Predictive Analytics is a branch of data analytics that aims at making predictions about future events based on analytical insights derived from historical data. Many techniques are used for predictive analytics, including statistical modeling, data mining, machine learning, artificial intelligence, etc. Predictive data analytics bring together IT, management, and various other business processes in a typical business database management environment to identify the risks and opportunities that lie ahead of time. By implementing a successful predictive analytics model, businesses can interpret big data effectively for their best benefit.
There are various tools used in predictive analytics, which different analytical models and algorithms power. In a business database scenario, predictive analytics can be applied to a wider range of use cases. Determining the most appropriate predictive modeling techniques for your organization is the key to get the most out of predictive analytics by leveraging data to make an insightful decision. Further in this post, we will discuss the most common models in predictive analytics and the types of algorithms helpful in fueling this model.
Top models in predictive analytics
There are many standard models used for predictive analysis. Further, we will discuss some of the top predictive analytics models, even though not comprehensive.
- Classification Model
The classification model is the simplest among various predictive analytics models. This model puts the data into various categories where it learns insights from historical data. Classification models are ideal for resolving any yes or no questions by doing a broader analysis to drive any decisive action. Classification model can answer questions like:
- “Are the customers going to churn out”?
- For a funding provider, “Will the cash advanced to be approved?” or “Is there a risk to default?”
- For banking operations, “Will it be a fraudulent transaction attempt?”
There are many possibilities with the classification model to be deployed in different industries and the ease by which it can retrain with historical and new data.
- Clustering Model
Clustering can sort data into many nested groups based on different attributes. If e-com companies are looking to implement targeted marketing campaigns, they have to go through millions of records to define a custom-tailored strategy for individual customers. This is not so easy to accomplish. With the use of the clustering model, they can easily separate the customers into different small groups or clusters with similar characteristics and custom devises the strategies for each group. Some other standard use cases are predictive modeling, including grouping the applicants to many smart buckets based on different attributes, identifiers, and benchmarking. To identify your possibilities of adopting a predictive analytics model, you may consult with experts like RemoteDBA.
- Forecast Model
The forecast model is also one of the widely used predictive analytics models, which deals with estimating the number values for the new data based on the insights from the historical data. It effectively does the prediction of metric values, and the model can be ideally applied in all such scenarios where historical data is available as:
- SaaS companies to estimate the number of customers they may probably convert within the given timeframe.
- Call centers to predict how many support calls they receive in a day/hour.
- A retail store to calculate how much inventory they need to stock to meet a specific season’s demand.
The forecast model may consider various input parameters such as the forthcoming events, environmental data like weather forecasts, and effect of any pandemic going on, etc.
- Outliers Model
Outliers model deals with anomalous data within the datasets. It will help to identify any anomalies either by themselves or in association with other categories. Outliers model can:
- Record a spike in activities like some support calls, which may be indicating a potential product failure.
- Find any anomalous data in the transactions to identify fraud on insurance claims etc.
- Find any unusual information as like any possibilities of unplanned downtime or so.
The outlier model is useful for doing predictive analytics in finance and retail etc. In finance, while identifying any possibilities of fraudulent transactions, the outlier model can identify the purchase history, location, time, amount, and so on.
- Time series model
The time series model covers a sequence of data points by considering time as the major input parameter. It used the date from the previous year to devise a numerical metric and predict the future data for the next one month or three months by using this metric. Typical use cases of this model include the number of daily calls received in a month, sales for the last three quarters, or the number of patients to a hospital in the last six weeks or so. This way, you can also understand how a singular matric develops over time with its accuracy above the simple averages. The time series model can also consider various seasons into account, which can impact the metric.
Predictive analytics algorithms
There are two types of predictive analytics algorithms:
- Machine learning algorithms – It involves structural data, which we can see in tables. Algorithms for machine learning comprise of linear and nonlinear varieties. Linear algorithms are quicker to train, whereas nonlinear are optimized for problems they are more likely to face.
- Deep learning is a subset of machine learning, which mostly deals with analyzing the text, audio, video, and images, etc.
Using the machine learning algorithms in predictive modeling, several different algorithms can be used as Random Forest, Gradient Boosted Model, K-Means, Prophet, etc.
Considering all the above, it is important to decide how you will determine which predictive analysis model is ideal for your use case. You have to start with the initiative to identify which predictive questions you are trying to answer and what you are looking forward to doing with the information. To decide on one over another, you can need to consider each model’s strengths and weaknesses in light of your requirements in hand and how each of them can be optimized with various analytical algorithms to decide the best choice for you.