Product research gets new life with AI

Joel Passen
February 22, 2023
8 Min Read

Product research is a crucial component of successful software product development. By understanding customer needs, preferences, and behaviors, technology companies can create products that create value for their customers and differentiate in the marketplace. Research helps businesses learn more about their target audience and users’ desired outcomes to develop features and functionality that increase customer engagement and dependency. Let’s face it, the name of the game is getting your customers addicted to your tool or platform. In addition, software product research provides valuable data that businesses can use to optimize customer acquisition and retention motions. 

Traditional product research 

To date, product research has been conducted through surveys, focus groups, and customer interviews. Traditionally, surveys have been emailed to customers immediately to gather qualitative and qualitative feedback. More recently, product experience platforms have given product researchers access to more dynamic in-app surveys, product usage analytics, and the ability to launch traditional surveys with fewer resources.

Customer interviews allow one to ask specific questions and dig deeper into customer motivations, pain points, and specific use cases. Interviews can be extremely useful when businesses try to develop new products or determine how to enhance existing ones. Customer interviews can also provide valuable insights into desired integrations, services, and more. 

Focus groups allow companies to observe how customers interact with products and better understand the user experience. Observing customers using the product can provide valuable insights that are unavailable through surveys or customer interviews. Additionally, observational research, such as shadowing customers in their own environment, can help uncover valuable insights that would otherwise remain hidden.

Here are a few other common ways teams conduct product research:

  • Examine competitors: Analyzing competitors' products and marketing strategies can give you valuable insights into customer preferences and behavior trends in the market.

  • Track sales data: Tracking sales data such as purchase histories, customer feedback, and website analytics can help you pinpoint which products are selling well and which are not so you can adjust your product design accordingly.

  • Monitor social media: Utilizing social media channels like Facebook, Twitter, LinkedIn, and Instagram can help you monitor customer conversations about your product or service and see what users are saying about it.

At the end of the day, what do all of the traditional product research methods have in common? They are labor-intensive, expensive, and time-consuming, requiring intricate expertise and specialization to operate. Another drawback to traditional product research methods is that the data and insights generated are typically used by a small group and not leveraged across the enterprise. 

AI is Changing How Teams Conduct Product Research

ChatGPT, the AI-powered natural language understanding (NLU) platform that helps automate conversations has catapulted AI into the business mainstream. Aside from being all the rage, business leaders are adopting AI now more than ever because of technological advancements that have made it more accurate and faster to deploy. Additionally, AI is becoming increasingly affordable, allowing businesses of all sizes to benefit from the latest advances in artificial intelligence. Furthermore, the increased availability of data has allowed for more sophisticated algorithms and models to be used, enabling better decision-making and providing a competitive edge for businesses that use AI. 

Product leaders recognize that customer expectations are changing rapidly, and AI can help them stay ahead of the curve. While AI and its practical applications are evolving quickly, here are a few ways that advanced data sciences are already impacting product research.

  1. Automating the data capture and cleaning processes

AI automation can take over mundane tasks such as data collection and normalization (cleaning or standardizing data for reuse and analysis), freeing up teams’ time to focus on more strategic initiatives. AI also facilitates the data cleaning and preprocessing (data joining and integration) activities required to glean knowledge from the raw data. 

  1. Eliminating privacy concerns

Privacy issues are often a roadblock for product researchers. Teams must be careful how they use personal data (PII) to discover product insights. Privacy restrictions and personal data limitations challenge legacy experimentation and research methods. AI is paving the way to alleviate these concerns so teams can move quickly. New advances in  PII Identification, de-Identification, synthetic PII generation, and pseudonymization provide teams with tools to iterate and innovate faster than ever without jeopardizing privacy regulations. 

  1. Making sense of previously untapped data sets

AI-powered platforms are making it possible to sift through data using natural language processing (NLP) and machine learning algorithms to quickly analyze large amounts of customer-generated information like email, tickets, call transcripts, and more. These data sets have, for the most part, been hard to access given, among other things, their unstructured nature. AI-based tools can search for patterns and recognize key signals that might be difficult and even impossible for humans to spot, especially at scale. 

AI is already accelerating product research by enabling teams to quickly and accurately collect, clean, and identify trends in customer behaviors related to product usage and specific future use cases. AI-based platforms can analyze vast amounts of data in real time, helping companies make decisions faster while reducing costs associated with human labor. Additionally, using natural language processing (NLP), companies can automate text-based research tasks, such as discovering specific product-related insights, which would otherwise take an immense amount of time and resources. With the help of AI, teams can gain valuable insights into their products more efficiently and more effectively than ever before.

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