Would it not be powerful and delightful to know what turns your customers on or off – before it is too late? AI-backed social sentiment analysis can help marketers do just that.

Consider these four examples.

First. Some brands were able to crack customer expectations right this Super Bowl and some failed. About 45% of the ads had casts that represented diverse and inclusive audiences – as gauged by the Association of National Advertisers’ Alliance for Inclusive & Multicultural Marketing (AIMM). But a lot of brands disappointed on a measurement used by AIMM for calculation of attention cultural insights, especially among Hispanic viewers. Interestingly, this year, Super Bowl LV had 23 first-time advertisers and brands saw more than 916,000 mentions (or 3,800 mentions a minute) on Twitter. What was calculated by an analytics firm through social mentions and sentiments on Twitter was a good hint of how brands performed in the marketing game. Brandwatch found out that some brands mostly saw positive sentiment, some saw backlash, and a lot had divided audiences.

Second. Curated social content is now more than entertainment. In a survey (Catch & Release) of 100 advertising professionals, 87% have expressed intent to increase the usage of UGC or User-Generated Content (that customers post on various platforms). These tools are helping marketers in gaining speed and precision in getting to just the right UGC content that fits their brand’s messaging and journey. This is, apparently, also helping marketers to elevate brand trust (39%), brand engagement (29%) and sales (17%).

Third. The pivotal social media unrest shared via ‘Black Lives Matter’ protests show that consumers are expecting more from brands than just lip-service or cursory attention to social issues.

Fourth. The consumer sentiment dipped by 1.6 percentage points, in August 2020 (according to the Refinitiv-Ipsos Primary Consumer Sentiment Index (PCSI) for India). These Refinitiv/Ipsos online polls were strengthened on precision using a Bayesian Credibility Interval.

Well! Turns out that a lot can be found out if brands are able to tune in to what people are saying or not saying – all the time. Social listening is an innate human trait. Whether we are in a café, at an airport lounge, in the traffic, or inside a marketing conference room – having your ears alert is always natural and useful. But keeping your ears on the ground has been made both easy and difficult with the advent of social media. On one hand, customers have more platforms and freedom to express their sentiments about a brand, a product or an experience. On the other hand, this is a mode of expression that is always on, out of control, and proliferating in many directions and formats.

Can Artificial Intelligence help marketers and business strategists to keep a pulse on the social sentiment irrespective of the scale, complexity, and frequency that these expressions manifest? Let’s find out.

Social Sentiment – A Marketing Cue That’s Strong and Slippery

Emotions have always been the fundamental driver of any human decision – especially the ones that customers make. Feelings are the guiding forces that can deflect, change, influence and amplify the purchase journeys and buyer behavior cycles in a strong way. It is, therefore, almost an imperative for marketers to have a sharp cognizance of what the customers are expressing, spreading and hinting at – out there. This can be done through a big spectrum of sources today – e-commerce reviews, tweets, social media feeds, music, images, videos, email, microblogging, chats, etc. In fact, the sentiment analytics market is expected (according to Market Research Future) to reach $6 billion by 2023.

With these tools, marketers can not only gather precious insights but can also use this as powerful data for their strategies and campaigns. A recent academic paper published by some Indian authors in ICSE also points out that opinion mining and sentiment analysis are gaining importance. The study of attitude towards an entity helps a lot – an entity can correspond to an event, topic, or individual and this analysis can be done at three different levels – document level, sentence level and aspect level.

Indeed, sentiment analysis is a great way to pick opinions, insights and attitudes that arm a brand with precious fuel and direction.

