Customers. Content. Connected.


Are you connecting your marketing to business results?

I was at the Marketing Analytics Summit a few weeks back, and talked to a lot of people who felt that:

  1. Measurement was really important
  2. Measurement was really, really hard

The eternal struggle for marketers seems to be connecting marketing actions to business results. But this can’t be a reason to give up on analytics. 

Are you driving sales? Prove it.

Marketers know how important we are to the process, but we’re also the first to face budget cuts when things get rough if we can’t directly prove our worth.

At the end of the day, no matter what your marketing techniques are, if you aren’t able to measure results, do you even know if they mattered? How do you, as a marketer, demonstrate the value you’re adding to the organization?

“Data is now fundamental to how people work, and the most successful companies have integrated it into everyone’s daily workflow. Data is the new application.” Frank Bien, CEO and President of Looker, sums up this need beautifully. Without centering data, marketing can falter.

People with a deep analytics background might already get this, but it is surprising both how difficult analytics remains, and how some people still don’t get it. They figure “I drop a bunch of stuff at the top of the funnel, and then my job is done.” Okay. But wait a minute. What about the middle and the end of the funnel? We need to be asking how this stuff progresses.

And let’s make no mistake, this is hard. B2C attribution is difficult but usually attainable. B2B attribution not only has a significant data integration challenge but there are process, technology, and often cultural impediments to measuring B2B results. So how do you work on this? Well, technology may be able to help.

Data Science is the Future.

Our Senior Strategist Mike Moran advocates for marketers to also become data scientists. He notes that “A lot of marketers got into marketing as a refuge from math.” They maybe liked the creative side — creating great copy, imagery, telling a story. Don’t get me wrong — those things are important. But if you don’t know your numbers in 2019 and 2020 you’re not going to live for very long in this business.

Getting the right data was the goal five years ago. Companies hired Chief Data Officers taking their data wrangling games to the next level. The reality? It’s more complicated than that. It’s not the data, it’s what you do with the data. If you haven’t seen results yet it’s because you haven’t yet put that data to work.

And that’s where data science is today, figuring out ways to put the data to work. Using advanced technologies like natural language processing (NLP) to understand the content the website visitors are consuming and pairing that data with machine learning educated models of desired journey patterns can help marketing teams qualify prospects and help sales reps picking up MQL progress the deals.  

Because we’re years, if not decades, away from any form of general AI we’re going to have to continue to rely on specific models that help with very specific pain points in our process. There are many applications available today that operate within this sort of framework. It’s still important to be able to connect our marketing actions with measurable results. Some of these advanced capabilities make our ability to do so a little bit easier.

Interested in personalization with proven results? Check out our technology solutions.

Opportunities And Gaps In MarTech

Last week I was asked what the biggest opportunities and gaps are in the MarTech stack. It’s an interesting question when one considers all the technology that’s available in the market. While I could opine on features and functions that I’d like to see, what really bugs me is the fact that so much of the overarching promise of marketing technology remains unfulfilled.

Effectiveness-Focused MarTech 

Efficiency has been the primary focus on MarTech since its beginning. If you look at early marketing automation, heck even much of what you see today, the focus is on getting rid of the manual efforts of marketing professionals. It feels sometimes like the tech is focused on helping marketers “make it up on volume”. Yes, automated testing will discover the best content to be delivered in a campaign, but the best relative to what? What grows revenue? More often “better” means some top-of-funnel activity.

That’s good for a start but the promise of marketing technology is delivering marketing activity that more directly connects to the business results. That’s the thing we were all sold way back at the beginning of this thing. Tim Peter and I talked recently about how martech is essential to personalization. Marketing technology was supposed to make marketers more relevant to the business. More focused on business results. More connected to the things that CEOs care about. It feels like the struggle is still very real.

Data Integration

Connecting marketing data to the things that happen downstream — sales, fulfillment, support — is another one of the promises of the marketing technology revolution. Most tech today is fairly open. APIs are abundant. 

In addition, there are ecosystems such as Salesforce and Hubspot that allow you to easily connect marketing and sales data. The great irony in all this is that it’s easier today for a small business to have a highly integrated marketing stack than it is for a large enterprise.

