LinkedIn Archives - Business Intelligence Info

Tag Archives: LinkedIn

New LinkedIn Sales Insights Coming in 2021

December 19, 2020 Microsoft Dynamics CRM

With so much going on in the news these days (hello COVID vaccines!), you may have missed LinkedIn’s recent announcement on a pretty major upgrade to its portfolio. Come this February, the online platform is launching LinkedIn Sales Insights (LSI) to help users better identify opportunities and gain a more comprehensive understanding of the market.

In her LinkedIn Sales Blog post earlier this month, LinkedIn Vice President of Product Management Lindsey Edwards said LSI is poised to deliver “clear visibility into the size and fast-growing nature of specific departments, functions, and accounts” and give users enhanced capabilities in accurately planning their sales strategies.

Data is the foundation of every great sales process. And LinkedIn, quite frankly, is loaded with a vast amount of mostly up-to-date data points on hundreds-of-millions of global members that, in my opinion, has been underleveraged up until now.

DATA IS THE FOUNDATION TO EVERY SUCCESSFUL SALES PROCESS

By leveraging LinkedIn Sales Insights, Sales Ops leaders, high-performance sales teams and Lead Sales Dogs benefit from clean, reliable data and will better be able to:

Segment accounts more effectively
Leverage the data for smarter sales planning
Identify whitespace within a target market
Gain better understanding on where to focus relationship-building efforts
Feed CRM with real-time data from LinkedIn

The LinkedIn Sales Insights platform offers truly enhanced insight into the size and growth of targeted departments, functions, and accounts, giving users a fast track to a more streamlined sales strategy and, ultimately, a boost in revenue growth.

With the changing roles in this new business and employment paradigm, it is more important than ever to have access to the real-time, accurate data that supports your sales initiatives. I applaud LinkedIn for stepping up to the plate and taking a good hard swing at better sales books and a stronger data game.

While its true functionality is yet to be seen, I look forward to LinkedIn’s LSI launch in just a few weeks and the profound impact it may have on good, clean data and enhanced sales processes.

Click here to watch a short LSI video on LinkedIn.

By Christopher Smith, Founder & CEO of Empellor CRM.

Let’s block ads! (Why?)

CRM Software Blog | Dynamics 365

LinkedIn open-sources Dagli, a machine learning library for Java

November 11, 2020 Big Data

LinkedIn today open-sourced Dagli, a machine learning library for Java (and other JVM languages) that ostensibly makes it easier to write bug-resistant, readable, modifiable, maintainable, and deployable model pipelines without incurring technical debt.

While machine learning maturity in the enterprise is generally increasing, the majority of companies (50%) spend between 8 and 90 days deploying a single machine learning model (with 18% taking longer than 90 days), a 2019 survey from Algorithmia found. Most peg the blame on failure to scale, followed by model reproducibility challenges, a lack of executive buy-in, and poor tooling.

With Dagli, the model pipeline is defined as a directed acyclic graph, a graph consisting of vertices and edges with each edge directed from one vertex to another for training and inference. The Dagli environment provides pipeline definitions, static typing, near-ubiquitous immutability, and other features preventing the large majority of potential logic errors.

“Models are typically part of an integrated pipeline … and constructing, training, and deploying these pipelines to production remains more cumbersome than it should be,” LinkedIn natural language processing research scientist Jeff Pasternack wrote in a blog post. “Duplicated or extraneous work is often required to accommodate both training and inference, engendering brittle ‘glue’ code that complicates future evolution and maintenance of the model.”

Dagli works on servers, Hadoop, command-line interfaces, IDEs, and other typical JVM contexts. Plenty of pipeline components are ready to use right out of the box, including neural networks, logistic regression, gradient boosted decision trees, FastText, cross-validation, cross-training, feature selection, data readers, evaluation, and feature transformations.

For experienced data scientists, Dagli offers a path to performant, production-ready AI models maintainable and extensible in the long term that can leverage an existing JVM technology stack. For software engineers with less experience, Dagli provides an API that can be used with a JVM language and tooling that’s designed to avoid typical logic bugs.

“With Dagli, we hope to make efficient, production-ready models easier to write, revise, and deploy, avoiding the technical debt and long-term maintenance challenges that so often accompany them,” Pasternack continued. “Dagli takes full advantage of modern, highly multicore processors and … powerful graphics cards for effective single-machine training of real-world models.”

