To say AI has changed business and marketing is an understatement. It has empowered entrepreneurs to run slimmer, more efficient businesses that can handle reams of data now, thanks to AI’s ability to derive insights from data in context. That’s what we’re focusing on today, the customer insights that have become possible thanks to AI.
Key Takeaways
- AI transforms business by deriving customer insights from data, enhancing efficiency and decision-making.
- Real-time churn and loyalty scoring allows businesses to automate user engagement and predict churn based on context-specific data.
- Generative AI enables scalable psychographic mapping, offering insights across entire audiences instead of small samples.
- AI can identify customers’ unspoken and unmet needs, streamlining product ideation and reducing the need for extensive market research.
- Overall, AI unlocks invaluable insights into customer behavior, improving marketing strategies and business outcomes.
Table of contents
Real-Time Churn and Loyalty Scoring
When it comes to customer insights management, predictive AI has supercharged the ability to monitor and quantify a user’s loyalty. Before, these systems were rigid and acted on the latest available data, often without considering context or drawing abstractions from their behavior. AI is better at that, allowing context-specific customer churn prediction while also crafting personalized remedies. Since AI can train off your audience’s data, not just assumed behavior, it learns what normal user activity is like and what it looks like when that user is about to leave.
What’s more important is what the entrepreneur can do with this extra insight. AI-powered churn detection can get flagged inside of live data flows, visible in profiles for each user. Using the same AI, it’s possible to automate the process of reaching out to users when their chance of leaving rises. Likewise, it can automate closing attempts for those identified as ready to buy again. In fact, AI agents can be trained to do a wide variety of things and orchestrate with one another.
Scalable Psychographic Mapping
Generative AI also enables scalable psychographic mapping, driving insights on thousands of people instead of small sample sizes. In the past, businesses would imagine their ideal customer and market towards them. This would be informed by interviews and customer feedback, which are mapped to identify your customers’ intentions. There’s still value in imagining your median customer, but now AI can look at your whole audience and tell you what kind of people they are.
It’s a shift from hard, active participant data to softer insights gleaned from user activity in chats, reviews and other communications. It mirrors what we saw from Google over the past decade – a transition away from hard metrics, like keywords and search volume, and toward tools that can read your words and identify user intent in context.
Psychographic mapping used to be very expensive and time-consuming, but generative AI can do it easily since models are built with natural language processing in mind. The same training that allows it to respond to your text, including reading tone and intention, can also do the same for your customers.
Reading Customers’ Unspoken, Unmet Needs
Following from our last customer insight, AI’s ability to read between the lines also benefits product ideation and development. It’s generally accepted that customers have three needs – the explicit ones they voice, the implicit ones set by industry standards and the unmet needs that are difficult to articulate, if they’ve been noticed at all. The best product meets all of them.
In the past, it’d take a marketing visionary to analyze a product/service and find an unmet need. It’d also take a lot of costly research and development. Even worse, sometimes a customer tries to speak an unspoken need and misidentifies the issue, leading to wasted time. It’s a common sentiment in writing – a reader who tells you something doesn’t work is almost always right; when they tell you how to fix it, they are almost always wrong.
Generative AI is perfect for that kind of inference by scanning over thousands of data points across your audience. It can then find commonalities in complaints and infer what’s missing, instead of being led astray by mistaken analysis.
One mode of reaching these insights is through synthetic customers, the ability to make that ideal customer a real thing you can chat with. Then you can interview them and model business changes, like product price hikes, and get a decent reflection of what your core user base will do in response.
It’s no crystal ball, but it’s a level of customer insights that didn’t exist a few short years ago. As AI technology continues to develop, we’re sure to unlock even more actionable insights that aren’t possible today.
