What do your customers really want?
In the past most customer interactions were reactive rather than proactive, but customer analytics are changing all that. How can you use analytics for better insight and service?
Your competition is after your top customers, and your customers have a lot of options. That’s the truth of it today, and it means companies have to work much harder – both to understand customers and to act on those insights, in order to retain that customer and drive additional revenues.
Customer analytics, centred on both structured and unstructured data, is playing a large and growing role in helping organisations use the data they have to identify what’s important to customers, pinpoint next steps, and follow through with the appropriate response. (Did you know that customers usually defect because of the excess effort it takes to interact with an organisation? A typical next-step action following an analytics program may be for the organisation to reduce the work customers must do to get issues resolved quickly).
Structured and unstructured interactions offer customer insight
A good analytics solution will allow you to look both at the structured data (transactional data, location) and unstructured information (social media, contract history) arising from customer interactions. By analysing this mix, organisations can identify the preferred channels of communications based upon the circumstances of the contact.
Real-time and near-real time analytics of voice calls from the contact centre are one of the more intriguing aspects of customer analytics, allowing an insight into changes in customer sentiment or the market. Keyword analysis of calls, for example, can provide the earliest warning that a competitor has launched a promotion. Tone of voice analysis, meanwhile, may indicate a developing problem with an agent whose calls frequently result in an agitated or angry tone on the part of the customer.
Cross-selling and upselling
Applying analytics to customer databases, especially if these stretch over years, also allows the building of rich customer profiles that can reveal cross-sell and upsell opportunities that have been hidden in the past. Smart analytics can enable the contact centre agent to be prompted with relevant information to introduce an upsell or cross-sell during a customer interaction; alternatively, the same mechanism can prompt an online customer with suggestions of what else they might like to purchase (“Customers who purchased this product also purchased…”)
Predictive analytics and decision management
Predictive analytics and decision management tools are useful in helping companies understand what prevents customer churn and contributes to retention. Simple things like text-message reminders (to warn customers who are about to go over their limit on a bank account, for example, or to remind customers of a consultant appointment the following day) all help prevent problems and lead to higher customer satisfaction.
The mountain of customer data is growing, and provides a significant opportunity for organisations to get closer to customers, improve retention and prevent churn. There may be obstacles – for example the integration of multiple customer databases following acquisitions, or data silos resulting from years of standalone systems. But the effort is worth it, allowing organisations to develop intelligence that can bring customer service to the next level, including communications with customers on their preferred channel, truly personalised offers and significantly improved retention.
What is your plan for integrating customer analytics in the enterprise? Could better insights about your customers help you react faster in a changing market?