Mastering Sales ROI in Manufacturing: Navigating Through a Sea of Data with Strategic Analytics and Reporting

Welcome back to our series’ fourth and last part: Mastering Sales ROI in Manufacturing: A SugarCRM Guide. You can check out the first three parts of our series in our blog section. This last part will discuss how you can strategically navigate analytics and reporting by monitoring and tracking with the right analytics tools and reporting structures. Learn why analytics matter for manufacturing enterprises, how to properly leverage analytics to secure a better market position, and the tools you need to achieve it. Let’s dive into it.

What Is Data Analytics?

The process of collecting, inspecting, cleaning, analyzing, or transforming data is called analytics. As a result of this process, enterprises can gather valuable insights regarding internal scenarios, strategies, and processes, as well as external factors such as market dynamics. The process can be applied to any data set, regardless of its size and other specificities. Data analysis has become a staple within organizations, especially as a foundation for decision-making.

Why Do Analytics Matter for Manufacturing Enterprises?

In the business world, knowledge is power. And how do you get this knowledge? Through data. But raw data can be overwhelming. That’s where analytics and reporting come in.

For manufacturers, data analytics are a critical part of their establishments. With the help of analytics, you can access real-time insights and get recommendations for process improvements, predictive maintenance, and more.

In this new technological era, the future of manufacturing is connected facilities, where data sourced from all departments and machines flows into a central repository, being processed and ready to use. Data analytics is essential for manufacturing enterprises because it can help improve processes, secure a better market position, and cultivate better relationships with existing customers and prospects. It can also help manufacturers:

Assess risks
Find trends
Predict outcomes
Evaluate customer satisfaction
Enhance the decision-making process

Types of Data Analytics

There are various types of data analytics, each serving a different purpose. Below are some of the main types of data analytics.

Predictive Analytics

As the name implies, predictive analytics are used to answer hypothetical questions within an enterprise. What will happen this summer with our production, based on internal and external factors? This method leverages historical data to evaluate trends and estimate the possibility of future similar outcomes. SugarPredict, for example, taps vast external data sources to analyze factors your data doesn’t cover—and makes predictions that enable businesses to make better decisions and focus on the highest priority sales activities. Such tools use artificial intelligence to offer your company a series of benefits:

Get full situational awareness of your business and customers based on historical data and customer journeys.
Uncover predictability across all your internal processes that help your sales reps focus on the right opportunities at the right time.
Transform your raw data into easy-to-understand charts and graphs for easy decision-making
Assess customer sentiment with sentiment analysis and accurately detect opportunities for improvement.
Accelerate processes by reducing time, costs, and technical expertise usually required for project-based AI predictive analytics.

Prescriptive Analytics

Prescriptive analytics is used to identify the likelihood of different outcomes by analyzing vast data sets. It is mainly used in critical scenarios where decision-makers feel unsure about the choices they need to make.
For example, SugarCRM steps in to translate that data into actionable insights. No more scratching your head over confusing numbers. These tools transform your raw data into easy-to-understand charts and graphs.

Suppose you make industrial pumps and you want to find out which product is your best seller over the last quarter. SugarCRM’s analytics tools can swiftly provide you with this information, presenting it in a clear, visual format. This allows you to pinpoint where your marketing and sales efforts should focus.

Diagnostic Analytics

This type of analytics is used to offer insights into the causes why an event occurred. For example, if you need to find out why your KPIs are not as stellar as you expected, diagnostics analytics can help you:
Spot data anomalies
Collect the right data sets that help you gain valuable insights into the “Why.”
Uses statistical techniques to explain the anomalies identified
Simply put, this type of analytics will help you understand why your teams are not meeting KPIs, for example.

Descriptive Analytics

This is the most straightforward type of analytics you can use as a manufacturer, and not only. This type of analytics offers clear explanations for why certain events occurred within your company by looking at data sets you already have.

To properly leverage data analytics, you must leverage certain technologies that usually work together flawlessly:

Data Management: Raw data is not helpful due to insufficient data management. Before being used for analytics purposes, data must be cleaned and transformed. There must be a protocol and standard used for data collection and processing, a protocol that ensures quality.
Data Mining: Data mining is the process of taking large sets of data and finding patterns. Only in this way can organizations leverage said data for decision-making.
Machine Learning: Machine learning is a subset of artificial intelligence. It allows software to learn patterns and automate models. It works on analyzing internal and external sets of data and generating complex predictions.

The SugarCRM Recipe for Analytics and Reporting

We know that data in its raw form is not of much use. This is why we translate that data into actionable insights with the help of predictive analytics, custom reports, and ROI analysis. For detailed analytics, Sugar Discover is the perfect tool you can use. This feature lets you dive deep into ROI analytics. You can dissect your data in various ways, finding insights you might not have seen otherwise. Let’s say you’ve had a spike in sales but aren’t sure why. Sugar Discover can help you analyze this uptick, identifying the specific factors that contributed—a seasonal trend or an effective sales promo.

We hope you enjoyed our Mastering Sales ROI in Manufacturing: A SugarCRM Guide. If you’d like to learn more about how a CRM can elevate your processes as a manufacturing organization, read our Connecting the Manufacturing Value Chain – Bridging the Gap Between Front & Back Office Guide.

Or if you’re interested in the first three parts of this series, we’ve linked them below for easy access:

Mihaela Chiurtu As a Marketing Content Writer, Mihaela is passionate about branding, content strategies, and customer interactions. When outside the office, Mihaela is a Netflix binge-watcher, skincare geek, and music lover.

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