As an e-commerce company, your business is built on data. Every click, transaction, and abandoned shopping cart provides valuable insights into your customers and their behaviors. But what about the qualitative data that can be found outside of the typical e-commerce metrics? Whether you have a DTC brand, subscription model, marketplace, or even wholesale, here are seven real-world data sources that every e-commerce brand should never overlook when it comes to understanding their customers, and how AI can be used to analyze this data.
1. Product Reviews
Every product manager at an e-commerce company knows that product reviews are a goldmine of qualitative data. Not only can you find out what your customers think about your products, but you can also gain insights into their preferences and pain points. But analyzing product reviews for every single SKU to uncover real, meaningful insights is extremely time-consuming work. By using AI-powered generative analysis, you can quickly and accurately identify the most common positive and negative sentiments in your reviews. This information can help you make data-driven decisions about product improvements or marketing strategies.
One use case for AI-powered analysis of product reviews is identifying features that customers love or dislike about your products. By analyzing the language used in reviews, AI can quickly identify patterns in the most commonly mentioned features or issues. This information can be used to guide product development and marketing strategies. For example, if customers consistently mention that a certain feature is difficult to use or doesn't work as expected, your company can prioritize improvements to that feature to improve customer satisfaction.
2. Net Promoter Scores (NPS)
Net Promoter Score surveys are typically used as a quantitative metric by most companies, but unstructured feedback is part of what makes NPS such an effective strategy for measuring customer satisfaction. It provides an extra layer of insight for your business, allowing you to act on a customer’s feedback rather than simply learning their opinion. By using AI to analyze the text of survey responses, companies can identify the most common themes and issues that arise. For example, if customers consistently mention that they had a negative experience with packing and shipping delays, your company can prioritize improvements to these processes to improve customer satisfaction and loyalty. When analyzing NPS, avoid tools that simply provide word clouds and positive/negative sentiment associations as these tools are limited in nature and unable to detect nuances in language such as sarcasm, vagueness, and multipolarity. Look for an AI tool that uses advanced NLP models to run true qualitative data analysis.
3. Customer support tickets
Buyers reach out to Customer Service regularly, but is your company revisiting those help desk tickets once they are resolved and analyzing them for insights? Customer support tickets can provide valuable insights into common issues and pain points for customers. By using AI-powered analysis tools, companies can categorize tickets and identify patterns in the most commonly reported issues. This information can be used to prioritize improvements to support processes and reduce customer frustration. For example, if customers frequently report difficulty with a certain coupon or promo code, a generative analysis platform could identify a spike in calls regarding a specific product code within your weekly reporting to help your company prioritize a fix to reduce support ticket volume.
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4. Transcripts (meetings, chat logs, phone calls)
Depending on your business, customers may reach out in a variety of ways: support forms, online chats, and phone as well. And while these touchpoints often spawn support documentation, imagine if you could analyze the original source data – the call or chat transcript itself – and understand the problem directly from the customer instead of a support worker’s interpretation without any additional resources required. Generative Analysis can help with that. The AI that powers this form of analysis can analyze all of those transcripts and pull out the most salient insights and trends, so that you never miss a thing.
Transcripts can provide valuable insights into customer feedback and preferences gathered from focus groups, sales calls, or other customer engagement activities. By using AI-powered analysis tools, companies can identify the most commonly mentioned themes and issues. This information can be used to guide product development and marketing strategies. For example, if customers consistently mention that they find a certain aspect of your product confusing or difficult to use, your company can prioritize improving that aspect to improve customer satisfaction.
5. Social media
Social media platforms provide a wealth of data about customer sentiment and preferences. By using AI-powered social listening tools, companies can track mentions of their own brand and products as well as competitors' brands and products. This information can be used to guide marketing strategies and product development. For example, if customers frequently mention that a competitor's product has a feature that they wish your product had, your company can prioritize adding that feature to your product. Hey, it worked for Twitter.
Email is another source of qualitative data that is often overlooked. By analyzing the text of customer emails, you can identify common issues and pain points, as well as customer preferences and feedback. AI-powered email analysis can help you quickly categorize emails and identify trends over time, so you can improve your customer service and product offerings.
7. Other Surveys
Finally, e-commerce companies should not overlook other surveys they may be conducting, such as internal employee engagement surveys or external marketing surveys. Imagine being able to ask your audience one freeform text question instead of 10 multiple choice questions to find the answers you're looking for. This is not only possible, but encouraged as a result of AI. Before, analyzing open-ended surveys was a nearly impossible task that required a ton of time and resources. Not anymore. By leveraging AI to analyze the text of these surveys, you can gain insights into employee satisfaction, customer preferences, and pain points. AI-powered survey analysis can help you quickly identify the most common themes in your survey responses, so you can make data-driven decisions about your business.
E-commerce companies have access to a wealth of qualitative data that can provide valuable insights into their customers and their behaviors. By using AI-powered analysis tools, companies can quickly and accurately identify common themes, pain points, and areas for improvement. This information can be used to make data-driven decisions about product improvements, marketing strategies, and customer service efforts, ultimately leading to a better customer experience and increased revenue.
Interested in learning how Viable’s Generative Analysis can help your e-commerce brand automate your qualitative data analysis with unparalleled speed and accuracy?