(POWER BI) LinkedIn Insights Unveiled: Unleashing the Power of Your Profile
Introduction:
LinkedIn, the leading professional networking platform, holds a treasure trove of data that can provide valuable insights into your profile and networking activities. In this report, we will explore the steps taken to analyze LinkedIn data and uncover a comprehensive analysis of your profile. By utilizing powerful tools like Power BI, we have transformed raw data into meaningful visualizations and derived actionable insights that can unlock the full potential of your LinkedIn profile.
Data Acquisition and Preparation:
To begin our analysis, I obtained a copy of my LinkedIn data by following these simple steps:
- Data Request: To access your LinkedIn profile settings. Navigate to Settings & Privacy > Data Privacy > Get a Copy of Your Data > Request Archive.
- Data Delivery: You will receive the requested data via email within 24 hours.
Once we received the data, we selected the following datasets for analysis: Calendar, Connection, Endorsements Received Info, Invitations, Messages, Reactions, Search Queries, and Shares. These datasets provide a comprehensive overview of your LinkedIn activity.
Next, we loaded the datasets into Power BI and performed thorough data cleaning using Power Query. We removed irrelevant columns and spaces, ensuring that the data is clean and ready for analysis. Additionally, we formatted the date columns in the calendar dataset to the appropriate data type for accurate time-based analysis.
DAX Measures Creation:
In order to gain deeper insights from the data, we created several Data Analysis Expressions (DAX) measures in Power BI. These measures capture specific metrics and enable us to derive meaningful conclusions. Some of the key DAX measures we created include:
- Calendar Table: We created a calendar table that facilitates data-based analysis. It includes measures such as Year, Year Month Number, Year Month, Month Number, Month, Week Number etc. This allows us to examine trends and patterns over time.
Calendar = VAR Days = CALENDAR ( DATE ( 2016, 1, 1 ), DATE ( 2023, 12, 31 ) ) RETURN ADDCOLUMNS ( Days, "Year", YEAR ( [Date] ), "Month Number", MONTH ( [Date] ), "Month", FORMAT ( [Date], "mmm" ), "Year Month Number", YEAR ( [Date] ) * 12 + MONTH ( [Date] ) - 1, "Year Month", FORMAT ( [Date], "mmm yy" ), "Week Number", WEEKNUM ( [Date] ), "Week Number and Year", "W" & WEEKNUM ( [Date] ) & " " & YEAR ( [Date] ), "WeekYearNumber", YEAR ( [Date] ) & 100 + WEEKNUM ( [Date] ), "Is Working Day",not WEEKDAY([Date]) in {1,7} )- TT Measures: The TT measures encompass various metrics related to my LinkedIn profile.
TT representing my name (Tolulope Taiwo), lol
Messages Sent = CALCULATE(COUNTA(messages[FROM]), FILTER(messages,messages[FROM] = "Tolulope Taiwo"))
Messages Received = CALCULATE(COUNT(messages[FROM]), NOT(messages[FROM] IN {"Tolulope Taiwo"}))
Endorsement = COUNT(Endorsement_Received_Info[Endorser First Name])
Invitation Received = COUNTROWS(FILTER(ALL(Invitations),Invitations[Direction]= "Incoming"))
Invitation Sent = COUNTROWS(FILTER(ALL(Invitations),Invitations[Direction]= "outgoing"))
Recommendation = DISTINCTCOUNT(Recommendations_Received[Status])
Total company = DISTINCTCOUNT(Connections[Company])
Total connection = COUNT(Connections[Connected On])
Total Reaction = count(Reactions[Type])
Total share = COUNT(Shares[ShareLink])- Messages Sent: Counts the number of messages sent by “Tolulope Taiwo.”
- Messages Received: Counts the number of messages received, excluding those sent by “Tolulope Taiwo.”
- Endorsement: Calculates the count of endorsements received.
- Invitation Received: Counts the number of incoming invitations.
- Invitation Sent: Counts the number of outgoing invitations.
- Recommendation: Calculates the distinct count of recommendations received.
- Total Company: Calculates the distinct count of connections based on the company.
- Total Connection: Counts the number of connections.
- Total Reaction: Calculates the count of reactions.
- Total Share: Counts the number of shares.
These measures provide a comprehensive overview of my profile activity and engagement on LinkedIn.
Data Modeling and Visualization:
To make the data more accessible and understandable, we established relationships among the tables in the data model. This enables us to create meaningful visualizations that present the analyzed LinkedIn data in a clear and concise manner.
We have developed graphs and a comprehensive dashboard that showcases the insights derived from the analysis. The visualizations allow us to gain a holistic view of my LinkedIn profile and its performance over the years.
Findings:
After the analysis, we have uncovered several key findings:
- Total Connections: From 2016 to 2023, the profile established a total of 1,946 connections. While the profile remained relatively dormant for most of the years, it gained substantial traction in recent times.
