Vous perdez des clients à cause de l’attrition. Comment pouvez-vous affiner votre approche de l’analyse des données pour l’éviter ?

Customers are crucial to business success, churn is a killer, yet most focus on gaining new customers rather than retention. The mobile industry is an example of the "Acquisition Trap.” Post-acquisition investments impact CX/EX, brand loyalty, and the bottom line. Utilizing contextual data, in-the-moment behaviors, and historical insights to personalize and orchestrate experiences enhance CSAT and NPS boosting Customer Lifetime Value. Too many companies hope existing MarTech can deliver post acquisition support but it’s just not designed to do so. It's vital to partner with software providers that offer proactive, intelligent, and personalized customer engagement that work with existing investments like MarTech, CRM, CDP, CCaaS, etc.

By analyzing patterns and trends, you can identify customers likely to leave. Act on customer feedback regardless how small or big it is and give them feedback. Also engage withy the clint and educate them and identify root cause prior implementing any solution and evaluate implemented solution if its working.

Maintaining a healthy customer base is essential for any successful business, yet especially for any business that relies on recurring revenue or repeat sales.Retaining customers has become more critical for businesses in any industry.The process of analyzing data to identify trends & patterns is important to determine what causes the customers to keep using our service,we can take action such as running new campaigns & new technologies to engage them, or provide special offers.Ultimately, this helps to improve retention rates by providing targeted offers customers who may be leaving or ignoring our services. Customer churn analysis can be a valuable tool for businesses looking to increase customer loyalty and maximize their ROI.

To prevent customer churn, you will have to identify regular warning signs from customers, understand the customer needs, create personalized retention plans, monitor customers activity and continuously improve your strategy.

Churn refers to the number of customers who cease doing business with a company within a given period. This could happen for many reasons - some customers may find a better or cheaper alternative, while others may be dissatisfied with the product or service.

Data Segmentation is the process of taking the data you hold and dividing it up and grouping similar data together based on the chosen parameters so that you can use it more efficiently within marketing and operations. Examples of Data Segmentation could be: Gender. Customers vs. Prospects.

Customer segmentation is key to understanding behaviors and needs. Divide based on demographics, purchasing, or usage. Identify churn risks and reasons to craft personalized retention strategies, boosting loyalty and reducing churn likelihood.

To refine our data analytics approach to prevent customer churn, we will need to focus on understanding churn patterns:- Segmentation: Segment customer based on behaviour, demographic, usage patterns, and engagement levels. Analytical techniques: Use techniques like cohort analysis, RFM (Recency, Frequency, Monetary) analysis, and predictive modeling to identify churn patterns.

A segmentação de clientes é essencial para entender e atender às necessidades específicas de diferentes grupos dentro da sua base de clientes.Existem algums formas de se fazer isso. Por exemplo: Segmentação Demográfica ( Idade: Clientes jovens (18-25 anos) versus clientes mais velhos (50 anos). Gênero: Homens versus mulheres). Com bases nessas informações é possível estabelecer a estratégia. Exemplo: Para clientes jovens, você pode criar campanhas que utilizem mídias sociais populares, oferecendo descontos em produtos mais modernos e tecnológicos. Para clientes mais velhos, campanhas que enfatizem a qualidade, durabilidade e suporte ao cliente podem ser mais eficazes.

Las campañas de retención empiezan desde el momento del primer contacto con el cliente, aún sin haber generado el cierre del negocio. Es por eso vital conocer y proyectar las necesidades del cliente para poder segmentarlo en el nicho adecuado y así lograr la fidelización del mismo. No es vender por cumplir una meta asignada , es crear relaciones comerciales a largo plazo .

One could start with basic models like Logistic Regression or Decision Trees, testing on historical data for accuracy. For higher precision, try gradient boosting machines or neural networks. Beyond accuracy, focus on precision and recall, using F1 score and cost-benefit analysis to measure false positives/negatives' impact on retention. Key Inputs: RFM, Engagement, NPS and CSAT Scores. Once model is are accurate enough, score customers in real-time, triggering personalised retention journeys for high-risk customers. I've learned that it is not a one-time solution but an ongoing process. Continuously adapting to customer behaviour is key to long-term success

Predictive analytics are vital for anticipating and preventing customer churn. Build models using historical data to predict likely churners based on usage, satisfaction, and responsiveness. Take preemptive action with special offers or enhancements to retain customers proactively.

