Category: Company

Visiting Local Brevery Loo-Blah-Nah!🍻

Since we’re a default remote first company majority of us work most of the time from home.

That is why we have monthly informal “meetups” as we call them so that we get some much needed face time 😅.

This month we’ve visited local brevery that gets its name from Slovenia’s capital Ljubljana, pronounced “Loo-Blah-Nah” 🐉.

We’ve learned how the beer gets made and a fact or two.

One of the two facts we’ve learned: India Pale Ale beer (IPA) got its name from the British colonial era. When Brits were taking their Pale Ale beer to India it very often turned bad due to long travels. So they’ve learned to add more hops and alcohol to it to last the long journey on the ships. And that is why it’s called IPA.

How the beer gets made:

1. Malting: Barley is soaked, germinated, and dried to produce malt. Slovenia does not produce it locally so the visited brevery imports it from Germany.

Barley (slo: ječmen)

2. Mashing: Malt is mixed with hot water, converting starches to sugars, forming wort. It was explained to us this is basically “sweet water”.

3. Boiling: Wort is boiled, and hops are added for bitterness and aroma.

Hops (slo: hmelj)

4. Fermentation: Yeast is added to the cooled wort, converting sugars to alcohol and CO₂. Each of the steps till now takes place in a different stainless steel barrels. No air should get in these barrels otherwise the beer gets bad.

Interesting fact #2: Per the 500 galons (2000 liters) of beer being made some
1,000 pounds (500kg) of Barley is needed and only some 10 pounds (5kg) of hops.

5. Conditioning: Beer is aged to develop flavors. In our case the guy said it takes some 3-4 weeks for the beer to develop. 

As the beer develops our evening also developed quite nicely with every new type of beer we tried along the evening 😅. Cheers! 🍻

Thank you for having us Loo-Blah-Nah!

Partnering up with Clay :)

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”.
  1. 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).
  2. 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 :).

Cheers, PredictLeads

Introducing PredictLeads’ New Technology Detection API Endpoint

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:

Here are some Technology IDs you can use to test the API:

What You Get

When you query this endpoint, the API returns data about where the technology has been detected, including:

  • Company Information: Details about the company using the technology.
  • Subpage Detections: Specific subpages where the technology has been found.
  • Technology Details: Information about the technology, such as its name, description, and category.

Sample cURL Request

Here’s an example of how you can make a request using cURL:

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!

Boost Your Lead Generation and Email Campaigns with Connections Dataset

Hey everyone!

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:

  1. Analyze Data: Dive into the Connections Dataset to find companies that share multiple connections with your current network.
  2. Prioritize Prospects: Rank these companies based on the number and quality of shared connections.
  3. 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:

  1. Gather Insights: Use the Connections Dataset to get detailed insights into the target company’s partnerships and integrations.
  2. Personalize Emails: Craft email content that references these relationships, making it super relevant.
  3. 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:

  1. Map Networks: Use the ConnectionsDataset to map out mutual connections between your company and target leads.
  2. Request Introductions: Reach out to these mutual connections for warm introductions, explaining the mutual benefits.
  3. 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: 

  1. Automated Analysis: AI analyzes the dataset to identify patterns and insights, like high-value prospects and mutual connections.
  2. Scale Personalization: AI personalizes email content at scale by incorporating insights from the dataset into email templates.
  3. Predictive Analytics: AI uses historical data to predict which prospects are most likely to convert, helping prioritize efforts.
  4. 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:)!

PredictLeads selected as one of top 10 Teams to participate in StartupBootcamp 18′ program!

PredictLeads joins Startupbootcamp Commerce, the leading global startup accelerator based in Netherlands. We will take part in its 2018 program in Amsterdam along with 9 other international startups.

We were first selected as one of the top twenty companies out of 600+ that applied. We attended Final Startupbootcamp Selection Days on 16th and 17th of January. Went through a vigorous screening and evaluation process in front of over a hundred mentors and partners at Startupbootcamp and were finally selected as one of the top 10 teams!

PredictLeads Top 10 teams StartupBootcamp

PredictLeads will now go through an intense 3-month acceleration program of workshops, one-to-one sessions and events in Amsterdam, starting off on 19th of February and ending with the Demo Day in front of hundreds of startup and tech influencers, mentors, partners, and investors on 17th of May 2018. They will be mentored by a network of entrepreneurs, top executives, and investors, and will have access to leading commerce companies in the Netherlands to validate and scale further. We will receive free office space, €15K seed money and support from Startupbootcamp partners, such as Amazon, Cisco, Ahold Delhaize, PwC, America Today and Rabobank, as well as over €500.000 in partners service and access to a network of Angels and VCs internationally for potential further investment opportunities.

We’re really excited to be given this opportunity. During the 3 month program our main goal is to use immense SBC business network to find new clients. We’ll be focusing on B2B companies that leverage Company Intelligence data in their products or services. We want to establish ourselves as the market leader in providing actionable Company Intelligence data to other vendors. Let’s do this!

About Startupbootcamp

Founded in 2010, Startupbootcamp is an award-winning global network of industry-focused accelerator programs. With 20+ programs in Europe, Asia, North & South America, MENA & Africa, selected startups gain access to the most relevant mentors, partners, and investors in their industry.

