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Dirty data has plagued businesses for decades, hindering AI\u2019s ability to deliver accurate results. The good news is that AI can help clean some of that dirty data or, in this case, duplicate data.<\/p>\n
Consider your phone number. When you share your number, you could use hyphens, dashes, periods, spaces or just numbers. There\u2019s nothing wrong with having a little variety in the way you share your phone number. When it comes to recognizing each of those different formats as a single data point, it can become complicated. Fortunately, AI uses relationship matching to recognize different formats of the same data. It then cleans up that data so you don\u2019t have five duplicate accounts for one customer.<\/p>\n
AI can help you make better decisions. By analyzing historical data matches, AI identifies trends and dynamically recalculates that data as conditions change. These types of AI-generated algorithms provide additional layers of information for better, more accurate decision-making. They are perfect for \u201cWhat-if\u201d questions and human explorations, such as whether to open a brick-and-mortar location in a new region.<\/p>\n
Understanding piles of data isn\u2019t easy, especially in our world of big data. What\u2019s more, this data is always changing and moving in relation to other data points. Without the help of AI, interpreting and acting on this data can be incredibly difficult.<\/p>\n
Even without the full picture, AI can use the mathematics of correlation to project values of missing data and indicate when important processes are diverging at a level that humans don\u2019t notice. Catching these divergences early can reduce risks and assist with cost avoidance.<\/p>\n
You\u2019ve experienced this type of AI, especially if you\u2019re an Amazon Prime shopper. For many retailers and contact centers, AI matches new users to existing customer patterns to predict their next actions and suggest next steps. For example, you might notice that after finishing the latest Stephen King novel, you\u2019ll receive ads and suggestions for similar science fiction or horror books by other authors. AI identifies your interests and recommends new products and resources based on your information and the actions of similar consumers.<\/p>\n
Not all useful information is spoken or typed, some of it\u2019s written, painted or photographed. AI machine vision has advanced greatly over the past decade. Soon, it may allow people to identify problems based on simple cellphone photos. For example, a picture of a troublesome connection for equipment could show a loose fitting. A picture of the manufacturer\u2019s label on an appliance would yield serial numbers, manufacture dates and other identification that can quickly narrow possible issues and solutions.<\/p>\n
NLP understands the spoken word. Consider Siri: People around the world ask Siri a multitude of questions every second. Many of those questions are asked in different ways. \u201cWhat\u2019s the temperature outside?\u201d \u201cWhat\u2019s the temp?\u201d \u201cWhat\u2019s the weather like?\u201d Each of these questions is phrased differently, but they\u2019re all seeking the same information.<\/p>\n
To identify and answer questions, AI transcribes and translates every spoken request into text. This enables the computer to answer the question \u2015 no matter how the question is phrased. Then it identifies the right answer and transitions it back to spoken word.<\/p>\n
AI does more than just make sense of the different phrases that humans use for the same questions or commands, it helps organizations better understand the intent that drives those requests. For instance, when a customer calls a contact center and uses the voice automation system, speech analysis AI analyzes the customer\u2019s word choice, energy and tone to detect emotion. It also breaks down keywords from incoming requests and finds context to act on, such as \u201cI want to move $500 from my checking to my savings account.\u201d<\/p>\n
Building on the previous AI abilities, organizations can directly and easily connect people to databases of answers for common incoming questions. After identifying the incoming request through speech analysis, Q&A disambiguation AI can automatically answer the question with a pre-drafted response. It also can compare and rate similar responses from multiple systems to deliver the best fit. This allows the customer to access their information quickly \u2015 and enables employees to focus on more complicated requests.<\/p>\n
AI can recognize and suggest automations for efficiency and cost savings. By locating repeat, high-cost processes through historical data analysis, you can automate back-end work. RPA AI organizes automated processes by categorizing and nesting dependent actions, making it easier for humans to better understand complex processes.<\/p>\n
Humans weren\u2019t made to consume and comprehend massive amounts of data. Without the help of AI, data is just a collection of rows and columns. AI gives you a full picture of the customer journey data by displaying it visually. Additional tools can further drill down into data to analyze and prioritize it for action.<\/p>\n
This type of AI-powered data summary can also highlight key events or related issues. For example, a contact center employee might handle hundreds of incoming requests each week. Instead of attempting to read through long conversations \u2015 and possibly miss important points \u2015 AI identifies common keywords, statements and questions for quick review and action. When a common question continually appears with a similar answer provided, that action is automated and added to the queue for a bot to manage.<\/p>\n
AI advances and improves customer and employee experiences. Even though it doesn\u2019t perform at the same level as your contact center employees, AI continues to grow more adept and beneficial with every customer interaction, proving that this is only the beginning. The future of AI and contact centers is (nearly) limitless.<\/p>\n<\/div>\n