As PredictLeads partnered up with clay.com it seemed only natural to then go work with clay in the literal sense.
As much as we try it’s really challenging for all of us to look at the camera at the same time 😀
Jokes aside, it was an interesting coincidence though not planned to happen :). Eva booked us for a nice evening of working with clay (literally).
Screenshot
When working with clay to create a finished ceramic product, two primary steps are needed when heating / firing the clay:
There were also two types of “tables” we were working at. This was the “Pottery Mill”.
Bisque Firing (First Firing): The purpose is to transform the raw clay into a hard ceramic state. This firing removes moisture from the clay and drives out organic materials. The temperature for bisque firing in our case was 950°C (1,750°F).
Glaze Firing (Second Firing): After bisque firing, the piece is coated with glaze, a glass-like coating that provides a smooth, glossy finish. The glazed piece is then subjected to a second firing, known as glaze firing. This firing melts the glaze and forms a glassy, non-porous surface on the ceramic piece. In our case Glaze firing temperature was 1,250°C (2,345°F).
Till the next team meetup! 🙂
We’re still waiting for the clay shop to go through these two steps and we can’t wait to see what we produced :).
We are excited to announce a new API endpoint from PredictLeads designed to help you discover which companies are utilizing specific technologies. Whether you’re tracking the adoption of CRM systems, cloud computing platforms, enterprise resource planning tools and more, this API offers a powerful way to gather and analyze technology usage data across the web.
How It Works
Our new endpoint allows you to ping a specific Technology ID and receive a detailed list of companies and websites utilizing that technology. This data can be invaluable for market research, sales prospecting, competitive analysis and more.
Example API Endpoint
You can use the following endpoint to start exploring technology detections:
Additional information can be found in our docs “here”.
Interested in Trying It Out?
We’re offering 100 free API calls to anyone who wants to test this new endpoint. Sign up at PredictLeads and start exploring + Feel free to let us know if there are any specific technologies or IDs you’d like to check the coverage of.
Note on Development
Please note that we are continually improving this endpoint, and your feedback is essential. If you encounter any issues or have suggestions, feel free to reach out to our support team.
Technology Data Snapshot
Technologies Tracked: ~15,000
Technology Adoptions Detected Since 2018: ~636 million
Websites Tracked: ~47 million
Technology Identifications Last Month: ~18 million
Technology Identifications Last Year: ~193 million
We look forward to seeing how you use this new feature to enhance your business intelligence and decision-making processes!
In sales, finding and engaging the right prospects can feel like searching for a needle in a haystack. Sending non personalized outreach is just a thing of the past and companies offering sales solutions are looking into data to add that personalized touch that increases those reply rates that we all like.
Job Openings Dataset as well as the News Events Dataset are incredibly useful and widely adopted for uncovering new leads and improving sales outreach. However, there is a unique dataset that is gaining significant attention. This dataset, which is not yet widely used due to its limited availability, holds great potential for transforming sales strategies. Here is why:
We all know that companies like to put logos of other companies they work with, on their website to gain credibility. Since those logos are often not backlinked, PredictLeads has built an image recognition system that connects these logos with company domain names. By checking the company’s Case studies pages, testimonials, “Our customers” sections and more allows PredictLeads systems to identify them as customers, partners, vendors, sponsors and more.
Here’s a quick rundown of how the Connections Dataset can revolutionize your sales efforts and how it’s used to target High-Value Prospects.
Identifying and Prioritizing Key Prospects
First up, let’s talk about finding those high-value prospects. With the Connections Dataset, you can pinpoint companies that already have significant relationships with your existing clients or partners. This means they’re more likely to convert because there’s already some trust and relevance built in.
How to Do It:
Analyze Data: Dive into the Connections Dataset to find companies that share multiple connections with your current network.
Prioritize Prospects: Rank these companies based on the number and quality of shared connections.
Sales Outreach: Focus your efforts on these high-value prospects. Make sure to highlight the mutual connections and the benefits of joining an established network.
Example: A SaaS company finds that several of its clients are partners with a leading industry player. By targeting this player and emphasizing the mutual benefits, they can craft a top notch outreach that’s hard to ignore.
Next, let’s make your emails shine.
Personalized outreach campaigns are the way to go because they address the specific needs of each recipient. By referencing the target company’s partnerships or integrations, your emails can be way more relevant and engaging.
How to Do It:
Gather Insights: Use the Connections Dataset to get detailed insights into the target company’s partnerships and integrations.
Personalize Emails: Craft email content that references these relationships, making it super relevant.
Automate Personalization: Use AI tools to scale this personalization process, ensuring each email is tailored to the recipient’s context.
Example: An AI-powered email platform identifies a potential client’s recent partnership with an e-commerce platform like Shopify. They send a personalized email campaign highlighting success stories of similar clients who benefited from this integration. Boom -> relevance and appeal.
Warm Introductions through Mutual Connections
Finally, let’s talk about using mutual connections for warm introductions. These can significantly boost your chances of successful engagement. The Connections Dataset can help you leverage existing relationships to approach leads with more trust and credibility.
How to Do It:
Map Networks: Use the ConnectionsDataset to map out mutual connections between your company and target leads.
Request Introductions: Reach out to these mutual connections for warm introductions, explaining the mutual benefits.
Follow-Up Strategy: Develop a follow-up strategy that leverages the credibility of the mutual connection.
Example: A lead generation company finds that one of its key clients is also a partner of a high-value prospect. They request an introduction from the key client, who provides a warm referral, significantly improving engagement chances.
