NobelBiz | Blog How to use customer data analytics to improve contact center performance and CX? Published on 28. June, 2022 Visiting a website, talking to customer service, chatting on social networks... When a consumer performs these actions, the company usually collects multiple data points. The data collected, measured, and analyzed for contact centers is an absolute gold mine. When used wisely, it can greatly improve the customer experience. In other words, the link between data and customer experience can become a virtuous circle. This article focuses on the crucial role of customer data analytics in contact center performance and customer experience. What is Customer data analytics? Customer data analytics uses customer behavior data to assist in crucial business decisions through market segmentation and predictive analysis. Client data from numerous touch points or communication channels are gathered and analyzed to offer insight into consumer behavior. In today's connected world, where the consumer wields enormous power, it is critical for a business to use analytics to come closer to the customer. Types of customer Data Contact centers, customer service, social media, and mobile applications offer a goldmine of textual and statistical data... Data! A center-stage buzzword in the last couple of years... Data is undoubtedly a new eldorado, particularly for organizations that may expand their customer knowledge and focus on enhancing the customer experience by analyzing it. Here are some data samples from each of these sources: \tCustomer service and contact centers: includes textual data, such as email exchanges, chat dialogues, and phone call transcripts. There are also behavioral data, such as the history of the brand's connection, the communication channels employed, etc. \tSatisfaction surveys include open, closed, multiple-choice, and free text zones. \tReview sites and mobile apps: text and ratings in the form of points or stars. \tSocial media: from 140 characters and hashtags on Twitter to replies on corporate page Facebook posts, comments on YouTube videos, and Instagram posts... Advantages of using Customer Data analytics for contact centers Call center analytics enables you to gather and analyze client data to prioritize them. These reports also boost your call center and business intelligence by providing actionable data. Here are some more advantages of employing call center analytics: 1. Increased call center agility The purpose of establishing a contact center is to have a dedicated group of employees who can prioritize your clients' requirements and needs. However, if your call center agents are overburdened by high call volumes and poor staffing levels, they will be unable to perform successfully. This results in extended average handling times, lower resolution rates, and more churn. You may avoid this by using call center analytics to predict when large call volumes are likely - such as during vacations or product launches. This allows you to respond rapidly to shifting demand while also having additional people available to answer all incoming calls. You may also use call center analytics to gather data from your customer interactions to uncover weaknesses in your systems and procedures. For example, if you find that many of your customers contact your support teams through social media, you may change your staffing requirements appropriately. Consequently, your call center workforce becomes more efficient and can swiftly adjust to your customers' demands. 2. Assisting your teams in aligning with strategy Call center activities are frequently treated as distinct from other departments. As a result, the data collected from your call center and the data collected from your sales, marketing, and product teams are frequently not combined and shared. Because call center analytics connects all data sources, exchanging information across teams is possible and straightforward. You understand how each department influences the others by making customer data available to all your staff. You may also find methods to improve your collaboration. This allows you to match strategies and goals to improve the customer experience and build stronger customer connections. For example, when your marketing team creates a campaign, you may share it with call center agents so that they can promote it in both inbound and outbound calls. And, if previous experience indicates that the campaign will boost call volume, you may staff your call center accordingly. Consequently, your total business intelligence skills increase and team operations are optimized. 3. Encourage unbiased decision-making Trusting your intuition generally leads to poor business judgments. Intuition will not teach you how to optimize your call center operations or meet your key performance indicators. It will not tell you why one business choice is superior to another. Call center analytics, on the other hand, foster a data-driven culture. Call center data analytics makes information available to everyone in your organization. Call center managers may evaluate agent productivity to see where workers are lagging behind and where they are succeeding. They may also see how a certain option affects call times, conversion rates, and handling times. Because call center analytics measures performance, you can employ focused coaching to enhance each agent's abilities and provide performance-based bonuses. Data analytics may also be used in the employment process. To locate the best people, you might focus on the performance indicators that your support agents or sales reps share. 