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.

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.

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.

SearchChat Podcast: AI Goes Back to the Basics

We at SeachChat frequently talk about how AI and site search produce value for your site. But let’s break that down for a minute. What this is all about at the end of the day is customer experience.

When a prospective customer arrives on your site: are you helping them? Are you answering their question? What value might you be creating — for them, and for yourself?

Steve and I focus on some of the most important ways to fix your site search improvement program. It might not sound like the most glamorous solution, but it’s the best way to ensure you can capitalize on site search insights. Site search offers some valuable information: what can you learn about a visitor and their intent.

As I wrote recently, site search is your company’s best salesperson. When powered by AI, your site search learns about your prospective customers and can tailor results to guide them. Machine learning lets site search deliver results that drive sales. If a salesperson was performing as poorly as your site search, would you even keep them around?

00m 00s — Intro and overview

02m 20s — Site search insights on Search Engine Land

13m 00s — Site search value and site search as your best salesperson

18m 50s — Developing a strong site search improvement program

23m 16s — AI and its connection to search

32m 30s — Customer experience

33m 23s — Subscription links and outro

SearchChat is now on

Check us out on FacebookTwitter, or email info@solosegment.com.

Originally post on Biznology

About Tim Peter