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About This Episode
As data has gotten cheaper, companies have started buying it. All of it.
But that’s actually the worst thing we can do. (Just ask your sales team.)
Instead, we should should focus on what attributes matter the most and dig deep on those, said Michael Pollack, our latest guest on The Sales Engagement podcast. He is the Co-Founder at Intricately, a company dedicated to helping cloud vendors understand how businesses are investing in their digital infrastructure.
In this episode, Michael explained the 6 main types of data. He also talked about optimizing your sales organization, outlining a great framework you can use to identify the data that matters most to you.
Here’s what he had to say.
All About the 6 Main Types of Data:
Currently there are 6 main buckets, or types, of data. Here’s a snapshot of each of those types.
This is cutting edge technology from about 1955, but it’s still pervasive today.
When you ask your sales manager or a marketer for some leads, or names of businesses, that’s typically going to come from firmographic data.
This data is fairly static. It’s updated a couple times a year, but it tends to be the oldest type of data. A lot of companies use this type of data for company sizing.
For example, you might see a revenue number in D&B or Factset. But here’s something to pay attention to — most of these numbers are calculated by estimating what vertical that business is in, how many employees they have, and the square footage of the space. It goes like this — if you have a factory with x amount of footage, you’re probably making x amount of widgets, so you’re probably earning x amount of revenue. But calculating revenue for digital companies using that kind of static formula will result in a number that’s probably incorrect. Tech companies just don’t work the same way that factories do.
And here’s something else you need to know about firmographic data. Most of it is collected via the US Postal service. And you thought dinosaurs were extinct!
#2: First Generation Technographic Data
Think about businesses like Datanyze. Companies like this have done a great job at scraping the HTML layer of the internet.
If you sell some types of cloud infrastructure, it can be a little harder to see that data, but the data can still be helpful.
#3: Behavioral Data
This kind of data is focused on users. It’s all about asking, “what have these users done?”
Each time you visit a website, you’re leaving a trail. These types of products monitor this trail. Finally, there’s some behavioral data that can be used around businesses, but generally we’re talking about people.
#4: Intent Data
This is all about looking at what prospects are searching for. For example, you can logically assume that if someone is searching for CDN, they might be a good candidate to buy CDN services.
The challenge with this type of data occurs with crowded topics, where there is lots of search traffic happening. The space can be a bit noisy.
When using this type of data, you always want to ask your marketer or sales ops person, whoever is buying data on your behalf, a few key questions. Ask:
- “How are we getting that intent data?”
- “Who’s providing it?”
- “What’s the quality of it?”
- “How are we validating it?”
Oh, and here’s something to keep in mind — just because someone is thinking about buying something doesn’t mean they’ll actually end up buying.
#5: Contextual Data
This data provides context about what a business is doing, what they are investing in.
The idea with contextual data is to save yourself some time. For example, you don’t have to ask someone if they’re currently an AWS customer. We can tell you that. We can tell what products someone’s using and the rate they’re using it.
#6: Contact Data
This is the bucket of data that every salesperson is familiar with. It’s phone numbers, emails, and WhatsApp nicknames — you get the idea.
Here’s the challenge — if you’re using Salesforce or another CRM, you’re drowning in fields on your lead records and opportunity records. The question you should be asking is, which of these fields actually matter? And how should I think about them? As you can see, there’s so much noise out there. So, how do you figure out what data is actually applicable to your purposes?
A Framework For Identifying What Data Matters Most:
There are four main ways to think about the data that is most important to you and your organization. Once you identify the attributes you want to focus on, you can ensure your marketing and sales ops team are focuses on providing that information to you.
- #1: Look at the available data sources you currently have.
Ask your marketing team to show you what they have. Make sure you pay attention to where each attribute comes from on the lead record in your CRM.
A lot of times, once the data is fed into the CRM, the labels that were on the data disappear. If that happens, you’ll just be stuck sorting through data goop.
- #2: Look at the current accounts in your list.
Identifying commonality across those accounts can be hard. But try to understand where the overlap is, where the data can really be helpful.
- #3: Clean your CRM database.
This probably sounds like a nightmare. But expunge your CRM of data that’s not relevant. Getting rid of any data terrifies people. But that is the poison of SaaS software — once it’s in your system, getting it out is hard.
- #4: Think about your accounts.
Think through your discovery questions. Think about times when you’ve had a great conversation with a prospect about their pain or their challenge. Generally, if you just write those questions down, you’ll start to see the first step of data.
Optimizing Your Sales Organization
There’s an over interest in qualifying everything. But to be an efficient sales organization, you really want to optimize disqualification.
To do this, you have to build an organization that can disqualify the no’s right away, get rid of the maybe’s that will turn into no’s, and then focus on the yes’s. But how can you do this? Think about your ICP, the ideal customer profile. What are the demographics of an individual who buys your product? What are the actions your prospect takes right before they buy from you? And can you define that with data?
Use these questions and the framework above to determine what data is most important to you. Then, go get it and use it to go after those yes’s.
So, here’s Michael’s challenge to you:
Go to your marketing person or sales ops team and ask questions. Make sure they’re getting the data that’s actually going to be most helpful.
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