Search Failure. You Can’t Stop Talking About It.

Since January I’ve had dozens of discussions with marketing professionals as part of a product roadmap listening tour. My goal is to hear what’s top of mind for thought leaders, understand the pain points, and figure out how to align our product and our marketing messages with what I’m hearing. While most of the conversation focused around our new product thesis: effective visitor journeys and customer experience powered by behavior-based personalized content recommendation, people couldn’t help but talk about search failure.

Site Search

As you already know, searchers are your best prospects and are more likely to convert according to the data our clients have shared with us. It’s true for B2C and it’s true for B2B. But selling this notion is hard because many companies have no clear owner for the search experience. So we don’t always talk about the positive effect our product has on search. But the people I’ve been talking to can’t help themselves. They universally volunteer that at best their search is a work in progress and at worst it’s a disaster.

So many stories

One large packaged goods company showed me a content marketing site that seemed incapable of providing search results of related articles. If you were looking for an article on living with teens, you were likely to see content marketing and products related to babies.

An executive at a leading insurance company lamented the fact that if you were looking for content related to their asset management business (a B2B play), you were just as likely to see information related to consumer offerings.

And there were also countless stories about personal B2C problems. Looking for the thing in white and only being offered red. Searching for a hoodie and being offered jackets without hoods. Seemingly random search results.

Why is that a problem?

Because of Google. You’ve spent a lot of money and effort improving SEO to attract visitors to the top of your funnel. If they land on your page and find the content wanting they have three choices: site search, navigate, or go back to Google. Navigation is only viable if your site is simple enough to understand. For most B2B companies, this is really hard. If site search isn’t working, well your visitors are left with only one choice. Google.

There are three concerns when visitors bounce back to Google:

  1. It potentially invalidates your SEO strategy. You’re either on the wrong keywords, have the wrong content or both.
  2. Your ability to progress journeys is stymied.
  3. Your competitors get another crack at your visitors.

The Promise of AI and Site Search

A lot of site search vendors are beginning to pitch AI as a technology that will make search better. The engine is smarter, therefore results are better. That’s a start. If text analytics or machine learning models can make search content more relevant, then eventually your visitors may begin to trust your site search.

But what’s really relevant about better search is not the “how” of better search, it’s the “why” of better search. Do the better results lead to fewer site bounces? Fewer exits? More conversions? Is your search data useful outside of search?

Is better search the answer?

Better search is only part of the answer. What you really want is better journey progression. What you really want are more conversions and more MQLs. That’s why we’re focused on building technologies that take better search data and use that elsewhere in the visitor journey.

If you’re a B2B company, it’s likely that site searchers make up a relatively small portion of your website visitors. But every visitor could use guidance as they try to navigate the complexities of your digital presence. Combining better search data with your journey data and your content data creates the opportunity to identify content that your visitors need, so that they’re more likely to progress their journeys and less likely to bounce and exit.

Make your search smarter. But make sure you’re focused on the true goal. Increasing the likelihood that your visitors are going to connect with content that matters and progress towards their goal. 

Want help getting the benefits of better site search? Check out our SearchBox software.

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: How Marketers Can Use Data to Keep Your Seat at the Table

There’s major power for automation within marketing, and not everyone is harnessing it. This episode of SearchChat Steve Zakur and I ask: how can CMOs use both automation and data to keep their seat at the table as companies evolve?

Now matter how long it takes to perfect, your work it will never be done. That’s because everything requires data and feedback. 61% of marketers said creating an automation strategy for their practices is a top priority, according to a recent study. The amount of data available to us defies human capability to process it. What’s more, people often struggle to believe that the data they are seeing is more accurate than their intuition.

Letting data lead often produces results we can’t get any other way. This year we saw a 6 year high in the percentage of time data is used in decision making–and it’s actually still a low number. In our last podcast we saw a similar trend, where most CEOs agree that AI will be bigger than the internet and yet 20% said they had no plans to do anything about it.

The DNA of marketing teams is creativity — but sometimes means data gets lost among unfounded opinions. One of the most powerful moments you can have as a marketing professional is refuting an executive’s intuition with hard proof.

Speak in the language of data to get your seat at the table.

0:00 Intro

1:50 Let the market tell you when you get it right

5:48 Automation is a top priority

10:55 Are you letting data lead?

21:40 Why CMOs need tech alliances

30:28 Outro

SearchChat is available on

Search Chat is SoloSegment’s podcast dedicated to all things search AI and content marketing related. Who is SoloSegment? We’re a technology company focused on site search analytics and AI driven content discovery to improve search results, increase customer satisfaction and unlock revenue for your company. If you think we might have the answer to your conversion problems, feel free to connect with us.

