Customer Support Solutions Melbourne To Redefine Customer Service With Smart Analytics

Data volumes are exploding in this hyper-connected business world. Do you know what to do with this data or how to leverage it for business advantage? Data on its own is static. You need to filter, organize and analyze data, and derive insights from it to make it smart and dynamic. 

Now, how can you turn your data into smart data and create value? How can you glean valuable insights to improve your overall customer experience? It’s time for you to become more data-driven and focus on smart analytics. Customer support solutions in Melbourne have built a solid strategy driven by data and smart analytics to redefine customer experience and unlock growth. 

Role of Customer Service Analytics In Elevating Customer Experience

Customer satisfaction is the key to build a positive brand image, improve customer loyalty and ultimately increase sales. The most important step towards creating an exemplary customer experience is to listen to them. Businesses usually get caught up in reinventing their products and services and means to serve around their offerings. But the actual customer expectations might be different or might change over time. Businesses are at the risk of alienating customers if they fail to collect and analyze customer data and act on valuable insights. Customer service analytics helps you bridge the gap between the real customer expectations and the quality of support you offer.  

Customer data analysis or customer service analytics is the process of gathering and analysing customer data to derive valuable insights. It will help you make smart business decisions on customer support, product development, marketing, sales, and more. Smart analytics help brands uncover customer issues and address those pain points which are most impactful for both customers as well as the business. 

Phykon’s customer support solutions in Melbourne use analytics to map out the customer journey.  It will help businesses easily identify patterns in customer behaviour. Analyzing customer behaviour gives you a better idea of customer needs. Understanding customer needs will improve the quality of your customer support and elevate the customer experience. 

What Kind Of Data Is Significant?

Customer data can either be quantitative or qualitative. Both are equally important and complement each other.  Customer support solutions in Melbourne make use of both to get a broader perspective and a complete understanding of customers, market, products and services. 

Qualitative Data 

It often exists in a narrative form and is non-structured and non-numerical information. It is comparatively difficult to analyze qualitative data but it adds more value to your data sets. Some businesses do not give much importance to qualitative data but it is the true voice of your customers. You get to know your customers on a personal level by analysing qualitative data. It gives you a glimpse of a customer’s individual feelings, life experiences and motivation.  Insights into their real-life experiences will let you tap into new opportunities. It will help you identify the real customer problems, what delights and frustrates them and how their overall experience with your product. Customer support solutions in Melbourne analyze these data to help you deliver truly valuable solutions and a supreme customer experience. You can collect qualitative data through customer support calls, support tickets, discussion forums or social media platforms.

Customer Support Calls:

Support tickets contain information about all the interactions between the customer and the support agent related to a specific issue or question. You can extract valuable information from support tickets to improve your customer service and product as a whole. You can categorize tickets based on their topic. It will help you understand the most common issues, common customer queries, and detect trends related to them. It is difficult to analyze large volumes of support tickets as they are highly unstructured. You need to employ the right tools to categorize, analyze and derive valuable insights from it.

Feedback:

You can ask open-ended questions to feel the pulse of your customers. Customer feedback can give you in-depth insights into how customers feel about your product or service and find out where they are having trouble. Open-ended customer responses too are unstructured. Innovative technologies like AI can help you segregate, analyze and capture valuable insights from your qualitative data.

Front-line Agents:

You can interview the front-line customer support agents or sales teams who directly interact with your audience daily. They will have a good understanding of customer needs and common general issues.

Qualitative data enables you to uncover unmet needs and form ideas for problem resolution. However, this information is subjective and needs to be validated — that is where quantitative data comes in.

Quantitative Data 

Opinions and emotions can vary depending on individuals. Quantifying customer feedback and opinions helps you prioritize what to build next. Quantitative data is structured and can be measured using numbers, such as data usage metrics, surveys, customer service metrics or poll results. After identifying a few new features that customers would like, you can conduct a quick survey to ask customers whether they would use it and if so, how often. This quantitative data will help you confirm or deny your assumptions. It helps businesses precisely measure customer behaviour or expectations. It allows you to identify patterns among a broader customer base. Product teams rely mostly on quantitative data as it is more reliable. You can derive different interpretations from a qualitative data set while interpretation of quantitative data is much easier. 

First Contact Resolution Rate:

It is one of the most commonly watched metrics in customer support call centres. It indicates your ability to resolve the customer issue during the first contact or interaction. This data can be correlated with the quality of customer service and customer satisfaction.

Support Ticket Resolution Rate:

This is dependent on the volume of tickets, the complexity of issues and the number of agents you have. It is the rate of support requests solved by your customer service team. It is calculated by analysing the number of tickets resolved and the total amount of tickets received. This data allows you to measure the productivity and efficiency of your customer support team.

Average Response Time:

It is the average time taken to respond to a customer support request. Longer response time results in lower CSAT scores.

CSAT Score:

It is a customer experience metric that directly measures your customer satisfaction level. It indicates the percentage of customers satisfied with your product and services. 

CES:

Customer Effort Score indicates the effort a customer takes to interact with your team and get their issue resolved. Low CES improves loyalty and increases repeat purchases. Long wait times, redirecting to multiple agents and explaining the issue multiple times to multiple agents results in high CES.

NPS:

Net Promoter Score is a growth indicator. This score is calculated by asking customers how likely they are to recommend your brand, products or services to their relatives, friends or acquaintances. You can use this metric to predict customer churn rate or measure brand loyalty. These quantitative data will give you more insight into how consumers view your brand and actions to be taken to enhance customer experience. 

Customer Support solutions in Melbourne from Phykon will enable you to make smart, informed business decisions with insights derived from smart analytics. It will improve the quality of your service, boost productivity and improve your CSAT scores. We help businesses leverage smart data insights to deliver a more personalized experience. 

    ×

    Powered by WhatsApp Chat

    ×