Okay, so, I recently had a fascinating chat with Kieran – a real whizz when it comes to leveraging AI in the business world. We were diving deep into how to use AI not just to sell more, but to actually provide better service through intelligent recommendations. Think less aggressive sales tactics and more ‘Hey, I think you might actually like this!’
Our starting point was something pretty fundamental: personalised recommendations. It’s more than just, ‘Oh, you bought this, so buy that too.’ We’re talking about truly understanding customer needs and proactively offering solutions. As Kieran put it, “It’s about moving beyond basic collaborative filtering. We’re talking about using deep learning to understand the nuances in someone’s behaviour, almost like reading their mind!”
Understanding the Data Landscape
First things first: data. Kieran stressed the importance of a holistic view. “It’s not just purchase history,” he explained. “It’s browsing behaviour, demographics, even social media activity (within ethical boundaries, of course!). The more data points you have, the more accurate your AI-powered predictions will be.”
Imagine this: A customer buys hiking boots from your online store. A simple cross-sell might suggest hiking socks. A personalised recommendation, powered by AI, could look at their browsing history – maybe they’ve been researching lightweight tents – and suggest a specific model that’s perfectly suited for the terrain they usually hike on, based on demographic data suggesting they live in a mountainous region.
AI in Action: Examples of Success
Kieran shared a few real-world examples that were incredibly insightful. One involved an online music streaming service. They used AI to analyse listening habits, not just to recommend similar artists, but to proactively suggest curated playlists based on the user’s mood, time of day, and even the weather! The result? Increased user engagement and subscription renewals.
Another example involved an e-commerce clothing retailer. They used AI to analyse customer purchase history and browsing behaviour, then created personalised ‘style boards’ for each customer. These boards featured outfits curated specifically for them, based on their past purchases, body type (estimated through purchase sizes), and stated style preferences. This led to a significant increase in average order value and customer loyalty.
Ethical Considerations and Data Privacy
Of course, this all comes with a huge asterisk: Ethics. Kieran was adamant about this. “Data privacy is paramount,” he said. “Transparency is key. Customers need to understand how their data is being used and have the option to opt out. We need to build trust, not creep people out!”
This means having a clear and concise privacy policy, offering users control over their data, and avoiding any practices that could be perceived as manipulative or discriminatory. Think about it: if your AI starts suggesting high-end luxury goods to low-income users, based on assumptions about their aspirations, that’s not just unethical, it’s bad business. You’re better of engaging them with items that better fit their spending habits and provide similar value.
Generating New Business: A Step-by-Step Guide
So, how can you implement this in your own business? Here’s a breakdown:
- Data Audit: Identify all the data you currently collect on your customers. Are there any gaps? Are you collecting data ethically and transparently?
- AI Platform Selection: Research different AI platforms that offer personalised recommendation engines. Consider your budget, technical expertise, and the specific needs of your business.
- Model Training: Train your AI model using your customer data. This will involve feeding the data into the AI platform and fine-tuning the algorithms to achieve the desired results.
- A/B Testing: Test different recommendation strategies to see what works best for your audience. Track key metrics like click-through rates, conversion rates, and average order value.
- Transparency and Control: Communicate clearly with your customers about how you’re using their data and give them control over their preferences.
Increased Revenue Potential
The potential for increased revenue is massive. By providing truly personalised recommendations, you can increase customer engagement, drive repeat purchases, and boost average order value. It’s a win-win: customers get a better experience, and you get a healthier bottom line.
So, there you have it. AI-powered personalised recommendations aren’t just a futuristic fantasy; they’re a powerful tool that can help you build stronger customer relationships and drive significant revenue growth. It requires a thoughtful approach, prioritising ethics and transparency. However, the rewards for getting it right are absolutely worth the effort. Remember, the key is to focus on genuine value, not just pushing products.
