Let’s Talk About Building Networks with Social Media Bots

Real-world examples of automancery at companies like yours

Keeping up with your social media following can be hard. We’ve automated nearly 100% of our social media networking, and we can for you too. Curious how?

Building a Fanbase by Connecting with Likers

Challenge: People who are serious about building their network want to do so with people who like them. But how to tell? Well on LinkedIn, connecting with people who like your posts is a great start. Plus, with the new limitations LinkedIn has for making new connections, this plays nicely into that.

Solution: Automation was created to analyze a person’s 10 most recent posts in their profile. From there it would look at the list of people who left a Like (of any type). Anyone in the scrollable list that wasn’t already connected with, gets a connection request with a somewhat personal message thanking them for liking the post, and asking to be connected.

Result: The quality of one’s social network goes through the roof when this approach is applied. Rather than reaching out to strangers and trying to connect and then foster relationships, building those relationships with people who have liked your content works wonders. It’s much more likely that they’d get along with you as a person if they’re resonating with the things you’re saying.

 

 

Consolidating Unanswered Comments

Challenge: One of the best ways to build relationships online is always make sure to answer comments. But tracking them down can be a chore. How to save time?

Solution: A custom automated script was built to analyze the last 10 posts on LinkedIn, and find any comment that didn’t have the profile owner as the respondent. Any posts found that had that condition were added to a simple HTML page where the profile owner could click a link and be taken right to the post (and comment) in question, where they could respond right then and there.

Result: Tremendous uptick in viewership on posts. When comments are replied to and conversations are fostered, the LinkedIn algorithm makes the post have higher reach. More eyes means more brand visibility.

 

 

Simple Messaging

Challenge: Similar to how we do email nurturing, we do nurturing on social media. Particularly, LinkedIn. People who want to keep up with the thousands of people they might be connected to find this challenging.

Solution: With some strategic automation, we’ve been able to message people based on their skills, conversation history, relationship strength, posts they’ve liked, and a number of other factors.

Result: Massive rapport being built. With many people getting spammed with messages right after connecting, the people who send thoughtful messages that are sent because of the recipient (and not because of the sender) go a long way in business.

 


 

Targeted Engagement Pods

Challenge: The concept of an “engagement pod” isn’t new–it’s a group of people who all agree to like, comment on and share other group members’ posts to boost visibility. However, this can be time consuming, and is technically a violation of terms since it games the algorithm. It can also lose momentum as people find other things to do than respond to possibly dozens of posts that aren’t really that interesting to them personally.

Solution: Flipping this on its head, we instead designed automation that sent particular people a link to a post. It’s content that they’ve shown interest in before, and who we genuinely want to hear back from (and yeah, possibly build a business relationship with later, sneaky sneaky!)

Result: End result is having yet another way to stay top-of-mind with people, while helping them be a valuable member of our network, and the networks of others who employ this approach with us.

 

 

Etsy Contact Collection

Challenge: Etsy shops don’t make it super easy to collect emails to reach back out later. We personally like to follow up with people individually after they place an order, thanking them, and asking specific questions about what they think of our products. Collecting this info manually is time consuming though.

Solution: A script was written that collects the name and email for each person that’s placed an order in our shop. From there, the data goes into another automation that can send specific emails to those same people, without using a mailing list.

Result: Because the emails that are sent are coming straight from us (just in an automated fashion) we’re able to build a die-hard fan base for our products. And we’re preparing to collect the info about what items they’ve bought, so we can further retarget with amazingly detailed questions and suggestions.