Predictive analytics takes CRM data and uses it to make predictions about future events. In terms of sales, predictive analytics looks at information from the entire customer life cycle to predict how customers will behave, such as whether or not they will convert.

As technology evolves and the world becomes more engaged digitally, you need to know your customers and prospects better than they know themselves. This helps you tell them what they need well before they realize it on their own. By leveraging CRM predictive analytics, businesses are able to connect and engage with existing and potential customers in a new and much more effective way.

While existing customers and potential customers are always waiting to have a need fulfilled, the question is – are you prepared to sell at the right time, in the right place, and using the right channel? How do you make sure each sale happens at the right moment? How do you ascertain each interaction provides a value-added experience for you and them?

The Need for CRM Predictive Analytics

Meeting your annual sales targets is critical to the success of your organization. This requires you to accurately forecast your sales revenue in order to make informed decisions and accelerate opportunities. Microsoft Dynamics 365 CRM’s predictive analytics models (built on Azure Machine Learning and algorithms such as latent semantic analysis and regression analysis) augment human judgment with seller feedback and ongoing model retraining, which results in analytics-based insights. These analytics capabilities help your sales executives to better plan and prioritize their opportunity pipelines and improve their forecast accuracy.

While your sales force is the best judge of whether or not your organization will win a deal or how successfully (or poorly) an opportunity is progressing, there are a host of data-related challenges that they face that hinders them in making the right sales decisions. Some of the top challenges include:

• The presence of humongous amounts of customer data – with practically no insights
• No access to valuable data that can help improve forecasting accuracy
• Absence of predictive capabilities in modern tools that results in disconnected experiences – with reduced productivity and increased inefficiency
• The incompleteness of CRM data in systems hampers good quality predictions (machine learning tools learn and train from complete, useful, accurate data)
• Data is tied to monthly and quarterly business tasks (data is typically entered during specific times, leading to insufficient real-time data)

Modernizing sales and marketing capabilities requires you to adopt customized sales solutions that streamline seller tasks – solutions that are built on advanced analytics models that increase the ability of your salespeople to make informed decisions, provide data about each customer opportunity (based on telemetry and visualization), and offer suggestions for specific actions. Access to advanced analytics supplements their decision-making abilities, enabling them to rethink and revamp their judgments. Having a better understanding of the risks in your pipelines allows you to adjust schedules and take advantage of hot opportunities.

Improving Sales with CRM Predictive Analytics Tools

IDC predicts that worldwide revenues for big data and business analytics will grow from $ 130.1 billion in 2016 to more than $ 203 billion in 2020. Predictive analytics methodologies make use of millions of previous opportunities sellers have worked on over the past couple of years and calculate near real-time opportunity win/loss predictions with sophisticated machine-learning algorithms. By aggregating end-to-end sales information and feeding it into predictive analytics models, you can receive powerful analytics-based insights and recommendations.

Key Capabilities of CRM Predictive Analytics

CRM predictive analytics can improve sales outcomes by enabling businesses to:

• Get an end-to-end view of the sales processes and desired outcomes
• Enable your sales staff (sellers, managers, and executives) to spend more focused time on customer-facing activities
• Get actionable suggestions along with opportunity indicators to drive sales in the right direction
• Leverage historical data to get concrete, analytics-based advice to finalize deals
• Get perspective and anticipate pain points before customers even realize they exist
• See which opportunities are hot, warm, or cold and get recommendations for data-driven actions
• Provide feedback to the tool and help improve recommendations (and value) the tool provides

With Dynamics 365, you can build and attach your own predictive model using Azure Machine Learning. Leverage the sophisticated predictive technology to have your historical data (sales orders) analyzed, while also easily evaluating your sales pipeline for positive outcomes.

Final Thoughts

Make the most of these predictive analytics tools by offering a way to engage with clients, learn more about individual opportunities, and improve sales productivity and customer satisfaction. With predictive analytics, you can take your CRM game to the next level and enjoy unprecedented profits in the long run.

To discover how a technology partner can help your business maximize CRM predictive analytics capabilities to meet your specific needs, contact an expert at Synoptek.

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CRM Software Blog | Dynamics 365