The main shift in the predictive analysis boom is that data scientists are no longer the only ones able to use these techniques. Machine learning and data analysis tools are now available in the hands of all professional users. These are just examples, showing how predictive analytics are changing the business world in depth.
We will begin this article with a very simple example in order to define predictive analysis. Take the example of a chain of stores that decides to launch its annual advertising campaign to sell its products. Augmented decisions and data visualization tools will make the tools used by the advertising campaign more accessible, more efficient and easier to use than ever before.
In order to foresee the impact of a campaign on its turnover, the company can confront past campaign results with its current results. The executive can also use augmented decisions to decipher the trends of a market. The objective here is to investigate the relationship between the turnover and the advertising budget in the pas.
The social media-marketing manager can also study demographic and social data to develop a campaign that will reach the targeted audience. The customer service representative can look for the main criticisms emitted by customers. Also, a customer service representative then generalizes the observed links on new unknown data.
The prescriptive analysis makes it possible to cross-reference information and action. The computational power of new agile business intelligence software makes predictive models much more accurate and simple to use by business users. However, predictive analysis remains limited.
Data remains a means and not an end. Dashboard software is a very common application today in terms of predictive analysis within companies. Data mining, as the name suggests, involves examining large data sets to discover patterns and new information. Data mining allows the business user to project themselves in the short and medium term by providing him with precise information about the possible impact of certain situations or decisions.
Parallel to regression analysis, predictive analysis are also increasingly using data mining and machine learning. In other words, models produce information, but not instructions explaining what to do with it. Usually resource intensive, predictive analytics often require advanced skills.
It is a real revolution in the decision-making process. Innovations in the field of machine learning, such as neural networks, explainable ai, or deep learning algorithms, allow unstructured data sets to be processed faster than a traditional data scientist with higher accuracy as algorithms improve. It is no longer necessary to have advanced statistical skills to produce a predictive analysis.
The algorithms and models cannot accurately predict whether your company’s next product will yield a billion dollars or if the market is about to collapse. Typically, algorithms will report on the most commonly used methods for predictive analysis within today’s businesses. We can define predictive analysis as the art of predicting a future situation by confronting a present situation with a past situation.
The other assertions are that the variables are the cost of the product, the role of that profile within the business and the company’s current profitability ratio. The first step is to train the machine learning from known statistical methods (polynomial regression, logarithmic regression, Linear regression, simple exponential smoothing, Fourier, Holt smoothing, etc.) to build the predictive model. The list of potential applications extends as far as the eye can see. The possibilities of integration in different industries and transformations of business through the various tools are very numerous and will continue to multiply as artificial intelligence evolves.
Imagine that the assertion is that this consumer profile will buy the product of the company. The predictive analysis guides you rationally in the possible choices to guide your strategy objectively. At the enterprise level, predictive analytics gives your non-computer and non-statistician employees the power to analyze trends and relationships in your data to predict changes in business indicators.
Using the example of supermarket infographics, the predictive model makes it possible to make decisions rationally based on past data and mathematical models. They help determine which data can predict the outcome the company wants to predict. Consider a sales representative looking for a typical profile on a CRM platform like Salesforce.
Contrary to traditional analyzes, this type of analysis does not make it possible to know in advance what data is important. What can predictive analysis do for your company and what are the key steps? Predictive analytics guides the retail industry, while financial startups use predictive models to analyze the risk of fraud.
By placing these variables in a regression equation, we get a predictive model from which to extrapolate an effective strategy to sell a product to the right profiles. However, it can also be interesting to choose predictive analytics for its simplicity of visualization (e.g. a straight line), which makes it quickly comprehensible by a large public. The prediction predicts the results using the intelligent model. This still emerging market could revolutionize the functioning of human resources.
Thus, depending on whether data is available with a trend or cycles, or if it is desired to make a model that is understandable by a large public, then different approaches will be used. Help centers have also started using predictive analysis to improve their software. The ideal application situation of this model is when the measurement to be predicted is proportional to the time axis.
Business intelligence tools and open-source frameworks like Hadoop allow for the democratization of data as a whole, but in parallel with B2B marketing, predictive analysis is also being used by more and more cloud software platforms in different industries. The choice of a predictive model depends on the situation as well as what one wants to show through its data.
For example, a site can suggest using predictive analysis for hiring, predicting which candidates will be most suitable for specific jobs. It looks for a linear relationship between the measure to be predicted and the time axis. Linear regression is one of the basic tools of modeling. The goal here is to help hiring services find problem areas using a data-driven alert system called achievement prediction. The last step (decision-making), more business-oriented, is to use the data obtained previously to adapt a strategy. The analytics functionality uses a machine learning algorithm to process the results, based on variables such as time to solve a problem, response time, or the most used words combined with a regression algorithm to calculate the achievement rate.