Communications Service Providers (CSPs) are undergoing a rapid evolution from traditional businesses centered on telecommunications, media, and hosting, towards new agile service models. Traditional CSPs find themselves in a revenue neutral or revenue erosion position as established revenue streams take hits from new competitors. Over-the-top (OTT) service providers, new unified communications providers, and growing adoption of cloud-based services put increasing pressure on CSPs to adapt to digital transformation.
Digital transformation is about improving the customer experience with customer-centric initiatives by modernizing tools and processes within an organization. Running predictive analytics on customer data sounds easy in practice but it takes the right blend of data sets and machine learning algorithms to understand when the customer is ready to purchase and then make them a compelling offer that results in higher conversion rates and growing revenue streams. According to telecoms.com, “a good offer comes at a time when it will increase both customer satisfaction and revenue. It’s unlikely that a subscriber will sign up for a new value-added service when they don’t have enough balance, or when it will use up their precious last bit of data that they need for everyday activities until then end of the month. The kind of offer needs to be at the beginning of the payment cycle, if subscribers are to be encouraged to splurge.”
With an abundance of customer information at their disposal, CSPs are constantly seeking how to utilize technology in new and innovative ways to capitalize on increasing revenue at the expense of the competition. Throwing more resources at predictive analytics initiatives isn’t likely to be a game changer to better predict the likelihood of a customer upgrading to a better package. A more strategic approach is to utilize new analytical models which allow you to train, score, and evaluate data sets to provide more meaningful insights. But for CSPs take advantage of this wealth of data and run predictive analytics, there is one elephant in the room that must be addressed – are the analytics using quality data to derive the best predictions?
The Importance of Data Quality for Predictive Analytics
Before predictive analytics can tell CSPs what to offer and when to offer it, data quality must be ensured. Data spans not one or two systems, but a series of systems that comprise an end-to-end business process that invites data errors to occur at the beginning, end, or anywhere in between.
Without quality data CSPs will not be able to personalize offers to individuals at the right point in time. What good is it to have all this data and not be able to capitalize on it? Having the proper data means CSPs control the amount of risk the company takes when upselling to customers.
The last thing a CSP wants to do is send out redundant marketing materials to a subscriber for a service they already have. Repeated experiences like this can cause customers to leave for a competitor. To ensure data quality and successfully leverage machine learning, CSPs are gaining economies of scale by relying on a single unified platform that includes both data quality and analytics capabilities.
Moving Toward Digital Transformation – How To Do It
In order for CSPs to capitalize on building their subscriptions and moving toward the digital transformation that so many want to achieve, they should focus on implementing an integrated platform that will help them help them effectively analyze and ensure the quality of big data. The platform should handle not one, but rather multiple steps from data acquisition and preparation to data analysis and operationalization. The platform should empower all users to aggregate and control data to accelerate and improve the subsequent data analysis process, and applying analytics to extract value from CSPs data.
This doesn’t mean abandoning existing tools but rather innovating in areas which have become stagnant with traditional approaches. With the proper platform, CSPs can deliver the most valuable and relevant services to their best customers. These upsells not only translate to millions in revenue but increased customer satisfaction leading to increased brand loyalty. With quality data, CSPs can take predictive analytics to the next level, helping CSPs convert subscribers, improve the customer experience and prevent revenue leakage.
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