A recent report from Accenture suggested 87% of people now use a second screen while watching TV to comment not only shows but advertisement.
Interactions in Social Media generate dialogue and analyzing all those coments can be overwhelming, but it's necessary to track the success of off-line campaigns. In this post we want to share with you how to measure these second screen interactions based in our experience with real clients.
Sometimes we do look up from our smartphones, take a break from social media, and engage our senses with the offline world. TV commercials, billboards, radio spots, banners and newspaper ads are as visible as ever. We see them and hear them and react to them. They generate interest, spark intrigue and arouse sentiment that makes them stick in the minds of the public.
But these days, the talk about what is seen and heard in offline ads doesn’t stay around the dinner table or at the office – it goes viral across social media channels.
A Nielsen survey from a few years back put the number of smartphone and tablet users that thumb away on their devices while watching TV at above 85% - a figure that is undoubtedly increasing by the day. Offline ads go online the second they air.
Aware of the role social media plays in business success, marketers are increasingly using second screen interaction tools to link offline ads to social media channels. Second screen interaction helps marketers turn offline campaigns into online dialogues that provide a rich source of customer sentiment and customer feedback.
But mining this valuable source of customer feedback for business-driving insights is a key challenge for businesses. How do companies measure the effectiveness of their offline campaigns? How can they make sense of the customer sentiment that is buried in uncountable social media posts that second screen interaction tools generate? How can they know if they need to pull or modify a campaign quickly due to negative customer feedback?
One of our clients launched an offline campaign but they knew to measure the efectiveness of it the place to look at was social media, particularly Twitter. The marketing team knew exactly at what time the ads were gonna be displayed on TV, so the decided to monitor during 30 minutes after the commercial was seen, they were tracking particular hashtags and also particular keywords like the brand name or the product name. However, 30 minutes in Twitter allow customers to create huge debates, so the amount of comments to analyze was significant. In that moment of the process was when Bitext CX helped the marketing team achieve their objective to track feedback in order to change the campaign as fast as possible in case it was required.
Bitex sentiment analysis tools allowed the client to quickly analyze large volumes of tweets and posts containing sentiment and feedback regarding the campaign. Using deep knowledge of sentence structure, sentiment analysis tools enabled text categorization that revealed exactly what customers were talking about.
Our client was able to identify what customers were thinking and feeling about every detail from the offline campaign and when the results came up, maarketers decided to modify the campaign, because they were impacting the audience but with the wrong message, so they weren't achieving the main objective from the offline campaign.
Bitext CX sentiment analysis tool uses deep linguistic knowledge and text categorization to help marketers quickly monitor second screen interaction. Bitext CX identifies customer sentiment with accuracy and turns volumes of unstructured text into insight in minutes.
Want to see for yourself how quickly and easily Bitext CX turns text-based data into real customer insight?