We’ve talked in our last couple of blogs about how the customer experience is changing, and how you can build an omni-channel listening strategy that pulls together unstructured data from your customers’ preferred channels, and puts it all in one unified platform. Now you have a new problem: how do you create actionable insights with all that data?
In other words, how do you actually use all this information to improve your customer’s experiences, build brand loyalty, and enhance your products and services? First, the good news: You are not alone. In fact, 97% of marketers say their organizations are ineffective at turning customer data into insights and actions.
The bad news? That number is so high because this is extremely challenging, if not impossible, if you don’t have artificial intelligence (AI) to help. No longer a “nice to have,” AI is an essential component of your customer experience strategy that turns your omni-channel listening into actionable insights.
At its core, artificial intelligence simulates how humans think, act, and learn at a massive scale, and has the ability to learn and adapt using petabytes of unstructured data in real time. Importantly, industry-leading AI can be trained to adapt to different customer or vertical models, ensuring that the insights you generate are specific to your business or industry. For example, the word “sick” might mean something very different for a sports media company than it does for the healthcare industry. But beyond that, this is how AI will give you more sophisticated results, moving from basic sentiment analysis to being able to analyze customer feedback across multiple dimensions, like location or product to get specific, actionable insights.
It’s also important to distinguish that all AI is not the same (and sometimes, people will call something AI when it’s not AI at all). It’s critically important to distinguish AI from dynamic functions and rule-based automations which don’t have the ability to learn and adapt the way AI does. Let’s talk about how it works.
Without AI, the sheer volume of unstructured data from modern customers on modern channels is just too difficult to handle. AI, in its basic form, is used to understand sentiment, but really begins to shine when expanded to include emotion, intent, and spam detection. More advanced applications of AI include training algorithms against industry or vertical characteristics, and building in feedback loops for continuous learning.
In order to turn listening into learning, it is important to capture as much information from a post or message and then aggregate this into themes, topics, tones, etc. This form of observation is built around the basic structure of sentences:
Time – gives us the timeliness of the message
Subject – audience or brand insights depending on the subject
Object – product or brand insights depending on the object
Place – location insights
Adjectives or Manner – keys to sentiment
Verbs – keys to trends/segmentation as actions speak louder than (other) words
Other contextual elements, e.g., hashtags, enable the attribution of data to sources, trends, or actions. Augmented with non-text elements such as emojis, photos, images, videos and gifs, and AI can help you draw a more complete picture of the conversations that could impact your business.
Effective AI uses machine learning and natural language processing to automatically enrich and classify data, highlight anomalies, and recommend content or actions. Industry-leading AI is over 80% accurate, and can detect certain people, places, objects and logos in social media images – empowering brands to understand how consumers feel about their products and how they use them. Here are a few examples of how these insights can help your brand:
Product Insights: Identify areas of improvement of overall product experience by quantifying and benchmarking the experience against competitor products.
Service Insights: Identify broken aspects of your customer journey by quantifying experiences at each touchpoint and benchmarking against competitors.
Visual Insights: Detect brand logos, objects, scenes, activities, gender, emotions expressed within an image.
Audience Insights: Understand what matters to an audience segment by discovering their preferences, affinities, and opinions.
Spam Detection: Quickly filter out unwanted messages leveraging inbuilt spam detection which identifies advertising and inappropriate content.
Content Insights: Compare engagement strategy of your brand and competitors by understanding factors behind engagement.
Location Insights: Understand what customers and consumers are saying about physical locations by discovering their ratings, reviews, and opinions
Smart Alerts: Helps to detect crises before they become viral or consequential.
Moderation Recommendations: Find and move high value messages to the top of your queues.
You can see how AI must quickly become a central part of your omni-channel listening strategy, giving you the ability to surface actionable insights from huge volumes of unstructured data quickly and at scale. Companies that adopt AI have the potential to increase business productivity by 40%, double their cash flow over the next 10 years from AI alone, and see an average of 8-10% higher margins.
There are a number of questions you should ask when evaluating an AI solution. Check out our new eBook for a quick list of things you should be looking for, and also get a first look at how you can democratize these AI-powered insights throughout your business to make your customers happier.
Download the 3 Steps to Transform Your Customer Experience with AI-Driven Insights eBook now.
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