The release of Dagli comes after LinkedIn made available the LinkedIn Fairness Toolkit (LiFT), an open source software library designed to enable the measurement of fairness in AI and machine learning workflows. Prior to LiFT, LinkedIn debuted DeText, an open source framework for natural language process-related ranking, classification, and language generation tasks that leverages semantic matching, using deep neural networks to understand member intents in search and recommender systems.

How startups are scaling communication:

The pandemic is making startups take a close look at ramping up their communication solutions. Learn how

Let’s block ads! (Why?)

Big Data – VentureBeat

LinkedIn: AI hiring is slowing down due to the pandemic

June 16, 2020 Big Data

AI jobs growth is slowing as a result of the pandemic. That’s according to a newly published report compiled by LinkedIn’s Economic Graph Research Insights team, which looked at the health crisis’ implications for the most impacted sectors.

LinkedIn’s analysis, which covered a post-COVID-19 period from March 16 to May 24 and a pre-COVID-19 period from January 6 to March 15, found that the virus dampened the demand for AI talent from employers as well as enthusiasm from job applicants. During the 10 weeks immediately following mid-March, growth rates in both slowed down. Listings for AI roles dropped to only 4.6% year-over-year compared with 14% prior to the outbreak, while applications — which were growing at 50.8% year-over-year — dipped to 30.2% post-COVID-19.

In a bright spot of news, LinkedIn found that AI jobs posted directly on its platform increased 8.3% during the 10 weeks after the COVID-19 outbreak in the U.S., when normalized against overall job postings. However, it also observed a 14.1% decline in AI applications during that same timeframe, suggesting candidates might be playing it safe during the uncertainty.

“AI specialist hiring is slowing … but that doesn’t mean that AI adoption is slowing down,” the report’s authors wrote. “The period we looked at is a special period when a large part of the country was under lockdown, business was interrupted, and many companies were struggling to move their workforce home including the recruiting process. It is not terribly surprising that we would observe a significant slowdown in AI specialist recruiting similar to the overall slowdown in the hiring demand. However, our data also shows that job applications are slowing down more than listings. It remains to be seen whether workers choose to play it safe in today’s economy or make a long-term bet on a career in AI.”

VB Transform 2020 Online – July 15-17. Join leading AI executives: Register for the free livestream.

LinkedIn’s findings agree with a survey conducted by IDC, which estimates the number of AI jobs globally could increase by as much as 16% this year (reaching 969,000), driven by stronger demand for AI workers as companies contend with the impact of the pandemic. (The firm’s worst-case scenario is 11% growth.) Moreover, IDC expects AI spending in 2020 to reach a high-end estimate of $ 50.7 billion, up 32% from last year.

Contrary to what layoffs in recent weeks by DataRobot, Textio, Yonder, Kodiak Robotics, Zoox, Cruise, and other AI and machine learning startups would suggest, the coronavirus has forced tech giants to increasingly rely on automation day-to-day. YouTube has begun to lean more on AI to moderate videos during the pandemic, since many of its human reviewers remain at home to limit the virus’ spread. For similar reasons, Facebook and Twitter have expanded the use of their automated tools to identify offensive material — with varying degrees of success.

It’s only logical that an uptick in demand for AI jobs would follow; specialists are no doubt needed to validate, maintain, and further develop the algorithms underpinning these systems. “AI is something that is … being delivered as a service or incorporated into the day-to-day work of people in other roles, like that of data scientists,” the LinkedIn report’s coauthors wrote. “[It] may become a tool in the toolbox for solving business problems, with expertise spread throughout people in many different roles at a company.”

Let’s block ads! (Why?)

Big Data – VentureBeat

LinkedIn is using AI to spot and remove inappropriate user accounts

January 17, 2020 Big Data

Social networks including Facebook, Twitter, and Pinterest tap AI and machine learning systems to detect and remove abusive content, as does LinkedIn. The Microsoft-owned platform — which has over 660 million users, 303 million of whom are active monthly — today detailed its approach to handling profiles containing inappropriate content, which ranges from profanity to advertisements for illegal services.

