Transforming decision making with real-time analytics
Published August 17, 2020
Last updated November 6, 2020
If you’re managing a customer support centre, how many problematic client conversations are failing during a week because your agents don’t have a better understanding of customer sentiment or their customer journey? None of your customers wants to feel as though they are just a revenue-source in the crowd. In the age of hyperpersonalisation they expect engagement tailored to their history, preferences, context and intent.
You’re not alone: In today’s volatile environment, businesses need to be agile and more flexible than ever to keep delivering the personalised CRM experiences that customers expect. Keeping pace with the rapidly changing expectations of customers was already challenging in an environment where roughly half of customers in the UK said they would switch to a competitor after just one bad experience. In the case of more than one bad experience, that number even snowballs to 80%. Now the uncertainty created by the pandemic has added to that.
Finding patterns, trends and understanding behaviour
It’s no wonder that CRM managers have drawn on current and historical data as an objective basis to gauge the success of their efforts and identify patterns, trends, behaviour - with the aim to objectively understand what areas need improvement. Integration with analytics simply makes CRM systems more intelligent in comprehending customers. It empowers managers to make data-driven decisions, regardless of company size or industry vertical. Cross-channel analytics can provide a unified view of the customer, allowing customer service teams to understand customer journeys, channel preferences and relevant account data. These insights then enable them to personalise the customer experience.
“CRM analytics can be defined as the process of capturing and processing customer data that resides in your CRM database to uncover and visualise useful insights about customers that you can act on to optimise your operations."
For customer service, the typically used metrics include CSAT (Customer Satisfaction) and NPS™ (Net Promoter Score™). Other factors can be first reply time (how long it took an agent to respond to the end user), full resolution time (how long it took for the ticket to be solved), or rate of one-touch resolution (the percentage of tickets resolved with a single interaction).
Turning real-time insights into actions
The downside of reviewing metrics post customer interaction is that it is too late to intervene in problematic conversations. That’s where predictive and real-time analytics come into play. The latest advances in AI, modern CRM systems you can now use your data to take customer satisfaction to a new level and drive long-term relationships.
Predictive analytics capitalise on patterns found in your historical and transactional data to anticipate what will happen in the future. It aims at suggesting actions for optimal results - empowering your team to help your customers while they are still in a conversation with them. In practice, this often looks like a live feed of all the necessary and important information on your dashboard, or can be a broadcast for support agents so they can see where to allocate their time.
With real-time analytics you can get a better understanding of how a conversation is going while you’re still in contact with the customer. Entering a conversation with more context helps to gauge the temperature of the relationship and give your support agent immediate feedback on their actions as the prediction updates in real time. This allows you to decide which action can be taken to ensure a positive outcome, thus reducing risk of failure. It drives retention, CLV (customer lifetime value) and can also offer an opportunity to improve sales conversion rates and identify upselling opportunities.
Internally, analytics can help optimise processes and the customer journey. Visualizing spikes in calls allows support centre managers to influence ticket distribution based on a satisfaction prediction score and route “high risk” interactions to more experienced agents or those with specialist knowledge. They can intervene to deliver a better customer experience and make customer support more efficient. With a more complete understanding of real-time customer behaviours, agents can resolve issues more quickly and more satisfyingly - stopping problems before they arise by prioritizing tickets with low satisfaction prediction scores, for example.
It definitely also pays off for the customer, who is getting a more satisfying, personalised solution that takes into account a holistic view of their relationship with the brand.
The most successful companies don’t stop there. They have made a data-driven approach their second nature and systematically capitalise on the value of their insights beyond the support center. They use customer feedback to make specific, data-backed improvements of product features or service design. Making your data internally accessible to everyone who needs it within your organisation boosts collaboration across the entire organisation.
With a better understanding of the types of customers you’re likely to gain the most value from any investment you make. You can also increase the reliability of your profitability analysis or better decide which projects and innovations are worth pursuing. This allows businesses to allocate more of their marketing and sales efforts on the most valuable segments, ultimately increasing their ROI over time.
Moving towards real-time analytics has become crucial to a modern support strategy to gain maximum business value out of a CRM system. But don’t expect the pace to slow down. Areas such as real-time speech analysis might currently still be a young discipline, but it is quickly developing and getting integrated in the omnichannel strategies of agile firms.