Customer analytics holds the key to greater business value from data.
How much more likely would you be to use a particular online travel site to book a hotel or flight if its search results more closely matched your preferences?
By analyzing more than 14 days of customer behavioral history and applying predictive and prescriptive analytics, one travel company managed to increase bookings by 2.6 percent or 50,000 additional transactions a day.
By 2020, more than 40 percent of all data analytics projects will relate to an aspect of customer experience.
“Investing in customer analytics will help you understand customer’s needs, satisfaction and value, and allow you to create a great experience for them,” says Melissa Davis, research director at Gartner.
Unfortunately, data and analytics leaders often receive requests with broad objectives such as “deliver the most value from customer data,” rather than specific requests grounded in business outcomes.
Follow these three steps to deliver business outcomes through customer analytics:
1. Prioritize the use cases that deliver the most value
Starting with a sea of data can be overwhelming. The best approach is to identify the business need and select up to three of the most requested use cases across the organization that most closely meet your business objectives.
Use cases might include optimizing marketing promotions, optimizing sales leads or improving the quality of customer service.
“For example, if your desired outcome is to increase or avoid losing revenue and your business discipline is customer service, select a relevant use case such as ‘reduce customer churn’, says Davis. “Next, identify the types of data to acquire, such as the customer’s interaction history (purchases and call center interactions) and profile data. Finally, select the analytics to apply.”
2. Build a foundation for the customer profile
A 360-degree view to support all customer analytics business use cases is an ultimate goal. Start small, with the minimum data elements that identify the customer.
Gartner research shows that transaction data is the most commonly analyzed data type today for organizations that have invested in big data initiatives, followed by log data, location data and social media profiles.
“The 360 degree view of the customer is important to ensure that a customer is recognized as they cross different departments of an organization,” Davis says.
3. Identify the data you have and the data you need
Decide what data will be needed for each use case and what data is missing and create a plan to acquire the missing data. The data inventory should be a living document that is revisited regularly.
Additional data can be collected from many sources, such as Internet of Things (IoT) sensors, commercial marketplaces and public data sources. Classify the missing data as either internal or external, and either “required” or “nice to have.”
“That way, you can decide if it is appropriate to acquire the data or not,” says Davis. “The most important question to ask is, ‘Will this data tell me something new about the customer?’.”
Gartner clients can read more in the report Three Steps to Yield the Most Value From Your Customer Data Using Analytics by Melissa Davis.
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