Churn Rate makes businesses worrisome and to get back those lost customers, they are in quest for solutions which help in bringing back those lost relationships. One way to deal with the situation is to review your sales and services of the last quarter and see if any mistake was made by the company, which back fired onto customers. The other way is to find a tool which can helping in knowing the reason behind “churned” customers.
Customer Churn Analytics can help in reviving those lost relationships. For instance, let’s take a business model which is lines with Walmart. Almost a decade ago, due to less competition and more offerings, retaining customers was not a tough job. But with growing competition, retaining customer base is becoming a tiresome job to almost every business and this includes retail sector.
As days are passing, customers are always running for best deals which are being offered. They do not care for the brand or for the previous relationship they have with the business. All they are interested is value for their money and the best they could get from the deal. Therefore, in order to attract customers, physical as well as online retailers are trying all sorts of tricks in trade.
And that’s where Customer Churn Analytics helps. It allows a service provider to prep, blend, and analyze all data about a customer to predict their overall satisfaction, as well as their experience with service quality, convenience, pricing, and many other factors.
By determining a customer’s propensity to churn, retailers can focus on retention campaigns and deliver preferential services to at-risk, high-value customers whose loss would cause the greatest impact to revenue.
Customer Churn analytics helps in the following way
- Fast access to all relevant data: Drag-and-drop tools allow you to connect to and cleanse data from BSS, OSS, CRM, and external data sources in a common analytics environment where you can rapidly process large volumes of data without writing any code.
- Advanced analytics tools, including predictive, statistical and spatial: Identify potential service level issues, competitive threats, and at-risk customers for proactive and immediate attention.
- Deeper insights in hours, not weeks: Data analysts and line-of‐business users can quickly and easily perform their own analytics, without waiting on IT or specialized programmers
- Simple deployment and sharing of analytic insight: All departments benefit from access to data for retention/acquisition campaigns, customer service inquiries, and identification of negative customer experience trends
So, planning to go for it? DNF will give you the tools to monitor multi-platform, multi-format data related to service experiences so you can optimize time, resources, and investments. Overall, DNF allows you to combine structured and unstructured data to predict your customers overall satisfaction, as well as their experience with service quality and convenience.
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