How to Run a Successful Predictive Analytics Project for your Business
Business intelligence tools have been the standard for organizations looking to remain ahead of the competition for the past few decades. With the expanding pace of digital changes in business, most analysts are increasingly asking, “What more can we do with data to assist business decisions?” Thankfully, there is predictive analytics.
There’s so much that could go wrong in a predictive analytics project. But it doesn’t have to. In this guide, our data scientists share some common pitfalls to avoid and tips on running your predictive analytics project successfully.
How to Run a Successful Predictive Analytics Project
Below are some of the expected points that you might consider when running a predictive analytics project:
1. Define your project
The first step is to find a problem that can be solved using predictive analytics. Recognize the end user for your project and identify their problems, goals, and the solutions they expect.
To make your job easier, create a document that specifies your project’s inputs and deliverables, and then double-check your resource and format criteria related to the description of your predictive analytics project. Doing this will ease your task and help you better understand what is expected from the project implementation.
While this may be an easy task, many organizations struggle to spot compelling problems that need predictive analytics. Below are some of the common issues which you can address by implementing predictive analytics:
- Revenue Forecasting
- Predicting customer churn
- Fraud detection and prevention
- Improvements in marketing campaigns
2. Identify your dataset
Gathering data is the most crucial stage of any data science project. Predictive analytics is all about analyzing the vast amount of data into trends. This collected data helps you with future predictions and remain ahead of the competition.
Note that the data you collected will work as the data lake for your project. It includes all the information you collect, ranging from structured to unstructured format, including tables, charts, and social media graphics. It is a collection of raw data, and it needs to reside in a database that is compatible with your chosen analytics tool.
Your predictive analytics model should be based on the data and demand patterns unique to your business offerings. Also, many organizations train their model solely on the generic data available on the internet. Doing so does not forecast precise results to suit your business case.
3. Analyze your data
Before moving further, it is crucial to ensure that your data collection is compatible with your predictive analytics tool. Once you gather the raw data, you can dissect and refine it until you retrieve the information needed for your predictive analytics project.
Consider working with your past events, successes, and failures to identify the truth behind your insights and implement it into your new dataset to predict the future.
Do not spend too much time filtering and cleansing your data, as it may delay your project timeline.
4. Choose the right team
One of the most important decisions you’ll make is to choose the right team for your data analytics project. Predictive analytics is a field that has vast potential, but it takes a skilled data analytics partner to execute it accurately. It’s essential to have people with diverse skill sets working for you for the success of your business.
To succeed in your predictive analytics project, choose a professional team that has experience building intelligent self-learning systems. Having the right team is the heart of your project, and it becomes challenging to create a strategy or set up the right goal without them.
5. Involve others in your plan
Now that you have defined the problem to solve and gathered the data that can help you reach your goal state, it is an excellent time to involve the stakeholders and executives of your organization in the plan.
As stakeholders are the critical aspects of your predictive analytics project, they can help you with the cross-functional data you will require while setting up your project and also help to promote the initiatives to others.
Gather a diverse group of people from various positions and departments of your organization to ensure that you receive well-rounded input on the solution. Don’t forget to consult the people involved in maintaining the IT operations of your predictive analytics project.
6. Consider statistical implementation
Even though machine learning predictive analytics largely depends on data analysis, implementing statistical models works as a cherry on the cake. Consumer behavior analysis and fraud identification are often carried out using statistical models by testing and validating the assumptions.
It is wise to prepare to have your ideas tested by evidence and understand that the obvious logical conclusions are not always confirmed by reality. Believe in your calculations, and at the same time, keep an open mind.
7. Prepare your model
The next step is to choose a predictive analytics model that best suits the requirements of your predictive analytics project.
With growing data-powered technologies around the market, many analytical services offer a wide range of predictive analytics tools based on different methods and mechanisms. Make sure you choose the right tool which empowers your predictive analytics project and is compatible with your data.
8. Close the gap between insights & actions
No data is worth anything if you cannot utilize it properly. The insights provided by the predictive analytics model are often not transparent or relevant to the person responsible for implementing those insights. In such a scenario, the insights are not utilized fully.
For instance, consider a sentiment analysis application in which the predictive analytics model has identified your customer being unsatisfied with your customer support team. How will you make this information helpful?
The information is helpful to those who work with the customer support team. The customer support team will resolve the issue and improve the brand image for future customers.
Therefore, while developing your predictive analytics model, it is crucial to identify who needs to know about your predictive analytics solutions and what they might want to do with them.
9. Deploy your project
Once you are done with the data analysis and statistical analysis, it is time to calibrate the model and interpret the results on daily routines. Remember, you don’t have to serve the numbers and statistics that show what is best for the organization unless those numbers translate into meaningful actions.
Instead of directly publishing your product to the market, it is recommended to create a prototype product and pass it to your executives and stakeholders for the beta test. There might be chances that your first few versions won’t be quite right, and it may take a few more iterations to create something that’s both useful and valuable.
10. Perform regular iteration
As the market trends change so fast, it does not take enough time for previous expectations to become old news. In such conditions, you should stay aware of the new predictive analytics features available and continuously improve your application into a more recent, better product.
It’s good to regularly examine and monitor your product and test it with the new data set to ensure it hasn’t lost its importance.
Read Full Article 13 Mistakes to Avoid in Implementing Predictive Analytics