Whether you realize it or not, big data is at the heart of practically everything we do today. Billboard companies, for example, are now leveraging eye tracking and traffic pattern analysis to gauge interest among drivers. Chances are one of those drivers owns a 4G-enabled vehicle that can track such things as performance and maintenance history. That person can also now record and analyze their utility usage via smart home solutions—anywhere, anytime. On a more critical level, doctors can now record and analyze patients’ heartbeats and breathing patterns to develop life-saving predictive algorithms.
In today’s smart, digital world, big data has opened the floodgates to never-before-seen possibilities. It has the power to course-correct potentially devastating outcomes, and it’s become a necessity for continually refining the customer experience. If you ask us, though, the best customer experiences today are supported by customer journey analytics.
The Need for Customer Journey Analytics
Customer journey analytics is a process that requires tracking and analyzing the way customers use a combination of available channels to interact with an organization. These channels range from human interaction (like speaking with a contact center agent) to fully automated interactions to assisted service (like live chat and co-browsing).
The need for customer journey analytics is simple: data solutions of the past simply won’t meet the next-generation customer needs of today and the future. Consider that just 10 years ago, channels like Web chat and social media were in their infancy (Facebook had only been around for two years). At the same time, the world’s first smartphone had only been on the market for one year. A lot has happened to transform the customer experience in a very short amount of time. As companies move forward in today’s age of rapid tech innovation, they must be armed with the right data strategy.
As mentioned, customers today use a vast number of channels and devices to interact with the brands they love. Each channel and device offers its own set of diverse scenarios for linking to other channels and devices, making no two customer experiences the same. Companies must be able to understand customers’ actions on any given channel or device in order to infer insights and create anticipatory engagement at the individual account level. For instance, why did one customer choose to purchase a product in a retail store verses online? Or, why did a customer end a live chat session before his or her inquiry was handled?
This level of understanding requires a comprehensive view of the data gathered from all channels and interactions that proceeded the moment in question. Customer journey analytics is a process designed to provide this comprehensive view and deliver deep benefits organization-wide—so much so that 60% of all large organizations are expected to develop customer journey mapping capabilities by 2018, according to Gartner.
Making Customer Journey Analytics Work for You
Companies need a data-driven customer approach to survive—and it needs to be effective to thrive. Many companies, however, struggle with taking their customer data and turning it into actionable results. In fact, a 2015 study conducted by PwC found that 43% of companies obtain little tangible benefit from their data, while 23% derive no benefit whatsoever.
To effectively apply your data, you must first determine what you wish to achieve with your data in the first place. In other words, what key objectives do you hope to achieve or improve upon by using big data (or specifically, customer journey analytics)?
Not sure? Here are four core initiatives to start you on a path to maximize your customer journey analysis efforts:
Self-service options—especially mobile—are rapidly increasing in popularity. Just consider that in 2015, Apple users downloaded over 51,000 mobile apps per minute. Also last year, 90% of customers used their smartphones in stores to make price comparisons, research specific products, and check online reviews.
In today’s mobile-first world, businesses should leverage customer journey analytics to develop a sophisticated and integrated mobile experience—one that seamlessly integrates self service into their mobile app via visual, in-app self-service options. Conversely, this experience should offer customers callback options (either immediate or scheduled), as well as mobile chat (automated or agent-assisted) and video service. In addition to offering a stellar mobile UX, businesses should ensure backend capabilities that intelligently route customers to agents based on available context in order to drive relevant, meaningful interactions.
- Improve resource matching. We live in a world today where cars can park themselves and doctors can 3D print new organs, yet we still struggle with routing callers to the right subject matter experts. The time for next-generation routing is now, and it all starts with improved resource matching—specifically, attribute-based matching. This means matching customers with agents based on rich context, business KPIs, and organizational goals across all work items, channels, and resources to drive segmentation, increase prioritization, and determine the best course of action per customer.
This also means choosing the right resources for each customer, regardless of where the resources reside within the organization. The right subject matter expert, for example, could be a contact center agent, a supervisor in your billing department, or your VP of sales. Customer journey analytics provides a 360-degree view of available resources organization-wide to support this level of attribute-based matching.
- Increase agent awareness. Not only is it important to collect the right information, but it must also be presented in a way that is visually understandable and easily accessible for agents. Imagine, for example, an agent being able to see where a customer has been on the company’s website over the last month, as well as that person’s live chat interactions last week. Imagine an agent being able to quickly see that a customer sent an email two days ago regarding a recent bill, or reached out via SMS because the company’s mobile app wasn’t working properly. Imagine if agents could gain this 360-degree, comprehensive view all in just one or two clicks of a mouse.
Data is continuously generated in different ways, and is consumed by different people across different processes and applications. Having the right information at the right time empowers agents to focus on customers’ needs without having to ask for the same information multiple times (which, as we all know, is one of today’s greatest customer frustrations).
Ensure continuous improvement.
When it comes to big data, businesses can’t manage what they can’t measure. Therefore, it’s important that companies measure their data both in real-time and historically to help improve systems, processes, and applications over time. This is what will enable them to consistently deliver on key business objectives, operate within budget, and maximize every customer experience. Here are four key technologies for ensuring continuous improvement:
- A data collector that can collect, standardize and normalize raw data across any data source so it can be used for enterprise-wide reporting and analytics.
- A processing engine that can correlate, translate, calculate and publish normalized data into meaningful business measures.
- A visual presentation platform that provides unified, real-time and historical reporting and analytics dashboards that can be used to visualize, analyze and explore key business measures.
- Predictive analytics to discover new trends, apply changes based on insights, and continuously improve applications, workflows, self service and routing decisions.
So, how can you succeed with these four objectives to fine tune your customer experiences? That’s an entirely new discussion—however, we can tell you this: invest in a customer engagement platform that:
- Provides a single view of customer interactions across all systems
- Allows you to add data sources quickly
- Can correlate data across both real-time and historical systems
- Boasts an open and extensible reporting and analytics framework
Experience is everything. Learn How Avaya Oceana Works.