Analytic Strategies for a Data-Driven Customer Experience

The data-driven customer experience is critical to the future growth and development of organizations, particularly in today’s hyper-competitive economy. There’s a prevailing understanding that opening up data and sharing it with customers will go a long way to advancing that customer experience. Things like predictive analytics and prescriptive analytics can also hold up the promise of radical change in the way organizations do business and then there are skill sets and obstacles to consider as you build your analytics story. Let’s look at how these aspects can help.

Improving customer experience is every marketer’s goal in today’s connected world. At its core is data, which we gather to better understand any interaction we have with our prospects and customers.

Companies nowadays actually map and produce marketing content through–ideally–interactive assets to each stage of their buyer’s journey. Smart marketers even include micro-surveying at different moments to gather more data at every touch point.

Moving forward, we see data-driven customer experience rapidly evolving, taking advantage of Artificial Intelligence and Machine Learning (AI/ML) algorithms alongside analytics and data visualization platforms. These technologies allow people to not just interpret the growing volumes of data better, but fine-tune relevant, personalized offers based on changing customer behavior. We will also be able to predict what a customer will need and respond in more efficient ways; as well as run many more concurrent tests of different offers or configurations of products and services with near-real time results that allow us to adapt to market demand.

It’s no wonder why analyst firm IDC states that by 2020, organizations that analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity gains over their less analytically-oriented peers.

Nowadays, we expect most people to educate themselves online or though mobile apps reading peer reviews, assessing a vendor’s reputation, and evaluating alternatives before they even contact the vendor to initiate a purchase. Sharing meaningful information doesn’t just improve your data-driven customer experience, it can be a differentiator. Furthermore, you can’t really personalize that experience without exchanging data with prospects. Strategies for a “next best offer/action” can’t be fully implemented without engaging your audience interactive manner to understand preferences and adjust to them.

For example, one of our large global airline customers created a solution for optimizing the web and kiosk check-in experience. It allows passengers to upgrade seats, buy miles, and select promotions. The price for these offers is very dynamic based on many variables, such as the customer’s profile; past preferences; aircraft capacity; number of booked seats, other factors previously known about the passengers, and captured during the interactive check-in process. The solution provides a recommendation on what the passenger is most likely to accept based on information captured on the spot. Other things are personalized as well, such as the layout of the offers; how many offers are shown; and direct emails.

Data integration is essential for accessing the right data sources for analytical purposes. But traditional data integration usually performed by IT or data engineers is now complemented with “distributed data preparation” or “self-service data preparation,” which is typically done by business analysts or data scientists.

  It’s no wonder why analyst firm IDC states that by 2020, organizations that analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity gains 

Gartner predicts that by 2020, self-service data preparation tools will be used in more than 50 percent new data integration efforts for analytics.