Conversational insights value – Every day 2.5 quintillion bytes of data are created globally to serve the sales force or marketing, or to guide the strategy of positioning new products on the market.
Artificial intelligence and machine learning tools make it possible to collect and analyze this data which, however, often remain an exclusive domain of a few.
Conversations with customers on a large scale and across all channels, have the potential to drive growth for the entire company.
The entry channel for this information is the contact center.
For this reason, an effective customer care strategy is not just about improving KPIs related to agent performance or average call handling time, but is about the ability to use insights from conversations with customers and direct them to improve all levels of the organization.
conversational insights value and data democratization
The term “data-driven” is often abused and also used by organizations that continue to make decisions based on instinct, rather than on data.
Conversational insights can be functional to decision-making in every company department: for example, at the product level to decide changes and improvements, or in marketing campaigns to make them more effective and based on customer feedback.
Companies must make data accessible at every organizational level and in every business function, regardless of department or role.
For this reason, it is necessary to encourage the “democratization of data” which means “making the data available to the average business user, regardless of level and experience”.
Many organizations are striving to achieve this and are investing in the right technology.
For example, a research by Forrester reports that 85% of business decision makers are prioritizing AI and machine learning technologies with the aim of modernizing the customer experience.
But it’s not just about giving everyone access to analytics platforms and customer data. It is about making your people aware of what they can do with this available data.
AI can provide a picture of the customers’ emotions, behaviors and opinions at every point of the customer journey.
This data may help business people to take targeted steps to improve business results in various areas.
For example:
- Data from customer conversations are immediately identified by customer care agents to improve their performance and what to say during the interaction.
At this level, the data help improve customer satisfaction and reduce customer churn. - Supervisors can use the data to extract best practices and use cases to be used for training new hires or update the rest of the team. In this way, results are obtained in terms of job satisfaction and people retention.
- Through the analysis of insights, those who work on the product are able to identify recurring customer requests and develop new features or make improvements based on these.
- In Marketing, understanding how people react to promotions provides a valuable source of knowledge for campaigns optimization to increase conversions.
- In Sales, the conversational insights help understand which sales strategy may work best.
- In Finance, understanding the pain points of the process can help make payments easier and improve cash flow.
- For those involved in Risk and Compliance, conversational insights allow you to constantly monitor how agents communicate with customers, in order to improve training and ensure compliance.
Conversational insights value – In summary, even if the source of the data are conversations with customers, the insights resulting from these interactions transcend the domain area of the contact center.
On the other hand, the future of the contact center is one in which insights from customer conversations are used to transform the entire business activity, from contact center agents to C-level executives.
To do this, it is essential to adopt solutions, such as Conversational AI, which can help generate useful information that is converted into measurable business results.
Source:
Call Miner – Future of Contact Center: a forecast
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