August 9, 2021

Written by

Collective[i] Team

  • Posted in
  • Artificial Intelligence
  • Neural Networks

What is the difference between machine learning and deep learning?

Machine learning, deep learning, and artificial intelligence (AI) each represent an incredible opportunity for enterprise sales teams to focus more of their efforts on selling. The third edition of the State of Sales report from Salesforce found that 66% of a seller’s time isn’t spent selling at all: Instead, sellers attention is diverted to sales forecasting, as well as the data entry and other administrative tasks that support it. But these activities should not take priority over the work that grows revenue — the work that their skill set is for.

Still, that’s where many sellers find themselves — and while they know AI could reduce the manual labor of forecasting and data entry, distinguishing among the options on the market is just one more administrative task that distracts from closing deals.

Those trying to understand the difference between machine learning and deep learning and artificial intelligence may be confused about how they are connected. “Artificial intelligence” is the umbrella term for any computer that mimics human intelligence. Machine learning is artificial intelligence, and deep learning is machine learning. Picture the rings of a dartboard: The outer ring represents artificial intelligence, and each smaller ring narrows in focus. Deep learning is the bull’s-eye at the center, helping sales teams achieve greater efficiency, close more deals, and make better sales forecasts.

Deep learning vs machine learning

“Machine learning” is a general term for when computer algorithms are trained by human developers to analyze data and make classifications or predictions that reveal insights. Machine learning programs can automate basic tasks, such as data entry. Humans not only provide the structured data for machine learning but also intervene and give corrections as the algorithm learns to recognize patterns.

In classical machine learning, the data is labeled, or structured, before the machine learning algorithm can process it and successfully identify patterns. A common example of this is basic personal contact information: When leads complete a form, filling in labeled fields with their name, job title, company, and more, that data is structured. Based on that labeled information, a machine learning algorithm could automatically assign certain types of leads to certain members of the sales team.

But if a lead were to send an email about their needs, a basic machine learning program couldn’t process it because that text would be unstructured, or unlabeled, data. This is where deep learning becomes an asset. Unlike machine learning algorithms, deep learning algorithms can process unstructured data such as an email or image and draw conclusions on its own: In this example, it could assign the lead based on the email and what it knows about the strengths of the sales team. Through platforms such as Collective[i]’s Intelligent InsightsTM, that assignment will even be accompanied by recommendations about who in the seller’s network could support moving the lead down the pipeline, plus insights about the buying habits and average time-to-close of the prospective client.

About 80% to 90% of the data collected by organizations is unstructured. This includes documents, email messages, chat logs, social media information and analytics, photos, audio files, videos, customer reviews — the list goes on. Before a machine learning program could process that data, it would first need to be labeled — a time-consuming process. Deep learning, on the other hand, can take in unstructured data and classify it automatically.

This capability of translating insights into action without the need for human intervention is what makes deep learning such a disruptive and exciting advancement in technology for sales teams who are distracted from their core work by administrative tasks.

Is deep learning easier than machine learning?

Deep learning makes use of a neural network, which comprises many layers of artificial neurons and learns logic and reasoning by processing massive quantities of data. While a basic machine learning algorithm can give outputs with just a few thousand structured data points, a deep learning algorithm is able to learn from millions of unstructured data points. This means deep learning provides better forecasts and insights.

Deep learning requires more data to learn from in the beginning, but the end result is that deep learning can scale better than machine learning. Once a deep learning algorithm has received enough data to make high-quality, accurate interpretations, it only becomes more accurate and useful the more data it consumes, without any human intervention. Basic machine learning cannot scale to process even large quantities of structured data, let alone unstructured data.

Collective[i] uses deep learning to analyze its vast network of dynamic market data collected from multiple sources to generate the highest-quality insights. Deep learning platforms are so useful to sales teams because they provide supportive insights using analytics that would be incredibly time-consuming for humans otherwise.

When to use deep learning vs machine learning

The decision to choose deep learning vs machine learning depends in part on the complexity of the problem to be solved. Deep learning harnesses a powerful neural network to carry out complex problem-solving such as sales forecasting much faster than a human could. This is how deep learning tools such as Collective[i]’s Intelligent ForecastTM — the first automated, daily, and adaptive forecast on the market — helps sales teams with efforts such as improving the accuracy and actionability of sales forecasts. Collective[i] draws on current events, market conditions, buyer behavior insights, and historical data to make analyses and drive insights on behalf of the sales team.

Basic machine learning tools, on the other hand, are helpful for streamlining administrative tasks such as CRM updates. Collective[i] has integrated such functionality into our platform in products such as Intelligent WriteBackTM, which eliminates the need for manual logging by automatically capturing data from any tool sellers use. With Intelligent WritebackTM, data can even be captured from approved non-CRM users, such as legal teams and resellers, to keep the CRM up to date.

Why is deep learning better than machine learning? Find out with Collective[i].

Deep learning is better than machine learning because it gives sales teams access to fast, accurate insights and automations that enable sellers to focus on what they do best: selling. From end to end, Collective[i] doesn’t just give sellers their time back but also lets them do more with the time they have. Explore our platform and schedule a free demo to see the difference for yourself.

Explore Collective[i]

On no, we ran into an issue submitting this form. Please ensure each field was filled out correctly and resubmit.

If this problem persists, please reach out to us and we will be more than happy to follow-up from there.

We’ll be in touch soon

In the meantime, explore Collective[i] and find answers to frequently asked questions.