Utilizing AI for Enhanced Personalization & amplify the Impact with automation
AI can take your use of the ConnectionsDataset to the next level by automating the analysis and personalization processes. Here are some tips:
Automated Analysis: AI analyzes the dataset to identify patterns and insights, like high-value prospects and mutual connections.
Scale Personalization: AI personalizes email content at scale by incorporating insights from the dataset into email templates.
Predictive Analytics: AI uses historical data to predict which prospects are most likely to convert, helping prioritize efforts.
Continuous Learning: AI systems learn from campaign outcomes, refining algorithms to improve future personalization and targeting.
Example Implementation:
An AI-powered email platform integrates with the Connections Dataset, analyzing the dataset to identify key relationships and generating personalized email content. It predicts which prospects will respond positively and continuously refines its personalization algorithms.
Conclusion
Since 2019, over 170 million business connections have been detected, with business connections data available for 38,5 million websites. Last month alone, there were approximately 12 million business connections, and around 57 million over the past year. The Connections Dataset is a goldmine for lead generation companies and those using AI for personalized emails. By providing detailed insights into company relationships, it helps you target high-value prospects, create relevant and engaging email campaigns, and leverage mutual connections for credible engagements. Combined with AI, it automates these processes and achieves personalization at scale, leading to higher engagement rates and better sales outcomes.
Feel free to let us know if you or if you’d like to learn more about it. We’re here to help:)!
Artificial intelligence is changing the job market, prompting significant shifts in workforce needs across various sectors. By analyzing job postings, investment companies can gain insights into which industries are reducing their hiring for roles likely to be automated. This helps them understand potential revenue impacts and growth opportunities.
Detecting AI Adoption Trends AI tools are increasingly integrated into business functions, ranging from data analysis to customer service and legal assistance. For example, paralegals, traditionally performing research and document review, are being replaced by AI systems that can quickly and accurately handle these tasks. This trend is highlighted in Nexford University’s article “How Will Artificial Intelligence Affect Jobs 2024-2030,” which underscores the growing use of AI in roles previously performed by humans. Monitoring job postings can reveal decreases in hiring for such roles, indicating a shift towards AI-driven solutions.
Strategic Insights for Investment Investment companies must stay ahead of market changes to make informed decisions. A decline in job openings for traditional roles, such as customer service representatives or paralegals, in sectors like customer service, sales, and legal services can signal a move towards AI automation. This information is crucial for identifying industries at risk of revenue loss due to a lack of automation foresight, helping investors focus on more promising areas.
For example, companies like Google and Duolingo are already replacing human roles with AI technologies. Google has integrated AI into its customer care and ad sales processes, while Duolingo uses AI for content translation, reducing the need for human contractors.
Economic Impact of AI The economic implications of AI are substantial. A McKinsey report predicts that AI could add $13 trillion to global economic activity by 2030, primarily through labor substitution and increased innovation. However, this growth comes with job displacement. Monitoring job opening trends helps investment firms gauge which companies and sectors are reducing their workforce due to AI, identifying potential risks and opportunities.
Understanding AI adoption through job postings allows investment companies to anticipate market shifts and focus on high-growth sectors. Sectors such as AI development, advanced manufacturing, and healthcare innovation are likely to attract more investment due to their proactive adoption of AI technologies. This foresight helps investors mitigate risks and capitalize on new growth opportunities.
Additional Data from the ADP National Employment Report The ADP National Employment Report for June 2024 provides a comprehensive overview of job trends. According to the report, private employers added 150,000 jobs in June, marking a slowdown in job creation for the third straight month. “Job growth has been solid, but not broad-based. Had it not been for a rebound in hiring in leisure and hospitality, June would have been a downbeat month,” said Nela Richardson, Chief Economist at ADP (ADP Media Center).
This data underscores the importance of monitoring employment trends to understand the broader economic impact of AI and inform strategic investment decisions.
The chart titled “ADP Employment: Establishment Size Year-over-Year Percent Change” tracks the year-over-year percentage change in employment across different establishment sizes from 2011 to 2024.
Here are some key points:
Trend Analysis: The chart illustrates fluctuations in employment growth across different establishment sizes over the years. A notable drop is observed around 2020, corresponding with the COVID-19 pandemic’s impact on employment. Post-2020, there is a marked recovery, with larger establishments (500+ employees) showing a more robust recovery compared to smaller establishments.
Recent Trends: As of June 2024, the growth rates have stabilized, though smaller establishments (1-19 employees) show slower growth compared to larger establishments. This indicates that larger companies are recovering and possibly investing more in automation and AI technologies, while smaller businesses are facing more challenges.
This chart helps visualize the employment dynamics and how different-sized businesses have been affected over the years, providing valuable context for understanding the broader economic landscape and the impact of AI on employment.
For more detailed insights and statistics, the full ADP Employment Report is available here.
Conclusion
By analyzing job openings data, investment companies can gain valuable insights into AI adoption trends and their impact on various sectors. This approach helps identify industries reducing traditional roles due to AI, enabling better-informed investment decisions. Utilizing datasets like those from PredictLeads can provide the detailed, real-time insights needed to stay ahead of market shifts, mitigate risks, and seize growth opportunities in an AI-driven economy.
Job Openings Data: Since 2018, there have been 166 million job openings detected.
Data Availability: Job openings data is available for 1.6 million websites.
Recent Trends: Last month, there were 5 million job openings, and over the past year, approximately 50 million job openings were recorded globally.
Active Job Openings: Currently, there are about 7 million active job openings uncovered by PredictLeads.
These statistics underscore the vast amount of data available to track AI adoption and its effects on the job market, providing investment firms with the necessary tools to make informed decisions.
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: info@predictleads.com
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.
Conclusion:
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.
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.”
