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A Signals Story: Hybrid Models to Create Contextual Intelligence

Artificial Intelligence (AI) is underpinning much of where the future of sales and marketing is headed—helping automate and augment processes that allow revenue teams to be solely focused on crucial revenue driving activities. While it may seem like there have been significant advances in using AI, it’s mostly around process related efficiencies. We are just at the beginning of using AI and predictive modeling for a strategic, data-driven advantage in sales and marketing. 

More Data Doesn’t Necessarily Lead to More Insights

In recent years, sales leaders see AI adoption growing faster than any other technology. As technologists in the industry think about AI, the number one question that comes to mind is whether they have enough data to run the kind of predictive modeling that would open up business horizons exponentially. However, one thing to keep in mind is that more data doesn’t necessarily lead to more insights. We are living in a technological era where having both a horizontal and vertical outlook is important. So the instinct to get all the data points possible and analyze them, doesn’t necessarily yield the optimal results. We need to know which data points are most critical and the impact they have on the problem we are trying to solve.

Random Forest: an example of a classification algorithm used to classify big volumes of data for predictions.

Exponential growth in information and data creates unnecessary noise making it difficult to get to actual insights. While looking at this problem specifically from a business perspective, we must consider the following:

When we look at the research related to AI and Machine Learning (ML), it is mostly centered around predictive behavior in a non-contractual setting. This primarily relates to anonymous visitors landing on your website, without having a history of purchase behavior, and then using their behavior to anticipate a purchase in the future. While this applies quite well towards B2C behavior, data indicates that B2B purchase behavior is more cumulative in nature. Meaning, a B2B prospect will return to a website and engage in multiple activities at least 4-5 times before strongly indicating an intent to buy.

From a data perspective, this is fundamentally where the noise is created. Multiple visits, followed by multiple activities, means it becomes difficult to filter which data points are actually informative towards a purchase. Spending the same amount of time with each prospect will eventually result in lower yields and longer sales cycles. The Qualified Signals AI model looks at these data points in an ecosystem style architecture—i.e the individual variables are contrasted against other variables and further analysis is done in conjunction with historical data. This way salespeople have access to a smaller, more insightful set of data points vs. having to field through a vast and somewhat random assortment of variables to guess which ones might be most useful.

How Can You Make These Signals Actionable?

A signal, by itself, without any contextual information is inherently not as useful. Once we have enough data points to predict whether a prospect has buying intent, we need to be able to look at this data in an actionable way to optimize converting them into an actual customer. The most common (and arguably the most effective) way would be to add a temporal dimension, which is meant to indicate areas of time sensitivity, which when coupled with additional signals, can be a good predictor of buying intent. 

With Qualified Signals, we coupled the high level overview of intents and categorized each trend with surging, heating, cooling, and neutral markers to denote whether purchase intent is climbing or waning. This allows for a more trend based outlook at the account level, rather than just the individual visitor, and also gives an indication of the most optimal time for action. We call this Signal Account Trends. With Salesforce, the data can be further personalized towards accounts that matter the most, taking into account filters such as ABM tier, account owner, region or industry.

Signals Account Trends show which accounts are demonstrating buying intent

How Do You Action These Signals to Yield Exponential Results?

Now that we have discussed the technology behind Signals, and the methodology we use to parse through terabytes of data, we come to the most impactful variable in the sales cycle: people. Some initial theories in marketing tech indicated that the introduction of AI and smart data would lead to a loss of human involvement in the sales process. However, at Qualified, we believe in the power of hybrid intelligence—the use of technology in conjunction with people leading to optimal results.

The actionable part of the Signals model relies on those involved having data and business acumen around the information being showcased to them. For instance, an account that is surging in the Signals model would require a temporal dependent action (i.e. needs to be actioned during the duration of the surge). Similarly, a sales manager can look at these data points on a cumulative basis to underpin capacity models. Given the nature of exposure to complex metrics, the possibilities of intelligent human intervention are endless.

Qualified Signals leverages AI to maximize efforts through a variety of features, but essentially, it boils down to smart sales intelligence. The combination of the right kind of human intervention alongside the correct signals allows for the best possible yield that is exponential in nature. 

If you’d like to learn more about Qualified Signals, feel free to chat with us on the website anytime. Our team is standing by.


About Tooba

As the Director of Business Intelligence and Strategy at Qualified, Tooba is currently finishing her Algorithmic Data Science PhD from MIT. She specializes in looking at AI models and how they interact with both big and small data to gain insights.

Posted on
December 2, 2021
by
Tooba Durraze

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