SearchChat Podcast: Customer Intent is New Again

Alternatively: Chicken Soup for the Customer’s Soul

It’s time to start thinking about the value of intent based marketing differently. The idea itself isn’t new, but now the data is finally there for people to solve their business problems.

What is your customer experience like if you could walk into a diner feeling under the weather, and are immediately offered chicken soup? Online companies don’t have to lose that personal touch. 

You can improve your buyer’s journey by optimizing results to find specific answers to specific questions. But those are hard to predict. Rather than optimizing the result, how can you optimize the experience — the full journey, whatever it might look like? 

These are questions that need answers. Because the reality is: you compete with all the experiences your customers have everywhere online. When a customer goes to Amazon and has a great search experience, they ask — why doesn’t everyone work this way? Your competition isn’t just other B2B companies, it’s Amazon too. High standards and a poor experience will send visitors looking somewhere else — anywhere else.

The data you gain from having a better site search lets you optimize the rest of the experience. Websites can be intelligent when this data is put to work. Do people who buy chicken soup also usually buy herbal tea? Desserts? Your data knows, and your site can make suggestions.

0:00 Intro

1:20 Intent based marketing is new again

11:45 Your competition is the whole internet

16:48 Search is intent, fundamentally

19:05 How do you utilize data to improve the customer journey?

29:05 Outro

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About Tim Peter

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

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Originally published on Biznology

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About Tim Peter

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

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

Originally posted on Biznology

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About Tim Peter

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.