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Predictive Analytics, Techniques for Getting Started

June 29, 2022

Predictive analytics, often also referred to as advanced analytics, has grown from a niche practice to an essential part of any company’s analytics arsenal. According to markets and research, the analytics industry will be worth $309 billion by 2026, a CAGR of almost 40%.

As new predictive analytics techniques and algorithms become available, the value of predictive analytics becomes more evident and more far-reaching. As a recent Forbes article points out, the predictive power of AI allows companies to get companies back on track following the disruptions of the pandemic and the ongoing supply chain issues. Predictive analytics, for example, is proving especially useful for inventory management and delivery optimization, helping companies operate more leanly and with less waste.

What is Predictive Analytics?

Investopedia describes predictive analytics techniques as “the use of statistics and modeling techniques to predict future outcomes and performance. Predictive analytics looks at current and historical data patterns to determine if those patterns are likely to emerge again.”

IBM calls the technique “a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques, and machine learning.”

From a business perspective, Predictive Analytics is the science of analyzing large volumes of historical data to identify scenarios where action can protect or maximize future business outcomes. In other words, historical data reveals trends that can predict future outcomes, allowing businesses to be more proactive in their business.

A recent HBR article explains, “analytical approaches that incorporate predictive models have begun to displace merely descriptive approaches.” Predictive analytics is widely used in economic forecasting and other fields where complex modeling of chaotic systems is vital. Businesses are now adopting predictive modeling to reduce reliance on guesswork.

There are two classic outcomes for predictive analytics:

Providing a risk profile. 

For example, when a new customer applies for a mortgage, how likely are they to default? Predictive analytics can look at potential customers’ demographics, credit history, level of borrowing, income, and other factors to develop a risk score. A credit risk profile based on millions of data points derived from similar customer behaviors can be far more accurate than basing decisions on credit scores.

Forecasting outcomes

By looking at historical engagement data, such as subscription renewals, cohorts of the most profitable customers can be identified. A SaaS company, for example, may want to target marketing efforts customers more likely to renew. Historical renewals data can provide characteristics that predict potential renewal and allow the company to focus its marketing spend better.

Predictive Analytics uses three common techniques:

1: CLASSIFICATION

What is Classification?

Classification predicts whether to place a specific case within a particular category.

An Example of Classification

By training an AI on a data pool of existing loan recipients and their repayment records, predictive analytics can develop algorithms for prospective customers, deciding which are likely to default on repayments and which will not. “Potential defaults” is one of the two categories used in the classification.

Business Value of Classification

Classification techniques are ideal for targeting risk allocations (as in the example above) but can also be used for revenue predictions and to provide guidance on actions to take – for example, deciding which marketing campaign is more likely to return a higher ROI.

2: REGRESSION

What is Regression?

Using historical data points to predict a numerical value of future outcomes.

An Example of Regression:

Customer Lifetime Value (CLV) is vital for ensuring profitability. This information is readily available for past customers. This pool of historical revenue data can create more accurate CLV predictions for new accounts.

Business Value of Regression:

Better decision-making by spotting trends within historical data that generate the highest value predictions. Critical parameters for improvement can be identified, and revenue increased. Prioritization becomes easier when you can target expenditure at customers likely to generate the most revenue.

3: FORECASTING

What is Forecasting?

Creating ongoing predictions for future value, such as the number of units sold or revenue.

An Example of Forecasting:

A store might use predictive analytics to examine historical sales figures and project these forward for future weeks, months, or even years.

Business Value of Forecasting using Predictive Analytics:

This method would prevent over-or under-stocking, minimizing operating costs (display space, storage space, lighting, refrigeration, etc.) and purchase outlay.

Key Steps to Your First Predictive Analytics Model

While there are specific practical values for incorporating predictive analytics techniques into your business model before adopting such an approach, there are a series of essential steps to consider:

1. Identify A Business Objective.

The ML algorithms used in predictive analytics must be trained on historical data, then used in specific types of analysis. Training takes time and requires clean data. It is essential to decide precisely what business problem or opportunity your business is trying to optimize. 

