More and more recurring-revenue businesses are looking at previously untapped sources of customer data from contact centers to create deeper intelligence about the drivers of customer experience. This deeper intelligence helps these companies to manage customer experience effectively and create a competitive edge.
By gathering customer perceptions of their experience, these businesses are able to use the data to get an insight into how to improve the quality of the customer relationship to improve customer loyalty. Such recurring-revenue companies use the data to identify where customer experience improvement efforts will have the greatest return on investment (ROI).
Business analytics in increasing customer loyalty
Business leaders often find it difficult to use customer feedback in the form of unstructured text for identifying loyalty improvement and ROI optimization opportunities. Here comes the crucial role of business analytics, which generally includes three phases: Descriptive, Predictive, and Prescriptive:
|Descriptive Analytics||Predictive Analytics||Prescriptive Analytics|
|Which questions are answered?||What happened?
How many customers?
Where revenue is less?
Why it is so?
|What will happen next?
What trends will continue?
What if we change pricing?
|What is the best course of action for a given situation?
Which offer is most likely to be accepted by a particular customer?
|How it is done?||Use of KPIs, dashboards, and charts||Use of statistical methods and predictive modeling to understand the relationships in input data & predict the outcomes.||Use of advanced statistical optimization and simulation techniques with inputs and constraints to recommend what actions to be taken.|
|General examples||How many customers have churned? Why did they churn?||
How many customers will churn in the next few months?
|What actions need to be taken to retain these predicted churners?|
Prescriptive analytics in preventing churn
According to Gartner, prescriptive analytics are the next step in the value chain after predictive analytics. Often referred to as the “final frontier of analytic capabilities”, prescriptive analytics involves the application of mathematical and computational sciences to suggest decision options to take advantage of the results of descriptive and predictive analytics.
With the capability to constantly gather new data to re-predict and re-prescribe, prescriptive analytics can automatically improve prediction accuracy and prescribe better decision options to business leaders.
|Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (IVR Logs, CRM Textual Notes), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities|
By using prescriptive analytics in a churn situation, business leaders can decide which type of remedial actions can be used for different customers likely to churn.
For example, a leading global airline was delayed on the tarmac for a half hour. As soon as the ﬂight landed, all the business class passengers received an email from the airline apologizing for the delay and offering a $50 voucher towards their next ﬂight. Similar actions can be taken in contact centers where high profile customers who are on the verge of leaving due to some glitch can be identified and a marketing plan is executed to retain them.
Automate decisions with prescriptive analytics
In contact centers, prescriptive analytics play an important role in providing the instructions to contact center leaders about what to do or what not to do. Particularly when there are too many options, variables, constraints and data for a person to evaluate without assistance from technology, prescriptive analytics enables contact center managers to take the lead. In contact centers, agents often have to depend on prescriptive analytics to know the appropriate options, amount, and under what conditions, a prospective customer can be extended varying levels of credit.
In business where the customer experience matters the most, prescriptive analytics can provide key decision makers with the capability to automate actions. With prescriptive analytics in place, it is easy to automate instructions and suggest the best options for the person to act upon.
Particularly in contact centers, prescriptive analytics can be put to use to automate decisions in churn situations like high risk callers who can be automatically diverted to special agents or emailed special retention offers.
With prescriptive analytics in place, decision makers can quickly extract actionable knowledge from the available data and define effective actions for the future. Based on advanced machine-learning, technology prescriptive analytics provides actionable rules and insights, which are inferred automatically from data coming from multiple heterogeneous sources. This helps leaders to approach and solve the problem in three ways – understanding why customers churned in the past (rules of behavior, ranking of drivers), forecasting who is going to churn and undertaking specific actions (marketing and sale) that prevent each individual decision to churn.