza team” and two pizza team.” I suspect that Brooks was probably more of a chateaubriand guy than a pizza connoisseur).

The reason some companies are struggling to figure out where AI fits into their businesses is that they either have too few people working on the problem or — far more likely — too many.

The right way to figure out how AI is going to work for your business is to assign a small group, one that you can feed with a single pizza (or two, tops), to investigate business problems that:

  1. Have clearly defined outcomes. You know what you want in terms of results. And…
  2. Currently flummox your organization. Even if you know what you want to accomplish, the issue to date has consistently resisted efforts to automate and improve.

There’s an old joke that claims a camel is nothing more than a horse designed by a committee. Want a better horse? Kill the committee. Focus on the folks who add value and ditch the rest.

If the puzzle you’re trying to solve requires a group larger than a two pizza team, break it into smaller pieces — kind of like “slices” — and assign those to your small, nimble team. When successful companies talk about “agile,” “lean,” or associated methodologies, that’s what they’re doing too.

Artificial intelligence isn’t some magic pixie dust you sprinkle onto existing initiatives in hopes that it will make some spectacular difference. It takes work. That work can be at enabled by focusing your team’s efforts in an effective direction and reducing the friction that frequently limits success. And, of course, fueled by a slice of pepperoni, mushroom, or plain ol’ cheese pizza.

So grab a pizza. Or two. But no more. Then round up a few folks at your company who like pizza and like learning to get started on putting AI to work for your future.

Happy Pizza Day, everyone!

  • Footnote 1. Yes, I’m aware pizza had a number of precursors like flatbreads that probably existed for thousands of years before the date I’m citing above. I’m using Wikipedia’s dating. Go fight with them if that matters to you.
  • Footnote to Footnote 1. Also, the stuff we think of as “modern” pizza probably only dates back to the 1800’s before emigrating to New York and New Jersey where we perfected it.  [Editor’s note: We also think Chicago deep-dish is pretty delicious.]
  • Footnote 2.Though I’d argue that the folks at Razza in Jersey City already have developed the world’s perfect pizza. Fight me.
  • Footnote 3. Just please, dear God, no Hawaiian. Yuck. [Editor’s note: Our correspondent could not be more wrong on this one. Who doesn’t like pineapple on pizza?]
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About Tim Peter

SearchChat Podcast: How Facebook Got Sent to App Jail

Facebook is having a terrible week. After experiencing a barrage of trouble over the last few months, they’ve finally crossed a line Apple won’t tolerate. They made available an app that gave themselves a scary amount of access to your device. It’s opt-in, but Facebook seems aware that it’s invading privacy — and appears to be preying on young people.

How well do people understand how you’re using their data? 


We also discuss the top trends people are talking about in 2019. After some keyword analysis and the input of sites like BiznologyCMO and more,  we can tell you all the most important digital marketing trends to watch. The biggest name will be no shock: Artificial Intelligence.

But do executives really know how to implement AI technology in a way that works, to create a seamless learning experience? The secret is starting small, with just what you know.

0:00 Intro

2:05 Facebook’s in App Jail

14:45 What are Top Trends pages saying?

17:40 How can executives get started with machine learning?

24:15 Seamless customer experience

27:00 Outro

SearchChat is available on

Originally published on Biznology

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About Tim Peter

2019 Themes in Digital Marketing

This year has started off strong on my end, but we’re also looking ahead towards what’s next. I’ve spent a fair amount of time talking to digital marketing professionals about what’s important to them, sharing some of our product roadmap, and seeing where there’s alignment and where there may be market opportunity.

While I try to structure these conversations to touch on a few specific topics, things usually don’t go as planned. The diversity of industries, experience and job roles of the people means that each conversation takes a unique journey through the landscape.

During the discussions, three themes have emerged. I’m sure you’ll see some of your challenges and focus areas in these. We’ll be paying attention to these as we build solutions and go to market.

AI/Machine Learning gets tactical in 2019. Businesses will stop waiting for some magic bullet and start taking very specific shots at specific pain points, starting small, going fast and iterating to find value.

Getting started with ML can be difficult. One executive I spoke to last week said that their way forward started with a domain where they had some expertise. They were in familiar territory. Familiar data, familiar business processes, and familiar business stakeholders made it easier to solve problems where they understand the value. The key learning point here is not to succumb to sales pitches for products that work on problems that you don’t fully understand. Find the familiar and start there. 

For many of our customers  that means their starting with visitor journeys. They have lots of data that can be explored and mobilized in interesting ways that reduce exits and improve the overall customer experience.

Mid-Funnel content gets the attention it deserves. When I speak to content authors one of the laments that I hear frequently is the fact that landing pages and top of funnel content get most of the attention from digital marketing. At the other end of the journey, conversions are instrumented and waiting to be counted. But the paths between the two anchor points aren’t well understood and thus don’t always get focus on their importance in the conversion process. Content owners also are discouraged that they’ve produced something that basically gets ignored both internally and by customers and prospects.

From a product perspective, this is a place where we’re looking at the data we have around what happens after those initial success, whether it’s campaign driven or search driven, and figuring out how to use mid-funnel journey data to get the right content in front of prospects to extend journeys. Early signal from the data should allow us to start some beta work with customers in 2Q/3Q to figure out if this mid-funnel problem is fixable.

Data-driven automation improves productivity. A lot of on-website marketing activity continues to be hand crafted. The placement of content on pages, whether that content is static or rules-driven, continues to be a big part of the workload of B2B marketing teams. A marketing executive at a large tech company spoke of the challenge of dealing with website pages that aren’t part of their current marketing cadence. Getting the right calls to action, content recommendations, etc. doesn’t scale beyond the team.

