Category: Competitive Intelligence

💜How Blueprint Supercharges Sales with PredictLeads Data💜

Navigating the sales landscape with data isn’t just about collecting information – It’s about turning it into actionable insights. This is exactly what Blueprint has mastered using PredictLeads.

🤘Let’s dive into how they do it. 🤘

Data at Work: Real Insights, Real Growth

Blueprint uses PredictLeads to perform deep technographic scoring, analyzing data on 500 new technologies each week. This isn’t just about knowing what’s out there -> it’s about predicting market trends and identifying emerging competitors, providing a clear advantage in crafting timely and relevant sales pitches.

Jordan Crawford, the founder of Blueprint, puts it simply: “It’s not about having more data, but about having the right data that you can actually use.” This is where PredictLeads shines, offering depth with actionable insights.

Key Stats and Strategic Decisions

With PredictLeads, Blueprint isn’t just collecting data -they’re strategically deploying it.

Here’s how:

  • Job Openings Data: By analyzing the hiring trends of potential clients, Blueprint can pinpoint when companies are expanding and tailor their pitches to meet these growth phases.
  • Technology Adoptions: Tracking 636 million technology adoptions helps Blueprint stay ahead, suggesting when companies are likely to need their cutting-edge solutions.

A Relationship Built on Success

Blueprint’s partnership with PredictLeads goes beyond data. It’s about continuous support and collaboration, which Crawford describes as unparalleled. “PredictLeads always finds a way to make it happen,” he says, emphasizing the personalized support that helps Blueprint leverage data effectively.

For those eager to dive deeper into this use-case, you can check it out here.

If you have any questions, please don’t hesitate to reach out to us at:

We are here to help:)!

💜Stay Awesome💜
PredictLeads team

Understanding Pension Funds’ Strategic Shift to Private Markets with PredictLeads’ Data

As global pension funds like CalPERS increasingly redirect investments from public equities to private markets, the demand for precise, actionable insights grows significantly. This strategic transition, aimed at securing higher yields and reducing market volatility, is particularly highlighted by pension funds’ systematic moves towards assets like private equity and private debt.

The Role of PredictLeads’ Data in Navigating Private Markets

Job Openings Data:

PredictLeads’ Job Openings Data provides real-time insights into hiring trends directly from company websites, reflecting growth and expansion activities within specific sectors. An increase in recruitment, especially within sectors like private equity and private debt, often suggests robust sector health and promising profitability prospects. For pension funds diversifying their portfolios into these private markets, such insights are critical. They help align investment strategies with sectors that demonstrate strong growth potential, making this data invaluable for funds adjusting to the dynamic conditions of private markets. Additionally, pension funds can use this data to identify emerging industries or regions where new skills are in demand, providing an early indicator of economic shifts that could influence long-term investment decisions.

Business Connections dataset:

The Business Connections dataset from PredictLeads employs advanced image recognition to scan and categorize logos on company websites, unveiling key customer relationships often obscured in conventional financial reports. This dataset is especially valuable for pension funds engaging in scoring potential (startup) investments. Identifying major clients, particularly public companies, allows funds to assess a startup’s credibility and market traction. Companies with esteemed, stable clients can be scored higher, indicating lower risk and potentially higher reliability as investment opportunities. This insight is crucial for informed decision-making in public market investments.

Pension funds can leverage this data to assess the market reach and network strength of potential investments, ensuring a more comprehensive risk assessment and decision-making process. Furthermore, this dataset allows pension funds to monitor the customer base stability of current investments, providing ongoing risk management and insight into market position changes.

Broader Market Implications:

The shift of pension funds toward private markets not only reshapes their investment strategies but also influences broader market dynamics. By utilizing PredictLeads’ alternative data, such as Job Openings and Business Connections, pension funds can gain deeper insights into the risk and potential of their private market investments, which are crucial for informed decision-making in an environment where traditional metrics fall short. 

Moreover, this strategic use of alternative data helps pension funds anticipate market trends, adapt to economic changes, and identify investment opportunities early, maintaining a competitive edge in an increasingly complex financial landscape. Whether through pinpointing emerging sectors with job openings data or evaluating the robustness of potential investments with key customer analysis, these datasets provide critical insights that enhance and refine investment strategies.


As the trend of pension funds moving towards private markets continues to grow, the importance of alternative data becomes increasingly central. Datasets like PredictLeads’ Job Openings and Business Connections dataset provide essential tools that enable large institutional investors to execute their strategies with greater precision and confidence. By leveraging these alternative data sources, pension funds can more effectively navigate the complexities of private markets, aligning their investment approaches with the most promising opportunities for sustainable returns.

Got questions? Don’t hesitate to reach out at!

Case Study: InReach Ventures & PredictLeads

InReach Ventures uses technology to help scale venture capital and make
investments in early stage startups throughout Europe. They built their own
proprietary software and developed a new model of investing to discover and invest in the most promising startups.

