Customer Experience Analytics: Why customer experience counts in today’s digital era?
Organizations with a customer-centric strategy deliver value, enhance customer experience (CX) at various touch points, and positively impact the bottom-line results. Marketers are leveraging artificial intelligence (AI) and predictive analytic tools like customer analytics to gain insights about their target market and existing customers. Artificial intelligence and big data analytics help businesses to not only streamline their processes but also in improving their customer experience. By integrating artificial intelligence and customer analytics within the business processes, companies can create a positive experience for the end-user, increase the consumer spend, build customer loyalty and drive brand advocacy to gain a competitive advantage in the market.
Customer Experience and Artificial Intelligence
Customer experience or user experience can be defined as the consumer’s perception about a brand or a product. It is essential for the organizations to direct their efforts towards creating a positive customer experience and retaining them with the brand. The costs of acquiring a new customer is five times more than retaining an existing one; thus, making it crucial for brands to offer a seamless customer journey across all touchpoints. User experience can determine the success or failure of any brand or business and can also be enhanced by integrating artificial intelligence, chat bots, or machine learning coupled with predictive analytics to leverage the customer and market insights.
How Does Artificial Intelligence Improve Customer Experience?
Artificial intelligence and chat bots help in creating personalized experiences and enabling intelligent, accessible engagement with the customers. It assists the end user to achieve their objective or offers solutions to their problems, thus, driving satisfaction and improving the overall customer experience. Artificial intelligence also enables businesses to gain insights into consumer behavior by sorting through a large amount of data generated, and helps them to navigate, understand, and enhance the sales or customers’ journey. But in what ways will artificial intelligence impact the customer experience, you ask? Here’s how.
- Artificial intelligence allows us to leverage the data available and comprehend consumer behavior and traits through customer analytics to streamline the customer interaction process by making information available and accessible across several touchpoints
- It leverages the overflowing and widely available consumer data through various devices and offers insights into consumer behavior and market trends. This helps businesses to incorporate personalization in customer experience by leveraging interactive applications such as chat bots e.g. Facebook messenger. These chat bots are advanced computer programs designed to simulate an online conversation with humans
- Artificial intelligence enables conversational commerce, by piecing together individual touchpoints and completing customer journeys to enhance and re-design customer experiences
To know more about customer analytics and its advantages
Business Challenge: Improving customer satisfaction by efficient order management. An Asian industrial cleaning products manufacturer wanted to improve its order management process, to reduce stock outs and improve customer satisfaction.
Situation: Lack of timely order fulfilment causing customer dissatisfaction
The client did not have a proper order management process in place. Consequently, the client was facing issues such as stock outs, large unused and out of date inventory, delayed shipments, and could not fulfil many orders on time, resulting in low customer satisfaction.
Solution/Approach: Order management analytics and predictive modeling to build an efficient order management solution
We used order management analytics on inventory data to cluster products based on sales, stock levels and lead times, at an individual distributor and retailer level, and map inventory data to sales forecasts and sales cycles. Based on this information, we built an order management model.
Impact: Improved customer satisfaction and revenues
The cleaning products manufacturer used our insights to predict and maintain appropriate inventory levels at distributor and retailer levels, to ensure availability of its products at all times. This helped the client in eventually improving the customer satisfaction levels and improving profits.
Business Challenge: Real time customer feedback for measuring customer satisfaction
A leading logistics client wanted to develop real-time customer feedback and analysis framework to measure customer satisfaction levels.
Situation: Need for new analytics based customer feedback process
The client had an existing survey based process for customer feedback which was old and was not capturing valuable customer data. The client wanted to run a customer satisfaction program and was looking for real-time data stream for analysis.
Solution/Approach: Big data analytics with prioritization framework
We conducted customer analysis and setup customer satisfaction process. The big data analytics model was created to collect and aggregate the customer data on areas such as billing, complaints, repairs, contracts and contact center calls. The model provided real-time feedback with prioritization framework and risk flagging methodology.
Impact: Reduction in customer complaints and improved satisfaction
Client gained a structured process to track customer advocacy and measure organization effectiveness. This helped in real-time customer interaction mapping and flagging risk episodes. As a result, there was considerable reduction in complaints with improved proactive issue resolution mechanism, as well as greater customer satisfaction.
Business Challenge: Improving customer satisfaction through better order management
A leading freight and logistics company wanted evaluation of its 3rd party logistics services providers and spend analysis to shortlist the most cost effective suppliers.
Situation: Providing real time information on order status
The client had recently conducted a survey in which the customers cited the need for improvements in timeliness and accuracy of the orders. To provide these services, the client wanted to utilize its customer portal, which could quickly provide the customers with accurate, personalized, and actionable information for tracking of their route, fleet and shipment schedules.
