Big Data is not just for the brave– and not just for the tech industry. While Internet companies such as Amazon, Google, Yahoo and others have historically leveraged Big Data to weave it into the foundation of their business, companies in other domains have been slow to follow suit.
Take the example of customer service and care. Customers interact with companies through many channels: phone, web, social media and chat. Research from Ovum shows that 74% of consumers use at least three channels when interacting with an enterprise for customer service. Customers may pay their bills on the website, seek billing information through web chat, or call the company to address a billing issue.
A “cross-channel” experience means a “consistent experience across channels.” For most companies, when a customer goes from one channel to the next they need to start over to explain who they are and what their issue is. This is exasperating to customers, but it does not have to be this way.
The ability to piece together the customer’s journey through different channels can be resolved through a robust Big Data platform. If solved correctly, this can completely transform a consumer’s interaction with a company, making it a far more intuitive…and even delightful engagement.
The goal for companies should be to make this happen. Powerful prediction and real-time decisioning solutions can guide consumers to their desired outcomes in an appealing way. This possibility is driven by a framework I like to call “Anticipate, Simplify, Learn” or “A-S-L.”
Briefly, here is how the A-S-L framework can provide a better customer experience.
- Anticipate customer needs – Who are you and what is your intent?
- Then Simplify the engagement – Now that your intent is known, what is the best way to engage with you?
- Learn from the interaction – How do I learn “at scale” every time I engage with you and feedback the learning to improve my ability to anticipate your needs better?
Prediction models themselves are built based on large volumes and a variety of structured and unstructured historical data. As customers interact with companies through multiple channels, the outcome of these interactions are recorded. Big Data Platforms can be used to “fuse” data from multiple channels to create a single view of the customer. This data can then be used to train statistics/machine learning-based intent models. These models are then deployed onto real-time interaction platforms to predict future intent for new and old customers.
All of this can happen “behind the scenes” to design a truly intuitive experience for customers. It’s the customer service of the future that we’ll all appreciate, and it’s possible with today’s technology.