Insights Update: Make Every Interaction Count
The shift to digital has greatly expanded the number of channels used by customers. This unprecedented growth in digital interactions from connected customers is fueling the rise of big data. Customers experience a journey comprised of a sequence of interactions that span channels and devices.
At the same time, customers, accustomed to interacting with leading digital innovators like Amazon, Google, and Facebook, expect every interaction to be easy, relevant, timely, tailored to their needs, and conveniently available on the device at hand.
Enterprises need to understand each customer’s journey as it spans channels. They must be armed with the ability to analyze real-time intent as well the current context (such as weather, location, and time of day) in which the interaction occurs, and use that as a foundation to anticipate their likely future actions. Finally, when they enable developers to use these predictions to adapt the behavior of apps, enterprises can deliver individualized, contextual, and consistent experiences that delight customers and accelerate profitable growth.
Introducing a new version of Apigee Insights
Today, we’re announcing a new version of Apigee Insights. Apigee Insights provides GRASP-powered big data predictive analytics that increases marketing effectiveness and customer satisfaction by enabling developers to build API-driven adaptive apps that anticipate, learn, and adapt to deliver contextual, individualized interactions. GRASP (graph and sequence processing), Apigee’s unique time-sequenced graph analysis on Hadoop, enables the analysis of entity and event data and finding hidden patterns in customer behavior across all digital interactions in the customer journey.
There are three key capabilities that are now available as part of this new version.
Customer journey analytics
Customer journey analytics enables enterprises to understand and optimize the customer journey by analyzing the sequence of all digital interactions with GRASP. With customer journey analytics, your data analysts and business users can visualize the customer journey, discover and profile common interaction paths, and optimize the journey. Using customer journey analytics, you can answer questions such as:
What journeys are most successful?
How do customers move across channels?
What actions did customers take after receiving a direct mail or mobile offer?
What actions did customers take before canceling service?
How does your mobile app and call center affect and drive in-store transactions?
Understand the customer journey, discover common interactions and influences
Predictive modeling in R
The updated Insights offers self-service predictive modeling that enables data scientists to use the popular R programming environment to build predictive models on GRASP. The models they build can incorporate unstructured and structured data. Now, data scientists can use their favorite R tools and their own analysis packages in conjunction with Insights.
Enterprises can realize more value from data by solving the last-mile problem of integrating predictive analytics into consumer-facing apps. This new version of Insights "wraps" predictive analytics in APIs and empowers mainstream developers to use predictive insights to build adaptive apps. Combined with backend-as-a-service (such as the Apigee Edge API BaaS capability) and Node.js, any developer can use APIs to build adaptive apps powered by GRASP to deliver digital experiences tailored to individual customer needs
To learn more, download the Apigee Insights datasheet.