In today’s ever-evolving business landscape, brands need to double down on their efforts to win and retain customers to stay resilient and push themselves ahead of the competition. To achieve this, brands must create unique and personalised experiences that make customers feel understood and recognised for what they are seeking.
This is where the power of data truly shines, as it can act as a compass for brands to deliver products that customers are likely to buy and stay on top of upcoming trends. In the article below, we will explain more about how your customer experience playbook can benefit from big data analytics.
Importance of big data in customer service
Big data and customer service are essential to ensuring business success in the modern era. By combining customer interactions and purchases, brands can use it to identify customers’ issues, recommend products they are more likely to buy, and personalise conversations.
Below are some of the roles in which big data can contribute to help brands win the hearts and minds of their customers:
- Competitive advantage: Big data can help businesses understand customers better, predict market changes, and respond proactively to trends. With this information, brands can recommend offerings and make continuous improvements to their services to ensure you have a delightful experience.
- Increased customer loyalty: Data on customer feedback and preferences can act as a blueprint for brands to tailor products and services. It can also guide brands to deliver articles, videos, or stories that customers might find interesting, leading to stronger relationships with the brand.
- Improved revenue: Data-driven marketing and personalisation can enhance customer relationships and your bottom line. By analysing historical data, brands can predict what customers will buy and create personalised offers to encourage more purchases. These efforts can boost sales and profits.
- Operational efficiency: With big data, brands can monitor operations and supply chains for any issues that arise. For example, brands can determine which equipment requires repairs or upgrades. This is crucial in maintaining operational efficiency and preventing downtimes from hampering brands’ abilities to meet customers’ demands.
Read: Why Is Data-Driven Analytics Of Interest To Companies?
Application of big data in customer service
There are many ways in which big data can be used by businesses to enhance customer service and encourage repeat business. If you’re a brand looking to change your customer service strategy, we encourage you to read this section below for ideas on how to harness the power of data science to its fullest potential.
Understanding customer behaviour
Analysing customers’ interactions and purchases can help brands uncover factors behind their purchasing decisions, such as the time of day or external events. Armed with this data, brands can offer timely offers during periods when customers are more likely to make a purchase, boosting the chances of a successful sale.
Personalising customer experiences
Besides gaining insight into customers’ behaviours, data from interactions and purchases enables brands to recommend products and services that customers may likely want. This makes for an easier and faster shopping experience for customers.
Brands can also create and deliver articles, videos, and marketing messages that customers are most likely to see. These efforts can help brands boost engagement and drive more people to their front doors.
Predictive analytics
Sales data and demand patterns can help brands anticipate customers’ needs in the future. From there, brands can tailor their products and services and optimise their inventory to prevent goods from being sold out.
Predictive analytics can also help users stay on top of issues before they occur. For instance, big data can inform IT teams of slow website loading times so they can take steps to optimise its speed. This includes reducing image size and deleting unnecessary plugins. By doing this, brands can provide a more convenient online experience for their customers.
Read: Customer Data Analytics: A Complete Guide
Customer segmentation
Insights into customer demographics, purchase history, and income level can help brands segment their customers. Segmenting customers is crucial as it allows brands to tailor their offerings and personalise their marketing strategies.
For example, brands can offer incentives like free deliveries to encourage customers who purchase less than $50 to add more items to their cart. They can also recommend accessories or related goods to customers who buy a specific product, thereby adding more value to their order.
Sentiment analysis
With the help of Natural Language Processing (NLP), brands can analyse customer feedback to figure out how their customers feel about their products or services. For instance, brands may find out that a specific product has more negative reviews than positive ones. By going through reviews, brands can get ideas on how they can improve their offerings so they can meet customers’ needs.
Chatbots and virtual assistants
Chatbots empower customer support teams by providing answers to frequently asked questions, leaving agents to focus on more complex queries. With the power of big data, chatbots can also provide personalised communications to help customers along their buyer journeys. In particular, chatbots can invite potential leads to read a blog or watch a video to introduce them to the brand and its services.
Recommendation engines
Recommendation engines use big data analytics and machine learning (ML) algorithms to understand what products customers are looking for. From there, they can provide relevant offerings based on this information.
For example, recommendation engines can offer customers suggestions on hotels, restaurants, and activities to do based on their travel budget and history. This strategy can help travel brands boost sales and conversion rates.
Agent training and development
Big data can help brands identify which team members need training as well as pinpoint opportunities to improve their customer service. In particular, data analysis may reveal that more of your customers interact with your brand through Twitter. This can motivate you to create a dedicated account and train your staff on how to communicate via the platform.
Customer satisfaction measurement
Businesses can use big data to understand how satisfied customers are with their products and identify any pain points they might have encountered throughout their interaction. With this information, businesses can take steps to resolve these issues, such as introducing requested features or using automated systems for faster response.
Improving operational efficiency
Smooth-running operations are crucial for brands to retain customers and provide the best experience possible. Harnessing data makes this possible by spotting issues before they arise and predicting which products or equipment might need maintenance. By adopting this proactive approach, brands can reduce the risk of services being affected by downtimes and disruptions.
Leveraging social media
Grappling with social media communications can be challenging. This is especially the case when your customer service teams have to handle thousands of posts daily, and customers expect instantaneous responses. With big data, agents can process massive sets of feedback and identify negative responses that agents should prioritise.
Analytics can also be used to identify which social media channels most customers interact with. This will give brands a better idea of where to focus their advertising and marketing efforts.
Conclusion
Big data is a game changer in ensuring success and improving brands’ survivability in the current business landscape. Whether you operate a small-scale retail shop or an established business, embracing data analytics is essential to keeping customers around through personalised experiences and relevant offerings.
