Nexa Lab Blog – Nowadays, everything is digital, including the data of your customers. The digitisation of customer data allows businesses to more easily compute the data in order to predict what their customers or clients might need. Learn more about what a predictive analytics is and how to doing it using CRM software.
This practice is called predictive analytics. With this practice, organisations can transform raw data into actionable insights.
But how do you do predictive analysis for your sales and marketing efforts?
Customer Relationship Management (CRM) software is one of the tools that can help you do that. This software provides a centralised platform for collecting, analysing, and utilising customer data.
Integrating predictive analytics capabilities into your CRM system can help you get valuable insights about your customers, optimize your sales processes, and drive business growth.
Today, we are going to explore predictive analytics in CRM. Is it possible to do that?
However, before we go into the details, let’s get to know about the predictive analytics concept first.
What is Predictive Analytics?
Predictive analytics is a branch of advanced analytics that uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.
It is more than just summarising historical events; it also makes predictions about potential future developments.
Predictive analytics helps businesses make data-driven choices and take preemptive measures to address possible problems or opportunities by identifying patterns and trends in their current data.
Predictive analytics has applications across a range of customer relationship and business operations domains when used in conjunction with CRM software.
It can help you identify which leads are most likely to convert, which customers are at risk of churning, or which products or services a particular customer is likely to be interested in.
This powerful capability allows you to tailor your marketing and sales strategies, optimise resource allocation, and enhance customer satisfaction.
How to Do Predictive Analytics in CRM Software
Implementing predictive analytics in your CRM software requires a systematic approach and a clear understanding of your business objectives.
First things first, as always, you need to define your objectives.
Outline what you want to achieve with predictive analytics. Are you looking to improve lead scoring, reduce customer churn, or optimize your product recommendations?
After that, you can start to evaluate your data availability.
Predictive analytics won’t work without a sufficient data supply. Make sure you have enough historical data in your CRM software to train your predictive models effectively.
Now, start choosing your predictive models.
You can base your choice on your objectives and the availability of data. Common techniques include regression analysis, decision trees, neural networks, and clustering algorithms. Your choice will depend on the specific predictions you want to make and the nature of your data.
To start the predictive model, you’ll need to clean up your data. This process includes steps like removing duplicates, handling missing values, and normalising data formats. The cleaner your data, the more accurate your predictions.
After you know what predictive models you want to do and have your data prepared, it’s time to do the testing.
You can test your method through training and validating your model. Split your data into training and validation sets to assess the accuracy of your models. Adjust your models as needed to improve their performance.
Now that you’ve found the models that fit your objectives, you can start to implement your models. To implement this in your CRM software, you’ll need to work with your CRM vendor or IT team to make sure seamless integration and proper data flow.
And that’s all the preparation you need to do for your predictive analytics.
When all the preparation is done, you can start generating the prediction you want to know and use that insight to do, for example, targeted marketing campaigns.
After preparation and implementation, the last thing you should always do is maintenance. Don’t forget to always monitor and refine your predictive models. Regularly assess the accuracy of your predictions and the impact of your actions.
You should also always review your strategy after applying predictive analytics to your CRM.
Does it produce favourable results? Or the same results? Or perhaps give you negative results?
Also, one thing to keep in mind is that this guide is only for the general use of predictive analytics in CRM. It is important to tailor your approach based on your specific industry and business needs for optimal results.
More on Nexalab’s blog: What Is Customer Insights and How to Gather That With CRM Software
CRM Software Features that Can Help You Do Predictive Analysis
Modern CRM software often comes equipped with built-in features and tools that can facilitate predictive analytics. Here are some key features to look for in your CRM software that can support your predictive analytics efforts:
Data Visualisation Tool
Advanced data visualisation capabilities allow you to represent complex data patterns and trends in easily understandable formats. Look for CRM software that offers customisable dashboards, interactive charts, and graphs to help you visualise your predictive analytics insights.
Machine Learning Integration
Some CRM platforms now offer integrated machine learning capabilities. These features can automatically analyse your data and generate predictive models without requiring extensive data science expertise.
Automated Data Collection
Look for CRM software that can automatically gather and update customer data from various touchpoints. This ensures that your predictive models always have access to the most current and relevant information.
Predictive Lead Scoring
Many CRM systems now offer built-in predictive lead scoring features. These tools use historical data to assess the likelihood of a lead conversion, helping you prioritise your sales efforts more effectively.
Churn Prediction
Advanced CRM software may include features that can predict which customers are at risk of churning. This allows you to implement proactive retention strategies and improve customer loyalty.
Recommendation Engines
Some CRM platforms incorporate recommendation engines that can suggest products or services based on a customer’s past behaviour and preferences. This can help you increase cross-selling and upselling opportunities.
Sentiment Analysis
Look for CRM software that can analyse customer interactions and feedback to gauge sentiment. This can help you predict customer satisfaction levels and address potential issues before they escalate.
Forecasting Tools
Advanced forecasting features in CRM software can help you predict future sales trends, resource requirements, and other key business metrics. Make sure your CRM software has this feature if you want to do predictive analytics.
Integration Capabilities
Make sure that your CRM software can integrate with other data sources and analytics tools. This allows you to incorporate external data and leverage specialised analytics capabilities when needed.
Customisable Reporting
Look for CRM software that allows you to create custom reports based on your predictive analytics insights. This enables you to share relevant information with different stakeholders in your organisation.
While it looks like CRM has many features to help you do predictive analytics, keep in mind that not every CRM has that. You should always keep an eye on the features of the software you want to use.
If predictive analytics is your objective, then make sure you choose CRM software that has analytical features so it can help you achieve your goals.
More on Nexalab’s Blog: What is Analytical CRM? All You Need to Know
Conclusion
Predictive analytics in CRM software offers a powerful means to transform your customer data into actionable insights. You can anticipate customer needs, optimize sales processes, and make data-driven decisions with that. In the end, what’s most important is how you can drive your MSP business’s growth and gain a competitive edge.
Investing in CRM software is a smart move for any business looking to improve sales, marketing, and customer service. But to get the most out of your CRM, it needs to work with your other tools.
Nexalab’s App Integration Services can help you connect your CRM with other essential platforms, like PSA software, for example. This means you get a complete view of your customer data, automate repetitive tasks, and personalise your customer interactions. It’s a solution that helps you get the most out of your CRM investment. Contact Nexalab today to explore how App Fusion can drive your business forward.