Much of the promise of integrated suites is undelivered. I’ve heard marketing execs talk about how they are unable to deploy some feature in their marketing stack because it requires some wiring that has to be performed by their IT team. The vendor calls it “configuration” but to marketing pros it’s still IT.


Data integration is a start, but having systems talk to one another only serves a purpose if it drives a process. You can’t make outcomes better if your business processes don’t support actions driven by the data that is being presented. 

The intersection of marketing and sales is a prime example. Long an area of contention — sales teams don’t move quickly enough on the leads presented by marketing and marketing teams don’t give quality leads worth progressing — nothing gets better despite data integration unless the processes are tightly integrated.

Process integration doesn’t happen because tools are wired together. Process integration happens when humans work together to design processes that support shared goals and shared methods. Only then can data and tech help make the business outcomes better.

These three challenges aren’t new. They may be the unattainable holy grails of business. That said, they are worth chasing. Vendors focused on outcomes and data supporting optimized processes are the things that will drive business growth even if we’re never able to achieve the perfection that we seek.

To learn how to improve conversions by putting your data to work with automation — connect with me.

How Are You Managing The Changing Nature Of B2B Customers?

What do customers want, and are you delivering?

Ask a business executive and they’ll tell you that everything they do is for their customers. Which is all well and good, but how many B2B companies are really paying attention to the changes in what customers want?

Is your personalization for your visitors… or for you?

Let’s view personalization through your visitor’s eyes

Anyone who has kept up with me on just about any social media platforms knows I’m all about music, and my guilty pleasure – always – would be investing in a new guitar. Or, y’know, guitars. More is better. And if I were to visit a music store that collected data on visitors, they’d know I like a certain make of guitar (I do) and that, for example, I bought two last year and have only bought one so far this year. Maybe, the bright salesperson thinks, “this guy needs need another axe.” 

Poor personalization, which we see all the time, might recommend a guitar exactly like the ones I’ve already bought. Or just knowing I like “guitars” and recommending one that isn’t the right price point, lacks the right features, or doesn’t fit my needs. Or even worse, knows that there’s a warehouse full of unsold Gibsons and suggests a Gibson because that’s what “we need to sell.”

True, effective personalization on the other hand recommends a guitar maybe one level up from what I bought last time or that people just like me also own. It needs to take into account me. The real me. It knows my interests, my behaviors, my goals, my spending habits, and much, much more. But it understands me, not as a target or as a prospect. But as a human being. 

High-quality personalization requires two types of data

What differentiates good personalization is an understanding of your visitor and what their preferences are. These preferences can be explicit or implicit.

  1. Explicit: Explicit data reflects what individuals have told you about themselves. Maybe you bought syndicated research or you have first-party information that human beings gave you. Explicit data often doesn’t feel like merchandising because you’re simply reflecting the best product for their particular needs based on what they’ve told you.
  2. Implicit: Implicit data on the other hand comes from looking at your customers’ patterns of behavior and performing pattern matching, suggesting content and products based on their activity or the activities of others like them. This is how behavior based personalization works

Study your visitor’s behavior

In behavior based personalization, the behavior that’s being exhibited on the site leads us to believe the customer is trying to achieve a certain goal. Once we understand the goal, we can test to see if we’re right (I say “we” because content recommendation is what I do with SoloSegment.) We say, “Hey, here’s some recommended content that seems to match what you want. Does this help you progress your journey?” (Clearly, we’re not explicitly stating that out loud, but that’s essentially what the models are looking to understand). Even better, the model gets a little smarter every time, learns from customer behaviors, delivers another iteration – or iterations, in practice – and makes the experience better for the customer. You’d expect a good salesperson to do this naturally, right? So why not expect the same of your website… your 24/7/365 salesperson?

Again, this is all about understanding your customer’s goal. Personalized content recommendations make suggestions to a visitor about the best content they can check out next. If you have data about the topics customers are interested in, why not help keep them on topic? Why not help them find what they really want? And as the data helps you discern customer intent, move them towards their ultimate objective. Which, I should point out, is what you want anyway. 

What is your visitor’s goal?

