Every now and then, brand messaging manages to sneak under my cynicism and provoke my curiosity. Take PathFactory:
Today’s consumerized B2B buyers self-educate more than ever, spending 67% of their journeys researching independently.
Sounds right - so what's the problem?
But buyers are experiencing a frustrating amount of friction along their path to purchase.
Ah yes - with raised expectations come disappointments with clunky/antiquated processes. And yes, PathFactory aims to fix that, by "enabling buyers." Granted, there is jargon here we need to parse. At Looker JOIN 2019, I had a chance to do just that - and learn how PathFactory's approach to analytics fits in.
PathFactory's Chris Vandermarel, Director of Product Marketing, is in a good position to answer those questions. Prior to joining PathFactory, he was hands-on in demand-gen himself, putting PathFactory to the daily test. As he told me:
The fact that our tool is primarily used by demand-gen marketers meant I had been using it for three years. That put me in the position to advocate for the product.
A B2B marketing dilemma - "the engagement bar is low"
Helping marketers to reduce buyer friction isn't easy. If it was, we would have solved it by now. So what's the problem? Vandermarel says it comes back to data:
PathFactory's minting a brand new, first party class of data for our customers. That is the data around, "Which individuals and accounts have spent how much time on all of their content?"
Ah yes, that magical and elusive word, "engagement."
As you know, the bar's pretty low for how people understand how people are engaged.
Yes it is. PathFactory aims to change that, by capturing and measuring what it calls "micro-moments." In layman's terms, those are the invaluable times when a prospect/customer is immersed in your content. And there is a huge difference between thirty seconds of watching your video, and ten minutes. Vandermarel:
On the individual level, most of what you know about people is the fact that they opened or clicked an email, filled out a form, downloaded a piece of content, or hit a web page.
And many of those visitors are anonymized. We're making pseudo-educated guesses on who they are because they are not logged in. But even if we know who those users are, when it comes to the engagement metrics that matter, we are often flying blind:
Even take an owned channel like email, where you know 100% of the people because they have all clicked through on your email. Even then, all you know is that likely they hit a landing page of yours, where they had to fill out a form, which is kind of odd because you already know how they are.
Then they clicked through and landed on a PDF. But when they landed on the PDF, you don't know whether they immediately closed the window - or spent 20 minutes on it.
I couldn't resist: All you know is that they were quivering with excitement to be opening another awesome PDF.
Then the sales team calls them and says, "Hey, did you read that PDF?" That's an odd way to start a conversation.
Raise your hand if you've gotten that awkward phone call. As I said to Vandermarel: "They're like, "We saw you downloaded this blah blah." I'm like, "What are you talking about?"
It's a binary interaction, and there's a lot of limitations as far as what you can do with that.
It's not just a binary interaction - it's a legacy interaction.
It totally is. But if you look, if you actually dig into what B2B marketing technologies do, and the data set that they're based on, it's alarming how many sophisticated companies, when you actually boil down to what the data is based on, it's a very simple binary interaction.
You have a huge amount of false positives in B2B data binaries. We think that's an alarming trend in the industry that we're trying to change.
The problem gets worse. The data shows that a buyer generally looks at eight to ten pieces of content before they are "sales-ready." Ergo, B2B marketers are playing a tedious game of catch-and-release:
B2B's answers to that problem, that's another area that's very alarming, because they want you to play a catch and release game. That's the best practice in B2B right now. You send an email. They click through, see a PDF. You send them an email a week later. They click through, see another PDF. You retarget them with an ad. They come back, click through, and see a video.
Catch-and-release is a drag.
Yes - that catch and release game is too slow. It's extremely expensive. You end up spamming people with emails, to your point.
Okay, so how does PathFactory intend to fix this?
We believe that there's a much smarter way to do it, and that's bundling your content together, but not bundling it in a hub. We're bundling it in such a way that when somebody lands on one piece of content, you're suggesting the next piece that they should see, so they can actually move from asset to asset in their buying journey, completely seamlessly.
So there you have it, the two key pieces of PathFactory's value prop:
- Package up the content journey, rather than thinly-disguised spray and pray.
- Provide better data on the quality of content interaction.
Measuring content interactions - how analytics tools fall short
Since we're at a data show, let's get back to that second piece. Vandermarel says our classic web analytics tools are falling short:
People throw Google Analytics on their website, and they think that they're sophisticated. [What PathFactory does differently] is when somebody hits a PDF, we can tell you, "John Smith spent eight minutes on that PDF." It's a much more detailed, granular level of data.
