Filip Vítek—the Executive Vice President of AI and Data at CommentSold—explains how customer-generated insights can drive e-commerce innovations in an age of “peak data.” This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.
When 97 percent of companies are adopting big data and AI strategies, it’s clear that data is key to innovation and growth. Good data can diagnose problems while uncovering hidden strengths. It can inform short-term and long-term goals and thereby inform the strategies to reach them. Nowadays, organizations have more access to data than ever before, with recent advancements in artificial intelligence toppling barriers to data collection and analysis. However, fewer than a quarter of executives say they’ve developed a data-driven company, indicating a veritable gold mine of untapped potential.
The e-commerce industry has a particularly unique opportunity with data analytics. E-commerce data accounts for a ballooning percentage of the world’s data, spanning various types, sources, and associated user behaviors. However, many e-commerce companies still don’t know how to optimize this glut of data to build a better product.
The instinct is to default to analyzing end consumer behavior patterns. After all, they are who merchants rely on e-commerce vendors to serve. However, for vendors, the most powerful data comes from these merchant customers themselves. Analyzing merchant data enables e-commerce platforms to understand how and where customers are getting the most out of their solutions, leading to better product strategy and output.
Winning Numbers: How to Choose the Right Data
Customers can unlock incredible new opportunities for e-commerce technology businesses, including new revenue streams. Being able to identify customer needs and promptly develop an effective solution is a business growth superpower. There are many sources of customer data to consider, including:
Business performance and utilization
This data can show how customers are doing on a platform and isolate which features are driving the most success. To gauge product value, e-commerce platforms might analyze common threads among their top-selling merchants. Key statistics to watch out for include those related to sales and invoices, conversion rates, return rates, and trends in-store activity.
Customer acquisition and retention
One of the more easily accessible data types, it can illuminate what draws people to adopt a product. Did an update or new feature bring a flood of new customers? Are there spikes and troughs in acquisition at certain times of the year? Did a rival vendor roll out a coveted new feature? Did it roll out a bad one or shut down? All of these factors and more can impact acquisition and retention.
Traffic and engagement
Changes in website traffic and social media engagement can reveal which products are resonating with customers, as well as which ones are causing trouble. A peak in traffic around a product update could be good or bad, so it’s key to track the sentiment.
Content
E-commerce is a content-heavy industry, reliant on images, video, and creative copy to communicate a product’s details to a potential buyer. The better a merchant’s content, the more likely they are to surpass their competitors.
Data is most powerful as an enabler when it can reveal potential revenue streams. For example, if customers are consistently integrating one product with a third-party product or plugin, it may be time to develop a similar solution. That said, not every platform has to be (or should be) all-in-one; when it comes to data utilization, learning what not to pursue is just as important as learning where to invest.
Merchant data trends can also help e-commerce companies whittle down costs. If the data suggests that customers aren’t benefitting from a particular feature, scaling back support or rolling it into a complementary feature may save valuable time and resources.
Ethical Considerations – Navigating Merchant Consent
Privacy and consent are non-negotiable considerations for any data analytics strategy: a product built on unethical data is an unethical product. While end-user data is theoretically useful to an e-commerce platform, it is more difficult to source ethically than customer data. Shoppers on a mobile app or a livestream sale don’t give consent in the same way that the sellers purchasing an e-commerce product do, which may make using their data invasive at best and illegal at worst. Customers, on the other hand, often consent to the use of their data in the user agreement. These contracts assure merchants that while the e-commerce vendor has access to their data, they will maintain ownership, and their consumers will be protected.
Ultimately, end-user data says more about the individual merchants than the e-commerce products those merchants are employing, so vendors will reap more value from customer trends, anyhow.
Data Alchemy: Translating Analysis into Action
When teams harness the power of customer data to power product innovation, they can genuinely make a difference in customers’ lives. The idea is to be indispensable, and that requires a thorough understanding of what customers actually need. However, data diminishes in value if distorted too heavily by human error and poor interpretation – no one wants tarnished gold.
This is where artificial intelligence enters the picture. Recent advances in generative AI (genAI) have had a transformative impact on the data analysis process; it uncovers patterns near-instantly that would take human analysts days – or even weeks or months – to parse out. From these patterns, certain LLMs and other frameworks can synthesize conclusions in plain language, graphs, and charts. Gartner predicts that 80 percent of companies will employ generative AI models by 2026, but I believe this could happen even faster for the e-commerce sector, which manages an extraordinary amount of data.
However, analysis is only half the battle. Companies should have a dedicated team to educate the rest of the business on how these findings apply to their work. If a vendor is going to build a new product based on a pattern unearthed from customer data, they need to ensure their teams have the right capabilities. Do they need to bring on more staff? Do they need to upskill? Fortunately, data helps answer these questions, too.
Empowering Customers with Their Own Data
Entrepreneurs are naturally curious—how many great companies began with the question, “What if?” Giving merchants insights into their own data allows them to translate that curiosity into action. E-commerce businesses should see every new advancement in artificial intelligence as a way to arm their sellers with new data insights. That way, they’ll get the best possible product experience while better serving their audience.
Data is everywhere, and when utilized correctly, it can unlock a deluge of opportunities for e-commerce vendors. By understanding that their most powerful data comes from their consenting customers, vendors take a necessary step towards building a better product. Combining this knowledge with the right analysis strategies, they can utilize their abundant merchant data to drive product innovation and business success.
Link to the original article: Solutions Review