May 14, 2020
There has emerged a trend where the two giants of the social media sphere have been typecast (at least in the mind) into different silos:
Most popular brands have “Fan Pages” and “Twitter Handles” catering to audiences in both the mediums. But with two-way exchange, experience, and user behavior, brands (and celebrities) have found Twitter as the ‘short and sweet’ spot to cater to their target audience. Be it campaigns, grievance addressal, contests or promotions, the preferred medium has weighed down onto Twitter. Well this isn’t a critique of social media so let me get back to the point.
One of the world leaders in logistics with over four decades of premier existence in the industry, headquartered in North America and spanning across 150+ countries, approached Decision Foundry with a gamut of requests like site auditing, dashboard creation, and mining of Twitter feeds. Our client also had multiple Twitter handles, one as a main handle, followed by country specific handles and a help handle.
The Twitter handles existed since early 2010, running regular campaigns, tweeting about new developments, news and updates of upcoming seasons, and running various contests. But they never had any one rack their brains or crunch the numbers. They never did a deep dive into the analytics of all the social media or specifically “Twitter” media efforts that they were putting in.
The engagement started on an exploratory mode to identify to what lengths insights can be mined from the Twitter data. There were multiple iterations of the exercise leading to a final consolidated outcome.
Prior to embarking on this engagement we had worked successfully on various text mining projects that were also presented as case studies to a wider audience (you can view the event here). We had worked for apparel brands, giant B2B IT consulting companies and now a top logistics company.
Coming onto the latest endeavor, we were provided with multiple 20k rows of datasheets (20k because the client had a tool which had the limitation of a max 20k pull at a time), with columns of data like the tweet, the author of the tweet, time stamp, followers, etc. The tweets were extracted to gauge the user behavior for a particular brand of services that they were running. The data wasn’t very rich in information but enough to draw some actionable insights latent in the data.
Looking at the variables available, we shortlisted four main approaches to bring deeper insights:
We delved deep again into tweet hashtags, looking into how many unique users did the campaigns end up engaging (given here only retweets were available as engagement parameters), and for how many days was the impact of the campaigns still looming once the campaigns were over. This revealed insights on how well did the brand involve users in conversations when there are no campaigns. It turned out that while campaign days resulted in as much as 300 tweets a day, non-campaigns days were as low as one tweet a day. This has implications as to what should be the optimal time gap between campaigns. It can also help in analyzing what should be the cost and frequency of running expensive campaigns to the point people naturally start conversing about the brand and it does not lose steam.
The text mining exercise of the Twitter feeds turned from being exploratory to a very robust set of outcome and recommendations for the client, allowing them to take well-defined actionable steps to achieve a stronger brand presence on Twitter. They have also made Twitter analytics a regular part of their intelligence activities.
August 02, 2022 | Article
Master Datorama Episode Two: Blending Data Sets with Lohith Paturu
With data coming from so many sources, you need a way to blend it together and get the complete picture of your marketing activity.