VOZIQ AI’s Post

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Your customers don’t decide to cancel overnight. There can be many instances of dissatisfaction, which your contact centers are usually blindsided with, making it difficult to retain your high-value, at-risk customers. Episode 19 of our #AIDrivenSubscriptionGrowth series is about how AI can proactively flag at-risk customers from your database and offer prescriptive guidance to retain each of your at-risk, high-value customers. Want to see how AI can fuel growth of your subscription business? Our No-Cost AI Growth Bootcamp is your chance to sprint test our AI solutions using your own data. Sign up for the bootcamp today using the link in comments. #CustomerRetention #HomeSecurity #PestControl #Churn #VOZIQAISolutions

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I always feel. Customers are there is addressable churn and unaccessible churn, right. Addressable churn is something someone is you know high churn risk because they're dissatisfied. Now we would love. That customer who is dissatisfied, it's a good thing if they're calling us and saying that I'm not happy and that's the reason I want to cancel, right. So they're calling the call center. Unfortunately, call centers are being blinded with, you know what happened to this customer a month ago, two months ago, three months ago. So dissatisfaction related things is what what I call addressable Chan and that's the first thing that we address and our models are also tuned to pick up those kinds of easy ones, right? The difficult ones are. You know, predicting someone who's already six months behind, there's no point in predicting they're going to cancel. Everyone in the world knows, right? That's what I would call an addressable churn, right? The, the ship has sailed and it's already passed. Let's not fight with too many things because you know, when we are looking for 2% churn reduction, 5% churn reduction, it doesn't matter which cancel reason we address, but we address reasons like dissatisfaction because of text analytics and sentiment analysis. We address things like, you know, pricing concerns, it's high price, low price because. The models are looking at geographic data that means they know what is my price and what is my neighbor price and we do not want them to go to a party and talk about you know $10 off here and there. We would be better off in kind of understanding that and feeding to the to the agent so they can kind of be proactively address these pricing differences that a salesperson. Wanted to sell, you know, three years ago, right so pricing related ones are easy to address, satisfaction related ones are easy to address, service related things are easy to address. And also because we actually show the home image it gives like talking points for the for the agent to customize the conversation. You just bought a new home or you have been living there for 10 years so. The homeowner is actually coming with certain expectations depending on big home and small home. So giving this context actually addresses multiple different reasons. So those are kind of top reasons that we typically address with predictive intelligence.

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https://bit.ly/no-cost-ai-growth-bootcamp

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