- Connection Distribution: Notably, a significant portion of the connections consists of business owners, including CEOs, MDs, and Founders. This suggests that my network comprises influential professionals on the platform, which can open doors to valuable opportunities.
3. Connection Growth: The highest number of connections (502), was observed in 2019, indicating a surge in networking activities. However, the number experiences a substantial drop-off from 2020 to 2023. This decline raises the question of whether the COVID-19 pandemic affected LinkedIn connections globally.
4. Connection Trends: An analysis of connection growth revealed a 22.4% increase between 2018 and 2019. Further examination showed that the majority of connections were established in April and September. These findings suggest potential correlations between certain months and increased networking activities.
5. Unspecified Company Names: Among the top 10 connections by companies, we noticed that 89 individuals did not provide the names of their respective companies on their LinkedIn profiles. This lack of information raises the need for further investigation into the nature of these connections and their potential value.
6. Message Activity: During the analyzed period, a total of 375 messages were received on my LinkedIn account, representing approximately 65% of the total messages. This indicates a substantial level of engagement and communication within the LinkedIn network.
Data Limitations:
While our analysis provides valuable insights, it is important to acknowledge certain limitations of the available data:
1. Lack of Country or Location Information: Unfortunately, the data does not include detailed information on the countries or locations associated with the connections. This missing data restricts our ability to perform in-depth analysis and gain insights into geographical locations and their influence on LinkedIn connections. Without this information, it becomes challenging to understand regional dynamics.
2. Limited Field Categorization: Another limitation is the absence of specific categorization of fields, such as technology (Tech) or non-technology (non-tech) sectors. The lack of this categorization hampers our ability to analyze and compare networking patterns across different industry sectors. It becomes difficult to identify trends, connections, and opportunities specific to certain fields or sectors.
Recommendations:
Based on the insights derived from the analysis, we recommend the following strategies to maximize the potential of the LinkedIn profile:
- Engagement Strategy: Given the observed drop-off in connection growth from 2020 onwards, it is recommended to actively engage with the LinkedIn community. Share valuable content, participate in discussions, and reach out to potential connections. By fostering meaningful interactions, you can enhance visibility and attract new opportunities.
- Targeted Networking: Considering the significant presence of business owners among the connections, focus your networking efforts on this influential group. Building relationships with CEOs, MDs, and Founders can provide opportunities for collaboration, mentorship, business and career growth.
- Improve Content Engagement: To maximize the potential of your LinkedIn connections, be more active in regularly sharing informative and useful content. This engagement strategy will attract the attention of potential employers or collaborators who may be seeking your expertise and offerings. Regular posting of relevant content can strengthen your professional visibility and increase the likelihood of new opportunities.
Conclusion:
The analysis of the data has provided valuable insights into my profile and networking activities. By leveraging the power of Power BI and data analysis techniques, we have been able to transform raw data into actionable information. The resulting dashboard offers a clear overview of my LinkedIn data analysis process and enables informed decision-making.
In conclusion, by leveraging the power of data analysis, I can optimize my LinkedIn presence and maximize its potential. Take advantage of the recommendations provided to enhance engagement, target key professionals, and share valuable content. By doing this I will strengthen my professional visibility, expand my network, and increase the likelihood of new opportunities.
Frequently Asked Questions (FAQs):
1. How can I request a copy of my LinkedIn data for analysis?
To obtain a copy of your LinkedIn data, navigate to Settings & Privacy > Data Privacy > Get a Copy of Your Data > Request Archive. Follow the prompts to submit your request, and you will receive the data via email within 24 hours.
2. Can I analyze LinkedIn data without using Power BI?
While Power BI offers powerful data analysis capabilities, you can still analyze LinkedIn data using other tools or software. However, Power BI provides a comprehensive and user-friendly platform specifically designed for data analysis and visualization.
3. How can I effectively engage with the LinkedIn community?
To engage with the LinkedIn community, share valuable content related to your industry or area of expertise. Participate in relevant discussions and groups, connect with professionals who share similar interests, and reach out to potential connections with personalized messages highlighting common interests or goals.
4. How can I improve content engagement on LinkedIn?
To improve content engagement on LinkedIn, consider the following tips: create compelling and informative posts, use visually appealing graphics or images, ask questions to encourage discussions, and engage with comments and messages from your connections. Consistency is key, so aim to post regularly and analyze the performance of your content to refine your approach.
5. How can I make the most of my LinkedIn connections?
To make the most of your LinkedIn connections, focus on building and nurturing relationships. Engage in meaningful conversations, offer support or assistance when appropriate, and seek opportunities for collaboration or mentorship. Remember that networking is a two-way street, so be proactive in providing value to your connections as well.
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