By Carmel Dibner
Machine learning and artificial intelligence are rapidly changing how companies gather and process all kinds of data. From image and speech recognition to medical diagnosis and data classification, AI is rapidly transforming a myriad of industries. The field of New Product Development is embracing the technique. Product Developers can now quickly and easily draw critical insights for innovation from existing data without having to devote a significant amount of time, effort, and expense to data collection.
Introducing ACETM (Automated Content Evaluator), our proprietary AI methodology developed in collaboration with researchers at MIT. ACETM uses convolutional neural networks – a type of supervised machine learning –to dramatically reduce the time and effort required to gather a comprehensive set of customer insights in a category. While many tools summarize key themes and keywords mentioned in big data, our groundbreaking tool dives deeper. It identifies “pearls” of insight that traditional market research methods like focus groups and interviews might overlook.
Working with researchers at MIT, we proved the effectiveness of the tool by comparing the machine’s results with the results of traditional research studies. For example, for the oral care category, the tool uncovered nearly every unique customer need identified with traditional research. Furthermore, we have compared the volume and quality of insights from the machine to those that can be obtained from a random sample of user generated content (UGC) of equal size. The machine unearths critical insights for innovation that are unlikely to surface through random sampling. Time and again the technique has proven itself. In cases where the data already exists, ACETM consistently gathers fascinating insights for innovation faster and cheaper than traditional market research methods.
From snowplows to kitchen blenders, the method has been used by both B2B and B2C companies to gather a complete database of customer insights in only a few weeks. We have applied the technique to both publicly available UGC (e.g., reviews, blog posts, etc.) and client-provided proprietary data (e.g., customer call center notes). ACETM has enabled our clients to examine their own category as well as product and service adjacencies. Multiple adjacencies can be explored in a single research study, contributing to the machine’s agility, speed, and efficiency.
To date, we have conducted engagements on a wide variety of B2B and consumer topics, each yielding a comprehensive set of customer insights for the categories studied. Our clients have found the results highly actionable. Even those in mature categories have discovered new insights that were not highlighted in prior research. For example, in the blender study we uncovered several customer needs that were infrequently mentioned but impactful, including the importance of making sure the blender is compatible with the surface it sits on (e.g., the blender doesn’t walk across the countertop or ruin the table’s surface).
The best part? The machine gets smarter with each application.
Tags: Machine Learning