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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?]

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

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

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.

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

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.

SearchChat Podcast: Ring in the Year by Putting Data to Work

Analytics matter: this is the unavoidable fact of digital marketing, even for those digital marketers that fear it. But are you even measuring the right things? Do you know how to make meaningful improvements?

In this episode of our SearchChat podcast, Steve and I talk about site search, personalization, and big data. In our work in website search, we’ve seen that clicks are a measure of activity, but not necessarily an indicator that something good happened. Did the click lead to a purchase? Did the click answer to a visitor’s question?

First, a brag: Marketing Tech Outlook named SoloSegment to its top 10 marketing analytics solutions. We talk about what we’ve learned and what we now offer our customers. When I first heard about receiving the award, SoloSegment was mostly collecting data. Now, we realized what sets us apart is automating changes using that data.

Our focus for 2019 is on  putting data to work. It’s not an easy task — it means determining if your data is accurate, as well as usable to measure success. 

We discuss personalization, which every marketer wants to jump into. Not everyone is ready.  Do you have the data to identify your audience, what the right content is, and identifying whether it’s working or not?

Tune in and discover more!

00m 00s — Intro and overview

02m 00s — SoloSegment named in top 10 marketing analytics solutions

5m 20s — Why measurements like clicks fail

9m 25s — Can you use your data to power success?

15m 15s — Why your B2B content marketing isn’t ready for personalization

20m 45s — How to think about Google Discover

28m 02s — Subscription links and outro

SearchChat is available on

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

Originally posted on Biznology

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

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

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