How AI can help to understand the customer

Ahead of us is a significant change in the way brands use customer experience (CX).  We are already starting to see the switch from companies competing on price and product to competing on CX. But what exactly do we mean by CX? Gartner defines CX as a customer’s perceptions and feelings caused by the one-off and cumulative effect of interactions with a supplier’s employees, systems, channels or products.   

Previously, the communication flow between customers and companies was either in person, writing or via a telephone call to the support line. Now, there are increasingly more ways customers can interact with brands, and when they do, they expect a high-quality experience “on demand.” 81% of marketing leaders were expected to mostly or completely compete based on customer experience by 2019, as revealed in the 2017 Gartner Customer Experience in Marketing Survey.  

There are many tools already giving insight to CX, such as NPS and Customer Success Scores. However, when companies need to make quick decisions, real-time insights are what’s helping decision makers. Technologies such as AI are now gathering these insights by allowing companies to organize and categorize data based on business needs, helping to make sense of all these interactions.  

To understand the customer from a CX perspective, and give some real-world examples, we can filter down a myriad of AI technologies and categorize them into three buckets: 

  • Speech Analytics: understanding, interpreting and analyzing voice conversations. Example: understand sentiment, IVR systems.
  • Image: capturing, processing and analyzing images, photos and video. Example: customer patterns, social media image analysis. 
  • Natural Language Processing: analyzing human expression and emotion. Example: text, chatbot, email analysis.  

The below table shows CX use cases and examples of these AI technologies in action:  

Source: Gartner 2019

Are data scientists the only ones needing to understand these technologies? No, it’s extremely valuable to both marketing and CX teams to gain an understanding of these tools. Every company has unique needs depending on CX goals and business objectives. Teams need to make a well-informed decision and understand which tools are most useful to their business, which will essentially lead to more accurate decision-making and a customer-first approach.     

Now, are people rushing to adopt these new AI technologies for CX? In Gartner´s 2018 Enterprise AI survey, it was revealed that businesses that are already deploying AI, 26% are implementing it to improve customer experience. Although it may not seem urgent to start implementing these technologies right away, it’s important that businesses are aware and start to familiarize themselves with these AI applications.  

A good place to start is mapping out a customer journey and finding the ‘dark spots’. These are the areas that could benefit from deeper real-time insights, such as understanding the mood of a customer when they are talking with a chatbot. Having these insights will allow you to hand over the conversation to a human based on the customer’s emotion.  

Companies are dealing with an increasing number of interactions happening across multiple channels and devices. With customer expectations are at an all-time high, it’s not easy to connect all these touch points and deliver an excellent customer. AI can help provide rich insights allowing you to get faster, real-time understandings, and optimize the overall customer journey. 

The Machine Learning Lover’s Holiday Book List

In the market for some last-minute gift recommendations for a machine learning “geek?” (we use the term affectionately around here). DefinedCrowd’s got you covered with our “machine learning lover’s book list,” hand-selected by our ML Team. From the ins and outs of speech and language processing to broad-level theoretical overviews of the machine learning field, these texts cover the wide-ranging topics we discuss in our office every day. Enjoy! And happy holidays from all of us at DefinedCrowd.

Speech and Language Processing An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition by Daniel Jurafsky

Summary: In this excellent intro to speech and language processing tecnologies, Jurafsky presents an empirical approach that comprehensively covers language technology based on applying statistical and machine-learning algorithms alongside modern technologies. The book largely emphasizes scientific evaluation and practical applications.

Foundations of Statistical Natural Language Processing by Christopher D. Manning and Hinrich Schütze

Summary:
A fantastic introduction to statistical natural language processing that uses an analytical approach to cover a range of mathematical and linguistic foundations for NLP technologies. Foundations of Statistical Natural Language Processing proves further useful in presenting theoretical and algorithmic building blocks for NLP technologies.

Crowdsourcing for Speech Processing: Applications to Data Collection, Transcription and Assessment by Maxine Eskenazi, Gina-Anne Levow, Helen Meng, Gabriel Parent and David Suendermann

Summary: An essential read for anyone interested in learning more about crowdsourcing training data for speech models. Offers a comprehensive overview from the basics of setting up a task, to tips for task interfaces and methodologies for quality assessment.

Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville and Francis Bach

Summary:
This book offers a great introduction to what many consider the “Holy Grail” of Machine Learning.
Topics covered, range from mathematical and conceptual background to deep learning techniques. The “research perspectives” that book-end chapters with specific case-studies make Deep Learning a great resource for students and software engineers alike.

Deep Learning for Computer Vison with Python by Adrian Rosebrock

Summary: For those looking to master deep learning for image recognition and classification, Deep Learning for Computer Vision offers practical walk-throughs, hands-on tutorials and a direct teaching style. Useful for both beginners and for the seasoned deep learning pro looking to brush up on the fundamentals.