Predictive analytics is an advanced form of data analytics that attempts to answer the question, “What might happen next?” As a branch of data science for business, the growth of predictive and augmented analytics coincides with that of big data systems, where larger, broader pools of data enable increased data mining.

To refine our data analytics approach to prevent customer churn, we will need to focus on understanding churn patterns:- Identify Indicators: Determine key factors that correlate with churn (eg. Drop in usage frequency, customer complaints, lack of engagement). Predictive models: Develop predictive models using machine learning algorithms (eg. Logistics regression, decision trees) to forecast churn likelihood.

Predictive analytics can be effectively used to prevent customer churn by analyzing historical data and identifying patterns that precede customer cancellations. By leveraging machine learning algorithms, businesses can create models that predict the likelihood of individual customers churning. These models consider various factors such as purchase frequency, customer service interactions, and engagement levels. Once at-risk customers are identified, targeted interventions such as personalized offers, improved customer support, or tailored engagement strategies can be implemented to retain them, ultimately reducing churn rates and enhancing customer loyalty.

Don’t underestimate gathering feedback in person from former customers. I’ve found that this approach enables you to gather more information and share your commitment to acting on feedback

While working with TCS for the Telstra process, Cx churn was a major pain area. It was then decided to stop the churn or the movement to other service providers by analyzing the problem and what impact it had on the Cx & Co, we decided to improve the TAT for new connections and enhance services by value adding to the existing subscription plans, these measures helped in curtailing the Cx Churn.

Customer feedback is the information, insights, issues, and input shared by your community about their experiences with your company, product, or services. This feedback guides improvements of the customer experience and can empower positive change in any business — even (and especially) when it's negative.

Listening to customer feedback is crucial for retention. Collect feedback across touchpoints like surveys or social media. Analyze to understand sentiment and make improvements customers value. Showing you value input builds stronger relationships and reduces churn.

To cut churn, dive into your data! At Shop2App, we leverage personalized customer journeys - and it work wonders. By leveraging Shopify Customer Tags and personalizing home screens, we've boosted engagement significantly. Using Klaviyo for tailored notifications based on browsing history and past purchases has also been significant. We constantly tweak our tactics based on ongoing data analysis to keep things fresh and relevant.

Segmenting customers according to pain points and planning personalized strategies for each group can help focus on every customer with different needs. Understanding customer journey while buying a product and segmenting into different funnels according to their interaction causing churn for more targeted approach.

It involves understanding the needs, preferences, and behaviors of the audience to create tailored experiences that resonate with them. Developing an engagement strategy starts with thorough market research and audience analysis.

Considero que las campañas de interacción específicas deben ser siempre personalizadas, emocionales, y generadoras de valores intangibles.

CSMs should have regular touch points setup for every client in their book when possible. It's even better to have a specific engagement strategy for accounts at risk of churn (red). These accounts deserve higher priority as the revenue to the business could be lost. It's important to be carful not to incentivize clients to escalate their account status for higher priority. An example of this would be to setup a task for a red account in which the CSM deep dives into the account and gives a full report on their usage, and clear areas for improvement. Look for features that are popular from other clients that perhaps this client isn't using yet.

A continual improvement process, also often called a continuous improvement process, is an ongoing effort to improve products, services, or processes. These efforts can seek "incremental" improvement over time or "breakthrough" improvement all at once.

Poor customer fit Selling your product to the wrong type of customer can result in them churning soon after signing on, immediately invalidating the cost of the resources used to win them.

Things are always changing,preferences change,to avoid churn the company should make improvements continuously to keep the customers engaged and satisfied.

See churn as a value spectrum and not just exits. Remember, not all customers are equal. Double click on cases like these: - System Abusers: Frequent refund requesters. - Low Profitability: Bargain hunters and cherry pickers. - High Resource Users: Customers using excessive support. However, consider that: - Future Buyers: Some bargain hunters sample products for future purchases. - Feedback Providers: High-support users can offer critical insights. - Referral Creators: Non-repeat customers might generate referrals. - Community Builders: Some non-paying users contribute to a vibrant community. If customer retention and loyalty is a long game for you, consider prioritising high-CLTV customers, even with lower current profitability.

Getting feedbacks after customers segmentation through direct call and carefully listening to them is also essential. These feedbacks help the organization to restrategize either to implement or remould existing policies to serve the customers better (All these steps should be carefully taken bearing in mind the compliance rules guiding the organization)