Solution/Approach: Delivery analysis, order management and monitoring
We churned all the customer and operational information from the client’s systems, and integrated them together to define key metrics such as order status, packaging time, dispatch time, vehicle route, and service personnel contacts. The finalized key metrics were then analyzed using delivery time analysis and order management analytics, to be utilized into the client’s customer portal for providing real time alerts to the customers.
Impact: USD 550,000 revenue and improved customer satisfaction
The client was able to provide real time information to its customers regarding the status and location of their orders, and also provided alerts in case of delays. This helped improve customer satisfaction and the client saw a surge in revenue by USD 550,000.
Business Challenge: Creating optimized transport network and improved customer service delivery
A leading transport and logistics company wanted to optimize its transportation network to reduce customer dissatisfaction and improve customer experience.
Situation: Service failures causing customer dissatisfaction
The client was facing persistent rise in customer dissatisfaction from its cargo delivery services. The client wanted complete network analysis to identify the cause of service failures and understand customer dissatisfaction patterns across various routes and channels, in order to create robust plan to eliminate all service issues and solve customer grievances.
Solution/Approach: Network optimization, channel management and customer analytics
We consolidated all of the company’s distribution and logistics data, and utilized network analytics for determining the best node and hub model and transportation planning. We also mapped customer complaints across various routes to identify the service delay as the major reason, and used channel optimization analytics and customer analytics for efficient service delivery.
‘Impact: Reduced service delay and improved customer satisfaction
The client used our network model for efficient transportation planning which eliminated service delays. The client also utilized our insights to modify its customer care service such that the customers were easily able to contact the front desk customer executives in case of delays, who offered first hand information on the status of delivery and delay resolution, thereby reducing customer dissatisfaction.
Business Challenge: Improving the order management process.
A leading lubricants manufacturer wanted to improve its order management process, to reduce stock outs and improve customer satisfaction.
Situation: Lack of proper order management resulting in reduced customer satisfaction.
The client was following traditional order management process and as a result of this, was facing regular issues of stock outs, large unused and out of date inventory, and delayed shipments. As a result, they were not able to fulfil many orders on time, resulting in low customer satisfaction.
Solution/Approach: Order management solution based on inventory data.
We deployed an order management solution that made use of inventory data for clustering the products based on sales, stock levels and lead times. Our solution also allowed the client to further classify the data at individual distributor and retailer levels, and mapping them to sales forecasts and sales cycles.
Impact: Better operations and improved customer satisfaction.
The client used our insights for predicting and maintaining appropriate inventory levels at distributor and retailer levels, to ensure availability of its products at all times. This helped the client in eventually improving the customer satisfaction levels and improving profits.
Business Challenge: Improving claims management process
A US based insurance provider wanted to improve the management of its claims resolution process in order to improve customer experience while reducing management costs and insurance claim frauds.
Situation: Non-cohesive claims management data obstructing claims management visibility and process
The client’s insurance claim data was stored across various silos in its legacy claims management system, which did not provide enterprise level visibility into the process. The result was high claims processing time causing customer dissatisfaction, as well as inefficient validation to reduce frauds and risks. Client needed a system which could deliver better claims management and resolution.
Solution/Approach: Claims predictive analysis, risks analysis and process optimization
We used claims predictive analysis and risk analytics on the client’s historical and real time data, to identify claim patterns, create KPIs to determine the validity of claims, anticipate potential suspicious claims and fraud risks. We also utilized process optimization to generate ideas for reducing the claims processing time and costs.
Impact: Improved customer satisfaction through reduced processing time
Our solutions helped the client improve claims accuracy and eliminate potential frauds. The client was also able to significantly reduce the turnaround time for claims processing and enhanced its service levels, which delivered improved customer satisfaction and cost savings.
Business Challenge: Improvement of network performance and optimization of operations
The client wanted to setup a robust solution that would help them successfully enhance network management and optimize network operations.
Situation: Reduced customer satisfaction due to traditional network management techniques
Due to the increasing number of subscribers and rising volume of data, the client was facing challenges in managing the network efficiently and in planning the network capacity. They were facing issues in managing huge volumes of information to derive required insights. The historic methods of network management resulted in lowered levels of customer service, thus creating a negative impact on customer satisfaction.
Solution/Approach: Network management solution for improving operations
We deployed an end-to-end network management solution that enabled the client to manage the network in an improved manner. Our solution defined key performance metrics and used predictive analytics techniques to identify and resolve network issues. Our solution also provided the clients with insights on optimizing network spend for improved profitability.
Impact: Improved customer satisfaction through implementing network analytics
Our solution helped the client in proactive identification of network issues and resolving them in a timely manner. This helped them in improving customer satisfaction levels and reduce customer churn rates. The client also was able to improve its capacity planning efforts for providing a greater quality of service.