Predictive AI for Business

Predictive AI takes large quantities of historical data and applies algorithms to it to analyze patterns and relationships in the data, so you can make better-informed guesses about what could happen under various conditions. The process involves four foundational steps.
 

1. Data collection

Predictive AI relies on access to big data — the bigger the better. In general, the more data you collect and run through your models, the better your results will be. AI models fed with larger, more diverse data sets can learn to recognize more complicated relationships among data in the training process, increasing accuracy.
 

2. Feature engineering

Feature engineering — sometimes referred to as data transformation, ETL (extract, transform, load) or data pipelines — is a critical step. Here, data scientists use a variety of methods to turn raw data into a format that’s easier for a machine learning model to interpret, grouping data by features that models can easily understand (and detect errors if needed).

Tools are available to automate feature engineering, but in many cases, data scientists or domain experts may opt to do it manually. This typically applies when the data domain is highly specialized, if you need to be able to show exactly how a model arrived at its conclusion (interpretability) or you need very fine control over the features in a data set.

Data teams typically select a feature engineering method to complement the ML model they plan to use for a given use case to help achieve optimal results. In a simple example, you can convert categorical data, such as color, to numerical data, so a linear model can correctly distinguish different colors. Using so-called one-hot encoding, you can create columns for red, blue and green, assigning a value of 1 in the corresponding column for “is_red,” “is_blue” and “is_green” for those colors and the numeral 0 in the other columns:
 

3. Model training and validation

Model training is the step during which you run prepared data through the predictive AI model so it can learn to spot patterns and make predictions.

To validate ML models, teams test the performance of the selected model by feeding it new data. Ideally, a group other than the one that designed the model (or a third party) will perform this step.
 

4. Deploying, monitoring and refining AI models

Deploying your predictive AI models is akin to releasing the production version of an application to your user base.

As with apps of any kind, AI models benefit from continuous monitoring and refinement to help ensure interpretability (the ability to understand how AI reached a certain conclusion), minimize model bias and maintain optimal performance.

Companies use predictive AI to help with business planning and decision-making in many areas, but it’s primarily used for — what else? — predicting what is likely to happen in the future under certain scenarios. Businesses use the predictions to help set strategy and inform decision-making. Here’s a look at a handful of common predictive AI examples.
 

Predicting customer churn

If repeat business and customer loyalty are key to your success, so is understanding why (and when) they might abandon you in favor of competitors. Machine learning can help by identifying patterns in customer behavior data to point to signs that certain customers might be ready to bolt. You can then devise offers just for them — such as custom product bundles or more attractive pricing — and track how well they do.
 

Forecasting demand

Demand forecasting using predictive AI can help you anticipate which products and services will be most popular most during a set time frame. This can help you optimize your supply chain, so you have enough raw materials on hand to keep pace with customer expectations and market trends.
 

Detecting fraud and mitigating risks

Fraudsters are everywhere, and their methods are only getting more sophisticated — ironically, in some cases, thanks to AI. In turn, IT security solution providers are fighting fire with fire, using predictive AI to analyze data from corporate networks to pinpoint suspicious activity and harden security to keep bad actors at bay. The finance industry, in particular, widely uses AI and advanced analytics to prevent an array of fraud, including phishing scams, identity theft and credit card and payment fraud. Related to supply chains, some companies are also using predictive AI in their third-party risk-management efforts, in which they try to anticipate and root out potential fraud committed by suppliers or other entities with whom they do business.
 

Streamlining maintenance and operations

Predictive AI can help companies with heavy machinery or large vehicle fleets, like Penske, keep those assets running at optimal levels by analyzing data from sensors built into their products. Many companies have integrated predictive analytics into their operations to target inefficiency, especially in areas of the business where technology is already an enabler, like supply chain management and customer service.
 

Personalizing marketing campaigns

Using customer behavior data, companies can use predictive AI to tailor marketing campaigns to certain buyer personas to deepen customer engagement. For example, streaming media services use predictive AI to keep subscribers coming back in an increasingly crowded field.

The simplest way to differentiate predictive and generative AI is found right in their names. Predictive AI analyzes historical data to identify patterns and make predictions about many things, from weather to traffic to customer behavior. Generative AI looks at the universe of content in a provided collection and, based on a prompt, generates new content in written or visual form.

Generative and predictive AI both employ machine learning and rely on access to massive collections of data for analysis and for use as source material, respectively.

As business tools, it makes the most sense to view predictive and generative AI as complementary. When it comes to customizing product recommendations, for example, a company could use predictive AI to look at customer behavior, then use generative AI to help craft marketing messages more likely to attract specific buyers, thereby preventing or reducing customer churn.

Predictive AI can help companies make better decisions, streamline operations and deepen customer relationships. Generative AI can help you create more engaging content as you follow through on decisions or business strategy shaped by predictive analytics.

Many successful businesses today have employed predictive AI. Here are five key benefits organizations may experience — and which you can enjoy as well.
 

1. Make more accurate decisions

Predictive AI can help companies make better-informed decisions by giving them the closest thing to a crystal ball they can get. More informed decisions create better business outcomes, among other benefits.
 

2. Increase operational efficiency

When you can more accurately anticipate the future needs of the business, you can look for ways to run operations more efficiently, eliminate bottlenecks and streamline processes. Predictive AI can help you get there by improving decision-making in all of these areas.
 

3. Enhance customer experience

Predictive AI provides a powerful tool to help you better understand customer behavior so you can cement buyers’ loyalty with tailored offers and make it that much easier for them to keep coming back.
 

4. Proactively manage risk

Using predictive AI in your risk management program can help you devise early warning systems to spot suspicious activity before it’s too late. In the same vein, machine learning can be applied to fraud detection by analyzing transactional data, so you can sidestep problems before they arise.
 

5. Reduce costs

When you operate more efficiently, enjoy robust sales to a loyal customer base and keep risks to a minimum, you organically lower the cost of doing business.