There’s a few major data challenges VC’s often face including data quality and the
time, effort and cost it takes to acquire or crawl data.

Here is a short interview with Ben Smith the Co-Founder / Partner / CTO of InReach Ventures and how PredictLeads company intelligence data helps InReach Ventures discover new companies and track growth signals for companies of interest.

How do you identify growing companies?

“InReach combines data from lots of different data sources. Some of that is around signals on how a company is performing like PredictLeads data, which helps us to find startups from all over Europe. This data, along with other types, allows us to look at how companies are growing, whether they’re growing their team, if they’re getting new customers or new business connections or partnering with different companies. “


Are there any specifics on how PredictLeads data is being used?

“With job postings in particular, outside the general idea that a company is growing positively, it gives us an idea whether there is real substance behind a company. Seeing that a company has a product and engineering DNA and are looking to invest more in it is a positive.”

What challenges were you able to overcome with PredictLeads data?

“It’s all about how best we leverage our own product and engineering resources. Having the InReach team focus on what we’re good at and working with partners that are better than us in areas is an important point of leverage.”

Why did you decide to subscribe to PredictLeads data?

“PredictLeads helped us by doing some of the work that we had always planned but never been able to prioritise. Finding news events around a particular company and identifying company customers through logos/connections is really interesting for us and also it’s something that takes significant time and effort to get right.”

What’s your view on the VC industry using data and what are the biggest challenges on the horizon in the industry?

“The value of data, machine learning and a data driven approach to capital is an ever growing trend. The point of venture capital is to fund innovation and how much innovation is happening in venture capital in the past 10 years is very limited. I think there is a change now where data and software is being seen as a way for venture firms to innovate their model. The issue that traditional VC firms first face is that culturally at their core, they are not a technology firm but a professional services organisation. Where we think we have an advantage is that we started as a technology, product and engineering organisation, taking a very data driven approach to venture capital. That’s where we think we will long term hold the advantage because we started doing this earlier. Traditional venture capital will start to utilise data over time, but at their core they are not tech or engineering organisations. Short term, data and tech will play a broader role in terms of the whole industry using it as it’s becoming more and more of a buzz and as data is becoming more demanded.”

What are some of the trends in Venture Capital?

“My co-founder and Investment Partner Roberto layed out the the data trend in VC well in his blog post: The Full Stack Venture Capitalist

How do you see PredictLeads to help you achieve your long term goals?

“Two things PredictLeads does and will continue to do is that it helps us discover that a startup exists in the first place and then tells us whether there’s something interesting happening that we might want to talk to them about.”

Company intelligence data in your Sales enablement platform

You’re already aware that the more insights and data you have on your prospects, the better. Exactly how could you take advantage of this data and how could it be integrated into your sales enablement platform?

Sales enablement platforms are without a doubt incredibly valuable to sales teams. They have endless use cases as illustrated in the “SalesTech Landscape” below – such as engaging clients with smart messaging tools, improving productivity with easy to use sales resources, identifying leads and scoring them, and of course, the CRM that helps you stay on top of customers and prospects.

In the CRM, all the information you have on your customers should be tracked, which can be made manually by the SDR itself or automated using a 3rd party system. Data like company name, contact information and demographics is usually logged into the CRM and creates a long lead list of prospects. For sales teams to become successful they usually need more information than this. Here’s how sales intelligence like company intelligence data can enrich sales enablement platforms and improve the performance of SDRs using it:

Personalized Outreach

All SDRs have heard the importance of being personal. No prospect wants to listen to some robot pitching their idea without knowing a single thing about them, their company or understanding what value their product can give them.  

PredictLeads can provide insights to increase Personalization such as: 

  • News Events alerts like “Company X receives Y Award” can give you an icebreaker to open up the conversation by congratulating them and letting them know you did your homework. 
  • Key Customers data insights can help you identify if you and the customer share the same partners or clients to build familiarity and trust. 

Sales Triggers

Imagine you have this very long list of leads in your CRM. You start making phone calls from the top but cannot seem to move the deal forward even though you’re having great rapport with prospects. The most likely reason is you’re not reaching out to the right prospects, the ones that are in the buy mode. Companies are not always looking to invest in products for many reasons. Maybe they’re already satisfied with what they have, or they don’t  currently have the budget to invest in new software or the product that you’re selling. With sales triggers you can identify companies with signals indicative of growth – meaning the company is more likely to invest. 

Examples of Sales Triggers PredictLeads provide are: 

  • Company signs new client.
  • Company launches new product. 
  • Company received financing.
  • Company increases headcount. 
  • Company made a new integration. 
  • Company expands facilities.

Targeting Leads

When sourcing for leads, most CRMs provides search functions with filters that help you target the right prospects. With more company data you can advance these search filters to create a more targeted approach. The more filters you can use, the better you can target prospects matching your ideal target persona.

