AI (and their human helpers) will power the next wave of consumer intelligence

Forrester has been writing about the rise of a new generation of AI-enabled consumer intelligence solutions over the past year. These solutions tap inside and outside data sources, and leverage the latest machine learning (ML) techniques to enable insights professionals to work at speed and scale and enable better decision making across the enterprise.

Most critically, these smart solutions operationalize AI, and bring its value not just to market research and customer insights groups – but to a diverse set of product, innovation, and customer service teams that are increasingly dependent on understanding (in real-time) consumer context and motivation, how they are talking about your products and brand, and even broader trends that are super important to product development, go to market, and customer love strategies.

As Forrester points out, these solutions have roots in and serve other market segments, including social listening, feedback management, customer analytics, text analytics, and insights services, and among late-stage vendors in the firm’s New Tech report, only 3 are recognized as true hybrid platform/service providers, including Synthesio.

We think AICI provides a great blueprint for the future of consumer intelligence (and the role of social listening within it). But it’s more than that. The “hybrid” segment represents how most organizations need to apply AI and machine learning given the shortage of data science talent – and the need for all parties to have access to insights earlier, to move faster and capture emerging market opportunities before the competition.

Democratizing the power of AI also requires a rethinking of what data is most helpful, and how to get more actionable, accessible small data into the hands of more people, and leave the big data to machines (which are way better at dealing with it vs. humans); a topic I've written about for almost a decade and more recently even Gartner has started to promote as well.

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Seeing trends sooner needs machine intelligence and human expertise    

If the path to consumer intelligence starts with social listening, we need a solution that is global-native and covers all the right sources. Only with this in place can we have a complete view of the market and its consumers. In addition, reducing the time it takes to turn insights into action is often the top goal for organizations that hope to become data-driven. Yet, there is always too much data, creating data overload. And the teams managing data are often not the teams that need it the most – really a challenge going back to early BI and centralized reporting functions.

This is where democratizing data, supported by smart machines, aka automation and AI, come in; like the use of ML for sifting through large volumes, noise reduction, and preliminary classification. Yet most organizations are relatively immature when it comes to adoption of techniques like deep learning. So, there needs to be tools that non-experts can use, and helpers on call to get teams started, provide the data science expertise when needed, and ensure accurate results.

Enter the role of hybrid providers that handle more data types, and more information, in more languages. They orchestrate the contributions of AI agents, along with expert built analytical frameworks and partner provided add-ons to allow teams to move faster, test and learn, and scale up more efficiently than using software or a service provider alone.

Here are the three ingredients found to be essential for uncovering untapped, unmet consumer needs or emerging opportunity spaces - and finally achieve consumer intelligence:

1. Next-gen AI and NLP technology provide the engine. Machine intelligence in the Natural Language Understanding domain allows for uncovering meaning and context behind human language at a gigantic data scale.

2. Expert built analytical frameworks provide the context. These models help supercharge intelligence delivery by injecting cultural insights, or shopper behavior data, or innovation best practices.

3. Human-machine teams and partnerships provide scale. Making progress with forward-looking trend prediction depends on this approach, especially when it comes to cultural analysis. This starts with having access to all relevant data sources (social, search, behavioral, etc.), along with connectors to third-party business systems to operationalize insights via dashboards, visualizations, and even in-app guidance.

Use cases for AI Consumer Intelligence

Let’s look at an example:

A kitesurfing equipment global brand was looking for a way to feed their product innovation strategy with meaningful insights and ensure they answer their consumers unmet needs.

This is where AICI has a great role to play. The mix of next-gen AI - using the last advancements in machine learning techniques for semantic analysis - and a proven analytical framework automated the exploration for user innovation discovery. The collection and analysis of 200,000 user-generated posts from over 9000 websites across the globe effectively identified more than 200 unmet needs over time with 20+ functionally novel innovations.

Then, confronting social and search data determined which ideas and innovations are gaining traction and thus worth commercializing, taking the guesswork out of the innovation process.

This approach is foundational for bringing the power of AI from the lab to across the enterprise. And also helping insight professionals and their peers get ahead by proving the most complete, most accurate, and most predictive picture of their consumers and markets. It balances the role of humans and machines and, most importantly, sets up AI as a true game-changer when it comes to the future of actionable, tech-enabled consumer intelligence.

This article was originally posted on Synthesio's website

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