The LinkedIn Data Driving Business Intelligence, Discovery, And Analytics For Better Decision-Making

Venture Capitalists are performing better screening, and discovery for smarter investments in early stage startups, Headhunting agencies are identifying candidates with unique skill sets, while Business Developers are better defining market opportunities. Discover how Data Sets can supercharge your business capabilities
LinkedIn datasets selling like hotcakes
Nadav Roiter - Bright Data content manager and writer
Nadav Roiter | Data Collection Expert
13-Dec-2021
Share:

In this article we will discuss:

4 lucrative LinkedIn Data use cases 

Here are four ways in which companies are currently leveraging Bright Data ready-to-use LinkedIn Datasets of publicly available data in order to save: 

  • Time
  • Money
  • Manpower
  • Cross reference queries on companies and people, which would otherwise not be possible otherwise

Company #1: Company discovery for investment 

This is a Venture Capital firm that invests in Software as a service (SaaS) companies globally. They query the LinkedIn Dataset to filter founders and developing new/early-stage companies, providing pertinent information that can help decide whether or not to invest in them. This customer leverages ‘Data Sets’ in order to cross-reference, filter, and aggregate LinkedIn company/personal profiles. They specifically look at:

  • Employee data including experience, and employment history of top leadership 
  • Changes in employment status or new positions
  • Growth or depletion in number of employees in a given company
  • Activity and brand engagement (gauging target audience interest in the product or service in question)
  • New product/feature release data

In order to analyze the current state of an entity, where it currently stands in the ‘corporate lifecycle’, and what the potential for growth might be.

Company #2: Data analytics for better decision-making

This company uses mass data analytics in order to provide their clients with strategic decision-making insights based on companies in their competitive landscape. They specifically leverage LinkedIn Data Sets in order to create strategic decisions and inform consultation projects in the areas of:

  • Marketing / Advertising -> Data Sets in this regard include finding potential customers by looking into specific industry segments and company attributes. 
  • Business development -> Data Sets include analyzing who the key decision makers are in an organization as well as who is the relevant Point of Contact for a specific type of deal.
  • Powering a boutique headhunting service for their customers -> Data Sets include collecting potential candidate ‘unique skill sets’ such as 3D modeling capabilities for architecture firms. Other things they collect include language skills, as well as people with a variety of interests/hobbies which often alludes to a more ‘well-rounded’ candidate. 

Company #3: Sales intelligence, and business development 

Offering loan products to SMBs across their market, this company needs to know in ‘relative real-time’ what loan products tier one and middle-tier banks are currently offering so they can compete with these institutions. Loan products are different in every state because of state laws and regulations. Additionally, loan:

  • Availability
  • Rates
  • Terms
  • And pricing 

may differ from one state to another. This is accomplished vis-a-vis Datasets from sites that track corporate financials, such as Owler, Crunchbase, and Glassdoor. 

They specifically leverage LinkedIn Datasets in order to gain access to:

Sales intelligence, this includes collecting information on competing entities and potent consumer audiences in order to:

  • Better define the market opportunity 
  • Fully grasp the needs, goals, and challenges that target audiences are currently experiencing on their ‘credit journey’ 
  • Pinpoint company decision-makers as well as finding the most effective way to initiate effective conversational engagement 

Company #4: Uncovering industry leaders and ‘Influencers’ 

This entity understands the importance of communities on LinkedIn. They have identified professionals that follow certain industry influencers who have become ‘informational authorities’. They leverage Data Sets in order to map target industries for their clientele enabling them to identify individuals that have meaningful clout with corporates. They then work to set up collaborations such as:

  • Co-publishing content with said individual
  • Sponsoring them to become an official ‘brand ambassador’

How LinkedIn Data is helping companies focus on their core business  

The reason why the above companies opt to use Datasets of publicly available data instead of collecting these data points in house is because Datasets enable them to use their resources in order to grow. They can then focus primarily on their core business while receiving their data from experts with know-how, experience, and the proper technology. 

  1. Data cleaning, and enrichment has already been taken care of – This means that things like duplicate values, and corrupted data files have already been removed. Data Sets are automatically correlated with information from other sites enriching them with additional layers of information so that they can be used straight away. This saves company employees time so that they can focus on operational / development tasks that carry a higher value for the company. 
  1. Leveraging top data collection hardware, and software – Collecting data in-house is accompanied by the need for developing, and maintaining complex data collection tech as well as the staff with the necessary expertise to handle day-to-day operations. This includes things like servers, Application Programming Interfaces (APIs), Networks, as well as being able to handle real-time target site operational changes, and proprietary code enhancements. 
  1. Data collection know-how – Being able to achieve full discovery of your target pages requires a lot of work, and prior knowledge. This may be anything from collecting entire/complete company profiles from online directories or collecting all comments and posts on an influencer account on a specific social media network. Whatever the use case, extensive data collection knowledge is necessary, for example well developed discovery methods based on crawling the target’s sitemap or directories, scanning all page categories, and sub categories or using semi-random URL discovery algorithms.

The bottom line 

‘Datasets’ are turning open-source web data into an affordable commodity that businesses can purchase in order to achieve quicker results utilizing fewer resources. Whether you are looking to identify: 

  • Companies that are ripe for investment
  • Suitable candidates for your headhunting agency
  • Market movers, and shakers for your marketing agency
  • Or sales intelligence for smarter business development

Datasets can provide you with a pre-collected, ready-to-use solution of public data. 

Nadav Roiter - Bright Data content manager and writer
Nadav Roiter | Data Collection Expert

Nadav Roiter is a data collection expert at Bright Data. Formerly the Marketing Manager at Subivi eCommerce CRM and Head of Digital Content at Novarize audience intelligence, he now dedicates his time to bringing businesses closer to their goals through the collection of big data.

Share:

You might also be interested in

If your company has even ONE developer dedicated to web data collection, you are wasting precious resources

The state of the economy in general, and of tech in particular, is leading many CEOs to put budget cut pressure on Information Technology execs. This article aims to help IT leaders improve their bottom lines by offering a more strategic approach to operational web data collection outsourcing

Shooting ourselves in the foot? Why we willingly killed 10% of our network

Bright Data believes in transparent and ethical practices, especially when it comes to dealing with users who make up its Residential peer network. To ensure compliance, we use advanced monitoring protocols and partner with top anti-virus companies. Sometimes, we make decisions which might seem a little crazy, like hurting our own network. That is what this post is about.
Web Data powering e-commerce

Mystery shoppers are so 2000 and late. Web data is the future of e-commerce.

We sat down with Charmagne Cruz from Shopee, the leading e-commerce platform in Southeast Asia, to discuss how the online conglomerate uses public web data to drive forward the company’s success as well as carve out a large section of the Asian e-commerce market.
Qualitative data collection methods

Qualitative data collection methods

Quantitative pertains to numbers such as competitor product fluctuations, while qualitative pertains to the ‘narrative’ such as audience social sentiment regarding a particular brand. This article explains all the key differences between the two, as well as offering tools to quickly and easily obtain target data points