YouLynq.me ft. Ajar A.I. Consulting – Linkedin Marketing Research
We (Rueben Alvarez, Co-Owner of Ajar and Michiel Verstraten, Co-Owner of YouLynq.me) first met in a bar in Amsterdam Zuid in January and immediately we gave each other so much energy: YouLynq.me has an incredible amount of data from 100+ clients and Ajar has the machine learning tools and knowledge to get insights in this data.
“Together we can improve online networking & business development and change the way we work forever.” #automate #robotise #standardise
Ajar A.I. Consulting
Ajar A.I. Consulting is a team of Artificial Intelligence academics with an entrepreneurial mindset, based in Amsterdam. Their love for technology and their natural instinct for business transformation brought us together.
They work closely with the University of Amsterdam and do as much work as research to provide excellent state-of-the-art solutions.
They believe that forming a bridge between university and business will evoke a new class of businesses, built on strong AI and smart solutions.
YouLynq.me is a marketing agency that is helping freelancers, sales managers, companies and executives with high-tech business development service.
Currently counting 45 clients from whom the LinkedIn performance has been analysed by Ajar A.I. Consulting. YouLynq.me claims to turn Sales & Recruitment 180 degrees and they call themselves Social Business Assistants.
Here’s the first idea.
YouLynq.me is managing the LinkedIn profiles of 100+ professionals. All of these profiles are performing in an equal manner because all profiles are optimized beforehand. This ensures that the quality of the profiles should not affect the performance. Nevertheless, we are observing differences in conversion rates. To improve our service and to get insights on how to grow business on LinkedIn, we want to analyze how different LinkedIn profiles perform on LinkedIn. In this case, performance is based on the acceptance rate of connection invites. This is the first part of the sales funnel. Before optimizing the conversion of later stages, we decided to start with optimizing the first step so that our clients have as many chances as possible. Later we will analyze the step connections, chats, leads, meetings, and deals. Profiles are grouped by their sector, seniority, availability, sex, and whether these are foreign profiles. (Foreign based on non-Dutch profile names).
Here’s what we did.
We collected all of our customers’ data. Our customers invited other LinkedIn users based on our clients’ wishes for expanding their network. Our clients range from juniors to seniors, exist of men and women, work in different sectors, and are of different nationalities. Our customers have invited from 184 to 6224 LinkedIn users to connect with them. All our customers have similar professional profile styles and copywriting. We’ve provided them with a certain Whitelabel LinkedIn profile that contains all the necessary information and is structured to readability and professionalism. We’ve grouped and counted all invitees per customer and compared the results.
Here’s how we did it.
Following GDPR guidelines and with the permission of our clients, Ajar preprocessed and cleaned our datasets. Moreover, using machine learning techniques they acquired additional relevant information necessary to form interesting conclusions. Using YouLynq’s expertise in marketing and behaviour on Linkedin, plus Ajar’s technical skills we were able to present these results. Most of the work was done in Jupyter Notebook, using python.
Here’s what we found out.
Foreign names (non-Dutch)
Shockingly, our clients with foreign names (non-Dutch) have a significant difference in acceptance rates. Compared to our clients with Dutch names, inviting as many Dutch and Non-Dutch LinkedIn profiles (50:50 on average), our non-Dutch customers were 5 times less likely to be accepted by a Dutch LinkedIn user. All YouLynq’s customer LinkedIn profiles are created through their high standard of profile structure and English copywriting, the only exception being their names. Therefore we have to conclude that discrimination based on simply a name is still very alive in our online communities in the Netherlands.
Freelance vs Business profiles
The majority of accepted connections are found in B2B profiles. Possibly more interesting for other employees, for freelancers require a different kind of work.
Senior, mediors, and juniors
Medior function profiles on LinkedIn have the highest average acceptance rate. Seniors second, and junior third.
IT vs innovation vs finance vs marketing
Marketing is the only sector that has a lower average acceptance rate, all other sectors were equal.
Our available clients show a difference in the average acceptance rate, compared to our unavailable clients. Clients who were not available for work at this moment had a higher chance of being accepted through their invite on Linkedin.