  1. Social sentiments can churn important directions on why a product is failing or succeeding
  2. They are sources of precise competitive analysis and real-time customer feedback
  3. These sentiments can immediately shape into major trends as a small individual post, or conversation can soon turn into a big wave representing a big community
  4. These nuggets can help marketers to quickly adapt their campaigns and messages to actual market realities and customer reception
  5. Skilful and outcome-centric monitoring of social media can yield insights that are too honest, deep, and raw to be reckoned through any other marketing survey or customer feedback tool
  6. These sentiments can help them construct useful bridges for customer engagement and help to forge strong customer affinity
  7. Marketers can tap the power of personalization, context and collaborative marketing by using these sources an platforms through the right tools
  8. They can avert any major crisis or customer disappointment wave before it builds up outside their isolated cubicles

So how to tap these advantages? Especially when what you see or hear is plain text or voice? And there is still a goldmine of sentiments that is tucked away inside or beneath those words or tones? That’s where it becomes difficult.

The challenges of actually leveraging insights out of social sentiment analysis are multi-fold:

  1. How to classify whether a certain post or conversation is positive or negative or casual or serious or neutral?
  2. How to extract meaningful information from the vast ocean of complexity of human emotions?
  3. How to convert descriptive analysis into predictive models for marketers – thus, equipping them for course-correction and better campaigns in the future instead of simply reflecting on mistakes?
  4. How to find out exact meaning and direction when a message is laden with adverbs, irony, sarcasm, negation, and humor?

Use AI and Convert Sentiments Into Insights

AI tools have been evolving at a great pace and maturity level to help businesses in solving these challenges. They can dig beyond the superficial layers of text and can help marketers reach the exact tone, feeling and intent of a social media text. The uptick in accuracy and speed is definitely a big plus here but what actually sets AI in a distinct zone now is the big help it gives in going deep into unstructured or subtle sentence fragments. AI can aid – massively – in picking up tough linguistic nuances. They are really redefining the importance of sentiment analysis and these tools come in various forms:

  1. Rule-based tools: They work according to a specified set of rules and expected outcomes as per a given marketing goal
  2. NLP-based tools: That use Natural Language Processing in a targeted way for digging out marketing connotations from language shared on social platforms
  3. Automatic tools: They add extra speed, processing, and output by automating statistical models, neural networks, and machine learning areas of sentiment analysis
  4. Algorithms for classification like Naive Bayes (NB), Support Vector Machines
  5. (SVM) and Maximum- Entropy (ME)
  6. Lexicon learning: This is where one uses predefined lists of words that are already associated with specific sentiments
  7. Affective Computing Tools – They read data from audio and video formats or facial recognition-based cognitive analysis
  8. API platforms for text analytics – They can help in understanding and analyzing varied formats like sentiment, entity, emotion, keyword, concept tagging, language, and taxonomy
  9. Customer Data Platforms or CDPs – They pack contextualization for both unstructured and structured data giving a holistic CX view to a brand

However, simply deploying any AI tool would not make the cut here. The tool should actually lead your brand into a better stance of control, confidence and insights. The evaluation and choice process here should be given due attention and time.

Pick A Good Partner

When you start looking for a tool that can get you the insights you are looking for, you need something that can brings signals out of all the noise out there. You should remember that selecting a partner that understands your needs and that has solid expertise is very crucial here.

Key requirements of a good AI tool would be:

  1. Availability of the right quantity and quality of training data
  2. Proper consideration for context  of  the  text  and  user preferences
  3. Ability to interpret analytical output for a given stage, need and circumstance of the business or brand
  4. Resources and minds that can translate interpretations into decision-enablers
  5. Expertise in scaling and integrating tools so that they do not turn into burdens for the marketing function
  6. Capabilities to execute the tool as per some unique challenges and constraints
  7. Attention to the flip side of sentiment analysis – such as privacy intrusion, ethical responsibilities, compliance, confusion, or overwhelming nature of output

As a smart marketer, you would harvest an incredible pipeline of insights and visibility into what is going on in the customer’s mind – if you can have the right technology by your side. As they say, a lot can be learnt from what the person says, but a lot more hides within – what the person doesn’t say but still expresses through other means.

Analyzing customer sentiments will help you understand them better to deliver better products and experiences and stay ahead of the curve. So keep a watch on those jokes, reviews, posts, tweets, pats, and brickbats – they are all valuable.