“How can we sell these guitars in the warehouse?” I’m sorry to say, is usually not the right question. It’s not about “What are we trying to achieve?” The right question is “What is the customer trying to achieve?”

That understanding, that focus on customer objectives and helping customers progress along their journey is what truly differentiates good personalization from bad. And its utility goes even beyond that; the better you understand your customer, the better you can merchandise whatever it is you sell, whether it’s enterprise technology, financial services, or, y’know, electric guitars. In practice, it doesn’t even matter whether it’s B2C or B2B

What’s your customer’s goal? What data do you have about the customer? And did you use that data in ways that help them reach their goal? That’s not just great personalization. That’s music to my ears. 
Interested in what implicit, behavior based personalization can do to help you drive revenue for your business? Check out SoloSegment’s technology solutions right here.

SearchChat Podcast: A Data Backlash Is Coming

There’s a serious need for changing the way we deal with customer data. How you can use data and still maintain a good relationship with your customers, even among the distrust? We also take a look at who has failed at that, so you can learn what not to do. Take a listen.

First: There’s a data backlash coming. Steve and I discuss Scott Monty who wrote recently on preparing for the gathering storm. We’ve all seen talk about the tech backlash, with firms like Amazon, Google, and Facebook in the cross-hairs of pundits and government officials. That’s centered around their consolidated power and use of data. And it’s a topic that deserves consideration. The backlash I’m predicting is a different one. It’s rooted less in operations and more in culture.

Is there a role for government in this? Google recently abandoned their “don’t be evil” motto and seems now… fine with being evil? More than half of the nation’s state attorneys general are readying an investigation into Google for potential antitrust violations

Meanwhile, who is doing data poorly? Instagram’s latest assault on Snapchat is a messaging app called Threads. Facebook wants more of your data by turning Instagram into Snapchat. 

Lastly, after decades of a focus on short-term quarterly profits and outlandish pay packages, The Business Roundtable execs proclaim that maybe shareholders aren’t the sole beneficiaries of company’s actions.

What is the future of trust in digital marketing? Is it possible to personalize without creepy data? There’s so much that marketers need to learn from GDPR. At the end of the day, we need to make substantial changes to business models built on data for customers to trust not only your brand, but also businesses altogether.

0:00 Intro

2:30 Prepping for a gathering storm

7:05 What is the role for government? For marketers?

14:30 How not to do data: Instagram

17:40 Do stakeholders matter the most?

28:10 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.

SearchChat Podcast: Surviving the Dog Days of Summer

As summer hits its hot, vacation-filled days the work world slows down for a minute. In these slower moments, there’s a chance to reflect on the speed of business. 

The Challenge Of B2B Personalization

Since the beginning of the year, I’ve had 147 conversations with B2B marketing leaders. These aren’t sales calls. I’m not pitching anything. They’re conversations about the issues that are top of mind for these professionals. I guide these discussions with questions around areas we’re interested in, but the main goal is to get a sense of the market. It turns out that personalization is a top of mind issue.

SearchChat Podcast: Can You Do Personalization Without MarTech? Would You Want To?

Is martech marketing? Can you market without it? Do mature marketing organizations need to be including martech? 

In a recent blog post we mentioned that 67% of B2B companies doing personalization are either entirely or mostly using manual processes for content personalization. That’s an enormous amount of people engaging in manual processes instead of automation. Why does this happen?

SearchChat Podcast: AI in Marketing–Where Are We On The Hype Curve?

“Come on. All this AI stuff, around marketing and sales, that’s all just hype, right? That’s not a real thing that matters. Maybe it’ll matter someday. But it doesn’t yet matter today.”

That’s a question I got recently, and it got Steve Zakur and I wondering where we are on the hype curve. Is it really just all hype? Maybe not — platform companies are snapping up data scientists the way early internet companies snapped up web developers.

Four things that prevent marketing teams from getting the most from AI

Why is it that with all the marketing technology vendors claiming their products are fortified with AI pixie dust, business results aren’t better? The same goes for internal IT projects. Everyone wants their process to be better–and artificial intelligence, machine learning, and text analytics can provide that something special. And yet, it doesn’t feel like something special is occurring. At least not in the way that lots of consumer products are getting smarter and better.