How to improve customer satisfaction with data analytics ?

Improving customer satisfaction

Consumers, in their never-ending search for something new and better, expect excellence. On the front lines of today’s digital trade revolution are customer service satisfaction agents, who are expected to do their best to deliver high-quality customer experiences.

However, in order for your teams to deliver excellence day by day, they must have the tools of analysis and data to understand customers’ wishes, needs and values at every stage of the customer’s journey.

In fact, 34% of customer experience professionals (CX) identified investing in advanced analytics as a top strategy to meet future customer expectations, according to Walker’s 2020 report.

Disassemble and detail data

Data repositories occur when stored data can only be accessed to one group in an organization instead of the entire organization. Data are known to lead to lower productivity and increased deficiencies. This can happen in a variety of ways, including data collection and analysis in a different way.

Different data collection and analysis

Different teams often collect and analyze different data in different tools, leading to separate perspectives on customer behavior and journeys. Problem solving customer experience is a multifunctional exercise, as different teams are responsible for different points of the customer’s journey.

To break data silos, data management and analytics strategies must be a multifunctional effort, with stakeholders from each team collaborating on how to identify the customer at each stage of their journey, build measures of success, and develop a customer lifecycle sharing strategy based on available data.

Lack of integration

If employees have to deal with excessive tool switching due to lack of mergers, data will be missed or duplicated, leading to low integration analysis.

It’s important that each source of your data flows into the same analytics platform – so that team members across the company receive consistent and reliable data.

Integration also helps to paint a complete picture of a customer satisfaction journey and experience, giving every stakeholder in the customer experience a 360-degree view of how people interact with their brand.

Otherwise, your teams risk making decisions that are influenced by unilateral data (e.g. relying on anecdotal data without looking at behavioral data).

Inadequate tools and techniques

Without the quality tool to analyze your data, teams will take matters into their own hands to get the job done. This may mean adopting a limited seat tool, building your shadow technology, or spending time on custom analytics in desktop spreadsheets.

On the other hand, when teams are equipped with access to relevant insights, teams can get rid of guesswork in understanding specific customer needs and difficulties.

Great experiences will constantly satisfy customers

It’s key when it comes to customer satisfaction wherever they are, on any channel. Outside of direct sharing, self-service tools provide customers with a starting point for common problems and can provide traceable insights into the problems your customers often face when dealing with your brand.

The real multi-channel support platform will also include automation that converts customers’ emails into help desk tickets, in order to track, prioritize and resolve orders in one place.

To take a step forward, advanced analytics can help you predict and improve business reports, identify and anticipate key factors for customer satisfaction quality, run downhill processes and collect raw data to find new patterns.

This comprehensive view of your support team can help identify customers who deserve positive recognition, as well as report potential problems and solve ongoing problems.

Great customer experiences achieve high scores in evaluating your business and also promote retention, loyalty, support, and growth. Providing these experiences can be facilitated by data analytics provided to your support team with relevant context and actionable insights at the point of action.

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