Tl;dr - It's common for software companies to use technographic data in prospect targeting. That's usually harvested from tags on the site which means many types of software remain invisible. Further, static technographics lack the context to understand dynamic opportunities. Contextual™ Technographic Data incorporates more expansive user identification into active intent signals for amazing insights.
Dynamic Activation of Technographic Data
Lots of companies include some technographic elements in their ICP (ideal customer profile.)
For instance, our friends at Databox focused on HubSpot users and agencies as they started to scale - they knew the space well, and had great integrations and agency solutions. Similarly we've had private cloud hosting clients, for example, who are specifically interested only in companies using some specific version of VMWare.
Those are fairly vanilla applications of technographics - a simple qualify/disqualify filter.
And it's why technographics are often part of a robust intent data stack. To dive deeper into common technographics use cases, here are good articles from ZoomInfo, and MarTech Advisor who says "Your competitive sweet spot is where firmographics and demographics intersect with technographics."
But that's not always the best solution. Sometimes the most important information isn't the static tech stack, but rather the current or likely tech stack COMBINED with other intent factors.
We call that Contextual Technographic Data, and it's enormously powerful.
Depth of Technographics
The first step with any data is to understand what it represents. If you assume, well, you know the aphorism.
How is it collected - method, frequency, etc? How broad is the reach? Can it confirm the negative - e.g. most technographics tell you what software a given company is running, but that's different than confidently confirming the absence of software.
Some of the common collection methods are based on a web crawl looking for code on sites as well as human calls/verification. In a PLG (product led growth) world with exploding freemium and trials, there are lots of obsolete codes floating around on sites. So existence is one signal - addition and removal may be more important. Human verification might feel like the gold standard - until you ponder for a moment how you'd feel about one of your colleagues sharing details of your tech stack with....someone on the phone.
How else can data be collected? What about job postings? Ellie Mirman (@Ellieeille) of Crayon had a great post recently on the Marketing AI Institute blog. In "How to Decode Your Competitor's Strategy with Predictive Intelligence" she wrote at length about the importance of job postings.
This job description notes details about a change in backend architecture that’s coming, including details on data storage and integrations. Companies will often divulge more details in job descriptions than they would otherwise volunteer on their website, so they can be a great source of intel.
This is key.
- You'll learn about the entire tech stack - not just the Martech and Salestech stacks with website tags
- You'll often gain inferred insight into large strategic priorities.
That means that technographics which incorporate the details embedded in job postings (which as Ellie notes would often be otherwise confidential) is often richer and more accurate. Certainly in conjunction with code on sites it's a much more robust source of information.
That's the approach our new technographics capability takes. It's really cool. 💪
But....even at that, it's a relatively static signal.
The potential value of data most often lies in the insights to be found at the intersection of various sources. Like technographics and 3rd party intent data for instance.
If you're working toward your Marketing Data Master Distiller designation, then I'm sure your creative juices have already started to bubble and boil thinking about amazing ways you could use this amazing combination. Let's look at a couple.
TechnoIntent "Imagine If" Scenarios
Scoop the Fruit Play
What if the "low-hanging fruit" isn't the easiest? What if the fruit that's perfectly ripe and already on the ground is the easiest to pick up? In other words, what if you could infer which companies should be using your category of software but aren't. And especially those that then start to take action that indicates they're trying to scratch an itch you know you help with? A simple harvest of perfectly ripe fruit. That's even better than the proverbial low hanging fruit.
Competitive Play
Imagine if you knew when the current user of a competitive product started to engage with other competitors. Maybe they haven't discovered you, or didn't understand the full range of your capabilities. Whatever the reason, here's your chance to put yourself on their shortlist just as they're doing their research and making critical comparative judgements.
Knockout Play
A sales classic - target the current user of a competitive product who is now taking action around a capability or problem where you have an edge - perhaps around regulatory considerations. For instance if key buying roles from an existing customer of a competitor which you know to be weak in HIPPA, is taking action around compliance related key terms, that could be your fast pitch down the middle.
Similarly if the customer of a competitor with clunky integrations starts to take action around a complimentary software, that could be a shorter path to revenue - and a great way to foster stronger bonds with channel partners who could win a twofer.
Product Launch Play
When launching a new product, is it easier to displace incumbents? Or to enter areas of the market ripe for new adoption? What if you could compare your ICP against competitive installs, look for the whitespace and focus there. You'd have a clear idea of appropriate messaging and targeting - knowing that they should but probably don't use a solution.
Take it a step further and aggressively pursue those that don't have a solution and are likely in market based on actions they're taking - maybe that's your list for beta users and early adopters!
Market Research Play
What market share do you and each of your competitors have, anyway? By Industry? By geography?
For existing users of incumbent products, is the buying journey the same as for new users? In other words, do the same roles & seniorities take the same sort of action? Or is the incumbent replacement driven more from the manager/director level, or vice versa, more from the CXO level?
Which competitors are strongest in which size companies? Which competitors have the highest churn rate?
Of course many of these insights will feed back into the competitive play.
Beyond Software - Products and Services
These scenarios primarily contemplate software marketing and sales. Contact level™ intent data has numerous applications in many industries. Use of key terms vs topics allows bespoke algorithms to work really well for B2B services and manufactured products in addition to software. Contextual technographic data can also identify opportunities for consultants, services and products.
Companies that manufacture recognition awards, for instance, might want to know about prospects considering employee engagement software. 3PLs might want to know about companies investigating the addition of pick to light software systems on top of specific ERPs. And medical billing service providers would sure like to know about physician practices which use a cumbersome billing software that are investigating alternatives.
The point is that because software touches so many pieces of every business, the ability to know which companies are using specific softwares AND taking certain other actions is an incredibly powerful capability.
It's Not Dispositive
It's probably clear, but a word of warning for eager sales teams. Like all intent data, these are signals; intelligence to be incorporated into your full data stack and integrated into your opportunity analysis. If the software doesn't use code on the site, and generally doesn't require the recruiting of special skills, this enhanced technographic data might not pick it up.
There are certainly situations for which it won't work. And it's not magic.
But it's pretty powerful for many, many applications.
