The Frame
In this article I will briefly examine the social media strategy I applied to Loopy Loyalty in a span of eight weeks time, particularly regarding Twitter and the results obtained from the 14th of September until the 5th of November 2015.
First and foremost, let’s frame the situation with a few numbers. All of these numbers refer to the Twitter profile @loopyloyalty.
Status on the 14th of September 2015:
- Number of Twitter Followers: 120
- Profile visits in the last 8 weeks: 119
- Tweet impressions in the last 8 weeks: 548
- Sales driven directly from Twitter followers: $0
Status on the 5th of November 2015:
- Number of Twitter followers: 1680(+1,500%)
- Profile visits in the last 8 weeks: 9,691 (+8,244%)
- Tweet impressions in the last 8 weeks: 56,400 (+10,392%)
- Sales driven directly from Twitter followers: $199
The screen below is from the Loopy Loyalty Twitter analytics dashboard and shows the Tweet impressions over the period of time we are taking into account.
The strategy
Before I began, I identified some hypothesis I wanted to test:
Hypothesis 1) If I follow someone, there is a chance this person will follow back.
Hypothesis 2) If the profile I follow has same or similar interests as Loopy Loyalty, then there is a chance this profile will visit the website or connect with Loopy Loyalty somehow.
Hypothesis 3) If I create valuable (Valuable as in people liking and sharing it) content consistently and interact with followers, then the overall audience will grow.
With these ideas in mind I had to develop an action plan to verify my assumptions. The next thing I did, was define a target. I determined two potential targets for Loopy Loyalty:
- Small-to-medium sized businesses that work with recurring customers (ie. bars, hairdressers, restaurants, gyms, nail salon etc),
- Marketing agencies which are willing to provide a new marketing solution to their clients.
In order to find them online, I used twitter advanced search which is an extremely powerful tool to laser-target people, profiles, hashtags or even questions and specific words. This allowed me to see how many people were looking for a loyalty program online, who was promoting one, and what keywords were more popular in this market segment.
Below is a screen of the tool.
This phase of research lasted a few days, and it helped me with learning who the competition is, what hashtags attract more attention, what people think and how people behave online about loyalty and loyalty programs.
Given this new knowledge, I decided to verify my hypothesis with a series of actions that ended up becoming my everyday activity for the first hour of work every morning.
Actions I took
To verify hypothesis 1 (if I follow, I’ll be followed back)
I followed a few dozen random people everyday and measured how many profiles would follow back. In order to do that I used Tweepi, a free online tool that enabled me to filter bots from real people, and choose profiles based on specific parameters I determined (eg. profiles who last tweeted not more than 2 days ago, or based in a specific cities or countries etc.)
To verify hypothesis 2 (similar interests profiles facilitate connection)
I used Tweepi to filter people by the specific hashtags I found in the previous experimentation phase, and followed a few dozen of them everyday. I also contacted some of them or tweeted to them.
To verify hypothesis 3 (interaction and valuable content increase audience)
I tweeted something almost everyday. Since this activity is quite a distraction and it takes time, I used Buffer, a free tool that enabled me to schedule tweets in advance. This way I managed to worry about tweets just once or twice a week. The content I posted was either containing the target hashtags I previously found, interesting retweets, videos/articles about Loopy Loyalty on PassKit blog, or some answers & questions I had with other accounts of Twitter followers. To measure the performance of my tweets I used the engagement section in the Twitter analytics tool.
The screen below is the Google Analytics for Loopy Loyalty traffic on the website generated by Twitter after the implementation of these actions starting from the 14th of September.
While verifying my hypothesis I monitored the following parameters: percentage of random people who followed back, percentage of people with similar interest who followed back, traffic to the website, and increase of followers after interactions with audience.
What I have found is that people with similar interests tend to follow back more than random people, and overall traffic from social media increased after consistent interaction with audience. This validated Hypothesis 1 and 2, but made hypothesis 1 not relevant to the strategy anymore, since data has proven that you can get more traffic and followers by engaging with people with similar interests. So I decided to only focus on them.
In terms of hypothesis 3, I defined a tweet as valuable if it got at least 5 favourites from non-PassKit related profiles and one retweet. What I noticed here is that there is no magic formula. Some tweets performed extremely well, others didn’t get much attention. So, one general principle I could deduce from this, is that increasing social media activity statistically increases the chances of posting the right content that gets visibility out there.
After validation of the hypothesis and determining what worked and what didn’t, I just had to schedule a BAU (business as usual) routine to keep generating free traffic, interactions, leads and twitter followers. I optimised this process by doing it first hour every morning.
An average first hour in the morning for me today most likely looks like this: open Twitter, check increased number of followers, check private messages to identify potential leads to get back to, check website analytics to verify the impact of Twitter on traffic for the previous day, post a tweet, unfollow who didn’t react or get back after 5 days from first interaction, find 100 – 200 targeted profiles to follow and follow them, look for people asking about loyalty programs and send them an image of a digital stamp card or tweet them about Loopy Loyalty, close Twitter.
Conclusion – This strategy can gain you more than just Twitter followers
From this experience I can say there is a learning curve in the process, and the more you do it, the easier it gets to be quick at it. I focused mainly on Twitter because I found it very suitable for this model, and because of the short available amount of time. I had to limit the platforms I was implementing also because I was working on other things. But I feel confident enough to say it’s possible to replicate similar strategies on other social media platforms, too.
A brief summary: how to increase Twitter followers in a nutshell
- Define your social media situation and goals in advance. Formulate some relevant hypothesis to your case and take specific actions to validate them.
- Find your Hashtags: take advantage of the free online tools such as Advanced Search by Twitter, Buffer or Tweepi to identify the keywords for your business, and segment users by preferences and interests.
- Measure the effectiveness of your actions and improve your goal definition, or change the actions you take if necessary.
- Once you found what works in your niche, just make it a habit and repeat the loop. It should consistently deliver the same results.
This is what I used to increase the Twitter followers of @loopyloyalty by 1,500%. Have a tactic that works for you? Let me know in the comments below, I’d love to hear about it.
