The Key Success Factors for Predictive Analytics

Technical and organizational factors to consider before launch

Predictive and prescriptive analytics go hand in hand when it comes to business decision-making and make powerful tools for organizations across every industry. Yet why are many CEOs skeptical of the accuracy in predictive data? Learn about the key factors that affect the accuracy of predictive models and set your organization up for successful prescriptive analytics.

Predictive analytics success factors

Whether it’s determining when a customer might unsubscribe from a service, an airplane part may fail, or a stock may rise, the potential of predictive analytics is clear across every industry. And if predictive analytics answers the “What will happen?” question, prescriptive analytics is the next step in the process, answering “What should I do now?” Both kinds of models play a crucial role in business decision-making. 

But despite the hype — or maybe because of it — enterprise leaders can be skeptical of these analytics approaches. In fact, KPMG’s 2018 Global CEO Outlook report found that more than half of CEOs surveyed were “less confident in the accuracy of predictive analytics compared to historic data.” It doesn’t help when predictive models are called into question in the public sphere when they fail to accurately determine the winner of an election or the prevalence of flu. And as we’re seeing with forecasts of COVID-19-related deaths and hospitalizations, the lack of clarity in such projections can shake the public’s confidence and profoundly impact public policy. 

There are certain key factors that contribute to accurate predictive models, which in turn leads to more accurate prescriptive analytics. And they aren’t limited to the technical aspects — in fact, part of ensuring that your predictive model succeeds is setting it up for success on an organizational level as well. 

Preparing Your Organization 

As Eric Siegel explains, planning operational deployment through a collaborative process between quantitative and business stakeholders is crucial to ensuring that predictive analytics work. This involves two key steps at each project’s outset: 

1. Establish the business objective

How will your predictive model be integrated in order to positively impact existing operations? For example, a customer churn model may help the marketing department more effectively target customer retention campaigns. 

​2. Define a specific prediction objective

This objective should be a specific question that seeks to support the business objective described in #1 and receives approval from business stakeholders. For example, an example prediction question might be, “Which current customers, who have been with us at least one year and purchased more than $1,500 worth of products, will most likely cancel our service in the next three months and not rejoin for the rest of the year?”  

After these steps are completed, you can move onto preparing your training data for your machine learning software, and deploy your model. You’ll want to integrate the model’s predictions into existing operations. For example, you could target a retention campaign to the top 5% of customers whom you think most likely fall under the “current customers” in your prediction objective question.   

But first, you’ll need to make sure you have the right technology features in place. 

Preparing Your Technology 

1. Multi-genre analytics

Integrating multi-genre analytics into your predictive modeling and machine learning classifications ensures that you leverage a diverse group of analytics techniques to determine the likelihood of a business outcome.  

For example, say you were trying to predict the likelihood of customer churn. You may need to include algorithms or functions related to data preparation, discovery, exploration, visualization, machine learning model building and scoring, and model evaluation, all delivered in a sequential workflow in order to deliver and operationalize insights. 

2. Diverse analytics incorporated into a single framework 

Predictive modeling is difficult and complex, particularly when analytic capabilities and algorithms need to be patched together manually, each with their own user experience, documentation, and workflows. Putting these capabilities together in one place and making it easy for users to call functions automatically makes their jobs much easier reduces the likelihood of human error. 

3. Scalability 

Your predictive capabilities will benefit from a platform that not only analyzes massive volumes of data but also repeatedly implements multi-genre analytics with as much frequency as the business demands. Large datasets inform model richness, leading to better predictions and classifications. Model scalability is also important in preventing your data scientists from having to manually recreate and operationalize models every time they’re required.  

4. Performance 

Keeping up with all the data streaming into the organization requires a platform that can shorten the lag time between data ingestion and operationalization. Every moment counts, particularly when you’re looking to encourage collaboration around real-time data across your enterprise. 

With a product like Teradata Vantage, there is a seamless, powerful platform that delivers on all of these fronts. The platform brings multi-genre analytics functions together into one interface, allowing users across the enterprise to call functions that are pre-coded without needing to write additional code or instructions. This not only speeds up the process of predictive modeling – it also makes human error less likely as more steps are automated. The platform is highly scalable, allowing users to easily access and analyze large volumes of data and repeatedly implement multiple models of varying complexity. Finally, Vantage’s performance is a key market differentiation that helps customers deliver business outcomes quickly and reliably. 

Curious about how Teradata Vantage can help you create accurate predictions?