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About Tim Peter

Tim Peter is the President of SoloSegment. An expert in e-commerce and digital marketing strategy, web development, search marketing, and analytics, Tim focuses on the growth of the social, local, mobile web and its impact on both consumer behavior and business results. SoloSegment provides analytics that improve site search conversion and machine learning technologies that improve content effectiveness.

Are You Considering Behavior-Based Personalization?

If you are like most marketers, you’ve probably been salivating over personalizing your website for years. It has always seemed like a good idea, but it’s never seemed possible.

At first, you thought, “If Amazon can do it, we can do it!” But then your IT folks told you the way Amazon does it. Amazon has so many products and so many purchases in its history–and so many repeat visitors–that it is relatively simple to guess what people want. But your site isn’t like Amazon.

Then you thought, “Well, if we know something about our visitors, we can use that to personalize.” But no one wanted to register on your site, so you didn’t know who they were. And privacy regulations came along, and you weren’t sure you wanted to know anything.

Does that mean that you have to give up the dream? No!

You actually can personalize using your visitors’ behavior. With the right technology, you can watch what visitors do on your site. With a bit more technology, you can find the patterns that lead them to success. And with one last dollop of tech, you can use that data to suggest successful paths to others on that same journey.

That’s the beauty of behavior-based personalization. It doesn’t require registrations. It’s GDPR-compliant, because it doesn’t require any personally-identifiable information. It doesn’t require a a slew of products or  return visitors. Or heavy traffic.

If you’ve been waiting for the easy way to add personalization to your site, it’s time to check out behavior-based personalization.

Originally posted on Biznology

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About Mike Moran

Mike Moran is an expert in internet marketing, search technology, social media, text analytics, web personalization, and web metrics. Mike serves as a senior strategist for Converseon, a leading digital media marketing consultancy based in New York City. He is also a senior strategist for SoloSegment, a marketing automation software solutions and services firm.

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! And if you’re interested in learning what SoloSegment is about, feel free to connect with us.

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

Search Chat is SoloSegment’s podcast dedicated to all things search AI and content marketing related. Who is SoloSegment? We’re a technology company focused on site search analytics and AI driven content discovery to improve search results, increase customer satisfaction and unlock revenue for your company.

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About Tim Peter

Tim Peter is the President of SoloSegment. An expert in e-commerce and digital marketing strategy, web development, search marketing, and analytics, Tim focuses on the growth of the social, local, mobile web and its impact on both consumer behavior and business results. SoloSegment provides analytics that improve site search conversion and machine learning technologies that improve content effectiveness.

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. But it’s necessary for marketers to keep your seat at the table. 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? If you think we can help, connect with us.

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

Tim Peter is the President of SoloSegment. An expert in e-commerce and digital marketing strategy, web development, search marketing, and analytics, Tim focuses on the growth of the social, local, mobile web and its impact on both consumer behavior and business results. SoloSegment provides analytics that improve site search conversion and machine learning technologies that improve content effectiveness.

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.

Clarke had it right, AI is magic

Any sufficiently advanced technology is indistinguishable from magic


Arthur C Clarke

It seems like AI has been on everyone’s minds lately. It definitely has been on ours, as Tim Peter and I spoke on AI on our latest podcast. AI has been particularly hyped up, with plenty of big ideas emerging about what it can do for website owners. But I’m fearing, that like blockchain, we’re heading for Gartner’s fabled Trough of Disillusionment if we’re not there already. AI can’t solve all your business problems, though there are those that are well suited with the tools that are available today. But like any solution you have to have a valuable problem and the right approach to applying the solution.

So, how do you get started? There are three real impediments to getting AI off the ground.

  1. Unreasonable expectations
  2. Concerns about data
  3. Skills and Experience

The AI Expectation Problem

We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten. Don’t let yourself be lulled into inaction.



Bill Gates

The Trough of Disillusionment is largely filled with folks, especially at B2B companies, who came to AI with unreasonable expectations. Like any new technology our expectations for near-term impact are always too high. There are no magical powers, there’s only hard work. So the first step in applying AI to any business problem is assessing the measurable value of the problem (make sure you have a business case) and think small.

Most “big bang” projects — large budgets, lengthy schedules, massive business cases — fail to meet expectations. With new technology the risk is even greater because not only are you proving that the project is valuable, but also that the platform can deliver.

To minimize your risk, think MVP (Minimum Viable Product) which is really just a fancy way of saying “Proof of Concept”. Identify a handful of experiments that you can run. This reduces the risk of failure — the likelihood that all the experiments fail is low — and set out goals that aren’t purely business value. For instance, teaching your dev team how to set-up a text analytics platform has a lot of value in the long run.

The AI Data Challenge

One of the intimidating challenges for AI projects is getting the data. Modeling can consume a fair amount of data but it’s not usually the volume of data that trips companies up, it’s that availability of that data. 

