Customer Feedback and Artificial Intelligence

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1 Customer Feedback and Artificial Intelligence The myths, the reality, and a guide to ge3ng started In just the last year, artificial intelligence (AI) has gone from being a buzzword to a capability that companies are actively using to solve business problems. Table of Contents Topic Page We all know that AI can help us somehow but the term is so nebulous that it can be difficult to identify properly how and why it should be used. A problem is that AI is currently both overestimated and underestimated in the business world. No, AI cannot entirely replace people; but it can find deeper connections and insights in data than otherwise possible. One of the clearest areas in which businesses can benefit from AI is in that of customer feedback. According to 2016 IDG Enterprise research, improving customer relationships and making one s business more data-focused are the top two business objectives driving investments in data-driven initiatives today. 1 The same research shows that some 78% of enterprises agree that the collection and analysis of big data for this purpose has the potential to fundamentally change the way they do business over the next one to three years and AI is a key part of that. AI and NLP: The state of the industry and what happened in 2016 Common misperceptions Key business applications for AI-based analytics Steps for successfully implementing AI-based analytics into a business By 2020, the amount of high-value data (data that yields actionable intelligence) is set to double 2 but value can only be derived from that data if it s properly analyzed. Artificial intelligence (AI) and Natural Language Processing (NLP) techniques have made it possible to gain value from the vast amounts of data available. The challenge is actually implementing AI and NLP solutions in your business. What developments in AI and NLP should businesses care about? What business problems can they help solve? What should companies look for when choosing a deep analytics provider? This goal of this white paper is to shed some light on those questions. Success story: Tidying up the filing cabinet Checklist: What to look for when selecting an AIbased analytics provider About Luminoso 5 5 7

2 AI and NLP: The state of the industry and what happened in 2016 Though the term AI may lack a simple, agreed definition, it is, broadly speaking, the theory and development of computer systems able to perform tasks that usually require human intelligence. Natural language processing, similarly broadly, is concerned with the interactions between machines and human (natural) languages, and teaching machines to understand language in the same way that people do. AI is not a new field by any stretch, but advancements in applied AI over the last ten years have dramatically changed the way businesses can process and make the most of their unstructured data. Even just a decade ago, unstructured or text-based data could only be analyzed using keyword searches or by creating algorithms to find certain terms. Such approaches required vast training datasets and hours of consultant time. AI-based solutions are replacing such methodologies. Three key trends in how businesses are applying AI and NLP emerged in 2016: companies recognizing and implementing AI as something that can help their business, the rise of chatbots, and advances in natural language processing and term embeddings. Businesses are beginning to see real applications for AI, rather than just pie-in-the-sky imaginings. Just a year or two ago, AI was commonly viewed as tech for tech s sake interesting, but with no real business purpose. That perception has rapidly changed as leading companies have started using AI to automate and improve processes. For example, chatbots have gone from being virtually unused to highly adopted in According to Forrester, Facebook Messenger had 0 bots in February 2016 and more than 18,000 by July Instant messenger service Kik Interactive has more than 20,000 chatbots, and in the first seven months that the company allowed Promoted Chats, its 200 million registered users exchanged 350 million messages with bots. 4 Customers desire to interact with companies via chatbots is likely to continue to grow as these chatbots get better which is being driven by continually-improving AI. With advances in natural language processing and term embeddings, there is movement towards understanding human language as it s naturally spoken, rather than having to adjust the way we speak for the machine. Amazon s Alexa and Google Home are two high-profile products relying on advanced NLP that were broadly discussed in This technology has much broader applications as well especially in automating contact centers.

3 Common misconceptions about AI and NLP Still, there are a few myths that persist about this technology. Help! We ll all be replaced by robots! The primary way people overestimate AI is in thinking it s going to make human involvement redundant. But AI is never going to replace humans. Businesses expecting to completely automate their workforce are going to be sorely disappointed. AI s strength is in doing one thing very well, increasing output and quality, and freeing people to work on more strategic projects instead of the tactical. According to Accenture, the impact of AI technologies on business is projected to boost labor productivity by up to 40% by fundamentally changing the way work is done and reinforcing the role of people to drive growth in business. 5 AI isn t just a higher-speed, automated replica of old business processes. AI and NLP can also draw new connections or find previously untapped insights. It frequently uses entirely new approaches to solve old problems, such as looking at text as concepts and ideas instead of words and focusing on relationships between those ideas instead of word counts. The speed is a welcome side effect. Concept-based deep analytics systems, for example, allow you to upload data and immediately begin to derive insights rather than having to tell a system what to look for. People are still underestimating what AI can do for them, but the business benefits are clear. But AI just helps us do the same thing faster. What s the added benefit? Key business applications for AI and NLP AI has clear benefits in every industry and an almost limitless number of applications within them. However, one of the clearest areas where businesses can benefit is in a clearer and quicker understanding of customer feedback. Consider how customer feedback was analyzed before the advent of AI and NLP, using traditional text analytics methods. Up until the 2000s, the predominant approach to analyzing unstructured feedback was to create long lists of keywords or ontologies, and then search for those terms in a dataset. This meant that for every type of data source, a new keyword list or ontology had to be created from scratch. Such processes took several weeks to set up, and maintaining such systems required a significant amount of resources and labor for upkeep.