How PredictLeads data can be used as filters for Targeting: 

  • Technologies used in the company can help you identify prospects that are more likely to have an interest in buying your product. For example, if you’re selling a Salesforce extension product you’d want to target companies that are actually using Salesforce. 
  • Companies Hiring for X category, if you’re selling a Marketing automation product you can filter companies hiring for Marketing category as they’re more likely to have a need for your product. 

These are just a few examples on how company intelligence data can be integrated into your CRM or other sales enablement tools, and I’m sure there are hundreds more creative ways. One thing is for certain though – sales teams will benefit from using this data, because it’ll be doing all the research for them. This means that they can focus on what brings them most value – selling.


Hiring intent data during pandemic

PredictLeads took the data for 5000 US based companies from different sectors and aggregated hiring intent data for each of the sectors. Our goal was to review how hiring intent correlates with pandemic consequences.

US states started pandemic lockdowns on March 19th and by mid April 90% of US population were under some form of lockdown.

Below we share three industries and how they were affected by Covid19 during this period and after.

For the IT industry our data shows downward trajectory of active job openings started on March 25th. On this day 20,000 job openings were slashed from the IT industry.

They were further decreased by nearly 130,000 job openings till May 28th 2020.


US unemployment rate rose from 3.8% to 4.5% in March. During last week of March 47,000 jobs were cut per PredictLeads data. In March the total number of job openings in the IT industry decreased by 6.8%.


US unemployment further rose in April to a record high of 14.4% during which 70,000 additional job openings were delisted in the IT industry alone – a staggering 12.8% decrease.


Data shows 51,000 job openings were removed in May (1st-28th). If there would be linear correlation between job listings and unemployment rate, this would suggest unemployment rate will further increase to ~21% by the end of Month of May. Up till now this means a 10.1% decrease in listed job openings.

Information Technology – eg. software & services, computer hardware, IT services …

Similar trends are seen for the Consumer Discretionary and Industrials industries as seen from below two graphs.

Consumer Discretionary – non essential goods eg. leisure products, entertainment, sporting, restaurants …
Industrials – finished products that can be used for construction and manufacturing industry

Covid19 definitely has a big impact on Hiring Intent.

If you’d like to get more detailed data on anything Hiring related please check out our APIs at or contact us at and we’ll be happy to help.


Introducing Key Customer Data

PredictLeads now provides four main datasets. News Events, Hiring Intent, Technologies and Key Customer data.

Key Customer data is our newest addition. As the name implies it provides key customer data for a given company. Eg. who are their customers, partners, sponsors, vendors, investors etc. We’re able to get this data by searching through news articles, blogs, case-studies pages, testimonials, “Our customer” sections and more.

We go as far as to use image recognition to connect a logo on a given website to a domain name it belongs to.

Key Customer data is served via clean APIs, with same structure as our other three endpoints. We follow best practices from

Besides using APIs data can also be provided via Flat Files or Webhooks.

If you’re interested in checking it out simply sign up here: and you’ll find an API key in the settings.

Documentation for the Key Customer data can be found here:

For more details please reach out to and we’ll be happy to help!

Cheers, Roq

Finding Top performing Companies

We’re working with many data driven teams – VCs, Corporate VCs, sales professionals and innovation departments.

Our aim is to provide data that helps them:

  1. enrich known companies -> to enable scoring and prioritization.
  2. discover up and coming challengers -> to not miss out on new market trends.

For the #1: goal is to find data that is indicative of a company performance. Currently we track and provide data on Hiring Intent, Events from news, Business Connections and Technographics. Each dataset covers a wide variety of data points and is available via clean APIs –

For the #2: goal is to find new up and coming challengers. These are companies that we’re seeing for the first time having expansion signals. For example hiring via Hacker News, expanding Offices, receiving Awards, signing new high value clients … and many more. You can find them under Discover endpoints:

We’re working extensively on increasing our match rates and having historic capabilities. Currently we track over 17 million companies and have performance data from 2015 onwards.

Often our clients try to find indicators of how well a company will be doing in the future. This is no easy task. One needs to first have accurate and complete data and secondly enough historic data points to make any kind of projections. The game is especially patience ridden for data driven VCs and CVCs. Startups easily need 5+ years to become successful and/or make an exit. So this is the timeframe data driven companies are bound to – to validate their prediction models. Which is quite a long wait time to train the models and iterate. Now one could train the models on past company trajectories and past exists. But often not all data points are available to do that.

So it is a long haul game we’re playing here. And the name of the game is patience. But since history has proved time and again that subjects with more knowledge, data and insight perform better than those with less we’re confident the results will be worth the efforts we’re putting in now.

If you’re interested into how we could provide value to your organization please contact us via one of the contact forms here

Looking forward! Roq

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