¿Qué hacer si las tasas de retención de clientes son bajas y desea aprovechar la IA y el aprendizaje automático?

I'm concerned about the question's lack of context. Retention is low compared to prior years, the industry, or both? Either way, gather relevant data about your prior years and industry (and competition). Then, and only then can you start to prepare for AI/ML modeling and training.

To address low customer retention rates and leverage AI and machine learning, start by analyzing existing data to understand customer behaviors, preferences, and churn patterns. Implement predictive analytics to forecast churn and intervene proactively. Utilize AI-powered recommendation systems for personalized experiences and improved engagement. Employ sentiment analysis to gauge satisfaction levels and address concerns promptly. Continuously refine models based on real-time feedback to optimize retention efforts. Consider AI-driven chatbots for efficient customer support. Overall, integrating AI enables data-driven decisions, strengthening relationships, and boosting retention rates.

To address low customer retention rates, leverage AI and machine learning by collecting and analyzing customer data to identify churn predictors and build predictive models. Segment your customer base for personalized interventions and continuously optimize strategies based on performance feedback. Implement targeted intervention tactics such as personalized offers and proactive customer support, while investing in customer success initiatives. Utilize experimentation and A/B testing (split testing) to refine approaches, ensuring that interventions are effective in improving customer retention rates over time.

AI only works as well as the data on which it is based. That's why I first collect all the customer data from my CRM and all the data from my marketing mix in one place and bring some structure to it. After that, I analyze the existing data and the purchasing behavior of my customers with the highest lifetime value using cohort analyses, touchpoint analyses, etc. Why? Because I want to understand how these customers think and act. I can already use AI here to gain initial insights. In the final step of the analysis, it is important to find the "blind spots" in my data and enrich them with new data. Post-purchase surveys are ideal for this in order to better understand customers and obtain psychographic data in addition to demographic data.

To improve low customer retention rates with AI, begin by analyzing data to uncover churn patterns. Utilize predictive modeling to identify at-risk customers and personalize interactions through AI-driven recommendations and communication. Integrate chatbots for instant support and sentiment analysis to gauge satisfaction levels. Automate marketing campaigns with AI for targeted outreach. Establish feedback loops to continuously refine strategies. By leveraging AI in these areas, businesses can gain insights, enhance engagement, and implement proactive measures to improve customer retention rates effectively.

As soon as I have collected my data, structured it and enriched it with missing data, the first patterns can be identified with the help of AI. From the analysis, we know what the customer buys, when they buy it and through which channel the impulse to buy came from. From this, we can determine which product, which campaign or which channel provides the highest customer lifetime value. I can therefore predict when a new customer is most likely to buy a certain product from me again. However, if a repeat purchase does not take place, I can also recognize patterns as to why this has happened. The psychographic customer data collected is particularly important for this.

A deep understanding of the customer is the basis for personalization. Brands often only see the first or last touchpoint and what the customer has bought. However, to really understand people, I need to collect more personal data with the help of post-purchase surveys and loyalty programs or through interactions on social media and in communities. This can be used to personalize a holistic customer experience: The message of paid & organic content, the search results or the whole UX/UI in an online shop as well as the right offer at the right time. All this can be done and communicated specifically with the help of the data and AI.

To automate the interaction with your customers with AI, it's crucial to speak the language of your audience. And by that, I mean to use the words they understand and to build messages they can feel. AI chatbots and virtual assistants can help when it comes to Customer Support, which is important to reduce churn rates. However if you want to increase your repurchase rates, start using AI for your e-mail automations to improve the aftersale-experience. Bonus: Sometimes it's not smart to push one offer after the other. Tell a story instead, why your customer should use your product again or why they should buy another product.

Too often companies view AI as the end goal and not the tool. When you realize that the goal is always to provide the best support possible, it shifts your mindset to focus on the best ways to achieve that. When implemented with the customer in mind, AI can help you scale your CX strategy by surfacing retention opportunities it would have otherwise taken you much longer to realize if at all.

It's important to track every conversation on your website, shop or support/ticket channels. Not only to improve the answers of your AI, but to understand your customers better. Remember that the best action you can take is to reduce the support inquiries, not to have the best support. A powerful way to do this, is to exchange with existing customers via social media, chat, calls or community events. Hear what they say, execute what they want and avoid what they dislike.

In my opinion there are four ways to boost the customer retention: - Increase frequency & intensity of your product usage - Increase feature usage - Make your customers remember why they started - Make sure they don't get bored You can focus on one of these ways or combine all these ways together. It depends on your retention strategy and what works best for your product/service or target group. And here is where AI and ML can help you with this decision by providing all the insights you need.

In my opinion customer loyalty is all about emotion! You gain fans to your brand through emotion and experience not transactions like coupon codes. Thats why it's important to identify your most valuable customers through data and design personalized and exciting experiences along the customer journey. If you have the right demographic and psychographic customer data, AI offers great opportunities to create unique touchpoints and an innovative shopping experience, especially online.