As software engineer Daniel Gorham explained in a blog post, LinkedIn initially relied on a block list — a set of human-curated words and phrases that ran afoul of its Terms of Service and Community Guidelines — to identify and remove potentially fraudulent accounts. However, maintaining it required a significant amount of engineering effort, and the list tended to handle context rather poorly. (For instance, while the word “escort” was sometimes associated with prostitution, it was also used in contexts like a “security escort” or “medical escort.”)

This motivated LinkedIn to adopt a machine learning approach involving a convolutional neural network — a class of algorithm commonly applied to imagery analysis — trained on public member profile content. The content in question contained accounts labeled as either “inappropriate” or “appropriate,” where the former comprised accounts removed due to inappropriate content as spotted using the block list and a manual review. Gorham notes that only a “very small” portion of accounts have every been restricted in this way, which necessitated downsampling from the entire LinkedIn member base to obtain the “appropriate” labeled accounts and prevent algorithmic bias.

To further tamp down on bias, LinkedIn identified problematic words responsible for high levels of false positives and sampled appropriate accounts from the member base containing these words. The accounts were then manually labeled and added to the training set, after which the model was trained and deployed in production.

Gorham says the abusive account detector scores new accounts daily, and that it was run on the existing member base to identify old accounts containing inappropriate content. Going forward, LinkedIn intends to use Microsoft translation services to ensure consistent performance across all languages, and to refine and expand the training set to increase the scope of content it is able to identify with the model.

“Detecting and preventing abuse on LinkedIn is an ongoing effort requiring extensive collaboration between multiple teams,” wrote Gorham. “Finding and removing profiles with inappropriate content in an effective, scalable manner is one way we’re constantly working to provide a safe and professional platform.”

LinkedIn’s uses of AI extend beyond abusive content detection. In October 2019, it pulled back the curtains on a model that automatically generates text descriptions for images uploaded to LinkedIn, achieved using Microsoft’s Cognitive Services platform and a unique LinkedIn-derived data set. Separately, its Recommended Candidates feature learns the hiring criteria for a given role and automatically surfaces relevant candidates in a dedicated tab. And its AI-driven search engine leverages data such as the kinds of things people post on their profiles and the searches that candidates perform to produce predictions for best-fit jobs and job seekers.

Let’s block ads! (Why?)

Big Data – VentureBeat

Analyze LinkedIn Sales Navigator usage data in Power BI

November 14, 2019 Self-Service BI

Power BI recently announced the GA of Power BI template apps, allowing Power BI users to gain immediate insights through prepackaged dashboards and reports that are created from a live data source without the need to connect to, model, or visualize the data.

The LinkedIn Sales Navigator Analytics Integration allows teams to seamlessly view and analyze Sales Navigator usage data within Power BI to increase sales effectiveness and maximize productivity.

The LinkedIn Sales Navigator for Sales Operations enables sales operations personnel to identify and analyze things like:

Who is using LinkedIn Sales Navigator
When the best day is to send an InMail
The Social Selling Index across all users

Using the insights found in LinkedIn Sales Navigator for Sales Operations template app, sales operations professionals can:

Uncover insights faster – Derive valuable insights faster by analyzing and visualizing your LinkedIn Sales Navigator usage data in Power BI within minutes
Run deeper analyses – Perform deeper analyses and run comprehensive reports by merging other data sources (e.g. HR, CRM, sales acceleration) into one single view
Optimize sales performance – Measure relationship-building activities and improve team and rep performance by identifying areas of opportunities for training or coaching

The LinkedIn Sales Navigator for Sales Operations template app also allows users to see Sales Navigator data next to data from other sources by pinning them to a common dashboard. For those users who want to mashup Sales Navigator product usage data with data from other sources, a new Sales Navigator connector is also available in Power BI Desktop.

In order to use this template app, you must:

Have a LinkedIn Sales Navigator Enterprise plan
Be an Admin or a Reporting User on the Sales Navigator Contract (Note: A reporting license on the Sales Navigator contract does not take up a seat on that contract)
Be a Power BI user

The LinkedIn Sales Navigator for Sales Operations template app is available for download from AppSource.

You can find out more about how to use the template app at https://aka.ms/pbilisn

To view the full set of LinkedIn Sales Navigator announcements, visit their website here.

Let’s block ads! (Why?)

