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How To Get The Contact Details Of Your 2nd And 3rd Connections on LinkedIn In Two Easy Steps
There’s no such thing as too many leads. Even if you have a rich LinkedIn network of sales leads already, we will show you how to easily harness your 2nd and 3rd connections.
LinkedIn won’t give you access to their personal data, but Linked Helper and Snov.io are here to solve this issue and help you generate more of those precious leads.
The process will be split up into two stages, first — using Linked Helper, second — using Snov.io.
Install Linked Helper plug-in from the Chrome Web Store then, click on the menu icon and a dropdown menu will appear. From the list choose Profile Extractor and create a new list with a new name. Navigate to LinkedIn’s search bar, choose people and set up your filter options by clicking on Connections and checking 2nd, 3rd checkboxes in the dropdown menu. Click Collect in the Linked Helper box and wait.
Linked Helper will collect all the email addresses page by page; you can stop this process by clicking on the same button again. Next, go to the Extract tab and click Extract collected profiles. The tool will visit each profile page and collect info about it. When its finished, click the Export tab and choose which format (either Google Sheets or Microsoft Excel) you want your contacts to be exported in, finish by clicking Download CSV file.
You will get a file with a bunch of columns, but don’t freak out. Find the Organization URL column and copy all of its content. Go back to Linked Helper and choose Companies extractor in the menu, again create a new list. Click View Collected followed by Upload Companies URLs, paste in all the urls you copied and click Import. Go to Extract and click Start Extraction. After the task is complete, download a new file again.
Now, prepare your file for the next step by clearing all columns, besides first name, last name and domain, added from the second file. Don’t forget to maintain this order:
- A column — first name
- B column — last name
- C column — company domain
Note: Make sure the domain order from the second file matches the name order in the first file
Go to the Snov.io account page and click on the Tools tab. In the dropdown menu choose the Emails From Names tool.
You will see a box asking for a file. Click on Choose File, choose the file containing your data from the previous step, and press Upload. Click Open List and you will be redirected to your main account page with the list you’ve uploaded. You can follow the whole process as Snov.io looks for the necessary emails.
On top of finding email addresses, Snov.io will verify every email address it finds and mark it with the respective indicator:
- not found — white
- valid — green
- potentially valid — yellow
- invalid — red
After its done click on the export button and in the pop up window choose the output format that suits your requirements — CSV, XLSX, or Google Sheets.
Decide which columns you want to extract:
- Email (address and validity)
- Name (first, last and full name)
- Company (name, url, social profile, size, locality)
- Location (country, state, city)
- User Social Profile
- User Locality
- Add date
And that’s it, you have a comprehensive list of enriched email leads, valid and ready to be used.
Thank you for reading, we hope this article helped you. May your leads always be hot and good luck!
The first phase of SNOVIAN.SPACE is here!
This is what you all have been waiting for.
Today we are proud to announce the launch of phase 1.0 of Snovian.Space
All of us at Snov.io have been working hard to perfect the platform, and we are pleased with what it came to be. You can now register and be one of the first people to explore the decentralized social networking platform that is Snovian.Space.
Phase one allows registrations and profile confirmation which helps build a user base. Next, we will enter Phase 2 adding metamask integration, messaging and airdrops — think of a major social network where you can easily find your audience and pitch your message simply by rewarding your listeners with crypto. No hidden payments to the platform and no paid plans!
We will soon start reaching out to all the people registered for the airdrop — the first Snovians who participated in the early bird program. The support of these 50k people reminded us what we were capable of and helped us along the way.
Soon we will be holding an AMA webinar to answer all of your questions regarding the platform. Make sure to check out the roadmap for Snovian.Space on the website later today, if you’d like to stay up-to-date on our progress.
Thank you, Snov.io Team
Data Mining Tools: The What, The Why And The How
With Big Data becoming more prevalent than ever, the demand for mining tools is growing. It’s becoming vital to know exactly what tools are capable of successfully dealing with huge amounts of data. In this article we will discuss the complex prospecting algorithms and data visualization libraries that will be your primary tools in building your lead generation platform.
Before we dive deeper into the details, first we need a clear vision of how a very large amount of data transforms from a huge amount of unorganized information into an organized and structured set of lists, ready to be used by sales, marketers or even HRs.
Common data processing looks like this:
- Find a lead data source. This is the primary place from where all your data will be mined. This can be a popular social media platform like Facebook, LinkedIn and Twitter. Now we have bulk data, most of which is no use to us.
- Target the relevant data. Here we define the targeted data type and source, suited for our purposes. We can have multiple associative data types, as well as several sub-sources to extract data from.
- Preprocess raw data for future processing. This part of the data mining process involves altering the data from a raw format into one that’s acceptable for further interactions.
- Convert preprocessed data into a readable format. Your original data language will be determined and transformed into one your system is able to process.
- Create Data Patterns/Models. Based on the data you have, you can determine common relationships between the subtypes of data and identify patterns, or create sets of tables connected by data relationships.
