Predictive Analytics: Applications and Business Benefits

Predictive analytics is a solution increasingly adopted by businesses to make more effective business decisions. According to the Facts&Factors report the global predictive analytics market is growing at an annual rate of approximately 24.5%, with its value projected to reach over $22 billion by 2026.   Discover the phenomenon behind using predictive analytics in business and see if it is the right solution for you.    

What is predictive analytics?

  Predictive analytics is the skillful use of an organization’s historical data, which, when combined with analytical techniques, can forecast future events. This can include consumer behavior, sales performance, product demand, and many other key business issues.    

Predictive analytics and business decisions

  Running a business relies not only on knowledge but, above all, on its effective application. As it turns out, this remains a challenge for most companies. According to the report Workforce 2020: The Looming Talent Crisis only 42% of enterprises are able to leverage the data they collect to improve their business efficiency. This is a significant oversight, as this practice offers numerous benefits, most notably by helping to mitigate the risks associated with poor investments.    

How does predictive analytics work?

  Predictive analytics is a technique that uses machine learning to forecast future outcomes. It relies on the precise selection of existing data to extract patterns that allow for the determination of the probability of specific future events. Predictive analytics typically consists of five basic stages:

1. Defining the objective — determining what the model is intended to verify;
2. Data preparation — involving the collection, export, and cleaning of data to create the most accurate database of positive and negative factors for the analyzed issue;
3. Data manipulation — to identify the greatest number of similarities and differences;
4. Model creation and training — designed to classify and recognize learned patterns in future data;
5. Model testing – involves verifying that data analysis is functioning correctly and producing the desired outcomes;
6. Prediction – the results generated by a trained model on a specific dataset.

  It is worth noting that the more accurate and well-prepared the data provided to the model, the higher its performance will be. Consequently, the quality of the results delivered by predictive analysis will be proportional to the time and effort invested in preparing the process.    

How can predictive analysis help your company?

  Predictive analysis primarily helps you determine the future direction of your business growth. Applying this solution allows you to identify potential risks and threats to your company’s development, while simultaneously highlighting strengths that can be leveraged to strengthen your market position. Furthermore, predictive analysis is crucial when launching a new product. It allows you to estimate whether your idea is viable, helping you avoid wasted investment. Beyond business strategy, predictive analysis is also vital for security. It helps prevent various threats, such as identifying patterns indicative of cybercriminal activity. In reality, this technique allows us to generate analysis on any topic—it all depends on the specifics of the enterprise, its needs, and the data available. See how our clients have chosen to leverage predictive analysis: Danone Polska and Credit Agricole.    

How can predictive analysis increase your business efficiency?

  Creating personalized marketing campaigns – predictive analysis allows you to identify the audience interested in your product, the expectations they want to meet, and the content that engages them. Moreover, it can also identify segments with different interests and needs that are not potential customers, allowing you to exclude them from your marketing efforts.   Reducing customer churn risk – business prediction also allows us to calculate the so-called churn risk, identifying current customers who are likely to lose interest in your offer soon. This is valuable information that can help prevent this situation, for example, by sending special discounts or other incentives.   Assisting in new product launches – you can analyze the products most frequently chosen by buyers and, based on existing customer preferences, assess whether you should introduce new solutions to your portfolio. This can also provide insights into which products or services to discontinue because they will soon cease to be profitable.   Better resource management – by applying data analysis, it is easy to see the actual demand for products and services across individual departments. This information can then be used to plan future expenditures, such as production materials.    

How can predictive analytics boost sales in your business?

  When making daily business decisions, we must consider many factors, such as:

• market price fluctuations,
• demand for our products,
• market trends,
• raw material costs,
• customer preferences and their reactions to our advertising campaigns.

  All of these elements can directly or indirectly impact sales performance. It is very easy to overlook or misinterpret some of them. With predictive analytics, you can easily analyze all these factors and gain valuable insights for the future. Most importantly, by leveraging data, you can determine which products or services will be most popular with customers in the near future. Furthermore, you can receive valuable guidance on what to introduce to the market to meet their needs. You can then focus your marketing efforts on the solutions that are forecasted to sell best.    

How can predictive analytics reduce costs in your business?

  Predictive analytics primarily helps identify where it is worth investing money and where it would simply be unprofitable. Thanks to this solution, you will avoid wasting your budget on marketing activities that are not tailored to your audience, and you will not invest in products for which there will be insufficient demand. It is worth noting that predictive analytics is crucial in sectors such as retail, food, and pharmaceuticals. Stores and pharmacies can better manage product deliveries and order only as much stock as they can sell. This not only saves money but also reduces the risk of wasting food and other products with limited shelf lives.    

What predictive analytics tools are available for businesses?

  There are many off-the-shelf predictive analytics solutions on the market that vary in complexity and functionality. Some of the most popular include:

• IBM Watson Studio
• Adobe Analytics
• Microsoft Azure Machine Learning
• SAP Predictive Analytics
• SAS
• SAP Analytics Cloud
• Alteryx
• Qlik Sense

  It is worth noting, however, that off-the-shelf solutions have many limitations and are not fully tailored to an organization’s specific needs. If you prioritize maximum forecasting accuracy and data security, you should consider commissioning a custom predictive analytics tool. While this may initially seem expensive, it is worth noting that developing such technology involves a single, predictable cost. In contrast, opting for off-the-shelf solutions requires ongoing license fees, and you have no control over potential price increases.    

What data do you need for predictive analytics?

  In reality, it all depends on your business needs and the objective of your predictive analytics model. For example, when analyzing marketing activities, you will need data on all past customer interactions, website behavior, purchasing trends, and previous marketing campaigns. For demand forecasting, you will need data on products, raw material and commodity prices, production costs, historical sales, supply chain logistics, and much more. Therefore, the proper selection of data to be analyzed is crucial. Initially, this data must be selected, cleaned, and mapped—creating the most complete model possible based on existing processes, which will then be used to test future possibilities and solutions through predictive analytics. The more high-quality data you have from a specific domain, the more accurate your analysis will be.    

Automating decision-making processes

  An interesting solution available to business clients is known as cRPA – Cognitive Robotic Process Automation, which is a more advanced version of RPA technology. Using it allows for the analysis of all types of sources, which, unlike in classic RPA, do not need to be structured data. How does this relate to predictive analytics? By incorporating predictive techniques, we can obtain solutions capable of automating typical decision-making processes. Analyses prepared with this tool are multi-level and performed with virtually no human intervention—the robot classifies the data and draws the necessary conclusions itself.    

What are the most common mistakes in predictive data analytics?

  Errors in this process can arise from both the provider offering the predictive model and your own team using it. Among the most common problems encountered in predictive data analytics are:  

• Lack of properly prepared data – which leads to inaccurate analysis results;
• Lack of a clearly defined goal – which leaves the provider unaware of the client’s expectations;
• Communication gaps between the provider and the client – resulting in a misunderstanding of client needs, causing the model to perform differently than the client envisioned;
• An overly complex system – which may result in your internal team not knowing how to use it correctly;
• Failure to account for changing conditions – predictive analytics can only work well if you account for all possible circumstances. For example, many predictive models lost their forecasting power at the onset of the COVID-19 pandemic. Although this was an extraordinary situation, models should be designed to adapt to changing conditions not only within the company but also globally.

   

Make better business decisions with predictive analytics

  As you can see, predictive analytics offers numerous benefits, such as improved planning and increased sales performance. If you believe this solution is right for your company, please contact us. We would be happy to discuss your needs and develop a solution tailored to your specific business requirements.