Over the past few years, data analytics has become an integral necessity in modern business. In a hyper-competitive world with ever-expanding sources and volumes of data, regardless of industry, the ability to leverage data analytics to manage the customer lifecycle is imperative for the success of companies across industries.

Yet most of the 100 respondents to a 2015 Gatepoint Research Pulse Report rated their ability to apply analytics to different scenarios as “good” less than 25 percent of the time. Respondents reported that two factors were largely to blame for their struggles: a lack of internal skills (49 percent) and a lack of accurate, reliable data (37 percent). Furthermore, half of the respondents said they had no experience whatsoever using any analytics tools within their enterprise.

Good Data Quality & Effective Analytics Equals a Competitive Edge

These struggles with data analytics are alarming given how important customer lifecycle management is to the industries in which the respondents work – including finance, healthcare, retail, telecom and business services. Customers are the lifeblood of these industries and competition is fierce. It is critical for companies to build customer loyalty from the very beginning of the lifecycle, combating churn and maximizing lifetime value of the customer.

Today’s customers are tricky. They’re constantly interacting with your organization, using the services you offer and communicating among themselves via social networks. Because of this, customers are quite selective about what they really want out of your services and the price they’re willing to pay for it.

Are you able to analyze and extract insights related to your customer from the available data?

By combining your data with external information, sophisticated profiles of individual customers can be built based upon past and present behavior. Using advanced analytics, historical data that exists within your organization can be leveraged to maximize current customer experience and lifetime value. Future customer behavior can be effectively predicted, thus allowing your organization to better leverage revenue streams, increase marketing efficiency (ROI and targets) and proactively manage risk before it manifests itself.

By the time an organization detects the risk posed by a customer, it's often too late and the damage has been done. It is critical to have analytics strategies to help detect risk at the point-of-sale. These analytics should include identifying risky customers as well as good prospects during the onboarding process. The risk monitoring should then continue via preemptive screening analytics throughout the entire customer lifecycle.

Churn and Retention Management

In any environment, and especially as service penetration reaches saturation, organizations will need to focus on retaining current customers. It is important to identify those customers who are at greatest risk of churn, understand what actions would be sufficient to retain them, and then compare the cost of saving those customers to their future lifetime value. Retaining the right customer has significant value, while retaining customers who pose subsequent revenue risk can be very expensive.

Keeping valuable customers starts by using analytics to determine a customer’s propensity to churn. What will cause it and when will it happen? Knowing this will allow organizations to discover the customers whose loyalty is at risk, highlighting the conditions that could change to lower their risk of leaving.

In addition, by analyzing the propensity of the customer to more buy products and services, the organization can easily evaluate whether the cost of retention exceeds future returns. Effective retention strategies can then be created to address those customers for whom the right actions will produce a positive life time value.

And determining churn isn’t the only benefit of customer analytics. Finding exactly the right moment to upsell or cross-sell a customer is a major opportunity for any organization. However, offering a promotion too soon or too late in the customer lifecycle is likely to be met with indifference or annoyance.

Analytics can be deployed to make sure that the customer is given the right offer upon on-boarding. As the customer relationship evolves, the key points in a customer lifecycle for a promotion or cross-sell can also be identified using advanced analytics.

Collections Effectiveness

Analytics may help keep customers loyal, but it can also be a key component to successful billing and collections operations.

For example, knowing the customer's propensity to pay the debt allows collection departments to segment their collections activities. Doing so reduces the resources spent focusing efforts on customers with the highest propensity to pay. This not only reduces expenses, but also increases revenues and customer paybacks. Understanding a customer's propensity to pay can also allow an operator to customize specific payment terms for customers who clearly do not pose a risk as long-term bad debtors. This capability provides not only revenue collection today, but also continued loyalty from a satisfied customer.

Understanding customer behavior and having the means to take timely action based on such insights is highly valuable to maximizing customer satisfaction and lifetime value.

Despite the paramount importance of effective data analytics in facilitating the above goals, far too many organizations are just starting to get familiar with how to best leverage different analytics capabilities. The sooner organizations develop and implement sound analytics strategies, the better they will be able to maintain the competitive edge in this global and already highly-competitive environment.  

Fortunately, organizations can seek the help of highly skilled and experienced partners to put these strategies in place and ensure they are getting maximum value from their data analytics. 

Ravi Rao is chief operations officer of analytics at Infogix, which has expertise in advanced analyticscustomer lifecycle management, and better data for better results.

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