AI algorithms can analyze a string of text and give an indication regarding what is the ‘intention’ underlying it. Sentiment analysis is a popular NLP technique which gives whether the tone of a particular text is positive, negative or neutral. However, intent analysis goes a level deeper and gives an idea of whether a string of text is a complaint, a suggestion or a query.
Gauging the intent of messages on social media opens a lot of new possibilities. Take a look at the following tweet.
Need to know if it has gone through or no. What happens to my money now? Don’t give me a template response @Paytm
— Pramathesh (@nextdoorharami) March 17, 2017
Now, if you run sentiment analysis on this tweet, it returns a positive sentiment but the inetmtion is to enquire. Intent analysis has several use cases and we have discussed some of the most important ones in this article.
Intent analysis can help a great deal in deciding the placement of ads. Once you extract intention from the social media posts, it becomes easier to identify the pattern. You can target your ads to the audience you procure from the intent analysis results. For instance, someone makes a post about a recent product experience and it has several other comments tagged along. You can extract the intent from the text on the web page and know if it’s your field to chime in. If the intent results are questioning the services of the brand mentioned, you can display your ad there and offer something better. Take a look at the following image for understanding it better.
One of the most practical applications of intent analysis is the management of customer services. If the intention in the posts made by existing customer is spotted and managed, resolving the issue becomes a lot easier. You can prioritize complaints from the customers and respond to them first without wasting much time.
Complete social media analysis
If you track your brand’s or your competitors’ brand on social media, knowing the intent is very important. The public posting platforms are a great source of performing a major part of online marketing and making random posts to kill time. So, tracking the success or failure remains ambiguous even after running sentiment analysis. Once you segregate on the basis of the intent of the posts, you can do your analysis on the relevant ones. This helps the social media marketers to filter noise from the corpus and focus on the opinion and feedback related text.
Take a look at the image below.
This is the image of intent analysis from our twitter study on #alldaybreakfast by McDonald’s. As you can see in the above image, most of the tweets are either random or marketing related. The tweets relevant for our study contribute 25% of the total. We filtered the opinion driven tweets and pulled out our insights based on this data.
Intent analysis can make your marketing process well grained and less painful. This is indeed a significant part of your entire market research and campaigning structure. Check out our list of contextual text analysis APIs or write to us at email@example.com for customized machine learning tools for your business.