Blogs by OpexAI

4 days ago . 4 min read
The Role Of Data Analytics In Customer Experience
By Opex AI Team | June 1, 2018
Once customers choose a product or service, the continuing success of their relationship with that company depends on customer support. Get it right and win loyal customers for life. Miss the mark and you lose customers, who may then broadcast their dissatisfaction and do more damage to your brand.
Their biggest challenge is processing all that feedback in order to extract those key insights. How do you even begin to analyze hundreds of thousands of product reviews or tweets, short of hiring an army of interns to read through and summarize them all?
Customer care executives must adapt to these changes in customer feedback channels and expectations by leveraging innovations in artificial intelligence, deep analytics, and natural language processing. These innovations allow them to help their teams get in front of customer issues, establish a common experience for all customers, regardless of channel, and contribute to a culture that’s customer-inspired.
Because AI-based approaches use machine learning and natural language processing to handle new data, processing time is more automated and thus faster. New words, slang, acronyms, and misspellings are identified and learned through context and machine learning, not as a result of analysts manually defining each term so the software can understand it. In contrast with other methodologies, analysts don’t need to spend time inputting long lists of new data sets for every customer service problem that arises. This means data is analyzed faster, giving companies the ability to quickly identify and fix any customer issues.
3 Building Blocks for Successful Application of AI in Customer Experience The successful application of AI in customer experience requires 3 fundamental capabilities:

1.Data Unification
Data unification is a must for any type of behavioral analytics. AI thrives on information—the more the better. The new generation of data unification tools make this daunting task cheap, fast, and relatively pain-free. Customer journey analytics platforms provide this service for a fraction of the cost of the dedicated data services providers of yore—even delivering a level of data integration free of charge.

2. Real-time Insights Delivery
For AI to impact the customer experience, insights need to be conveyed in the moment through the customer’s chosen touchpoint. Integrating with these touchpoints is the key to in-the-moment engagement. The truth is that, in addition to elegant SaaS data streams, most enterprises must rely on myriad on-site, home-grown and legacy touchpoint data sources—product interfaces, payment platforms, point-of-sale systems, customer care, etc.. This reality creates a challenge for delivering real-time insights and are still very much a custom affair.

3.Business Context
For a simple, isolated interaction, AI is able to deliver results by simply knowing that an email is an email and a campaign