The unexpected emotions I discovered from 4 major bank’s customer experiences
A number of customers today interact with their banks on Twitter especially for the customer service. Many banks have dedicated customer care handles on Twitter to enable better customer engagement.
Analysis of Twitter interactions can provide deeper insights in to Customer experiences. My interest was to gain insights about their experience through the lens of human emotion and perceptions especially when they have problems with the Bank service. To explore this, I sampled 944 tweets with negative response orientation from 4 major US bank’s twitter customer care handles in the last 6 months period.
Approach: The entire analysis was done on the aggregated twitter data using the Cognitive analytics platform we have developed at our company. It took about 1 hour to run the entire data processing and analytics.
Analysis Findings and Interpretation
The emotional analysis of the negative customer responses (text data) is shown below. The dominant emotions are Distress, Fear and Pity.
- Distress: Creates a feeling of severe anxiety or strain. It’s evoked when people are displeased about an actual consequence (of an event). — — Essentially this suggests that something in the service which is supposed to work well or is in short of the expectations hasn’t worked well for the customers.
- Fear :This is an unexpected emotion discovered. Generally in my analysis across other similar scenarios /sectors, I have mostly come across Anger, Distress, & Disgust. Fear was a rarely a dominant emotion.What does Fear indicate? Fear is evoked when people are displeased due to the prospective consequence of events. (prospective: likely to happen at a future date.). This means people are strongly feeling that things are not going to work as expected. Imagine what would happen to customer loyalty and commitments when there is fear in the heart?
— Feeling Fear creates a fight or a flight response. Banks should be more concerned about the Flight response which means customers may choose other better options.
- Pity: Another Interesting & Unexpected Emotion at play. It is evoked when customers feel the bank is incompetent in delivering the services. We have to understand that the stature and reputation of the bank in the minds of the customer is not felt high when pity is felt. This is another important emotion that bank’s don't want to evoke.
Predicting perceptions in the Mind of the Customer
Customer experiences create negative perceptions about the Bank. Lets look at what kind of Perceptions are at play . In the cognitive analytics of tweets, I have modeled the perceptions felt as Brand personality Archetypes with each archetype (attribute) having specific characteristics. This means the bank is identified having certain (negative brand) personality. The dominant negative brand personas predicted are
- Bumbling: Indicates that the Bank’ service does not provide tangible results. The Bank (perceived as a person) may not be agile and may not have the capabilities to produce the required outcomes.
- Disorganized: Perceived to lack a sense of order and organization. Indicates a weaker foundation or fundamentals in rendering the basics.
- Immature: Brand is perceived to be like a person who is unrefined or unsophisticated (crude) and who may be acting against the norms and principles. Indicates lack of maturity and/or perfection.
All these 3 dominant dimensions can also be seen from the perspective of qualitative risk to the Bank.
The context discovery is done through advanced text mining and analytics on the twitter response (text) data. It provides cues to what is behind all the emotions and perceptions. Context analysis consists of uncovering the Topics, Influencing theme, Unique theme and Concepts from the conversation data.
- Topics (Understand the Talking Points)
Topics are key-words or phrases that are the talking points of the customers. In a way, they describe the customer communication. The discovered topics are shown below as a word-cloud with the key words scored and sized by its
salience. We can infer that people are talking more about the bank accounts and cards.
2. Influencing Theme (Spot influencing aspects of service)
Influencing themes are the common attributes decoded as keywords from the customer responses that has the capacity to have an effect on the entire context (i.e Bank service) or they could represent the effect itself. The word cloud below indicates the influencing attributes ranked by their relevance. The size and meaning of the words provide cues to the influencing aspects. (service issues, phone calls perceived as fraudulent etc.)
3. Unique & Important Theme (Spot distinctive aspects of service)
Unique and Important themes are the distinctive and salient attributes decoded from the customer responses that has the capacity to have an effect on the analytics context (i.e. Bank services) or they could represent the effect itself. The attributes discovered is shown in the word cloud below scored and sorted by its relevance. If you see it indicates for instance in cards: credit card , debit card,cash-pay card,visa cars etc. In Account: business account, online account etc.
4. Concept Discovery
Concept discovery reveals the abstract ideas or the high level aspects predicted from the whole conversation by comparing the pattern of the twitter conversation with thousands of similar data patterns . The below table shows that the concepts related to the customer conversation are mainly related to the debit card and the credit card in the context of the Service.
Human Emotions and perceptions strongly felt in the hearts and minds can make or break businesses depending on how positive or negative they are in customer experience. They are the vital signals to understand the customer narrowly and intimately across different organisational functions such as Bank customer care .
Interestingly, we can infer from the analysis of the aggregated sample data from 4 large banks , there seems to be a larger problem service efficacy (tangible service), lack of order and maturity in common. Also there seems to be a common problem with the cards.
My belief is Social media customer care touch points (Twitter) for Banks across the globe today is increasingly becoming critical. However, banks have to understand how their business works deeper through the lens of the human-truth (minds and heart of the customers) as customers are humans and not just data points as shown above in the emotions and perceptions.
Through advanced human-centric analytics like we do at our company, Banks today have the opportunity to maximize the value of the social media data such as from Twitter or Facebook for risk assessment, improving customer service, finding unique opportunities so on..
The views mentioned here are my own.
Data if used for this post have been sourced from available information in the public domain. But no representation or warranty, expressed or implied, is made as to its accuracy, completeness, timeliness, or correctness. I am not liable for any errors or inaccuracies, regardless of cause to you. (readers)