Many problems where AI can help requires data from across the organization. Building the connections, both technically and within the management system, with other organizations to access the data is critically important. Ideally, availing yourself of data from work that’s already being done within the company will provide you with the right access. Of course, normalizing that data to work together can still be a challenge.

The AI Barrier: Cost

One of the largest barriers to getting started is skills and expertise. Competition for data scientists is fierce and consultants who do this work can be costly. There are essentially two types of consultants that can help. Domain experts with software that focuses on one specific type of problem and custom development shops. 

Working with a software vendors can provide you with a quick start, but it often presumes that you have a problem that fits with the software that they’re selling. What we’ve seen in the marketplace is that the best packaged AI solutions are in very narrow domains. If that’s a fit for you it can be a great accelerator.

Custom development is a great option when you have a rather unique problem. The downside of this approach is that you’re often building both the platform for the application and the application itself. The timelines for this approach can be long and the cost high. 

One of the the ways we’ve found successful is to find a vendor who has both domain expertise and a good platform but not necessarily an application that meets the need. If they have application expertise in a close swimlane, they may be able to provide you with something that is specialized for your use case but not rigid like a prebuilt application. This allows you to enter with a modest investment and a solution that meets your solution needs.

It’s not magic, it’s work. Valuable Work.

When AI works, I think Clarke was right, it does seem magical. And what business can’t use a little magic? But don’t buy into the hype. Don’t be frightened by the expectations curve. Do find a valuable problem. Do run a few experiments. Do start. Build the muscle memory. Find the place where AI allows you to build a valuable customer experience.

If you want help figuring out how to use AI to convert your customers, check out SoloSegment’s technology solutions.

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.

The Secret Value of Site Search

While rushing to close 2018 deals, business teams everywhere are also finalizing 2019 plans. Business cases have been calculated, lists have been prioritized and they’re getting to green light 2019 initiatives. All of these are focused on yielding the greatest return for businesses. Increasingly, site search is on the list because of the hidden value in this capability.

Among the value being found by companies are: 

  • Site searchers are 87% more likely to respond to marketing goals than non-searchers
  • Site Searchers are 43% more likely to buy — and in some cases a lot more (up to 600%) — than non-searchers
  • Effective site search retains visitors increasing SEO & SEM Yields

Site Searchers 87% more likely to achieve marketing goals

We were recently looking at one of our client’s Google Analytics dashboards. One of the reports reported the marketing goal achievement of searchers vs non-searchers. While we’ve known for a long time the positive impact that site search has on transactional and commerce experiences (see next section) we were encouraged to see the data that showed that marketing goal achievement was 87% higher for searchers than for non-searchers. That’s almost double!

Now you can only get to these levels of performance if your search is really good. It has to do with what we expect most search engines do: Answer our questions accurately. This client has made the investment in understanding the sources of search failure and fixing many of those. Their search success rate is 44% and going higher. This is the type of experience that your visitors expect.

Site Searchers 43% more likely to buy

Higher conversion rates from site searchers is a well documented phenomenon. It’s intuitive that this is so. The person who is searching knows what they want and thinks you have that thing. All you have to do is point them to the thing that they want. Some studies of highly transactional businesses have indicated that searchers are up to six times more likely to buy.

Again, the table stakes for getting this value is to make sure that your search engine experience delivers highly relevant answers that allow the buyer to move forward with their journey.

SEO & SEM yields Increase

Anyone who tracks traffic on their website knows how much of their traffic comes from Google. The data that not everyone looks at is the amount of traffic that goes back to Google. For many companies the amount of traffic that goes back to Google is almost equivalent to what they got in the first place.

We looked at data for a couple of companies and found that on average 30.5% of their traffic comes from Google. We also found that 24.5% of their traffic exits to Google. What is frustrating is that many companies spend a tremendous amount of money (it’s an $80B industry) on search engine optimization and search engine marketing. They’re paying to attract the traffic. What they struggle with is retaining that traffic.

What’s the value of retaining that traffic? One large chemical company we’re working with has determined that their digital lead value is $10,000. We recently calculated that just focusing on increased search retention of one product could yield over $60,000 in incremental revenue. This is a number you should be able to calculate for your business to demonstrate the value of better search. (If you want to find out your potential increased revenue, contact us for a free consultation).

Get them. Keep them. Grow.

Great search improves marketing goal achievement, improves commerce conversions and improves the yield on your SEO and SEM spend. What could be more valuable than that? Invest in better search. Site search is your hidden growth engine. What’s in your budget?

If you want to get started, feel free to check out Two Things You Can Do Now To Improve Your Site Search. And if you want to generate the most revenue from your site search, don’t be afraid to drop us a line.

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.