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An introduction to predictive analytics
Tune into Michel Hayet, Senior Product Marketing Manager at AppsFlyer, and get familiar with predictive analytics and how they can help you anticipate changes in the market that will impact your business. The more data you enter into the algorithms over time, the more accurate predictions will become.
Hi there, my name is Michel Hayet, and I’m a senior product marketing manager at AppsFlyer.
Over the next few sessions, we’re going to talk about predictive analytics. Specifically, we’ll cover what it is, its uses in mobile marketing, and how it can help you address the challenges to UA that came with iOS 14.
For this first lesson, we’re focusing on what predictive analytics is, providing an introduction to predictive modeling, and discussing what results to expect. So let’s dive in.
What is predictive analytics?
Predictive analytics can help you accurately anticipate market shifts and reactions that could affect your business. Using those data points can help you adapt so your performance doesn’t take a hit.
By using advanced deep learning and machine learning algorithms, you can improve the accuracy of your predictions over time. Meaning, the more data you feed your predictive model, the more reliable it becomes. After all, a prediction that isn’t accurate enough is nothing more than a guess.
Businesses have already been using predictive analytics for years to come up with strategies that plan for shifts in their markets.
Airlines will try to predict when destinations are more, or less, popular at certain times of the year, then they’ll adjust their ticket prices to reflect the spikes and dips.
Large retail stores and supermarkets will plan the inventory they need across their different locations based on how and when people shop.
And stock market traders will use predictive analysis to forecast market volatility.
But to actually get these insights, you first need to make an accurate predictive model.
How? Let’s talk about predictive modeling 101.
Predictive modeling 101
A predictive model is basically a set of rules that relies on data you’ve already gathered. These relevant metrics are analyzed using the AI we mentioned earlier so you can identify trends more accurately and easily over time.
This AI uses a ton of data points when constructing the model. It’s an amount of information that the human brain couldn’t process alone. So your role is feeding the algorithms and deep learning systems as much historical data as possible so they can make a predictive model that’s more precise.
Giving the AI a lot of performance data is key, but it’s just as important that it’s the right data. Before you start pulling any and all metrics to use in your model, ask yourself: how much of an impact will this measurement make?
Now we’re not going to just leave you hanging. To properly answer this question, you’ll need to think in two different ways:
The first is thinking objectively by looking at the results of the machine’s analysis. It’s the most important point of view because, again, the AI can identify patterns that humans alone can’t.
The second approach? Practical thinking. Put yourself in the shoes of the person who’s eventually going to use the predictions from the model.
Once you’ve decided which data to use in your predictive model, it’s time for testing to see if it’s working. Start introducing real-time data in addition to the historical metrics so the model begins to improve its core logic. The AI will modify itself on the go and provide more accurate results as time goes by.
Now let’s discuss what types of results you’re going to get from your model.
What to expect from your model
When we’re talking about the actual results from a predictive model, we mean insights like scores, trend directions (going up or down), prices, and time periods.
These results are based on two main factors:
The actual measurements put in, and what you’re looking to do with the insights
This second factor goes back to what we said about the end user. Thinking about who’s going to use the model and what they’ll look to get out of it should help you decide what measurements to feed into it.
Let’s think about the airline example again. Imagine you’re creating a predictive model for an airline company about the price they should charge for a first-class ticket from Paris to New York in May. The purpose of the model is to help them come up with a reasonable price according to demand during that month. So the results should be in the form of a price – not a score or time period.
The more that you align who the end user is and what the output should be, the more useful your model will be.
Which leads us to what we’ll cover in our next episode – solving the SKAN measurement issue. See you then!