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Predictive analytics techniques and models

What are predictive analytics models?

Predictive analytics tools work by using clever models and algorithms to assess in-depth historical data, highlight any patterns and predict future trends. Predictive analytics tools are important as they can help businesses when they are seeking to optimise their performance. This is because the software can predict consumer behaviour and purchasing habits, which can lead to companies attracting, retaining and nurturing their most profitable consumers. Also, by using predictive analytics, companies can spot sales opportunities and target campaigns at consumers to encourage them through the marketing funnel. 

There is no doubt that when used correctly, the economic value of analytics tools is high. However, the different types of analytical models are rarely talked about. Predictive analytics data visualisation

Types of predictive analytics models 

There are 5 key models when it comes to analytics. These are the forecast model, classification model, outliers model, time series model and the clustering model. Here’s a little bit about how each work:

 

Forecast Model

The forecast model is one of the most used models by industry professionals. The forecast model is powered by metric value prediction. This means that the software analyses historical data and uses it to predict future trends. For this model to work, historical data must be readily available.  A working example or a company that could use the forecast model would be a restaurant. A restaurant owner could predict how many customers they will likely have in the next week by using previous weeks data. The best forecasting model platforms have added artificial intelligence and can consider potential nearby events which could impact the forecasting result. 

 

Classification Model

The classification model is simpler than the forecasting model, however, still works by analysing historical data. The classification model will categorise data from the basis it has learnt from the historical data. The data can be classified by different techniques, for example, decision trees. A working example for the classification model would be an online banking platform questioning whether transactions could be fraudulent. 

 

Outliers Model

The outliers model operates by using anomalous data from within the dataset. The outliers model is a particularly useful tool for finance companies. The model can detect the volume, time and location of a purchase, which could assist in highlighting fraud. An example would be fraudulent activity on a website of a consumer spending thousands of pounds on products which is out of character.  

 

Time Series Model

The time series model works by assessing snapshots of data from the previous year, to then produce and develop a numerical metric sequence that will predict the following 3-6 weeks of data. The further back and more in-depth the historical data, the more accurate the forecasting time series model will be. The time series model will also consider the seasons and yearly events that could impact results. For example, if a restaurant wanted to predict their consumer visits, the metric would take into consideration that in the previous year, due to Covid-19 and lockdowns, restaurants were shut and sales were at an all-time low.  

 

Clustering model

The final model is the clustering model. This model groups together data that is within a similar category. Companies can use the clustering model to categorise consumers into categories to target according to their similar needs. This is a far more time-efficient method of targeting than individual targeting and aids companies wishing to successfully target the masses with similar characteristics. Another working example of this would be a loan company clustering individuals from high crime level areas in the same data set to protect themselves from crime. 

 

Benefits of predictive modelling

There are so many benefits for companies using predictive analytics tools, such as:

  • Saves forecasting time
  • Cuts forecasting costs 
  • Saves manual forecasting efforts
  • Provides competitive advantage
  • Increased ROI 

 

Challenges and limitations of predictive modelling

Although predictive modelling comes with many advantages, we, of course, have to mention some of the challenges. They can be: 

  • Overload of data – not all data is needed for the success of a company, and it can be difficult to differentiate between useful and not so useful data. 
  • Skewed data – for example, by following seasonal trends such as weather, consumers will buy fans as the temperature increases. However, they will not necessarily buy more fans if the temperature increases from 25 degrees to 35 degrees. Companies must be able to spot the change in the patterns. 
  • Errors in historical data – as the technology is powered by an algorithm, it will not be able to spot errors in data as a human would. However, this can be overcome by reinforcement learning. 
  • Unable to explain results – as the software is a machine, it will not be able to explain why the trends will be like the prediction, however accurate the predictions are. 

 

The future of predictive analytics

Predictive modelling is still young and will improve in accuracy as more companies opt-in. So, the more historical data that companies can provide, the more accurate the data is. Similarly, the longer that a company uses predictive analytics software, the more time the algorithms have to learn, further increasing the accuracy.  

Our advice for companies wishing to invest in predictive technology is to do it sooner rather than later. This is due to software becoming more intelligent as the boom of sign-ups continues to increase. This will ultimately cause late joiners to struggle to remain competitive with those who have been involved for longer.

BOSCO™ powered by Modo25 is a predictive analytics platform that uses the time series model to predict trends and show companies where best to spend their marketing budget. BOSCO™ considers competitor marketing actions and creates a league table for companies to benchmark and remain competitive. Furthermore, we have a team of data and marketing experts behind the software to bring clients inhouse marketing to the next level. 

 

If you have any questions about BOSCO™ or our services at Modo25, please contact us at [email protected] 

Jonny Griffin - Modo25
Author
Jonny Griffin
Jonny Griffin - Modo25
Author
Jonny Griffin
 

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