SELECT distinct(b.rankyear) FROM magazine_details a left outer join ranking b on b.mag_id=a.sno where b.app_status != 0 and a.cat_id=58 and b.rnkid is not null group by a.year order by a.sno desc limit 1
SELECT distinct(a.year), count(*) as count_yr, a.image_name,a.url,a.label1,a.label2,a.date, b.rnkid,b.rnktitle,b.rankpicname,b.weburl,a.cat_id,b.app_status,b.mag_id,b.master_stat FROM magazine_details a left outer join ranking b on b.mag_id=a.sno where b.app_status != 0 and a.cat_id=58 and b.rnkid is not null and b.rankyear = '2019' group by a.year order by a.sno desc
Moving From Business Intelligence To Predictive Analytics
Business intelligence solutions are designed to aggregate data from disparate systems and report on what has happened in your business. They excel at showing trends of data over time as well as the ability to slice and dice data to analyze past business operations. Over the years of enhancing these solutions, many businesses now have great repositories of data that have been documented and contain higher quality data than source systems. To get started with data mining and predictive analytics you can leverage the data assets built to deploy business intelligence solutions.
Getting Started with Data Mining Models
By building models on your historical data is how to get started on the path to predictive analytics. Data mining models are designed and built to analyze data to discover patterns, trends or behaviors. There are two types of machine learning that you can apply to the data– supervised learning and unsupervised learning. Supervised learning algorithms predict future outcomes by training on historical data with a known outcome. For example, a decision tree algorithm can be used to create a model to predict customers that are likely to churn. In order to do this the model requires sample data that indicates each customer in the system as active or churned. Unsupervised learning makes predictions only on the available data. There is no known historically correct answer. An example of unsupervised learning is market basket analysis. Models such as these discover relationships in data to determine items that are frequently purchased together. Regardless of the type of model, building an appropriate and accurate data mining model is the first step to move from business intelligence to predictive analytics.
Ways to Leverage Business Intelligence Data
There are two ways to get started with leveraging your business intelligence assets to move to advanced analytics. Once you have identified the business problem, start by reviewing the data elements that could be useful to answer the problem.
Often you will need a variety of data at various levels of detail and aggregation to build a model. For instance, to build a customer churn model you would need basic contract details like term, product, contract start and end date, as well as demographic data. This detail level information can then be mashed up with aggregated data such as number of missed payments, number of inbound calls, or number of responses to outbound campaigns. Sourcing the required data elements from your business intelligence solution can help accelerate building the model. Often the business intelligence database contains most of the information to get started. If the data is not in the proper format or the required data is in source applications, you can leverage the logic or queries in the business intelligence ETL processes. Using these queries can help you understand where data is coming from and what transformations have been done on the data. A key to any successful data mining model is ensuring you understand the data and quality of the data that is being leveraged.
Applying the Models to Create Predictions
Once you have a working data mining model you can move from business intelligence to predictions. In this stage, the model is applied to your operational data. For example, a customer churn model would be applied to active customers. Each customer‘s probability of churn would be calculated by comparing the individual’s specific data to the model. Once the probability is assigned, operational processes can be created on modified data to utilize the new information. In the sample below the confidence (Churned) column shows the probability of each of these customers’ churning.
This information can be saved in a database and leveraged to improve your operations. CRM applications can integrate this information to give call center agents more insights into each customer.
Another example is creating marketing campaigns based on predicting the customers who are most likely to accept an offer. Once the model is built, apply your contact list to this model and each customer will be scored with a probability to accept the offer. In the example below, each customer is given a probability of responding to the campaign.
This will decrease your expenses and increase your conversion rate by matching the offer to the right potential customer.
To begin using predictive analytics in your operations, start by defining business problems where you have a good understanding of the data available. Then build out models to see what patterns or behaviors can be found in the data. Once a model is developed and run against your production data, ensure you have business processes ready to use the output from the predictions to improve your business. Data mining and predictive analytics can give your company an edge to improve revenue, decrease expenses or improve services.