Emotional Journey Mapping
Emotional journey mapping is the process of creating a visual map of your customers’ emotional state at each stage of their journey. It’s a vague process that can be visualized using emojis, graphs, or charts. The idea is to add a more qualitative touch to the analysis of a customers’ emotional state over time, which lets you see which touchpoints are high and low points for a customer and where things could use improvement.
Mapping an emotional journey involves taking the touchpoints and steps involved in a customer journey (whether on or offline) and investigating what said customer is feeling at each step. Remember, it is not a linear process, as different customers will have different desires and needs depending on their demographics, so it might be useful to create many different customer profiles in order to analyze the journey. Some points might contradict each other with the different groups, and you need to be prepared to compromise when deciding how to alter your approach thanks to an emotional map; for example, a younger consumer might prefer that you use an email and despise phone calls, whereas an older consumer might prefer the opposite.
In the end, deciding what to do with the emotional journey map is going to be complex and nobody will be able to get every step right for every potential customer.
Sentiment analysis — a powerful tool
Sentiment analysis, also known as opinion mining, is the process of extracting detailed information on consumers’ opinions and feelings from text using AI analysis — linguistics and text analysis being two of the main tools used. It’s often applied to reviews, surveys, and social media posts, where there are usually large amounts of text to analyze.
Sentiment analysis relies on detecting the tone of the text, something which a native speaker reading it should be able to pick up with ease, but a basic computer might have more difficulty with. Because of the large amounts of text to study, it’s impractical to do the analysis manually, while computers can do the job in a fraction of the time.
The downsides of computer analysis are mitigated with the use of databases that hold examples of tone and attitude to cross-reference against when checking text. For example, the phrase “I really love the fact this keeps breaking” would easily be read as sarcasm by a human, but might be missed by a basic program that is simply reading the text. It’s all about current linguistic trends, and these can change over time with slang evolving and the meaning of words changing, so sentimental analysis software is constantly updating to be on top of their game. Today’s best opinion mining softwares can easily circumvent these issues as they are advanced enough to detect the tone and interpret slang.