As a fellow data scientist, I understand the grind involved in trying to squeeze as much performance as possible out of a propensity estimation model. I can tune parameters, change the model framework, or get more data from internal and external sources. Working at integrate.ai, I’ve seen how external data can significantly improve model performance firsthand. And, I’ve also experienced the material challenges of working with external data sources:
- Incorporating external data into your training set requires considerable preprocessing and feature engineering effort,
- Boosting model performance after wrangling external data into your training set isn’t guaranteed, and
- Joining datasets can introduce significant privacy risks when using typical approaches that rely on unique identifiers.
Problem solved—with Signals
My team of data scientists at integrate.ai is all-too familiar with these struggles. That’s why we’re leading the movement to Signals.
- Rich, machine-learning-derived insights about consumers’ needs that are generated from real consumer behavior,
- Packaged to easily connect heterogeneous datasets and help machine learning systems make more accurate predictions that drive business outcomes, and
- Built on pseudonymous real-time and historical data and are anonymous to protect consumer and business privacy.
Signals provide you with predictions on your consumer base that are generated from machine learning models built on external datasets. When you leverage Signals, you are tapping into a network of rich insights into consumer behavior—such as conversion propensity, expected lifetime value, and brand loyalty—generated from consumer transactional data from different companies across multiple industries.
We designed Signals to seamlessly integrate with your training set, be relevant to the problem you are solving, and be differentially private. The result? Data scientists can easily improve model performance without having to “own” the underlying data from which the Signals were generated.
Signals in action
So how can you get access to Signals and boost your model performance? All you need to do is send us your training set with target labels—and we send you back a file with privacy-safe, anonymous Signals at the same granularity as the training set you provided. Behind the scenes, our platform trains machine learning models on common features between your training set and numerous other datasets that are in our library. And we do this in such a way that your data is never shared, pooled, nor mixed with other customers or third parties. Once you receive the Signals, file you can seamlessly use them as an extended features set to create a new, enhanced model.
Using this approach, Signals can help you boost the performance of a variety of propensity models that predict different outcomes, such as a customer’s likelihood to convert to, churn from, or engage with your products (among others).
So what’s the bottom line?
As a data scientist, I’m thrilled about the movement from data to Signals because Signals help alleviate the headaches I outlined above:
- I can seamlessly integrate Signals into my training set set because they are purpose built for machine learning models,
- I can be confident that my model performance will increase because I am tapping into a network of rich insights that are relevant to the problem I’m solving, and
- I can mitigate privacy and security risks because Signals are differentially private and I don’t need to own the underlying data from which they were generated.
But, the thing I’m most excited about? Better model performance leads to business outcomes that I could never have achieved without Signals in my training set. In fact, our experiments show that with Signals, we see at least a 5% increase model performance. And companies that have worked with integrate.ai to leverage Signals have seen a 10% to 20% increase in their marketing campaign conversion rates.
I’m not the only one who sees the value of harnessing Signals. In addition to the thousands of customers who have already tapped into Signals, integrate.ai has received over $50 million USD in funding to bring our vision of a secure and privacy-centered solution to your team. And we’re the first AI-company in the world to hold a Privacy by Design certification while being PIPEDA, CCPA, and GDPR compliant.
Ready to take a closer look at how we create Signals—and how they can help you boost model performance? Download our white paper, “Signals for Data Scientists.”