Episode 68: – Technology Companies and Competitive Intelligence – The Ugly
During this podcast we cover:
- The Ugly – What Technology Companies typically get wrong when building Competitive Intelligence teams and generating analysis.
Subscribe to the monthly B2B Market Research newsletter for the latest CI information.
Modified Transcript:
Welcome to another episode of the B2B Market Research podcast. In this podcast we’re going to extend on the topic we talked about last time where we talked about the good and the bad when it comes to CI teams in B2B tech companies. In essence, what do they tend to do well and what tends to be problematic for them?
Now we’re going to talk about the things that are truly ugly, the things that are really systemic problems that almost every CI team in tech seems to face. Before we get into that, though, a couple brief programming notes.
First, if you want to find past episodes of this podcast you can find them on our site, on iTunes, or on other podcast repositories like Stitcher Radio. If you have any questions about the podcast or if you just would like to suggest a topic for a future podcast, feel free to send me an email at sean@cascadeinsights.com. With that, let’s get into the topic.
Previously we talked about the good and the bad in tech CI. A little bit of context here: The comments I’m making come from interacting with different types of very large tech companies (very large meaning predominantly Fortune 500, with a smattering down to the Fortune 1000 level.) These are complex organizations with usually inherently complex market intelligence and competitive intelligence functions. What I want to highlight here are systemic and constant problems that I see across organizations and talk a little bit about the impact of these things.
The first problem is this incredible overreliance on feature-function, tear-down types of analysis. It infects almost any kind of deliverable the CI team produces. Really I think this has to do with product development or engineering’s oversized emphasis on the output from a CI team. I don’t necessarily think it’s bad to have that input. Feature, function, benefit: these are things that customers decide upon when they’re making a purchase of a B2B piece of software or hardware. But it’s not the whole picture. What happens is you have all of this analysis that’s based on what’s the next feature, what’s the next function you need. How are we doing on a feature comparison?
I remember one time I saw a CI team (in air quotes) basically hand me the thing that they were most proud of recently and it was essentially this 600-page binder. This goes back a couple years ago so it was probably wouldn’t be a binder today. Probably just be a PDF everybody would circulate, but it was a binder that basically detailed in infinite detail, screenshots, everything, all of the comparisons of these two complex software products. I thought, “I understand why engineering needs that but I don’t really see that as a CI deliverable.”
CI needs to stand a little bit above the fray and give deliverables that can impact the organization from a sales, marketing and engineering standpoint across that whole set of business functions. Because functionally, from a customer standpoint, they see the buying decision as one part what the product is, one part how the sales team interacted with them, one part how it was priced, another part about the market authority (unless that the organization has that they’re buying from,) issues about post-sale support – so how is customer support dealt with? These things are are dealt with later if product engineering has this oversized emphasis.
Now the next thing, and a very related thing that I think keeps this from happening, is if tech company CI teams just invested a little more in human intelligence. It’s pretty common for me to see analysis that when I ask what data supported the analysis, what data drove the creation of this, the answer is “Well, it was data from this research report and this one. It was from an internal portal. It was maybe from some quantitative study, etc.” What you find is there’s very little direct interaction with customers. Occasionally there’s a study that’s being leveraged, but there’s very little rich interaction with competitor customers, competitor partners – even limited interaction sometimes with a company’s own losses, which is truly incomprehensible in some ways.
When you look at all of this, what happens then is that this whole human perspective on how an organization actually is going to buy and continue to interact with you as an organization is somewhat lost. Instead, it gets driven back to this feature-function, which I think is sometimes perceived as more objective perhaps than the subjective buying cycle. The fact is, the buying cycle is what leads to that purchase, so you have to look at all of that. You have to look at not just the sales motion but the marketing motion, and then also obviously include some of the product feature-function benefit.
One other thing that’s really interesting to me is that there also seems to be a really small internal human intelligence network. I do this exercise pretty regularly when I deliver training, whether it’s a webinar or in person. I’ll ask the room, virtually or otherwise, to say, “How many people do you know that you can go to tomorrow and write down their names that could help you answer a key intelligence question you may have?” Inevitably there’s one person who I could probably leave them to it for four or five hours and they would just keep writing names, but the bulk of the room, every time 80 to 85% of the room stops somewhere before they get to five names.
Again, even if they have the names they tend to be clustered around a given function. They tend to know a lot of people in product engineering, a lot of people in sales, a lot of people in marketing, but they don’t have a lot of cross-functional relationships. It’s important, even if you’re challenged to do external human intelligence.
Some companies have real concerns about how that’s conducted or they particular dates the teams have to go through before they commission that type of work. I completely understand that but there’s very limited restrictions usually on your ability to interact with internal team members. This is a network that you should build and foster and have lots of relationships and lots of different groups. If only because it gives you a shadowy reflection of what real customers thought, but at least you’re getting it filtered by the actual groups driving those decisions, driving marketing, driving sales, driving customer support. That’s a really important thing to go do.
Now with that we’ll wrap up this podcast. If you have any questions about what we covered in this podcast or the podcast in general, again, feel free to send me an email at sean@cascadeinsights.com.