Company Signals API

We started PredictLeads with the mission to find actionable business  data in public documents.

 

HISTORY

Categorized news articles about companies was our first offering, with which our partners were able to display highly relevant news to their customers. But quickly we saw even bigger demand for more structured business data.

The main issue was that there was no way to extract such data with our existing classification approach. For example, there was no way to know which company was acquired and which one did acquiring. Or, who was hired to which position within a company.

Although we knew the solution to the problem, we wanted to build something for the long term. Today, after 6 months of building systems, gathering training data and QA processes, we’re proud to finally launch Business Signals API.

 

FUTURE

At its core is PredictLeads Entity and Relationship Extraction System (PERES) which performs 3 steps:

  1. It starts with scanning millions of public documents every day.
    1. Public document examples: news articles, blog posts, SEC filings, job openings, PR releases, industry news etc.
  2. System then extracts 20 different types of entities from the those documents.
    1. Entity examples: organizations, persons, locations, money, job titles, business divisions etc.
  3. At the last step, it tries to identify relevant relationships between these entities.
    1. Relationship examples: acquisitions, personnel changes, investments, expansions, office closures, recognitions etc.

Well, actually, there’s a fourth step, human supervision. We know that machines are not yet fully reliable, so most of the relationships are still human approved for highest quality.

 

To get a better picture what types of data are we talking about, here are two examples of data in JSON format:

{
    "id":1656497,
    "type":"signal",
    "categories":[
      "launches"
    ],
    "title":"BMW launches ReachNow car-sharing service on Sep 19th 16'.",
    "body":"BMW will launch its ReachNow car-sharing service in Portland on Sept. 19, the car-maker announced today.",
    "domain":"bmw.com",       "url":"http://www.geekwire.com/2016/bmw-unveils-details-reachnow-car-sharing-launch-portland-next-month/",
    "found_at":"2016-08-15T02:00:00.000+02:00",
    "removed_at":null,
    "data":{
      "location":"Portland",
      "product":"ReachNow car-sharing service",
      "date":"2016-09-19T02:00:00.000+02:00",
      "organizations":[
        "bmw.com"
      ]
    }
  }

 

Or

 

{
    "id":1915491,
    "type":"signal",
    "categories":[
      "invests_into_assets"
    ],
    "title":"Domino Printing Sciences Plc invests into assets : factory on Aug 18th 16'.",
    "body":"Cambridge-headquartered commercial inkjet printing business Domino Printing Sciences has opened its new £19m factory in China.",
    "domain":"domino-printing.com",
    "url":"http://www.insidermedia.com/insider/central-and-east/domino-printing-sciences-opens-chinese-factory",
    "found_at":"2016-08-18T09:44:26.000+02:00",
    "removed_at":null,
    "data":{
      "location":"China",
      "amount":"25332000",
      "assets":"factory",
      "date":"2016-08-18T09:44:26.000+02:00",
      "organizations":[
        "domino-printing.com"
      ]
    }
  }

Above are 2 of 30+ possible types of signals. Full list is available here: https://predictleads.com/docs/#signal

 

APPLICATIONS

Types of applications are (almost) endless, but I will list a few we had in mind:

  1. Sales Enablement Solutions: empower customers to know everything about their prospects’ business without Googling around.
  2. Predictive Lead Scoring: high quality buying signals should be core data for predictive models
  3. Account Based Marketing or Sales: enable customers to generate automated highly personalized and relevant messages to their existing or potential customers.

 

Contact us for your API key or to find out more.

sales@predictleads.com

Rowing upriver

So we’ve been developing our solution for some 6 months now and are finally ready to open it up for you to test it. You’ll be able to register on November the 1st!

Simply upload a file with your leads (domain, website or email address) and we’ll do our best to automatically find you fresh relevant triggers. Identifying opportunities that are out there right now should become a breeze. You’ll know exactly when to reach out and build better relationships with your potential and existing clients because of it.

We hope you’ll find it helpful!

Product

We go through millions of websites searching for information about your clients to find triggers that matter most to you. Finding triggers signalling that a prospect is now more likely to buy or upgrade your service. These are events such as leadership changes, received funding, new technology installs, new products offered, new valuable PR mentions, newly opened job positions etc.

With the aforementioned info at hand you can simply filter out the best 50-100 prospects out of thousands and contact them now.

We use machine learning, information extraction, natural language processing, text analysis and stream mining. We’re now working closely with our first customers to create a product that will bring value. You’d like a test drive? Shoot us an email at roq@predictleads.com and we’ll do our best to help you reach out at the right time to the right prospects.

Keep up the good work!

Our mission

There’s so many startups coming out each and every day. More and more people harnessing the power of web. With time the trend will just accelerate.

When products find their product-market fit and show their value to the world our goal is to help them find the audience they deserve.

Our aim is to connect the top-notch tools with companies that will greatly benefit from using them.

To find the right companies for your products on which you work so hard we use machine learning algorithms on big publicly available data.

Since being best in one thing is the way to go when faced with multitudes of competition we’re now solving just one part of connecting you with companies.

It’s following up. It’s making that awkward contact a bit less awkward. We find you reasons why now is the time to reach out to your prospect. We find you things to say when you make a call to your long lost touch with a prospect. We first need to ignite the conversation. So we bring you the icebreaker.

More on how we do that in the following post …

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