Customer Experience Management (CXM) is the process of understanding and managing customers’ interactions with and perceptions about the company/brand. In our Big Data world, improving the customer experience is increasingly becoming data-intensive endeavor. Consider CRM systems, surveys, social media sources, telemetry systems, and publicly available data sources; using the combined power of statistics and today’s processing capabilities, these sources of data can help businesses model the processes that drive the customer customer experience (CX). Consequently, the tools and methods of artificial intelligence, machine learning and predictive analytics will play a major role in helping businesses better understand and manage the customer experience.
In fact, a recent survey by retail management firm BRP Consulting found that 45% of retailers are expected to increase the use of artificial intelligence for customer experience in the next three years, and 55% of retailers are focused on optimizing the customer experience to increase customer loyalty.
According to Gartner, 85% of all customer interactions with a business will be managed without human interaction by 2020. Accenture predicts AI will double annual economic growth in 12 countries by 2035. They predict that AI technologies will boost labor productivity by up to 40 percent by changing the way work is done.
What is Artificial Intelligence, Machine Learning and Deep Learning?
Data science includes advanced tools and methods for leveraging the plethora of data sources available to CX professionals. These tools and methods include artificial intelligence, machine learning and deep learning. These are the ways that companies are automating insights to drive their company forward. Artificial intelligence is a field in computer science that focuses on developing computer systems to perform tasks that usually require human intelligence, including visual perception, speech recognition, decision-making, and translation between languages. Machine learning uses statistics/math to allow computers to find hidden insights (i.e., make predictions) without being explicitly programmed where to look. Deep learning is a class of machine learning algorithms that are modeled after the information processing and communication patterns of the brain. Deep learning uses layers of units or nodes for feature extraction and transformation, each layer using the output of the previous layer as input. Higher level features are derived from lower level features to form a hierarchical representation.
How Can AI Help Businesses Improve the Customer Experience?
Iterative in nature, these methods continually learn from data. The more data they ingest, the better they get at making predictions. Based on math, statistics and probability, these methods find connections among variables that help optimize important organizational outcomes. In the CX space, these outcomes typically reflect customer loyalty metrics (e.g., retention, repeat purchases, increased purchases). Below are a few ways AI can help improve the customer experience.
- Automate customer interactions while providing a more immediate and personalized response that eliminates those frustrating moments, like searching through pages and pages of images to find one particular item. North Face created a shopping assistant (Q&A with customers to help them find products).
- Handle customer complaints using chatbots, reducing service costs.
- Customize the customer experience: Black Diamond, as reported in MIT Technology Review, has improved the online shopping experience by using past purchases, current weather conditions, and other points of insight to inform its decisions on what products it pushes to shoppers.
- Detect fraud. Machine learning can detect anomalies in data to identify/prevent security breaches.
- Improve CX surveys. AI can help reduce survey length by using conversational surveys that employ NLP and machine learning to identify why customers are satisfied/dissatisfied.
- Improve customer service. AI systems will improve customer service by allowing customers to ask questions in their own language instead of reading FAQs, alerting front-facing employees to at-risk customers so they can focus their efforts where they matter most and improving engagement (customers don’t have to interact with a human).
We live in a Big Data world and more businesses are leveraging their vast amounts of customer data to improve how they manage and improve the customer relationship. And this trend will likely continue. Since 2012, the investments in AI startups have increased; AI startups received a record-setting $5 billion in venture-capital funding in 2016, according to CB Insights. IDC projects that worldwide revenues from cognitive systems and AI will reach $47 billion by 2020.
To improve the customer experience, businesses must be able to make sense of their data that represents their customers’ attitudes and behaviors. An understanding of these customer metrics now, more than ever, requires the proper application of math and statistics that underlie such fields as artificial intelligence and machine learning. The companies that are better able to leverage the data science tools and methods of AI and machine/deep learning will be able to outperform their analytics-challenged competitors.