Broadly speaking, predictive analytics is the science of using historical data to predict the likelihood of future outcomes. While this is one of the oldest problems faced by businesses, recent advances in AI have made it possible to achieve much better accuracy and address a much wider range of applications than ever before.

Recommender Systems

The most famous recommender system is probably Netflix: based on your viewing patterns and the patterns of other viewers, Netflix recommends new movies to you. There was even a famous contest called the Netflix Prize where they challenged researchers to come up with new techniques for recommending movies that could significantly outperform the current systems.

These systems use a variety of techniques, but at their core they rely on some common ideas. First, users are likely to prefer items similar to items they’ve already enjoyed. Second, they’re also likely to prefer the favorite items of other users who have similar preferences to themselves. AI techniques can improve the performance of these systems, for example by removing the need for manual feature engineering and letting the algorithm extract features automatically.

Marketing Analytics

Similar to recommender systems, marketing analytics uses your existing customer data to predict what new offers and products to market to customers, and how. Tools such as Klaviyo and Mailchimp tie together AI and analytics with your marketing funnel, allowing you to predict when your customers will make new purchases, how much, and how often, and helping you craft the messages and campaigns that will increase those numbers.

Supply Chains

Accurately forecasting inventory can increase margins and reduce waste. According to McKinsey, AI will produce up to $2 trillion in impact on the supply chain and manufacturing sectors, completely transforming them.

AI-powered forecasting tools like Llamasoft, AnyLogistix, JDA / Blue Yonder, E2Open help enterprises optimize their supply chain. Employing AI in these forecasts allow them to adapt more quickly to changing market conditions as well as automatically taking into account external variables like weather.

For example, Proctor & Gamble has adopted an end-to-end supply chain model to help scale its distribution and manufacturing across the 130 manufacturing sites serving 180 countries across the world.

Demand Forecasting

Many modern companies now rely on real-time data to forecast demand for resources like drivers. A company like Uber has to decide how many drivers it needs on the roads and offer the right incentives to encourage that number to sign in to drive at a given time. One of the approaches they took to forecast demand in extreme events (such as New Year’s Eve) is to apply neural networks to historical data in order to predict demand for drivers in future events, achieving gains in performance of 2%-18%.

How to use predictive analytics in your business?

You’re most likely already using predictive analytics in your planning, whether you know it or not. Marketing tools such as Mailchimp, business tools like Quickbooks, and more already incorporate predictive analytics as part of their offering and their recommendations for actions.

Going to the next level and making more direct use of predictive analytics requires building and maintaining a data pipeline. First, identify which data you capture from your customers and your operations. Then, clean and normalize the data, and finally use analytics software to visualize and create predictions from them. Tools from companies like Microsoft and SAS can help you analyze the data, but usually the most challenging part is the data collection and organization.

Building your own data pipeline usually requires deep knowledge about the specifics of your business, and needs to be custom-tailored for your situation. Research shows that while many businesses have invested heavily in analytics software, many have not simultaneously invested in the talent needed to make best use of the software. When planning your business’s analytics strategy, make sure to keep the human element in mind and not let the technology outpace your team’s ability to utilize it to its full potential.

This is the fourth in our series on how AI will affect your business. Check out our other posts to learn more.

By Published On: December 10th, 2020Categories: AI0 Comments

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