Microsoft Power BI Blog | Microsoft Power BI

5 Killer Tips for Creating LinkedIn Posts that Hook Readers

March 14, 2018 CRM News and Info

Type any text into a Word document and spell check the text. Once finished, the tool will display “readability statistics.” At the bottom of this box under the “readability” subheading, it will include a Flesch-Kincaid reading ease score and the grade level. The higher the reading ease score, the simpler the content is for your LinkedIn readers to understand.

Create a length that is optimal for the platform.

Creating an amazing LinkedIn post also involves understanding how much content your audience wants to read. In the past, marketers believed that shorter was better, but today experts find this is not always true. If you write amazing content that is truly valuable, not only will the audience read it, they will read it even if it’s very long.

An article published by the Content Marketing Institute found that short content, with word counts of 1,000 or less, dominate LinkedIn. But surprisingly, this is not the content that readers want most. Posts with 1,000 to 3,000 words get the most shares. Check this out:

Up to 1,000 words: Average shares of 6,439.

Medium content of 1,000 to 2,000 words: Average shares of 7,771.

Long content of 2,000 to 3,000 words: Average shares of 8,702.

The bottom line? If you want to hook LinkedIn readers, you must publish long-form, high-quality content to capture their attention and inspire them to share it with their peers.

Deepen the relationship with a strong CTA.

Creating great content that draws readers in and delivers the information they crave is just the beginning. Once they read the material and say, “Wow, that was really amazing,” readers want more. Sadly, many great LinkedIn articles stop short of asking readers to take that next big step: the call to action.

Creating an effective CTA starts with a goal. After reading your content, what do you want readers to do next? Maybe your goal is to entice them into viewing more content, in which case you might include links to related content on your blog, where you can continue building that relationship.

Or perhaps your goal is to capture the prospects’ email addresses so you can launch a nurturing campaign, in which you might encourage them to download a road map or guide to solving whatever pain point the content addresses. For example, you might say, “To get our free white paper on the three most common mistakes people make when doing X and how to solve them, click here.”

Every piece of content that you publish on LinkedIn should be part of an overarching strategy, and the CTA should be carefully designed to support and meet those goals.

Keep readers engaged with the perfect visual count.

Here’s a well-kept secret about creating content on LinkedIn: It’s not all about the words. How? Research shows that visuals are very important with all content, but especially on LinkedIn. One large study of LinkedIn posts found that LinkedIn content with images received a greater number of shares, likes, comments and views.

On average, a LinkedIn post with zero images receives about 6,413 views. However, when eight images are included, this number jumps up to 57,575 views — a sizable increase!

Let’s block ads! (Why?)

Act-On Blog

10 Things about LinkedIn Sales Navigator a CRM Admin Needs to Know

March 13, 2018 Microsoft Dynamics CRM

In this blog, we will review the 10 things a CRM Admin needs to know about LinkedIn Sales Navigator and walk through some settings.

If you would like to learn more about the LinkedIn Sales widget, please check out this link: LinkedIn for Microsoft Dynamics CRM Widget – Overview. We recommend that you review the Microsoft Dynamics 2016 and Office 365 Installation Guide as well. If you are unfamiliar with the LinkedIn CRM Sales Widget, below is a very brief illustration because we are going to talk about Sales Navigator today!

The LinkedIn for Microsoft Dynamics 365 Widget works with the Accounts, Leads, Contacts and Opportunity entities. The widget allows a user to see a LinkedIn Member Profile and Company Profile as sections on the entity form. How does it work? Well, while creating a Lead, the widget lets the user search LinkedIn.com directly from the Entity form and users can view information from LinkedIn.com about the Lead’s LinkedIn Member Profile. Below are a couple of screen shots.

If you want to know more about the LinkedIn Sales Widget, please see our video Dynamics 365 In Focus: LinkedIn Sales Navigator and read our blog How Microsoft’s Acquisition of LinkedIn is Revolutionizing the Sales Game for Dynamics 365 Users.

So, what is the Sales Navigator CRM sync about for a CRM Admin?

The Sales Navigator Admin CRM sync allows CRM Admins to:

Connect to an Online D365 Organization
Auto sync all seat holders in CRM
Set a Business Process Stage where Accounts and Leads from CRM will be imported to Sales Navigator
Allow Sales Navigator to size and group a won Opportunity
Enable Sales Navigator data to sync back to your CRM
Sales Navigator data items as Activities in CRM
CRM Data sync Statistics
Copy InMail Messages to CRM

Now let’s break down the Sales Navigator CRM Settings!

