Case Study: Social Media Automation in iGaming

Dynamic social media automation is a powerful tool for online marketers. When used as part of a well balanced strategy it can result in huge time and cost savings as well as offering many other benefits, including scalability.

It is hard to come across social media automation case studies or statistics, so in the name of science we have devised and implemented a few dynamic automation campaigns of our own which delivered some very positive and interesting results.

social media automation statistics

The Test Website & Associated Social Media Account

Our test-bed for our social media automation experiments is a website & Twitter account which targets the saturated and difficult niche of online poker. is a live database of free-to-enter poker tournaments (known as ‘freerolls’). The site lists over 1500+ tournaments each day, offering poker players over £3,000,000 in risk-free poker tournaments every year. The associated Twitter account is @Frreerollsdb.

We decided to use the Twitter platform due to their powerful API – which makes it easy to interact with the platform programmatically, and their excellent Twitter Analytics service makes analysing your social campaigns easy.

Target demographic: Poker players, Gamblers, Sports Fans, Males 18-35

The Social Automation Campaign

We devised and deployed two different types of social media automation initiatives in order to gather data on the effectiveness and ROI of these types of activities.

All social media automation activities took place on Twitter over a four-week period (in tandem with other manual and automated interactions which took place on the account). Remember that maintaining a balance of automation and real human interactions is important.

Automation Test #1: Freeroll Auto-posting

Activity: Broadcasting upcoming freeroll tournament event information (and a trackable affiliate link) from the FreerollsDB database to the target social media channel (Twitter).

Test Campaign Goals in order of importance

  • Goal 1: Link Clicks
  • Goal 2: Stimulate interactions
  • Goal 3: Brand Amplification (retweets, shares)
  • Goal 4: Gain new targeted account followers.

Campaign Description

  • Activity Frequency: Every 20 minutes
  • Analysed data sample size: 1226 Tweets
  • Example Tweet:

sample tweet for automation test #1

Results (after 30 days)

Campaign activity

Automation Test #1 – Campaign Metrics & Results

Metric Result Comments
Total Tweets: 1224
Tweet Impressions: 78,501
URL Clicks: 171 (15.18%) – this is an excellent click-through rate.
Retweets: 81 (7.1%)
Favorites: 66 (5.86%) Our (existing) audience likes the information we are sharing.
Replies: 4 (0.3%)
User Profile Clicks: 73 (6.48%)
Engagement: 563 (50%) Quite a high level of engagement.
Engagement Rate: 3.5% this metric is relative to the # of followers on the account. Only a small subset of the account followers engage with these types of tweets.
Hastag Clicks: 3 (0.2%)
Detail Expands: 95 (8.4%)
Embedded Media Clicks: 68 (6%)
Follows: 2 (0.01%) This type of automation performs poorly in terms of new follower acquisition Click Data (7 days):

Total Click data for the social media automation test campaign Click Data (30 days):
Click data From Twitter for the social media automation test campaign


  • All-in-all this was a really successful social media automation campaign which really delivered well in terms of our main goal of stimulating clicks to merchant directly from auto-generated tweets.
  • According to, we received 180 clicks from our tracked auto-generated tweets on Twitter directly to merchants (using FreerollsDB’s own affiliate links of course.)
  • As an extra bonus, it’s also possible to get some extra click-yield / value from this tactic by sharing these Twitter posts automatically on your Facebook page and other social properties (we gained an extra 68 clicks, around 32% extra clicks from Facebook users using this method.)

Automation Test #2: Tactical Search + Reply

If we know that we can be 100% certain about the semantics of a specific tweet in advance (because it contains a specific string of text which we know), celebrex to buy then we can actually pre-create a great response to it, and know with absolute certainty that when we reply to it, our reply always make sense to the recipient – with no exceptions. This is a great opportunity for automation – and can offer really high conversion rates (in terms of follower acquisition).

Imagine the following scenario:

  • Tetley Tea bags is running a competition.
  • To be eligible to win a monthly prize, brand followers must Tweet about Tetley
  • The Tweet must begin with the text ‘I love drinking Tetley tea because …..”

With advance knowledge of the search ‘fingerprint’ (“I love drinking Tetley tea because”) and understanding the meaning of this particular communication – we know that we can reply to all of those people and say “milk and sugar in mine thanks!”, with very high confidence that the recipient will never suspect that they were just tweeted at by a customized bot, as our reply makes perfect sense, 100% of the time, and dumb bots can’t do that – can they? There’s nothing dumb about this bot!

So naturally in our second social media automation test we chose to use this tactic, and to reply to Twitter users who showed interest Poker Freerolls.

Lucky for us, 888 Poker has made it exceptionally easy to fingerprint their customers (see the search results below) who are actively interested in playing in poker freerolls. These people want to play in the 888Poker monthly twitter freeroll which demands a tweet containing specific text. This is fabulous because we can automatically search for these users via the text-string which they must use in their tweet.

Automation #2 Execution

Activity: Every hour we search for Twitter users with a very specific text-string in their tweet (which identifies them as high probability potential customers and ‘easy targets’) and replying with a specific prefabricated reply, a trackable link (thanks Twitter Analytics) and a call-to-action.

Fingerprinting users for auto-tweet replies

And Destroy: – We can automatically reply to these tweets with some well wishes and a useful link to our [relevant] website or product.

If the recipient of one of our specifically crafted automatic interactions replies to the tweet then we can (and should) take over the conversation manually.

Sample Interaction:
Automation and resulting conversation

Automation Test #2 – Campaign Metrics & Results

Metric Result Comments
Total Tweets: 1090
Tweet Impressions: 8,139
Replies: 28 (2.5%)
Favorites: 14 (1.3%)
URL Clicks: 124 (11.3%) More than 10% of recipients are visiting the FreerollsDB website – Winner!
Retweets: 4 (0.4%)
User Profile Clicks: 42 (3.85%))
Engagement: 303
Engagement Rate: 29.9% High Engagement Rate
Hastag Clicks: N/A
Detail Expands: 83 (7.6%)
Follows: 8 (0.7%) Really low follower ROI on this initiative, but we’re referring people to our website not our Twitter page.


  • High engagement – a 30% engagement rate was achieved.
  • Low direct follow-back rate – but this seems reasonable given our call to action actually leads the user away from Twitter. In the next iteration of this automation we can test the effects of directing the user towards the FreerollsDB account Twitter account page rather than the website.
  • High Click Through Rate – This automation produced a good click-through rate to the target website (over 10%)

With some creativity, some experimentation and analysis – it’s possible to give your online marketing efforts a significant boost by adding powerful custom social media automation into the mix.

Contact us to discuss how we can build a powerful social media automation campaign for your business centered around your specific business goals and needs.