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What are the differences between descriptive, predictive, and prescriptive analytics in CRM?

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Customer relationship management (CRM) is a key strategy for improving service operations and customer satisfaction. CRM analytics and metrics help you measure and optimize your CRM performance and outcomes. However, not all analytics and metrics are the same. In this article, you will learn about the differences between descriptive, predictive, and prescriptive analytics in CRM, and how they can benefit your service operations.

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1 Descriptive analytics

Descriptive analytics is the most basic and common type of CRM analytics. It tells you what happened in the past and what is happening now with your CRM activities and results. For example, descriptive analytics can show you how many customers you have, how often they interact with you, how much they spend, and how satisfied they are. Descriptive analytics can help you understand your current situation, identify trends and patterns, and monitor your key performance indicators (KPIs).

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José Luis González García

Customer Success Manager en Valor de Ley | Marketing e innovación
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La analítica descriptiva en CRM se enfoca en el pasado y presente. Revela datos sobre clientes, interacciones, gastos y satisfacción. Esta forma de análisis es fundamental para comprender la situación actual, detectar tendencias y monitorear KPIs. Aunque básica, su valor radica en proporcionar una base sólida para decisiones informadas y estrategias efectivas en la gestión de relaciones con el cliente.

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2 Predictive analytics

Predictive analytics is the next level of CRM analytics. It tells you what is likely to happen in the future based on your historical and current data. For example, predictive analytics can show you which customers are most likely to churn, buy, or respond to your offers, and how much revenue they will generate. Predictive analytics can help you anticipate customer behavior, segment and target your customers, and optimize your marketing and sales strategies.

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José Luis González García

Customer Success Manager en Valor de Ley | Marketing e innovación
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El análisis predictivo en CRM va más allá del pasado y presente, proyectando lo que es probable que ocurra en el futuro. Identifica clientes propensos a abandonar, comprar o responder a ofertas, junto con estimaciones de ingresos. Esta herramienta permite anticipar comportamientos, segmentar audiencias y optimizar estrategias de marketing y ventas. Es un paso crucial hacia la personalización y eficacia en la gestión de relaciones, basado en el poder de los datos para prever y responder a las necesidades del cliente.

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3 Prescriptive analytics

Prescriptive analytics is the most advanced and complex type of CRM analytics. It tells you what you should do to achieve your desired outcomes and goals. For example, prescriptive analytics can show you which actions to take, when to take them, and how to allocate your resources to maximize your customer retention, loyalty, and profitability. Prescriptive analytics can help you make better decisions, automate your processes, and personalize your customer experiences.

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José Luis González García

Customer Success Manager en Valor de Ley | Marketing e innovación
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La analítica prescriptiva en CRM es la cúspide de la toma de decisiones inteligente. Indica acciones específicas, tiempos y recursos para potenciar retención, lealtad y rentabilidad. Este enfoque optimiza decisiones, automatiza procesos y mejora la personalización del cliente. Es un paso esencial hacia el futuro de la gestión de relaciones, destacando por su capacidad de maximizar resultados con precisión y eficiencia.

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4 The benefits of different analytics

Each type of CRM analytics has its own benefits and limitations. Descriptive analytics is easy to use and understand, but it does not provide any insights or recommendations for the future. Predictive analytics is more powerful and valuable, but it requires more data and sophisticated models, and it does not guarantee accuracy or causality. Prescriptive analytics is the most impactful and actionable, but it is also the most difficult and costly to implement and maintain, and it may face ethical and practical challenges.

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5 How to choose the right analytics

The best type of CRM analytics for your service operations depends on your objectives, resources, and capabilities. You should start with descriptive analytics to establish a solid foundation and baseline for your CRM performance. Then, you should move to predictive analytics to gain more insights and foresight for your CRM opportunities and risks. Finally, you should consider prescriptive analytics to leverage the full potential and value of your CRM data and actions.

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6 How to apply the different analytics

To apply the different types of CRM analytics to your service operations, you need to follow some best practices. First, you need to define your CRM goals and KPIs clearly and align them with your business strategy. Second, you need to collect, integrate, and clean your CRM data from various sources and systems. Third, you need to choose and use the appropriate tools and methods for each type of analytics, such as dashboards, reports, charts, algorithms, and simulations. Fourth, you need to communicate and share your analytics results and recommendations with your stakeholders and customers. Fifth, you need to test and evaluate your analytics outcomes and actions, and adjust them as needed.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the differences between descriptive, predictive, and prescriptive analytics in CRM?

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5

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1 Descriptive analytics

2 Predictive analytics

3 Prescriptive analytics

4 The benefits of different analytics

5 How to choose the right analytics

6 How to apply the different analytics

7 Here’s what else to consider

Service Operations

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What are the differences between descriptive, predictive, and prescriptive analytics in CRM?

1

2

3

4

5

6

7

1 Descriptive analytics

2 Predictive analytics

3 Prescriptive analytics

4 The benefits of different analytics

5 How to choose the right analytics

6 How to apply the different analytics

7 Here’s what else to consider

Service Operations

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