Sales Navigator Administration Settings

1. System Requirements:

Microsoft Dynamics Administrator User with CRM Admin security role
CRM Instances: Dynamics 2016 online or on premise and/or Dynamics 365 Online
Integration user account for CRM sync
LinkedIn Sales Navigator Enterprise Edition
Assumption that the LinkedIn Sales widget is successfully installed

2. Connect to CRM and then enter subdomain for the Dynamics 365 online instance, this will be followed by a request for credentials. The recommendation is to use a service account for these credentials. The reason is that when LinkedIn Activities (InMail, Messages etc.) are copied to Dynamics 365 these LinkedIn Activities are “Completed by” the CRM Admin for the Sales Navigator account versus the record Owner.

3. Confirmation of Connection to Dynamics 365 and the date of the last sync with Dynamics 365.

Below are the other configurable settings:

4. Auto Sync CRM allows Accounts and Leads to be automatically imported to Sales Navigator

5. Use Business Process Stage to import Accounts and Leads into Sales Navigator. The drop-down menu allows for five optionset values. The first is “Not Sure” the remaining four values are the Dynamic Out-of-the-Box business process stage names, 1-Qualify, 2- Develop, 3-Propose, 4-Close.

6. Storing the value for a won Opportunity allows Sales Navigator to size and identify the profile of deals. There are two available values for this option set, “Not Sure” and “Amount”

7. Sales Navigator syncs back to CRM. These data items become Activities in CRM. Below is a sample InMail message created in Sales Navigator with the copy to CRM feature enabled. The third view is this same message showing as an Activity item in Dynamics 365, how cool!

8. Sales Navigator data items as Activities in CRM. Users can create LinkedIn messages while working in Dynamics 365.

9. Sales Navigator Sync Statistics can be viewed by clicking on the View details link of the connected Dynamics instance. The table of CRM Data Sync Statistics shows stats for records the Accounts, Contacts and Leads entities.

10. The Admin sub menu item “Seat Management” allows enabling of the CRM Sync feature for a Sales Navigator Seat (aka Dynamics 366 User).

Pretty great stuff, huh? We hope this has provided relevant information about the Linkedin Sales Navigator from the CRM Administrator’s perspective. In review, we connected and configured Sales Navigator settings, showed the sync with CRM Activities, the CRM Data Sync Statistics and enabling CRM sync at the seat level. For more information about the LinkedIn Dynamics Sales Navigator see these tutorials.

Happy Dynamics 365’ing!

Let’s block ads! (Why?)

PowerObjects- Bringing Focus to Dynamics CRM

LinkedIn B2B Marketing Strategy: 5 Powerful Reasons to Why

February 26, 2018 CRM News and Info

At the top of the page, you will see several tabs. Click on the one that says “Groups.” In this example, the technology marketing keywords generate 1,622 groups. Start by finding a few to test and requesting permission to join.

Once you’ve joined, leave comments in existing conversations and focus on adding high value. In addition, it’s very powerful to start posting high-quality content to groups. There are more than 130,000 posts published weekly on LinkedIn Pulse, but only a fraction of this content is posted to groups. Plus, the credibility of this platform is high, with 71 percent of users reporting they feel that LinkedIn is a credible source for professional content.

Create a plan to integrate LinkedIn into your existing content marketing strategy. For example, you might create a blog post on a trending topic and publish it to the company blog. Take that content and also post it to LinkedIn groups and share it through LinkedIn updates to generate maximum exposure.

Key takeaway: LinkedIn groups are a powerful way to get in front of specific target markets and contribute in a meaningful way. Join groups, post content, and contribute in an authentic way to generate results.

It’s a hidden opportunity to drive greater traffic.

One great benefit of LinkedIn is that you can use it to generate not only leads but also traffic for your company’s website. How? It starts with creating great content that’s not only interesting but also truly amazing ― the kind that leaves people wanting to read more about what your company does.

For example, let’s say that you publish a post to LinkedIn Pulse. At the end of that article, you can link to other popular pieces of your content. Review analytics to discover what pieces of content prospects like best, and then link to this content to drive prospects to your website and create a steady flow of traffic.