When you have that data, you can determine who's actually sales-ready.
Even if I touched eight pieces of your content, that doesn't mean I dug into them. PathFactory wants to redefine/clarify what it means to be "sales-ready," to avoid wasting the time of either party.
[Now you can see], "This person has hit all of the assets that we need them to see, but not only did they hit them, they actually spent greater than the time threshold we've specified for those assets, so now we truly know this person's actually sales-ready." The sales team can see that and say, "Okay, this person spent this much time on the content," pick up the phone and have the call in confidence.
PathFactory's primary metric is the amount of time spent on a content asset. But they also provide a deeper look into the topics potential buyers are consuming. Their NLP processing engine can go beyond "John Smith spent eight minutes on this e-book," to noting the topics that Mr. Smith engaged with.
We gather quite a lot of intelligence on interaction with content, what the content's about, who the audience is, and try to marry that data in a way that's extremely actionable for sales and marketing organizations.
From homegrown BI tools to Looker
Fair enough - so how does Looker fit in? Prior to Looker, PathFactory built their own dashboards and customer reports. He pointed to Tyler Dewald, Senior Web Developer at PathFactory, who joined our interview:
Tyler would attest to how time-consuming it was, how hard it was to get right, and the infrastructure necessary.
That issue showed up exactly where you don't want it to - serving data to customers.
We had huge backlogs of customer requests for, "This dash would be would be nice, but we need to filter it this way," or, "We need to slice it this way. Can you produce a panel that says this? Can I schedule this to be sent to my boss on the first of every month? Can I download this as a PDF? Can I download this as an Excel spreadsheet?"
Every one of those requests was just painful for our development team, and it ultimately meant that, with a finite amount of development resources, we needed to put customer requests on the back burner. No product marketer ever wants to do that. That's a worst-case scenario.
Enter Looker. When PathFactory started with Looker two years ago, they didn't utilize any of Looker's self-service functions yet. It was more like a standalone BI system tied into PathFactory's database. Still, Looker had an impact. Example? PathFactory was able to configure reports to automatically go out to customers, and provide customers with requested dashboard views quickly.
It was enough to prove the concept. From there, the real fun began:
Then we got some really great customer feedback saying, "This report's great. This one's not so great. Change it in this way." We were able to make a lot of modifications on the fly really come up with the reports that were most useful to our customers, reports and dashboards. At some point, it's kind of became a no-brainer.
But it didn't stop there:
[We asked ourselves], "We have so much of this happening for so many of our customers now, why not add it into the product?" That was around the time that we learned about Powered by Looker, Looker's embedded offering. That was about June this year. We signed a contract to upgrade our license from standalone BI to embedded BI.
As of October 1, PathFactory now provides embedded Looker reports to their entire customer base. It was a quick process:
It was a pretty fast turnaround for us, four months, to go from contract signings to live. There was only maybe the last month of that where there was some heavy lifting happening from the development team.
Okay, so what's the difference for the customer?
We've had Cisco go on record for us saying that there is a night and day difference between the way that the analytics worked before and after, and just the flexibility. We just have more reports overall now, more reports and dashboards.
We have way more dimensions that you can filter on. You can drill down to any date range. We had some date range restrictions previously. We had no ability to download as a PDF or an Excel spreadsheet. We had no ability to schedule a report so it would automatically be sent.
Being able to do this without straining resources is a key. Dewald told me he's been able to do this with a two-to-three person development team:
We're using Redshift right now to power Looker, and then Looker's ability to just kind of interact with that database, and be able to quickly query, and also just have so much functionality out of the box has really helped us out. In terms of person-hours spent, if we wanted to get even one dashboard up, we would be looking at a team of three taking about two weeks to set that all up from back-end to front. As Chris pointed out, now, to get a bevy of Looker dashboards, we're able to do that in a month or less.
But efficiency is not the heart of it. The true win is deploying talent into better projects. Vandermarel says that's exactly what's happening.
Previously, we needed to spend a lot of development resources on building all of these reporting capabilities. Now that we have less of our resources focused on that, because there's a lot more self-service that customers can do and we can build it faster, we can devote more of our resources to strengthening our AI offering.
Vandermarel and Dewald believe that will give PathFactory a needed edge in a crowded marketing analytics market:
We believe that we're uniquely positioned in B2B, having the correct data inputs to model out and understand what individual people should see next.
We're about to find out.