Listening to and acting on customer feedback is essential to improve your CSAT ratings. Not only does feedback provide a window into real-world customer expectations, but negative feedback when shared publicly could damage brand reputation. Harvard Business Review went as far as to say that companies shouldn’t ignore listening to customer feedback in favour of Big Data analysis practices. While quantifiable data points and generic market research can reveal broad trends, customer feedback as shared via calls, emails, live chat, social media, and review forums offer insights into hidden improvement areas.
As a customer-centric brand, you probably have a number of feedback collection channels in place, feeding into a centralised CRM. Here are 6 strategies to help you make the most of this feedback data and act on them effectively:
1. Unprompted Feedback Analysis
Data from structured surveys, questionnaires, polls, etc., will be ingested by your CRM and a centralised analytics engine – but what about unprompted feedback? These often indicate a customer’s most pressing issues, which may not fit into a feedback template. Ask open questions when requesting feedback and ensure agents flag unprompted suggestions either manually or with the help of real-time speech analytics. Speech analytics would be able to extract important keywords and phrases from unprompted feedback and find dominant trends across your customer base.
2. Volume and Repetition Analysis
Mapping the number of feedback inputs pertaining to a specific brand aspect (e.ga new feature or a website update) is useful for cutting through recency bias. This means that you can focus on high volume feedback that impacts the largest customer base, instead of being limited to the most recent piece of feedback. Similarly, repetition analysis reveals helpful suggestions that may otherwise go unnoticed simply because they are discussed (and overlooked) so often. Remember, with a high volume of feedback coming through different channels, there’s always a risk of bias influencing your decision-making. This methodology makes feedback analysis more objective, making sure that you can utilise the full potential of your data.
3. Dependent and Independent Variable Behaviour
Dependent variables like NPS, CSAT, customer effort score, etc. are quantifiable elements of customer feedback that are directly linked to a business outcome. Independent variables like agent politeness, response time over emails, website/shop floor design, etc., have no direct correlation with business outcomes. Studying dependent and independent variable behaviour using feedback data tells you how the latter could drive the former and how descriptive actions could bring about quantifiable improvements in the business. For example, you could leverage training intervention to help agents handle problematic callers more politely, and this methodology will ensure that the training results in a quantifiable uptick in NPS, CSAT, etc.
4. Sentiment Analysis
This customer feedback data analysis methodology converts unstructured information like the customer’s mood, attitude, or sentiment into an action point. Monitor for specific keywords (positive or negative) to intervene with the appropriate action. For example, you can identify which moment in the conversation is best for upselling/cross-selling, or when supervisor intervention might help to avoid a possible dispute. Sentiment analysis can be applied to multichannel interactions across voice, chat, email, and social media. To achieve this, you would need to couple sentiment analysis with speech analytics (for processing audio) and text analytics, with the results feeding into a centralised CRM database.