You're facing recurring customer complaints. How can you leverage data analytics to effectively address them?

Try as we might, the Customer Experience will never be perfect all the time, but with recurring complaints, it's crucial to first understand the root cause. Then, in order to get to a swift resolution, you need to bucket the data into areas that really matter to the business. The most impactful measure will always be tying issues back to revenue. - Use data to highlight how this issue is increasing Churn, reducing ability to Cross Sell/Upsell, and/or increasing internal COGS headcount due to volumes. - Show also what the impact will be of resolution. Use actual $$$ wherever you can.

First, gather and consolidate data from all customer touchpoints, including support tickets, call logs, feedback forms, and social media interactions. Use data analytics tools to track and analyze this data to identify common themes or specific areas that consistently lead to customer dissatisfaction. For instance, if multiple complaints are about delayed shipment times, data analytics can confirm if this is isolated to specific regions or products.

After cleaning data, Categorization and Tagging data can be helpful. We can categorize complaints into delivery issues, product quality, and customer service, and tag them with keywords like “late delivery,” “damaged product,” and “rude staff.” After categorizing, we can use Descriptive Analytics: We use Statistical Tools: The company can use 1. Frequency analysis to determine the number of complaints in each category. For example, they find: ? Delivery issues: 500 complaints ? Product quality issues: 300 complaints ? Customer service issues: 200 complaints

Get mre information by visiting them if need be or assigning an account manager who will work closely with them. Ask open ended questions to understand the issue in depth. After collecting the data, analyse it and put in place specific points of implementation to tackle this complains. Implement and put them into actions.Once in place.. go back to stage one and collect data of the implementations made. This is cycle that should stay frequent in order to regain and maintain the trust from the customers.

Secondly, once patterns are identified, drill down into the data to understand the underlying causes. For example, if customers frequently complain about poor product quality, analyze return data, quality control reports, and supplier histories to pinpoint quality breakdowns in the supply chain. Use predictive analytics to forecast the potential impact of these issues on future customer satisfaction and business performance.

Avoid being too granular in your trend analysis. For the average product or services, if you have more than 6-8 big buckets of features and functionality, it's too many. Once you have your big groups you can start to cluster similar challenges and focus on the biggest pain points first.

Implement changes based on these insights. If data shows customers are dissatisfied with customer service response times, consider optimizing workflows, adding staff, or introducing automated solutions like chatbots. Monitor the effects of these changes with ongoing data analysis to see if the issue declines over time. By systematically using data analytics to identify, understand, and address recurring customer complaints, you can improve the customer experience, enhance satisfaction, and reduce the frequency of similar complaints in the future.

One way to help develop actionable insights is to frame them within a customer journey. By doing this, you’ll help your audience understand the context and impact from a customer perspective. Here are a few steps to help do this: 1?? Collate all your feedback and group them into a handful of overarching customer journey steps (3 to 6) 2?? Translate each individual feedback type into a pain point statement (you’ll likely to have a few statements within each customer journey step) 3?? Make sure each statement is written how the customer would say it, rather than internal business language 4?? Create a visual that conveys outputs of steps 1-3 5?? For each journey step - create (ideally with SME’s) the ‘so what..’ & best next action

Small changes now are always preferable to big changes later. Why? From the company perspective: - Fast - Less Investment Cost - Less Risk - Quick Time to Value - We Can Fail Fast (if it didn't hit the mark we can pivot) From the customer perspective: - I can give feedback while it's still being finalized - I get some improvement right away - Less change at once means less learning curve

Siempre resultará más útil y será más valorado por los clientes, que la resolución de problemas sea supervisada y efectiva, antes que ser inmediata. La inmediatez puede prestarse en muchas ocasiones a cometer más errores por lo que es preferible prestar mayor atención al proceso que se implementará para resolver.