In today’s competitive market where customer service has become the most influential brand differentiator, corporate executives need to focus their decisions on reliable research, data, and information.
A company’s way of interaction with its consumers affects how they perceive them, which influences potential customers in addition to their most loyal ones.
It makes organizations eager to find ways to analyze their customer’s service. Here, Big data analytics comes into the picture. Big data has a set of information that can help to analyze computationally to reveal patterns, associations, trends, etc. to review human behaviors and interactions.
Let’s first have a brief about customer analytics.
Customer analytics is a process that allows using customers’ data to understand their behavior which helps an organization to make smart and well-informed business decisions. It helps to view market segmentation and predictive analytics for enhanced marketing and helps to manage the relationship with the customer.
Big data services simply refer to a large dataset which can be stored and analyzed by the latest technologies to provide business insights and to help any company to make the right decisions. It is the process of collecting and analyzing online customers’ activities data of any site or app, that helps companies to deliver enhanced services to their customers.
Gathering and analyzing data is crucial to make healthy business decisions, it enables companies to research consumer behavior and to use those conclusions to enhance customer satisfaction.
Reviews are an excellent way to know about the performance. Depending on the service, you can take reviews on Google, App Store, yelp, etc. Reviews help to know about customer satisfaction, their issues, experience, and the voice of customers. It gives insights into customer experience and impacts hugely on any business either by uplifting via recommendation or by criticizing it publicly. In this, an automated software analyzes all the submitted reviews and the customer’s opinion about any particular product/service and classifies reviews as positive, negative, and neutral.
Big data analytics enables the ability to gather customer feedback through online reviews, social media, and from the internet, that makes companies able to take the feedback of customers and put it into improving the overall experience.
Big data analytics tools help to get an insight into the requirements and choices of each customer. It allows us to make separate strategies for each client, and does not work on a ‘one size fits all’ approach. Catering to each customer allows expanding loyal customers, which is the biggest long-term business driver. A study also shows that increased 5 % retention helps any organization to generate 25% more profit. Big data helps here to analyze all of the previous consumers’ accounts for all action details to determine their personal preferences accordingly.
Big data management metrics says a lot about the customer’s experience. As longer AHT (average handling time) low rate of FCR (first contact resolution) indicated communication issues which needed to be resolved. It helps to identify any process and training guidelines such as CRM database handling etc.
With big data analytics on your side, you can easily calculate customer response time across multiple channels. It can help to determine the most explicit explanations for all customer inquiries, weak & the strong spots in customer service procedures, and compile it into a customer service guideline, to help agents in increasing their problem-solving efficiency. Understanding metrics can help improve contact center practices and the overall customer experience.
Big data is critical to implement targeted marketing practices. Such as click-through rates of links communicated through text messages, marketing emails, and other channels can reveal whether a marketing strategy is leading to actual sales or not. A geolocation data helps to determine that brands are able to target the right people or not. Brands can use this information regarding sales events and extra discounts that may be applied in-store. This data can be used to make customer-centric strategies, and interpreting this data can lead to improved service practices and more reliable customer experiences.
A good customer service depends on competent and reliable employees; they work as a frontline worrier and can also break a business. As per a survey, 90% of global buyers play a crucial role in the choice of the brand. Big data analytics help to find agents who are lacking in their performance, such as, response time average, abandonment rate, hold time, and all other factors that influence customer service productivity.
On the other hand, NPS is a popular method for conducting customer experience analysis and measuring customer satisfaction. It is a numerical score calculated by customer recommendation about any particular brand service/product on a scale from 1 to 10 (1 is lowest and 10 is highest). NPS allows us to segment customers based on their rating, enthusiasm, and loyalty.
Though Big data analytics uses a relatively new method to analyze customer service, it represents meaningful conclusions out of the massive pile of seamlessly irrelevant information, enabling companies to increase consumer satisfaction, opportunities to reach leads and increase their sales to fine-tune existing marketing campaigns, and of-course the retention rate.