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How implementing customer predictive analytics boosts the bottom line
There's a lot of hype behind customer predictive analytics. Though the technology has been around for years, the real-world potential of analytics to create more sales is now reaching new heights, thanks to recent developments in processing power and data storage.
And it's not just sales processes that CRM data analytics is improving. Examining customer interactions can also augment agile workforce management, customer retention efforts and fraud detection. Moreover, analytics-driven automation is helping organizations fulfill service-level agreements more efficiently, which also facilitates customer satisfaction with less of a lift than manual processes had required.
Customer predictive analytics can help cut down on the overwhelming amount of processing required in today's customer service environment. Customers perceive any contact with an organization -- whether in-person or through email, social media, dialing a call center or website interactions -- as "talking to the company." Keeping track of all that on the inside can be a monumental challenge. Automation and data mining can help create an experience that customers have come to expect in the digital age and quickly discover opportunities once out of reach because of their time and cost requirements.
But the technology is still only half the equation when it comes to finding a meaningful return on investment. Human thought is required to create thoughtful implementations and to train staff on getting the most out of insights derived from customer predictive analytics. Analytics also requires constant fine-tuning after the initial implementation as companies respond to changing market conditions, seasonal adjustments and economic ups and downs.
In this handbook, you'll learn how cutting-edge companies are applying rapidly evolving analytics tools to find more prospective customers and better improve their experiences after the initial sale. More importantly, you'll learn about the human factors that analytics can't replace.