Content recommendation engines can help here. Enabled by algorithms that look at a lot of the data your already have about visitors, journeys and content allow them to suggest content allowing marketing professionals to continue to focus on top priorities while putting data to work to help improve visitors who are elsewhere on the site.

I’m sure I’ll find other common topics as I speak with digital marketing professionals but this seems like a good list to focus on during 1Q. What are you top predictions for 2019? What are you focused on improving? Where are you learning?

Originally posted on Biznology

Steve Zakur

About Steve Zakur

Stephen Zakur is CEO of SoloSegment. SoloSegment provides analytics that improve site search conversion and machine learning technologies that improve content effectiveness.

Why AI Has Come a Long Way Since HAL in 2001

January is a special month in AI history. Because in both the novel and movie 2001: A Space Odyssey, January 12 is when the HAL 9000 sentient computer — (spoiler alert!) the story’s antagonistic artificial intelligence — goes live. Depending on whether you date HAL to its “birth” in the film, the novel, or when those media originated, HAL is anywhere between 22 years to 51 years old now (For trivia buffs, of which I’m one: The book and film were released in 1968, making HAL’s conception over 50 years ago; if you go by the dates given in the film or the book, respectively, HAL is either 27 or 22 years old). HAL is then placed aboard the Discovery One spacecraft to participate in a journey of, well, discovery to the planet Jupiter.

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About Tim Peter

SearchChat Podcast: Can You Personalize Without Creepy Data?

Is the dream of the visitor journey dying? How do we make journeys more functional without using data people don’t want us to have?

Marketers are starting to learn they can’t just orchestrate a visitor journey from start to finish. It’s all about improving the journeys people actually make. They’re complex, not straightforward. Steve and I discuss how visitor journeys are a big data problem. Machine learning allows you not to have to manually deal with that data. It makes Big Data little.

Data can be put to work automatically to make the journey better — and it doesn’t have to be a ton of data. We often start with search data, and it works great since it connects people directly with the thing they want. “Behavioral personalization” means personalization but without all the creepy data. Instead, it’s personalization that customers are asking for. This matters in a post-GDPR world.

Google’s policy is to get right up to the creepy line without crossing it.  Most people don’t know that smart TVs are cheap because they are tracking your data.  How long will models built on creepy data survive?

The three laws of robotics initially were just about making sure robots don’t kill humans. Now we’re thinking much further beyond that — how to create ethical artificial intelligence for business.

Tune in and discover more!

00m 00s — Intro and overview

2m 00s Visitor journeys are changing

7m 05s AI for developing visitor journeys

11m 05s Behavioral personalization

15m 25s Creepy Data

19m 30s 3 Laws of Robotics — how do we create ethical AI?

22m 45s Is it just “legal,” or is it good for customers?

29m 35s Outro

SearchChat is available on

Originally published on Biznology

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About Tim Peter

Do hidden robots need guiding standards too?

Even back in 1942, there were dreamers about what the days of artificial intelligence would look like. Futurists like Isaac Asimov were considering the risks of new autonomous technologies. It was during that year that Asimov wrote a short story entitled “Runaround” in which he unveiled the three laws of robotics.

The key theme for these laws was that a robot could not through action or inaction allow harm to come to humans. Over the years both philosophers and writers have examined these laws in myriad ways showing the loopholes in the language and the challenges that can arise in edge cases. Regardless, the principles seem like the sort of thing we’d want if robots walked among us. They should serve to enhance our lives.

If you’ve ever seen a video of Boston Dynamics’ robots, you understand why the three laws are needed, at least at an emotional level. Boston Dynamics makes all sorts of animal/human-like machines and they seem like something out of a science fiction movie where the robots are not benevolent servants but instead determined to be our overlords. The videos of those robots are evidence to support the need to get those laws right before Atlas walks among us.

But what about the hidden robots, the robots that exist only as lines of code buried on a web server in a cloud hosting facility and don’t look menacing? Should we also be giving thought to guiding principles of design for these engines that are fed our data and are allegedly supposed to make our user experience better?

It seems like a no-brainer. However, anyone can sign-up for their own cloud-based hosting account which likely includes a machine learning starter kit. With a little skill and the right data, a journeyman data scientist can create technology that can do things that would have seemed magical twenty years ago. In the hands of more talented operator far more extraordinary possibilities exist. So what responsibility do each of these developers have to society before they unleash their machines upon us?

I suspect that the European Union is going to lead in this space much as they did with privacy. I also suspect that the initial laws of robotics/AI are going to me more focused on disclosure than compliance with behavioral norms. But this is the sort of thing that could get out of hand, not in the Skynet manner but more in the way that Facebook struggled with privacy. I’ve written previously about whether business models based on personal data will survive. It seems the technology will be always two steps ahead of our understanding of how both it, and the humans who created it, will be using it.

I’m optimistic about the possibilities for AI to have an almost magical ability to improve many aspects our lives. But like with privacy, I think we have to be looking forward to the risks that such technology to have a negative impact. We need to be intentional about ensuring that the machines are learning to work to our benefit.

And if you want to learn more about personalization using behavioral data instead of personal information, check out our GuideBox technology.

Steve Zakur

About Steve Zakur

Stephen Zakur is CEO of SoloSegment. SoloSegment provides analytics that improve site search conversion and machine learning technologies that improve content effectiveness.