Commerce.AI versus Sprinklr: Comparing Product Data Analysis Solutions
We've previously compared Commerce.AI and Qualtrics, two product data solutions. You may have heard of Sprinklr, a data platform with features for social listening.
Social listening has become a popular approach to understand customer desires, but it is becoming clear that there are pitfalls with this strategy that make it less reliable. Social listening often fails to provide adequate knowledge of the specific product features that customers are asking about, and also lacks the ability to forecast trends in the market. This makes social listening’s effectiveness on a company’s operations questionable.
Additionally, social listening is expensive and time-consuming. Firms have found themselves incurring significant costs. The cost is debated in the industry, but it is clear that the investment is high and not seen as necessary for smaller firms.
Commerce.AI’s product data engine addresses these concerns by providing product insights at scale from over a trillion data points, largely directly sourced from commerce channels.
This engine relies on artificial intelligence (AI) to harness data from commerce firms like Amazon, Walmart, and Target, to social media channels like Facebook, to bring the data to you, rather than you constantly hunting for it or acquiring specific data through tools like surveys. At scale, these unstructured data sources become an unnervingly accurate look into what customers want and how they feel about your brand or products, which can become the impetus for change in marketing or product campaigns.
We know that when it comes to commerce, companies need to listen and manage an ever-growing number of touchpoints. Managing and excelling at these touchpoints needs data, and using Sprinklr for social listening is one solution to gain insights.
Clearly all industries are headed for digital—but many companies still think of a marketing department as a one-size-fits-all solution for customer engagement. They build one website and product catalog for each customer segment or region instead of creating a personalized experience. They use an army of data vendors and disparate downstream integrations to bring together insights—of course they do this months after those insights could have had an impact on decisions like product pricing or design. And they manage all of this out of separate offices by IT teams far away from user experience designers who know the customers like no one else does. Result: inefficient marketing processes.
Sprinklr attempts to break down those siloes and provide teams with easier access to social data. That said, social data often isn't enough, as it lacks the needed quality and relevance to be truly valuable for the product innovation process. Moreover, teams have to go out and spend precious time acquiring social data, which should be spent on product innovation.
Commerce.AI—Bringing Product Data to You
With case studies that span across industries and an engine built on over a trillion product data points, Commerce.AI has become the platform behind some of today’s most influential firms, from Unilever to Coca Cola.
One of the first things brands notice about Commerce.AI is its in-depth and insider knowledge of product data. Unlike many other products who rely on user-sourced social data, Commerce.AI boasts a proprietary analytics engine with access to Amazon, Walmart, Target, and other important data sources.
This combined with their diverse product strategy, market intelligence, and research insights features means that Commerce AI is a one-stop-shop for those who need to reduce the workload traditionally associated with perfecting a product's strategy.
Excited by this technology's potential for creating new opportunities for rapid innovation in marketing and business development in the global marketplace, many Fortune 500 companies have already begun leveraging this innovative tech tool to redefine how they design products and segment customers.
Commerce AI offers an in-depth understanding of thousands of product segments, reached through their unmatched dataset - which is precisely what merchants need to stay ahead of increasing competition in today's global marketplace.