As a business owner or decision maker, to stay ahead, it is essential that you have tools and insights to help you make informed decisions on what your business or client market may or may not do. This practice will help optimise your business’s outcomes and ensure strong, long-term growth.
Predictive analytics is taking relevant historical data and using a series of advanced machine learning techniques, statistical and modelling techniques to help predict unknown outcomes or future events.
Ultimately, the question predictive analytics strives to answer is; What is likely to happen?
Let’s build a data analytics case study. A business could use statistics and data mining coupled with machine learning techniques to find relationships and patterns in relevant unstructured and structured data. This data includes factors such as a specific age group, gender, marital status and sales or income. Through the use of different techniques, you can take these patterns and use them to predict what your customer base will do in the future. This may result in you actioning an adjustment to your sales or service plans to capitalise on your predicted outcomes.
The Predictive Analytic Process
Knowing the predictive analytic process is important for the business owner or decision maker, but most importantly; the executive sponsor. This is primarily to ensure that the collective data analytics efforts are aimed towards a common goal, the wider organisation understands what is trying to be achieved, and the potential actions that come of it.
Let’s break each step down into a high-level overview;
- Define the Project– This step is where a business defines the data analytics project’s deliverables, business objectives and outcomes. It is imperative you enter this stage understanding the problem you want solved or the question you want answered. You identify the data sets that you’ll use throughout the rest of the process, and this gives you team concrete data to work with.
- Data Mining or Collection – Data collection or mining pulls data from several sources and compiles it in one centralised location to give a comprehensive view of all customer interactions. It is imperative that your company has the data sets in the first place and they are in good working order. If not, more work may need to be done here to capture and collate.
- Data Analysis – At this stage, your team inspects, cleans up, models and transforms the raw data to help them arrive at data-backed conclusions about historical trends.
- Statistical Analysis – Your team takes that raw data statistics and uses it to validate their hypotheses or assumptions about future events. They can test their assumptions against historical data to get a more defined prediction.
- Modelling – Predictive modelling takes all of the data and statistics from previous steps and compiles them to make accurate predictions about the future. You can also set up several models to ensure you choose the best outcome.
- Deployment – Your team takes the best predictions from step five and deploys them in the businesses’ everyday decision making. This allows you to see results and adjust quickly if you don’t see the outcomes your team predicted.
- Monitoring– The final stage is monitoring. Your team carefully monitors your predictive models to ensure your business gets the predicted results.
It is important to note that while the step by step process can be viewed as a linear progression, the level of effort, time and complexity varies greatly depending on organisational maturity, resources and many other factors. It also needs to be viewed as an ‘always on’ process where we continually ask questions of the data and understand the actual outputs and outcomes.
How Predictive Analytics and Descriptive/Diagnostic Analytics Combine
Descriptive and diagnostic analytics allow your team to look back and understand the root cause of any patterns or issues your company may have, such as a downturn in sales or shift in the market. When you combine it with predictive analytics, you’ll get an in-depth understanding of why something happened in the past with your business and how to adjust your processes depending on what happened. In turn, you can see a more stable and consistent long-term growth.
The applicability and opportunity that predictive analytics brings to businesses and industry is far reaching. By building in capabilities to make predications on future events, organisations can look to plan much more effectively, but more importantly; harness opportunity. This may include optimising marketing campaigns, improving operations, allocating or planning of resources and future sales plays.
We help bring data & insights and data analytics strategies to life through data visualisations and business intelligence platforms such as Microsoft Power BI, Tableau and Qlik, and embed data analytics processes and strategies throughout our 3D Growth Model. The end result is a data-driven organisation.