Customer service is a core component of almost every successful business model, whether or not an organization is in a service-oriented industry. But as consumer preferences continue to evolve, companies will increasingly turn to digital technologies to help them meet or exceed their customers’ expectations.
Data analytics, in particular, can provide business leaders with a wealth of actionable information about support interactions, consumer trends, sales conversions and more. However, harnessing big data to its fullest requires the right combination of knowledge, tools and experience. So how do companies leverage data to provide better overall customer experiences?
Keeping up with consumer preferences
According to a 2017 study by Microsoft, around 54% of global consumers report having higher customer service expectations than they did one year prior. What’s more, close to 96% of respondents believe that customer service is critically important for building long-term relationships and cultivating brand loyalty. While it’s clear that consumers are starting to expect more from their brands in terms of personalization and convenience, many companies have struggled to meaningfully adapt their practices to modern preferences.
Generally speaking, the push toward increased customer service automation has introduced new complexities into traditional call center frameworks. For example, organizations across the globe have started integrating chatbots and other artificial intelligence tools into their existing customer service environments. And although some consumers may appreciate self-service features, many still prefer to speak with a human representative when resolving issues and making high-value purchases. In fact, a survey from GetApp discovered that 88% of customers say that talking to a person is their preferred method of customer service interaction. But where does big data fit in?
Collecting and analyzing customer service data can help companies understand how consumers are contacting and engaging with their support features. This allows key decision-makers to weigh the benefits and costs of AI-driven platforms, advocate for evidence-based solutions and optimize their consumer engagement practices. In most cases, a multi-channel communication strategy will likely stand as the best option, but knowing where to divert resources can help streamline new deployments and avoid potentially damaging support line updates.
Merging big data and customer service
Today’s B2B marketplace is saturated with customer service management software and web-based applications, which can make it difficult for organizations to locate products that align with their unique business objectives. Luckily, most industry-leading CRM solutions offer somewhat comparable data collection and analysis capabilities. But when it comes to data-driven customer service, information sharing is by far the most important feature. When contacting a support representative, around 72% of consumers expect the agent to “know who they are, what they have purchased and have insights into their previous engagements,” per Microsoft’s 2017 study. This heightened personalization can help customers feel acknowledged and appreciated, while also creating an efficient documenting system for use in internal reviews. Some of the key metrics businesses should track include:
- CSAT scores: Customer satisfaction scores provide businesses with real-time information about how consumers perceive their products, services and support features. This feedback is crucial for gauging whether existing customer service practices are effective and for locating areas of improvement. A study published in the Harvard Business Review found that customers who rate their experiences highly typically spend 140% more than those who give low ratings, suggesting that superior customer service can help boost a company’s profitability.
- Support ticket volume: This metric is vitally important for businesses that utilize multi-channel customer support strategies, as it can reveal which platforms consumers are gravitating toward. Additionally, ticket volume can be an indicator of broader issues a company may be facing, from product defects to public relations challenges. Monitoring ticket volume can not only optimize agent staffing, but can also identify behavioral trends that may inform future marketing and customer service decisions.
- Full resolution times: Tracking time-to-resolution can help organizations pinpoint complex customer service queries and establish detailed knowledge bases on specific topics. Keeping pace with consumer trends can be difficult without data collection and analysis software, which may leave organizations unprepared for spikes in call volume and website traffic.
- Sales conversions: Some companies have started launching several digital marketing campaigns simultaneously in the hopes of locating the most effective advertising techniques. This strategy is also useful for learning more about specific customer demographics, shopping preferences and consumer pain points. Of course, before this information can be leveraged, organizations must be able to track unique engagements across several distinct communication channels and contact numbers.
Now that mobile phones have become commonplace, companies need to offer customers convenient and reliable methods of resolving their issues, purchasing products and lodging complaints. According to the global consulting firm BIA/Kelsey, businesses will likely receive a projected 169 billion mobile calls by 2020, which demonstrates the importance of timely call center optimization.
If your company is looking to improve its customer service practices, Dial800 is here to help. For over twenty-five years, we have offered the enhanced business phone services our customers need to stay informed about the volume and quality of their sales leads. To learn what Dial800 can do for you, read through our product pages or contact a representative today!