How Our Smart AI™ Auto Learn Capability Can Make (Almost) Anyone a Data Scientist
In the last few years, advancements in Artificial Intelligence and Machine Learning have progressed leaps and bounds, but it’s adoption in businesses has not followed at the same pace.
According to McKinsey, 50% of companies globally have adopted Ai in at least one business function. If you are a glass-half-full type of person, that is good. But if you’re a CX, Marketing, or Contact Center leader who must deal with the vast levels of data companies are generating every year, it absolutely seems like not enough. There’s so much data available and not enough insights being generated.
Let’s take the CX organization, for example. According to another McKinsey survey, they found that the typical CX survey only samples 7% of a company’s customers on average. As a result, companies are waiting for results from an unrepresentative sample of their customer base. At the same time, there’s dozens of other data points that can help quantify the customer experience, such as call transcripts from the Contact Center or digital experience data. But without the resources or the people power to deploy AI strategically, CX teams are left without a way to uncover the insights in that data.
Companies need the AI tools and the data science talent to take advantage of the data they already have. So why isn’t it happening?
That same McKinsey study on AI found that among AI high performers, only 45% know the type of AI talent needed (e.g., By role and skill level) to support AI initiatives. It drops to 33% for the rest of the field. What that means is that there’s a big delta between the need for AI to help companies manage their massive amounts of data and the amount of people who have the talent to build and deploy A.I. models.
Another reason that companies can’t take advantage of the data they already have is because the costs and risks of traditional A.I. projects have, in the eyes of executives, not lived up to the benefits. Traditional Artificial Intelligence projects can be slow to deploy, and their insights can be disconnected from the current needs of the teams they’re designed to support.
Bring on Smart AI™ and Auto Learn
Artificial Intelligence doesn’t have to be an exclusive club. Whether you’re a data scientist or working in a department that has tons of customer, operational, or behavioral data, AI can help. But the technical barriers for traditional AI put it out of reach for most.
At Stratifyd, that’s why we’ve built our Smart AI-powered Experience Analytics Platform to empower people of all skill levels to move beyond traditional analytics. Businesses need the power of AI now, so we’re democratizing AI to help ever surface hidden signals in themes in whatever data they need to analyze.
Even though we’re bringing AI to the masses, we’re not serving it up in a black box. Knowing how your AI models are working is key to ensuring you’re getting the most out of your data. This is especially important for predictive models, because you’re often making consequential decisions and acting based on those predictions.
But what if you don’t even know where to start with analyzing your data?
One of the ways our platform helps anyone be a little more like a trained data scientist is through our Auto Learn function.
What is Auto Learn?
Since it’s not always clear what the best algorithm is to use for your business goals—especially when you have diverse, unstructured sets of data to begin with—our platform allows you to apply a multitude of models. All of this is done with no coding involved, and the best model will automatically be picked based on accuracy.
Once you’ve brought your data into the platform, you simply apply the Auto Learn function to the data you want to train it on, choose what you’re predicting for, and then see which model ends up producing the most accurate results based on the data.
What does this mean in real life?
Trying to Tame the Madness of College Hoops Data
The beauty of the Auto Learn feature is that it can be applied to so many types of data. To showcase how it works, we took a shot at predicting the outcome of the recent college hoops tournament. (Necessary caveat upfront: predicting sports is hard, especially sports that take place in March!) It took some fiddling with the data like all good models, but because the model training was so simple, we could run multiple iterations quickly to fine-tune to get the most predictive model possible based on the data we used.
To get a good sense of who would win these head-to-head matchups, we took historical data—the regular season statistics from the last 8 years—and brought that data into the platform using our CSV connector to train our models on.
We ended up using five different models before we found the one that was most accurate—over 91%. What that means is that it was able to predict our ground truth—whether the home team won or lost—over 9 out of 10 times.
In our testing in the round of 64, the model was highly predictive, getting upsets for Oral Roberts and Abilene Christian right. It certainly did better than my bracket. When we applied the model to predict the round of 16 teams this year, our model had mixed results, getting four out of eight correct. That is essentially a coin toss, which is fitting for what was officially the most upset-laden tournament in history.
For next year, we plan to make some modifications to our models. One way is starting with better data: our current training data did not include strength of schedule. A high-scoring Oral Roberts had the same weight as a high-scoring Gonzaga, even though the divisions they play in are different calibers. Similarly, the data we used was for the regular season; it may be worth bringing in post-season data as well to provide a better sample.
Predicting sports is not what Auto Learn and Smart AI were built for, though. If they were, we’d be in Vegas, not Charlotte. So, what are businesses using Auto Learn to understand about their customers and businesses?
Three Ways Companies Use Auto Learn Today
When you are looking to predict outcomes, but you’re not sure whether you need a logistic regression model, recurrent neural network, or deep neural network model, Auto Learn is here to help you figure out the right one—even if you are not entirely sure what those words mean.
Let’s talk about the ways companies are using Auto Learn today.
Reviewing customer tickets to figure out whether there is a compliance issue that needs to be addressed. This is particularly important for regulated industries like financial services. With the Auto Learn training models on this form of customer data, companies can help mitigate risk automatically, with much less effort from their compliance teams.
Filling in the gaps that NPS scores can leave behind. With many customers not filling out NPS surveys, AI can be used to analyze the entire customer base to better quantify the percent of customers that are a detractor, passive, or a promoter.
Identifying churn risks based on customer behavior. By applying Auto Learn to behavior data, like clicking and ordering, for specific outcomes, the likelihood a customer will churn can be predicted and the processes can be improved to lower the likelihood.
How our Auto Learn capability can help you will depend on the data you have and the outcomes you need to predict. With a faster time-to-insights and more flexibility than traditional AI, you don’t have to predict today exactly how you will use tomorrow it though. Our technology grows with you and your business needs.