Article By: Matt Hogan, Head of Customer Success at Intricately
Data is infinitely expanding – it’s the informational universe that taunts us with its enormity. Kind of cool, right? Though, up until recently, the popular growth strategy for revenue teams involved digesting as much of this data as you could stomach.
We should have known that strategy wouldn’t last long. While it’s an admirable goal, revenue teams are increasingly aware of how absurd and ineffective it really is.
For these teams, which typically consist of marketing, sales and customer success, managing intake and actionability of data has become the latest topic of discussion. But where do you start?
While you can theoretically find a use for any type of data, that doesn’t mean you should pay attention to all of it. You need to know how to discern which data is worthwhile so you can implement your findings effectively.
What kinds of data actually help revenue teams succeed, and what kinds are just white noise? We’re breaking down the six different types of data, how teams can optimize management of them and how they can analyze their entire data strategy to better execute campaigns across departments.
Why does the right data matter?
Your revenue team could spend weeks combing through all the data available to you, but to what end? While all that data can paint a clearer landscape of leads and customers, how much of it directly helps your team generate more demand, close more deals, and make customers happier?
If you’re like most SaaS companies struggling with revenue operations, you’ve come to realize more data does not directly equate to more revenue growth. Data is so plentiful, overwhelmed revenue teams often overlook less-obvious qualified leads that don’t shine as brightly as no-brainer qualified leads in the clutter.
To avoid this, they need to capture and analyze the right data. The right data will help your team zero in on qualified leads and close more deals. But before you go rummaging through your current database for the right data like your aunt Lisa at a thrift store, take a step back and review your data strategy.
What is your data strategy? How does your team leverage different types of data to make smarter decisions? When your team comes together around an aligned data strategy, big things start to click into place. A data strategy naturally aligns Sales and Marketing around an ideal customer profile, ensuring everyone is aware of the different types of data available to them.
An aligned data strategy also provides Sales with the insights they need to upsell current customers, while allowing Marketing to understand the content that’s resonating the most throughout the entire funnel.
Once your team is on track with an aligned data strategy, they need to take note of the various types of data available to them.
The different types of data for revenue teams
What types of data are most valuable to your team? How many types of data do you currently lean on for success? Depending on your industry, you may favor behavioral data over firmographic data, for example.
But if you’ve made it this far into the post, we’re guessing you’re in SaaS and prefer a blend of several types of data. For revenue teams, there are six that you can use to make decisions, understand prospects and engage customers.
- Firmographic data refers to characteristics of firms, companies, nonprofits or governmental institutes. Data points can include information about employees, revenue, customers, location and more.
- Intent data gives insights about when leads are researching products or solutions online. It’s a somewhat black-box form of data, and sales and marketing teams use it to identify when highly qualified leads are prepared to purchase.
- Behavioral data is accumulated by observation of consumer behavior, namely their engagement with digital content including your website, downloadable content, emails and more.
- Context data provides relevant insights into a company or individual about their need, ability to purchase and potential budget.
- Contact data refers to all the contact information for an individual or company.
Review these different types of data with your team, identify which of these you’re currently working with and list them under each category. This simple organization and review tactic will provide clarity about the types of data you’re using. Soon, your team will begin to make smarter decisions when selling your platform.
Flipping the switch from qualifying to disqualifying
So you’ve identified and categorized the right data available to your teams. Now how do you put it to work? Start by flipping the switch.
Instead of hacking your way through data to identify qualified leads, use your data to weed out companies who don’t need your product. Stay with us – clearing the clutter of disqualified leads quiets some of that noise and may even reveal some hidden gems you previously overlooked.
Sifting out disqualified leads will look different depending on the type of product you sell. If you sell a physical product, for example, you may find success by looking at a company’s physical footprint through firmographic data.
But as we’ve already guessed, you’re probably selling software. In your case, firmographic data like company size and revenue will give you only a fuzzy glimpse of whether the company is an ideal customer. By analyzing both technographic and context data, you can glean clearer insights about a company’s technology stacks and its spend and usage potential for consumption-based cloud products.
When shifting to disqualifying accounts, your team should take the time to:
- Understand your ideal customer so you’re clearing out disqualified leads with a firm understanding of what “yes” looks like
- Clear your database of everyone who doesn’t match your ideal customer profile
- Look for new accounts outside the database that do match
- Supplement new accounts with different types of available data
If you can’t disqualify accounts that are unfit for your product, chances are you don’t have the right types of data your team needs to do their best job of selling and marketing your products. If you come to this roadblock, hit pause and revert to step one: categorize your data.
Finding the right data for your team
Curb the temptation to implement every scrap of data available to your team; you simply don’t need it all. Instead, focus on developing a basic, personalized data stack that serves your team and helps you disqualify unfit companies.