Over the last decade, the world has become highly data-centric. Every move we make, every step we take, generates data. As a result, big data, including segments like business analytics, have grown to form the foundation of every successful and sustainable business model.
Today, almost every product on the marketplace, whether it’s digital or physical, is based on what the end-user likes, dislikes, wants, and needs.
No decision is made (and nothing goes into production) without engaging in data and analytics. It’s an approach that helps companies make smart decisions, identify new opportunities, improve productivity, and save money.
What is Business Analytics?
Business analytics can be described as a systematic investigation into your organization, the industry, competitors, customers, and customer behavior. It’s a technique made up of statistical models that can be applied to specific processes, projects, or even products.
It can also be deployed to analyze the performance of the whole company. Business analytics protocols leverage both historical data and data generated in real-time to forecast future trends.
This technology-driven approach helps derive actionable insights. The more you understand your business, your industry, and your customers, the better you will be placed to make accurate predictions and boost your bottom line.
This is the primary reason why business analytics is being absorbed into business processes across industries. According to Gartner, more than half of all new business systems will integrate continuous business intelligence by 2022.
So how do you build a robust business analytics system? Let’s take a look.
1. Formulate a Clear Objective
Like any other project, you have to have a clear idea about what you’re trying to achieve. Start this process with that in mind and develop a strategy that will help you achieve your business goals.
This exercise will be a lot easier if you ask a lot of questions. Your questions should be focused on both your business and your customers.
For example, you can ask questions like:
- Who are my customers?
- What are their pain points?
- What can we do to make their lives easier?
- Where do they congregate?
- How do they like to communicate with each other?
- How do they like to communicate with us?
The answers to these questions will help you determine the project’s limitations and justify the budget. Following this approach can also make it much easier to place the customer at the center of all your business decisions.
2. Collect Data from Multiple Sources
We’re all generating copious amounts of data in real-time from a variety of different sources. In fact, you’re generating data right now (as you read this blog post).
But raw data on its own is meaningless. To get the most out of customer data, you have to analyze it. To get accurate results, you have to have a solution that can seamlessly integrate data from several different end-points.
Data is generated from web browsers, mobile apps, the Internet of Things (IoT), and more at various touchpoints in the customer’s journey. So your business analytics system must be built to seamlessly collect all this data from multiple sources in real-time to deliver real business value.
3. Track Key Business Metrics
Once the data has been collected, you’ll be ready to track the performance of your predefined metrics. You can, for example, keep track of the data collected from in-store IoT sensors, social media platforms, and your e-commerce platform. If things aren’t going to plan, you’ll have an opportunity to adapt your strategy.
It’s also an excellent idea to build a highly user-friendly dashboard that can efficiently track key performance indicators. When applied effectively, dashboards can help data professionals access the information they need without wasting time or resources.
4. Develop a Predictive/Classification Analytics Model
The next natural step is to process, clean, prepare, and analyze the raw data. To better understand your results, it’s best to visualize it. Data visualization can help data scientists identify patterns, outliers, and develop techniques for further analysis and data modeling.
Based on the project’s objectives, you can explore several classifications and predictive analytics models and choose the one that performs best. It’ll all be relative to your specific project goals.
5. Evaluate the Analytics Model
Once the classification or predictive analytics model has been built, you have to see if all the variables are statistically significant (with a p-value less than 0.05) and optimize it.
You can also check with a domain expert to ascertain if the variables in the final model are appropriate to meet your objectives.
This is a critical step in the whole process because whenever checks aren’t performed, it’s highly likely to lead to errors. So the decisions you make based on faulty conclusions can have disastrous consequences.
6. Engage in Automation and Real-Time Monitoring
The world is continuously changing, and so are your predictive models. They may even evolve and decay over an extended period of time.
As these models are highly dynamic, it’ll be vital for the company to monitor changes in real-time. This approach will present an opportunity to modify your strategy when your campaigns are no longer effective.
Once everything is working like a well-oiled machine, you can save time and manpower by automating the whole process. You can do this by setting up triggers to alert the system whenever there are changes to the model’s accuracy. These alerts can also be leveraged to target potential customers based on real-time data, proactively.
When you have an intimate understanding of your business and your target market, you will be well-placed to deliver highly personalized customer experiences. All these different ingredients can also come together to improve revenue streams and scale your operation.
Do you need help building a robust business analytics system? We can help! Reach out to one of our in-house experts for a FREE no-commitment consultation.