Our team has started using AI to evaluate leads, and the results are truly impressive. Bots analyze customer behavior on the website and assign them a score based on the likelihood of conversion. But the question remains: how can we strike the right balance between high scores and the customer's actual readiness to buy so as not to lose potentially valuable contacts?
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Doesn't a high lead score sometimes feel misleading? It did for me. I once chased a "hot" lead with a perfect score, only to find out they were just browsing, not buying. How do you avoid that pitfall? Through careful segmentation and multi-faceted scoring, considering behavioral AND demographic data. Have you played Basket Random ?
In my experience, AI is great for filtering leads and saves managers a lot of time. But you shouldn't rely entirely on algorithms: sometimes they can miss a non-standard customer who is still ready to buy. That's why it's better to combine automatic evaluation with human verification.
I was looking for a way to improve the performance of the sales department and came across an article with interesting case studies: https://aloa.co/blog/the-role-of-ai-in-reshaping-customer-outreach-for-startups. I decided to try one of the AI tools for predictive analytics — it analyzed past transactions and automatically identified the most promising leads. Initially, I was concerned that the system would make mistakes, but it turned out that the conversion rate increased almost immediately, and the quality of leads improved significantly.