Make sure that once prospects finish reading the content, they know what to do next so you don’t lose them forever. Create strategic lead magnets to entice prospects to provide their email addresses. This could be a high-value white paper that addresses a specific pain point or some other piece of amazing content. Once you collect emails, you can start nurturing, providing value, and moving those contacts closer to the sale.

Key takeaway: Drive more traffic from LinkedIn to your website by strategically linking to related content at the bottom of your posts. Then create a plan for how to move those visitors into a nurturing strategy.

You have the ability to tap into the genius of thought leaders.

LinkedIn recently implemented a new feature that allows users to identify and learn from top influencers in specific industries.

Let’s block ads! (Why?)

Act-On Blog

D365 In Focus: LinkedIn Sales Navigator and Dynamics 365 Integration [VIDEO]

February 23, 2018 Microsoft Dynamics CRM

In today’s Dynamics 365 In Focus, our Director of Solution Design talks about the LinkedIn Sales Navigator and how it integrates with Dynamics 365. LinkedIn Sales Navigator is a tool that gives your sellers the ability to tap into their social networks, as well as the social networks of their team members to find new leads and prospects. Learn more about it in our Dynamics 365 In Focus video:

Let’s block ads! (Why?)

PowerObjects- Bringing Focus to Dynamics CRM

LinkedIn open-sources Dynamometer for Hadoop performance testing at scale

February 11, 2018 Big Data

LinkedIn today released an open source project called Dynamometer to help businesses stress-test large-scale Hadoop big data processing systems without using a massive amount of infrastructure.

The tool is designed to prevent an issue that the enterprise social network encountered in early 2015 when the company added 500 machines to its Hadoop Distributed Filesystem (HDFS) cluster in an attempt to improve performance. Instead, the team ran into a bug that only showed up at large scale and that caused jobs targeting the cluster to time out.

Dynamometer, which is named after a tool used to test cars, simulates large-scale clusters while only requiring roughly 5 percent of the actual underlying infrastructure. That helps developers get around one of the key issues with testing software at scale: Actually provisioning all of the machines can be costly, even in a public cloud environment.

Instead, customers can use Dynamometer to test the same sorts of workloads they see in production and ensure that the system will stand up to software changes. LinkedIn used the tool to analyze migration of the company’s HDFS clusters from Hadoop 2.3 to 2.6, a change that required adjusting certain parameters of the clusters in order to avoid performance issues.

Erik Krogen, a lead engineer on Dynamometer, told VentureBeat in an email that the tool is meant both for companies working with Hadoop at large scale, like LinkedIn, and smaller shops that are proposing changes to the HDFS open source project and want to make sure they won’t affect performance at scale.

In the long run, Krogen hopes that Dynamometer will become part of release testing for HDFS, as well as regular continuous integration of new code changes between releases. That’s why LinkedIn released it to the public as an open source project. The company already used Dynamometer to help with the release of Hadoop 2.7.4, which allowed it to verify that the maintenance release didn’t negatively impact performance.

Let’s block ads! (Why?)

Big Data – VentureBeat

Tag Archives: LinkedIn

New LinkedIn Sales Insights Coming in 2021

DATA IS THE FOUNDATION TO EVERY SUCCESSFUL SALES PROCESS

LinkedIn open-sources Dagli, a machine learning library for Java

LinkedIn: AI hiring is slowing down due to the pandemic

LinkedIn is using AI to spot and remove inappropriate user accounts

Analyze LinkedIn Sales Navigator usage data in Power BI

5 Killer Tips for Creating LinkedIn Posts that Hook Readers

Create a length that is optimal for the platform.

Deepen the relationship with a strong CTA.

Keep readers engaged with the perfect visual count.

10 Things about LinkedIn Sales Navigator a CRM Admin Needs to Know

Sales Navigator Administration Settings

LinkedIn B2B Marketing Strategy: 5 Powerful Reasons to Why

It’s a hidden opportunity to drive greater traffic.

You have the ability to tap into the genius of thought leaders.

D365 In Focus: LinkedIn Sales Navigator and Dynamics 365 Integration [VIDEO]

LinkedIn open-sources Dynamometer for Hadoop performance testing at scale

Recent Posts

Categories

Archives