4. Boosting your conversions rates A strong analytics platform should not only enhance your call center's efficiency and productivity, but it should also proactively look for methods to increase income. It accomplishes this by predicting what clients could be interested in in the future based on behavioral profiles, demographics, and purchase history. Consequently, your sales staff can recommend that product to consumers or notify them when there is a special deal on it. Another option is to assist you in determining the most effective outbound call methods. For example, phoning prospects in the afternoon may result in higher conversion rates than calling in the morning. Based on the past sales approaches that have performed best, analytics may also educate your salespeople on how to properly phrase queries or adjust their pitch to convince clients to buy. 5. Enhancing agent performance As previously stated, call center analytics technologies do more than merely collect consumer data. They also assist you in analyzing your agents' performance. Analytics reports may show you where an agent excels and where they may want further assistance. They also enable you to identify top-performing agents objectively by using specific call center key performance indicators (KPIs) like wait times and first-call resolution rates for support agents or closure rates and deal value for sales reps. You may establish the best methods to arrange your call center operations and teams for optimal outcomes by determining the KPIs that fit your company goals. 6. A seamless customer experience Data analysis aims to match market demands by increasing the consumer experience ultimately. This necessitates an omnichannel approach and linking the various ways customers interact with your brand. Data will be especially crucial in ensuring a flawless consumer experience. This data will be processed to provide a smooth experience. For example, the aim is to enable the client to begin their experience on the internet and conclude it at the store without interruption. To provide this experience, information must be collected and updated in real-time. 7. A personalized customer experience In order to provide a personalized customer experience for each individual, the contact center must pay special attention to customer data in the face of competition. Offering individualized service is critical for standing out and providing the best client experience. A personalized customer experience necessitates a high amount of consumer information, which analytics can provide. You may better understand your clients and predict future demands by successfully leveraging the most relevant information. Many customer experience gains are stopped because executives fail to recognize their worth. But when they see it for themselves, it puts everything into perspective. Colin Shaw, Founder & CEO of Beyond Philosophy LLC,has decades of expertise in this field, assists leaders in establishing the proper perspective on the potential of CX. Find out more from our podcast episode " Customer Memories and The True Value of CX, with Colin Shaw " 8. Deeper customer insight Using data analytics, you can better understand your client base and why they call or drop by your business in the first place. For instance, if a person interacts with your call center, your call center agents can determine if they need to be educated about your products or services or if they need someone to talk to. This knowledge can then be used to solve future customer issues that arise. 9. Greater knowledge of the customer lifecycle By using call center analytics, you can learn more about your customers' behavior and expectations and improve your efficiency. For instance, when you see that someone calls your business on a Monday and makes an appointment for Thursday, you can automatically use this information to make arrangements to handle such a request in a way that will be more efficient. 10. Improved customer retention Many organizations suffer from high churn, especially in customer retention. Suppose you can better understand your client base and employ call center analytics to better serve your client base. In that case, you can boost customer loyalty and thus increase your customer lifetime value. 11. Improved quality of service Using customer data analytics, you can determine if your customer service team members need to meet deadlines for making appointments, for example. If the answer is yes, you can adjust the task for the next available agent. How to use Customer data analytics? There are several applications of customer data analytics in customer care or call center management. Here are a few major ones: \tConsumer analytics: This is the most common application of Customer data analytics because it plays a key role in understanding the consumer's requirements. For instance, what's happening in the customer's day? What do they want to communicate? How do they prefer to be approached? What would be the ideal time to contact the customer? \tTransaction analytics: This technology provides data and statistics on how customers have used the services provided by the company or call center. This helps improve the service the company or call center provides for a particular product. For instance, it helps give information on how customers use the products. \tField Service Analytics: This is based on the analytics of the work