How Generative AI is Creating Opportunities for Harnessing Customer Data
AI is everywhere now, from your social media inbox to your smart doorbell to cat’s heart rate monitor (no, seriously). However, even if it’s infiltrated every part of your life, you’re probably not using it to its potential in the workplace. You know generative AI can help you triage customer complaints, rewrite your website copy, replace expensive photo shoots, and a million things in between. Still, you might not be fully aware of everything it can do behind the scenes.
Beyond its capacity to help you prevent your best face to customers, AI can go a long way in helping you understand them better. It’s capable of collecting and analyzing vast amounts of customer data in order to understand exactly why your customers behave the way they do. From there, it can tell you what they actually want, make predictions about future trends, and more. Here are just some of the ways that generative AI is harnessing the power of customer data.
Understanding Conversational Agents and Virtual Assistants
If your company uses webchat in any capacity — say, to respond to customer queries — you’re already collecting customer data. Conversational agents and virtual assistants, gather all kinds of data about each and every user interaction. Simpler ones collect basic information like mentions of specific products or services, or location and demographic data. More advanced conversational agents can detect more specific cues, like those discussed below.
As generative AI-based chatbots grow in popularity, they are also becoming more specialized, to maximize their potential for sector- and industry specific-contributions. Innovative technology companies are developing purpose-built AI tools to serve certain types of businesses, like healthcare platforms or automotive sales software with chat functionality. These types of sector-focused tools may show more commercial promise than more general AI applications.
Sentiment Analysis and Trend Detection
One reason industry experts are excited about more specialized tools is that covering less ground allows for greater levels of sophistication. It’s sort of like the difference between an internal medicine doctor and a cardiovascular surgeon — to advance higher, you get more specific. There’s a time and place for both general and specialized generative AI, because you need both types of data. You need broad insights about large groups and more granular, individual detail.
More advanced webchat and AI tools can get to this deeper level, closely exploring each interaction with every customer or client. Some can actually analyze customer sentiment with natural language processing — in other words, they can detect, based on their words, how your customers feel. Some implications of this include better future predictions about what types of products or services to offer, as well as improved customer service interactions.
Predictive Analytics and Churn Prevention
In terms of customer service, some AI tools are quite good at predicting when a customer is planning to switch to a competitor. They may do this through sentiment analysis, or by spotting behavior patterns (clicks-throughs, open rates, etc.) that have led to churn in the past. This can happen during webchat interactions, or through other applications of artificial intelligence. For example, there are designated predictive analytics platforms designed for churn and lead scoring.
Many tools are designed to aggregate customer data across various customer touch points. For example, the same tool might be watching a customer’s behavior across email and website interactions, chat, and even phone calls. Using the data they’ve collected across these platforms, they can predict the customer’s next action (buy, churn, etc). They can also recommend when and how a salesperson should reach out to that customer, for maximum chances of success.
Integration with Other Tools
That multichannel functionality is especially useful in industries like e-commerce, finance, and automotive sales. Each one has various online and in-person customer touchpoints, which all must be linked together electronically. Keeping information and data collection consistent across these touchpoints is one of the keys to a better customer experience. AI data collection tools, then, work best when integrated with other platforms, like CRMs and email marketing tools.
With a CRM, for example, information about each and every customer interaction is stored in one central dashboard. Users can access all that real-time customer information from their phones, company computers, and more. As more information is collected by the system, AI tools can make more accurate predictions and recommendations based on larger data sets. If data is siloed, on the other hand, predictions aren’t as accurate, because AI doesn’t have the full picture.
Targeting, Segmentation, and Personalization
AI tools are also used, with impressive results, to segment and personalize the customer experience. The more they understand about behavior and the customer journey, the better equipped they are to offer bespoke marketing and recommendations. Trained AI tools can make strong predictions about customer preferences, from ads to landing pages to product features. They do this by harnessing the data they collect across all these different customer touchpoints.
For example, an apparel company can use AI data collection to predict a customer’s interest in a specific pair of leggins. The next time the customer interacts with that company’s chatbot, the bot can recommend similar leggings. Or, it could put that customer on a list to receive an email about the company’s latest leggings line or sale. Finally, it could show the customer those leggings to try and upsell them at checkout when the customer is buying something else.
Data is Everything
There’s a reason that customer data is now considered one of the most valuable assets for a company to hold. The more you understand about your customer, the greater your ability to grow your profits and beat out competitors. At the same time, a plethora of AI tools are evolving to serve nearly every potential use-case for data collection. Decision-makers will need to plan carefully to choose the right tools and decide exactly how to invest in generative AI.
