Improve Operational Efficiencies with AI-Based Predictive Analytics

Featured article by Amit Levi, VP of product and marketing at Anodot

As a business leader, you can’t see into the future, but you can forecast what’s going to happen. When you have the right kind of assistance, your business forecasts become more accurate. And with greater accuracy, forecasts can help guide your business decisions and put you ahead of your competition.

This, in a nutshell, is the premise of predictive analytics. Predictive analytics takes data from past events – such as the number of customers that convert into prospects, or the amount of products sold within a given interval – and uses it to make informed guesses about the future.

AI Predictive Analytics Surpass Traditional Platforms

If you employ data scientists to help you forecast revenues, it’s likely that you’re already using some form of predictive analytics. If you stick with traditional predictive models, however, you’re likely finding it difficult to scale and improve the accuracy of your forecasts. AI analytics are built not only to scale but to work in the real-time environment that many businesses now operate.

AI and ML can factor thousands, even millions, of metrics and events into a forecast. For example, for forecasting a product’s sales, ML can learnnot just from the buying pattern ofthat product but also learn from the buying patterns of all your products, plus the number of tech support calls related to each product, the number of related social media posts, and so on.

By incorporating this data, machine learning analytics can generate more accurate forecasts.

Why and How Does Predictive Analytics Provide Business Value?

Imagine that your business is a chessboard. Each square is a variable that affects your business – such as customer loyalty, the price of your product, the cost of raw materials, the location of your stores, etc.

Each piece represents a way in which you can interact with those variables. The castle is your CRM, the knight is your marketing automation stack, and the pawns work in your contact center.

The metaphor breaks down, of course, when you consider that there are far more variables affecting your business than there are squares on a chessboard. If you want to calculate the actions you can take that lead to the best possible outcomes, you’re going to have to use AI and ML.

If AI can win at chess (a game that contains more possibilities than there are atoms in the universe), it can also help you beat your competitors. AI can harvest the millions and millions of data points that affect your business, process them, and create accurate forecasts for your most critical KPIs.

More importantly, AI and ML have real-time capabilities. Manual forecasting takes up to a week to process. By contrast, AI and ML can create a forecast in an instant. When you have more detailed and accurate forecasts available faster, you’ll find yourself on a completely different playing field from your competitors.

How to Start Empowering Your Business

Let’s say that you have a call center company. You want to predict how many calls are going to come in during a given night so you can understand how many technicians you need to have on staff. You can begin to estimate this by asking yourself how many calls there were on this evening last week, but that’s not a method that will scale very well if you have a large number of customers. There are many factors to consider and using AI can provide you with an accurate result.

First, your data may experience seasonality — there may be natural variations on a cycle that takes longer than a week. There may be a product recall or an outage that you don’t know about. Similarly, your product may have gained traction in a new market, with a lot of people wanting to know how it works. Alternatively, there might even be a problem with cell service or another issue that depresses the number of support calls you receive.

AI can parse all these factors and more. It can tell you, in effect, that there was an outage last week that raised the number of calls — but that it’s been solved, and there will be fewer calls today. It can use social listening to track complaints about your service to see if there’s an ongoing issue. By weighing each of these metrics and considering them together, you can get an accurate forecast.

When you start adopting automated forecasting, you’ll be able to make similar gains. With AI analytics, you can capitalize on the opportunities that likely would have remained hidden within millions of metrics. With this new generation of predictive analytics, it’s about letting AI and ML create instant, accurate reporting across your critical KPIs, making your business better equipped to make agile, data-aware decisions.

Author Bio:

Amit Levi is VP of product and marketing at Anodot. He is passionate about turning data into insights. Over the past 15 years, he’s been proud to accompany the development of the analytics market. Having held managerial positions in several leading startups, Amit brings vast experience in planning, developing, and shipping large scale data and analytics products to top mobile and web companies. An expert in product and data, his mantra is “Good judgment comes from experience and experience comes from bad judgment.