done by customer care agents, field staff, or dealers. The information is collected by calling the existing customers to collect information about their purchases. It provides insights into the demand for the product and helps handle the customers who are frequently receiving negative complaints. This information helps optimize the customer experience and also helps identify the problem areas. The process of data analytics involves the collection, aggregation, and interpretation of data. The data analytics is then used to solve business problems using aggregated data. The data analytics can be used to: \tStrengthen the customer experience \tGather customer insights \tDistinguish between high-value and low-value customer segments \tMaintain accurate records \tMonitor customer behavior The analysis of contact center verbatims allows for a better understanding of the customers; for example, the study of spontaneous consumer speech on social media allows for the identification of dissatisfactions with products and services, the relationship with the company in general, and its customer service in particular. It also enables you to discover consumer ambassadors and what pleases and promotes contentment! The analytical method employs a two-pronged approach: \tFirst, create an upstream analysis grid based on the identified individuals, known irritants, and various "contact points" with the business. \tIn exploratory mode, to uncover more causes for contact, growing concerns with our goods and services, weak signals... The results are then visualized: the insights are shown on dashboards as key indicators and ratings based on the various analytical models (sectorial, business, etc.). Customized Dashboards are everything! Indeed, Customized dashboards provide fast scanning of information to comprehend client comments and behavior. This is the point at which you transition from big data (thousands of documents and data) to Smart data (clear information that can be viewed and comprehended in the blink of an eye). To be smart, this data must be supplied to the right person, at the right time, and in the right way: disseminating customer knowledge inside helps to develop a customer-centric culture and allows everyone to understand the complete customer experience and their part in it. Each department involved in or impacted by the customer experience must have access to the "voice of the customer." Whether in the form of dashboards sharing meetings, targeted newsletters, or alert emails on sensitive subjects, consider these insights when designing and implementing new projects, launching products, selecting tools, or training agents. The voice of the customer Voice of the customer analysis enables you to be proactive by identifying problems with products and services as early as possible, adjusting customer journeys by including all positives and negatives at each stage, completing and illustrating NPS surveys, and documenting them to understand this score. It is simple to measure campaign performance, but it is more challenging to study satisfaction in the sense of trust, emotions, and those components that allow people to adhere to the brand's values, a connection based on sentiment, and a relationship that goes beyond the NPS score. The analysis helps you gain insights after gathering, extracting, and classifying the data. \tGain a more comprehensive understanding of its prospects and customers (reasons for contact, recurrent irritants, expectations and wants, etc.) to modify customer journeys, and improve FAQs and information accessible at ports of sale, internet, and mobile, for example. \tRequest subjects and grounds for contact (We know who they are, their history with the brand, their chosen channels, and their preferred items) to reply swiftly and appropriately and to provide adapted or personalized offers. For example, a phone company needed to know the stages between selling a mobile subscription and when the consumer could use it. It turns out that the process was sophisticated and resulted in a large number of incoming communications. The task was to streamline this approach, which was costing the organization money and causing displeasure. This was resolved by making an outgoing phone call upon the signing of each contract to answer all of the listed questions. The end result is tailored to help create a streamlined procedure and save costs. Conclusion Customer data is currently a complex challenge. Effective and meaningful processing of this information enables you to better align your strategy with the future of your contact center. In this respect, analytics and diverse tools for processing consumer data will become significant business assets. Today, these technologies may be used in all professional sectors, allowing for the creation of a superior customer experience that will accompany customers throughout their purchase process. Implementing a robust analytics foundation may help you adapt your business strategy. And that’s where NobelBiz Omni+ comes in! With our cloud contact center software OMNI+, you have access to a customized Reports Engine that enables you to leverage the power of data. Get trustworthy data from numerous analytics and reporting systems that provide historical and real-time data that you can manipulate to build comprehensive performance reports. You can also accommodate virtually any communication channel: Voice Calls, Facebook Messenger, Twitter, WhatsApp, Telegram, SMS, Email, Live Chat, Webchat and Voicemail.