With the relational data patterns identified, we are able to build all sorts of meaningful infographics and visualize them using third party services or libraries. These third party solutions don’t have a high learning curve, however analyzing the libraries directly would require the assistance of a developer who is familiar with the languages used in any given library. Here you can see the list of the most commonly used 3rd party tools for data visualization:
- Tableau (big data tool for corporate use)
- Infogram (simple tool for big data)
- Datawrapper (data tool for journalists and news publishers)
- Google Charts (user friendly library based on HTML5 and SVG for Android, iOS and browsers)
With these tools we can create infographics that will show all the data we need for our sales and marketing departments to create a successful marketing campaign. Moreover, collected data can be used for outreach to potential prospects. Lead generation cannot exist without a solid data foundation. If you want to generate leads – generate data.
So, what is data mining, why do we need it and how can we use it to generate enriched lead data? Let’s explore, starting with what data mining actually is.
What is data mining?
Data mining is the process of analyzing bulk data to find new unknown patterns and hidden correlations. With data mining enterprises we can use these models and patterns to generate quality leads.
Data mining was created to work on the following tasks:
- Predict. Have an ability to foresee undefined or future values in one or another feature of your data.
- Descriptive. Make your data understandably organized through user friendly patterns and models.
Within these tasks are several techniques essential to the data mining process that can’t be neglected:
- Association. Data is being generated by analyzing the association between items in a given data set. This technique is often used by sales to determine which products customers buy together.
- Clustering. Here data is treated like an object which is stored in automatically defined classes. To make it clearer, data is kept in clusters, with particular similarities between them.
- Classification. This technique breaks data into relative classes and groups. With it you can classify leads into separate groups, like who is more likely to become your sales lead or who has no potential whatsoever.
- Regression. Used to predict a range of numeric values in a precise data object. With regression you can predict the flow of leads to your platform.
It’s important to know about these techniques, even if you don’t know how to properly use them. This is where the data mining tools come in handy for performing the analyses of your data. These tools have different features and ways of implementing them.
Some of them are more complex and take significantly more time to implement. It all boils down to the goals you are trying to achieve. You might ask, if it’s so complex, why should I care? Well, let’s jump into the next section and explore why.
Why are data mining tools so useful?
Data is the oil of the 21st century, and oil equals money. Data mining tools will help you generate more revenue by creating informational assets, used both by sales and marketing departments. They can study the behavior of your clients, their location, position and create solid marketing strategies.
Enterprises thrive on the features of data mining tools, with them they can get detailed business intel, plan their business decisions and cut costs drastically. They can also help you detect anomalies inside your models and patterns to prevent your system from being exploited by third persons.
With all those features on board, you won’t need to implement complex algorithms from the ground up. Moreover, you can adjust those features with some additional tweaking to the code base (if it’s an open source tool), as your demands grow.
Overall, data mining tools were created to define and achieve numerous objectives, helping you generate more profit in the end. Now you see why these tools are genuinely useful. Let’s end this with the last but not least important question – how.
How can we implement them?
Different tools require different approaches. Some require zero to no coding experience, others would most likely demand some programming skills depending on the coding used. These tools are generally open-source and don’t have any paid plans.
Here is a list of the most commonly used data mining tools. Starting from entry level to enterprise grade businesses:
It’s an open source ready to use tool that requires no programming knowledge, with numerous features for data analytics. Thanks to built-in template frameworks, this tool speeds up the work of the data miner and cuts the amount of errors during the runtime. This tool is written in Java and has multiple mining options like pre-processing, converting and prediction techniques. It can be used with other tools like WEKA and R-tool to give models written in the code of those two. Existing patterns, models and algorithms can be enhanced by the following programming languages:
- R – a programming language used for data mining, extraction, exploration and analytical tasks;
- Python – a programming language used for rapid prototyping of software solutions.
They are well suited for rapid prototyping and data manipulation.
RapidMiner has all the data analysis features from the simplest to the most advanced ones. With plugins from Rapidminer Marketplace, they extend the already vast functionality. Moreover, developers and data analysts can use the marketplace for publishing their plugins or algorithms.
WEKA contains a selection of algorithms, visualization tools for machine learning and data analytics. You can use this tool directly on your sets of data. With WEKA you can perform numerous data tasks, regression, clustering, classification, visualization and data processing. The main advantages of this software are:
- Completely free
- Portable, can be used on multiple platforms
- Compilation of numerous machine learning and data mining algorithms
- Compelling user experience with graphical user interface
Besides, this tool can be used for creating various machine learning schemes.
Orange is a Python library with a component-based structure for machine learning, data mining, analysis and visualization. These components are also called widgets, they help not only with simple tasks like data preprocessing and visualization, but also with creating complex algorithms and prediction models.
Orange has visual programming implemented into it for creating a solid workflow by linking user-made widgets. It can also be used as a Python library to change widgets and manipulate data.
R is both a free programming language and an environment for manipulating data and statistical computing. Thanks to the numerous packages R is commonly used for data mining and creating statistics by data scientists and analysts. These packages include community-created libraries for data manipulation.
What we’ve learned
Data mining tools are an essential part of enriching your leads. With these tools at your disposal, you can create patterns based on the user’s behavior and apply it to your marketing strategies. These patterns can also be used to enrich your leads with new data. There are various techniques to describe data by associations or split it into separate clusters, to predict the changes in data by classifying it or using regression.
Overall, data mining tools help us enrich our leads and make our lead generation campaigns more successful.