Whether it’s focusing marketing efforts for a higher ROI, providing more accurate sales forecasts, or minimizing customer churn, any of these goals can be achieved with the help of predictive analytics. Regardless of the goal, the outcome of any predictive analytics process must meet a high-value objective for your organization.

2. Determine Available Data Assets.

Carry out a full audit of data you hold or could reasonably obtain. How many years of data do you have? Does it need to be cleaned and optimized? Do you have all the data sources you will need to train the algorithm? 

Ensuring that you have a good volume and the diversity of data is critical since predictive analytics relies on historical patterns to make accurate predictions.

3. Prepare And Engineer the Data.

Cleaning and preparing your data for the Machine Learning process at the heart of Predictive Analytics could take weeks or even months, depending on the state of your data and how many data points you will use in your prediction algorithm. 

You must make sure there are as few details missing as possible and that numbers, dates, and amounts are normalized (i.e., no mixing of currencies or dates written in different ways). Some manual labor may be involved in re-entering data to ensure it is compatible, but data hygiene and automation tools are also used to speed up this process.

4. Build, Validate, Iterate and Tune.

Once you know your data is ready, you can create the first models and test their effectiveness on a subset of your data. You’ll be able to spot places for improvement and reiterate your model. Once you have a more extensive working system, you can tune it to return precisely the predictions you find most useful. You’ll also want to plan how this will be displayed (dashboards, reports, presentations) and to whom you’ll grant access.

5. Share the Process and Learning. With a fully-functional predictive analytics process in place, now you can devise a methodology for incorporating predictions into your decision-making. Remember to schedule enough time for training whenever teams have access to predictive analytics data. Bring stakeholders onside quickly with vivid demonstrations of your new tool’s power, and make sure all parties have a process for incorporating the valuable insights you’ll glean.

6. Monitoring and Retraining. While not necessarily a step needed to “deploy” your first predictive analytics model, monitoring the accuracy and validity of a model that is in production is critical. External factors like changes in economic conditions and shifts in market competitiveness are just a couple of examples of influencers that might cause your model to become less accurate over time. All models eventually need to be “tuned” and retrained, and you must have a plan to retrain your models over time as they drift.

Common Problems with Predictive Analytics Adoption

While numerous technical challenges can slow or halt a predictive analytics initiative, two main challenges are commonplace and often derail projects. The first is a lack of know-how. Businesses contemplating predictive analytics for the first time often lack the data engineering, data science, and machine learning skills necessary to build accurate models. Hiring people with these skill sets can be a costly and slow proposition not easily achieved for small to mid-sized organizations.

A second common challenge afflicting large and smaller organizations is the lack of available talent. Historically, finding the people needed for data science and engineering skills has been problematic even for larger enterprises. Skills shortages become especially critical when dealing with steps three and four in the earlier process. Manually prepping data and evaluating models is time-consuming and requires a significant investment in expensive human resources.

Automation has created viable options for companies with either of these challenges. AutoML systems have made selecting the correct algorithm and fine-tuning its parameters a much faster process, and feature engineering solutions have come to market that dramatically accelerate the discovery of meaningful data patterns for predictive analytics predictions. 

How dotData can Help

dotData’s automated solutions are ideal for companies trying to maximize their return on investment from Predictive Analytics techniques. Established data science teams can leverage our dotData Py platform to automatically process raw enterprise data and prep it for machine learning.

Companies that are just getting started with Predictive Analytics can benefit from dotData’s Managed Predictive Analytics program that couples our dotData Enterprise end-to-end automation platform with dedicated enablement support from our Data Science team.

dotData’s product can automate steps 3 and 4 of the process outlined earlier. At the same time, our team will provide dedicated support to help your business choose the right business problem to solve with Predictive Analytics and identify your data sources, deploy your models into production, and understand how and when to retrain your models.

Our full-service approach goes beyond “no-code” AI to help businesses make the most of their initial investment in Predictive Analytics and gives them a platform from which to scale.

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dotData Automated Feature Engineering powers our full-cycle data science automation platform to help enterprise organizations accelerate ML and AI projects and deliver more business value by automating the hardest part of the data science and AI process – feature engineering and operationalization. Learn more at dotdata.com, and join us on Twitter and LinkedIn.