Prospect Prioritization: How Intricately Solves the #1 Problem for Cloud Sellers
In this post, Intricately CTO and Co-Founder Fima Leshinsky explains what applications can reveal about a company's digital footprint – and how we can use application infrastructure to better understand the cloud market as a whole. Continue reading below.
A problem I see sales and marketing teams struggle with is prioritizing their prospect universe. Every team I’ve spoken to dreams of something quite similar — the ability to segment their prospect universe into four buckets:
- The ideal prospects. My sales team should pursue these prospects immediately.
- The remaining ideal prospects. These prospects should be customers, but an inside sales team can pursue them as opposed to my most expensive sales resources.
- Not an ideal prospect but could be a customer. Qualified for marketing for nurture.
- Not a good fit. These companies don’t make sense for our business, let’s move on.
The first three buckets make up a company’s TAM. You can narrow your TAM with basic firmographic data, but once that’s done, you have no way of prioritizing the companies in your TAM with firmographic data.
Prioritization requires you to size the potential of a lead or prospect – but how can you do that with your existing data set? In this article, I’ll walk you through this common prioritization problem, why companies struggle with it, and how you can solve it with Intricately.
Prioritizing your prospecting universe in the cloud
Most mature sales organizations have a good sense of what their ideal customer profile looks like. Their challenge is determining if a prospect actually matches that profile and to what degree.
Problem #1: Inaccurate and incomplete information
Companies can attempt to use a combination of firmographic and technographic data to disqualify prospects, but every data provider we’ve evaluated runs into the same issues: Inaccurate and incomplete data.
Use a data provider to enrich your prospect list with firmographics alone (company size, location, industry and revenue) and you’re guaranteed to have 20% - 30% of that data be inaccurate or missing altogether. This assumes your prospects are in North America.... expect another 2x decline in accuracy if you’re prospecting in APJ or LATAM.
And even with this basic firmographic data appended to your prospect list, you’re still not any closer to figuring out how to prioritize this list for your sales team. Removing noise from prospecting lists and lead channels is a crucial piece of figuring out where to focus your sales and marketing resources.
Problem #2: Difficulty sizing a prospect potential
Let’s say you’ve crossed the first hurdle of unreliable data and identified 1,000 account targets for your sales team to pursue. Where should they start? That’s a lot of companies and if you’re selling into the Mid-Market or Enterprise, and it will take time to penetrate each account.
So what’s a sales team to do? The most common answer: follow their gut. If you have a seasoned sales team, each sales executive will be able to produce a list of prospects they believe are good sales targets. This tends to be based on past sales experiences, anecdotal data from LinkedIn browsing or news sightings.
This approach doesn’t scale, and often causes unintentional friction with the marketing team. Sales and marketing will debate the merits of a prospect and how it should be prioritized or score, often using a different set of evaluation criteria.
Long-term sales success means sales and marketing alignment. Both sales and marketing need an objective source of truth on their prospect universe that everyone can agree on.
How are today's cloud companies attempting to solve this problem?
1. Buying multiple data sets
Many companies with large sales ops budgets tackle this problem by acquiring multiple data sets. More data is better right?
Not always. Unfortunately, this strategy results in a ‘Frankenstein’-like data set. By piecing different data sets together, there's a lack of consistency with how the data is shaped. The noise that results from combining different data sets together makes it difficult to work with and even more difficult to make actionable for your sales team.
2. Finding low cost resources to manually build prospect lists
Some companies hire offshore resources to manually go through lists of leads and append data to them. Teams of people are hired to scrape (primarily) LinkedIn and manually clean and enrich that data.
This can be highly effective when needing to build small prospect lists but quickly becomes cost-prohibitive at scale. LinkedIn has engineering teams dedicated to protecting their data from mass scraping operations – you won’t beat them. You’ll also miss out on fast-growing companies who were too small last quarter but have either raised a round of financing or accelerated their hiring.
3. Using existing customer data to build account potential models
Some companies have substantial data science resources and lots of customer data. They attempt to use both to build account potential models.
The key challenge here is that they usually have little data about their prospects. The only way to effectively predict if a company will become a customer is if you have enough data on the company to pattern match with your own customer data set.
"There is a major prioritization problem that teams don't stand a chance at solving. Teams are forced to discard a large percentage of cloud prospects and leads because they don’t have enough data on them."
How Intricately solves the prospect prioritization problem for cloud infrastructure providers
1. Reliable and complete datasets
We felt the pain of incomplete and inaccurate data first-hand. In response, we’ve designed our data set to ensure 100% coverage on all of the firmographic data necessary for basic prospect qualification and the best product adoption, spend, and growth insights in the market. Your teams won't have to rely on scraping LinkedIn or building a ‘Frankenstein’-esque compilation of data sets.
2. Prioritization by spend
In order to prioritize prospects, you’ll need a way to size a prospect’s potential. We use our own data set to build models to help you do just that. Every infrastructure and platform as a service product we identify includes a detailed spend estimate. Our spend estimates reflect the amount of product adoption and usage present. Spend estimates can be rolled up to the provider, product category or company levels.
3. Prioritization by use case
Use case spending is a common way for customers to supercharge their prospect prioritization. Use cases are powerful rollups of Intricately data that represent a company’s areas of investment. If your team has a market segment you want to pursue that isn’t easily identifiable through product adoption – e.g., Machine Learning, Big Data, and even blockchain – our use case data set can identify it.
4. DIY: Build your own models with Intricately Data
If you already have a data science team and want to build your own account potential models, you can utilize the same feature set our engineers use to build our own predictive models. Our feature set was designed for machine learning and includes hundreds of raw features aimed to help your data science team build your own predictive models in-house. Examples of data models we’ve built using include: Spend potential, revenue, company size and product spend.
If your team is ready to start prioritizing prospects based on cloud adoption usage and spend data, contact us!
We’d be happy to share how leading cloud providers like AWS, Snowflake, Google Cloud, Verizon Fastly and more leverage Intricately data to solve their lead prioritization problems. Reach out to our team today.