4 AI- and NLP-based approaches, particularly those that use word embeddings (that is, where words or phrases are turned into mathematical vectors and plotted out based on their relationships to each other), have enabled companies to move away from those more traditional and resource-consuming approaches. These new approaches represent a huge decrease in resources and setup time, narrowing the distance between insight and action. This has a direct impact on companies bottom lines; according to the IDC, organizations that analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity gains over less analytically-oriented companies by Steps for successfully implementing AI-based analytics 1 Think about the process before you begin. Many companies dive right into analyzing data without considering where that data will come from, how often it will be analyzed, and how the insights from that data will be shared and acted upon. Without having a firm plan in place, many of these companies end up doing nothing - so it s crucial to have a plan from the beginning. 2 Your data is multichannel. Combine it and use it all. More likely than not, you have customer feedback and data coming in from multiple sources and through different departments. To get a full picture of your customer, you should aggregate this data and look at it all together. Analyze your data regularly (and more often than you re likely doing now). 3 Because unstructured data can be so difficult to analyze without AI-based methods, many companies only look at their data once a quarter or annually. This means it can be harder to spot trends or identify issues before they re out of control. Look at your customer feedback much more often as close to real-time as you can get to effectively monitor and manage customer satisfaction. Measure and track unstructured feedback. 4 Treat unstructured feedback just as you would structured data. For example, keep tabs on the percentage of customer comments about a particular issue. This makes it easier for you to see the impact that analyzing and taking action on your customer feedback can have. It also makes it easier to prove out the ROI and effectiveness of your unstructured feedback program.

5 5 Make customer insights a part of your business process. Doing this allows you to build KPIs around the unstructured data that you re tracking, for example reducing the number of comments about a particular issue. Success story: Tidying up the filing cabinet To get a clearer picture of how exactly companies can effectively integrate AI into their business practices, let s take a look at a specific example. A large office supplies retailer was struggling to process and analyze its contact center data. The retailer recognized that this customer feedback could be a valuable source of insight to help identify and resolve issues contributing to customer churn. However, the retailer faced three challenges: most of the data was unstructured (i.e. text-based), they received tens of thousands of messages a day, and their data was scattered across multiple channels and departments. Traditional methods of analyzing text-based data simply wouldn t cope with the problem. The company used an AI-based deep analytics solution with an open application programming interface (API) to analyze the data. The API was critical because it enabled them to easily and quickly link their various data sources to the same location and analyze it all together. This omnichannel view of their customers enabled the retailer to spot issues and root causes that otherwise would have been missed. For instance, the retailer discovered that certain customers were having problems logging into their online account but only in certain circumstances. While the details of those circumstances would have remained a mystery using their previous analytics approach, using AI to compile and analyze all the data helped them discover the underlying issue; a difficult merger between the company s rewards website and general shopping website. What to look for when selecting an AI-based deep analytics provider For most companies, incorporating AI and NLP into business practices is accomplished by hiring a vendor who specializes in such techniques. Fortunately, there s a large and growing number of AI startups in the United States alone.

6 However, this makes selecting a vendor that much more difficult. How can you tell which vendor will best suit your needs and is actually using AI and NLP to do so? There are some questions you should ask any company you re considering before you engage their services. How will your data be uploaded into their system? Can you hand over a file or connect directly via an API, or will your data require cleanup and processing before you can even upload it? Many companies analyze unstructured data by coding it, effectively turning it into structured data. This means a great deal of important detail and nuance can be lost in the process. And this is the gold you don t want to lose. How long will it take to get set up and running with their system? If setup time is longer than a week or two, this is a sure sign that the company is using training data sets in the background instead of AI and NLP, which can adapt to new data or changes in data automatically. Do they ask for sample or training data sets (or say they provide their own)? If they need sample data sets before they can get you set up, or can t answer the question of how they handle data that may change over time, you re probably working with a team of consultants not a system based on AI and NLP. How long is the data processing time? True AI and NLP solutions can process data in a manner of minutes. If it s much longer than that, it could be a good sign that your vendor is using a team of data coders or consultants. This can increase both the cost to you and the time it takes to find insights. How complex is the process for integrating the unstructured data analytics solution with your existing systems? The more complicated or longer the process is, the longer it will take for you to get set up and begin to uncover these all-important insights. Is it a specialized or general system? A general toolbox of solutions won t perform as well as a tool designed specifically to analyze unstructured data.

7 Used properly, AI can have a significant impact on your customer interactions, improving feedback, maintaining loyalty and having a significant impact on the bottom line. Retailers who leverage the full power of big data could increase their operating margins by as much as 60%. Are you making the most of yours? References 1. IDG. (July 5, 2016.) IDG Enterprise 2016 Data & Analytics Research. 2. IDC. (November 2015.) IDC FutureScape: Worldwide Big Data and Analytics 2016 Predictions. 3. Forrester. (October 20, 2016.) The State of Chatbots. 4. Ibid. 5. Accenture. (September 28, 2016.) Why Artificial Intelligence is the Future of Growth. 6. IDC. About Luminoso Luminoso Technologies, Inc. is a leading AI-based deep analytics company that enables clients to rapidly discover value in their data. Luminoso s solution reveals an accurate, unbiased, real-time understanding of what consumers are saying, uncovering actionable insights and the unknown unknowns. These insights are used to increase business performance and build better customer experiences. Luminoso s software is flexible; it natively analyzes data in 12 languages and can be deployed in either a standalone Cloud or On Premise solution or integrated into an end-to-end client platform via the API. Clients include Sprint, Staples, Scott s Miracle Gro, Roche, Microchip, and CenturyLink. Luminoso also serves a growing set of channel partners, including Sapient Government Services. The company is privately held with headquarters in Cambridge, MA. For more information, please visit. Interested in learning more? Request a custom demo: /demo