Analysts depend on massive amounts of customer data to produce actionable insights so that decision-makers can quickly react to ever-shifting customer demands. To keep up with changing customer demands, you rely on customer analytics for deeper customer insight, targeted interactions and improved marketing efforts.

Customer data comes in many forms and from multiple sources. But that data can be messy, incomplete, and difficult to process. Often times, customer data files require significant manual cleansing and integration before you can analyze results and deliver answers to your organization in a timely manner. It’s no secret that analysts spend significant amounts of time in the prep & blend phase manually writing formulas in Excel or programming/coding processes to prepare lots of data.

In addition, you might have to enrich the data as well as perform advanced analytics, like predictive analytics, to generate deeper insights into what your customer data is telling you. Then you need to analyze and share the customer data to help your decision-makers visualize and understand trends in order to better target customers and prospects as well as optimize marketing efforts.

Typically this entire data preparation to visualization process could take you up to several weeks to perform. And in today’s fast-paced world, spending weeks to generate insights is not ideal. Reacting rapidly is very important as your decision-makers strive to keep up with changing customer demands.

Hence, in your work with data you need better ways to reduce the time and complexity spent in manual data preparation and cleansing, as well as speed up your time to insight to keep up with the rapid pace of business.

Learn how you can go from weeks to hours and be more efficient in your work with data. Register for the Modern Analyst webinar Four Ways to Optimize Customer Data for Deeper Insights and learn how to speed up your time to insight while reducing the complexity and time spent in data drudgery.

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