Why more is more when it comes to networked data
A neural network is a computing system with interconnected nodes that work much like neurons in the human brain. In fact, these nodes are even called neurons. But how does a neural network learn? The answer is data, data, and more data.
In the training phase, a neural network takes in data. Each layer of neurons apply analysis and labels to the data before passing it to the layer beneath for deeper analysis. This enables the neural network to make predictions in the prediction phase. Based on the accuracy of the outputs, the neural network is then given feedback, from which it learns to improve performance.
This back-and-forth between the training phase and the learning phase is the constant learning life cycle of a neural network. The opportunity to keep adding richer and more current data sources is one reason the innovation of the neural network is so transformative for sales forecasting.
Salespeople typically spend between two and three hours a week on forecasting…yet that work results in forecasts that are only 75% accurate. This misalignment between effort and outcomes is because sales teams often use only historical data to inform the sales forecast. Predictions about the future based on what happened in the past don’t account for the realities of the present-day market.
Many sales forecasting tools that claim to use artificial intelligence (AI) to correct this issue actually fall victim to the same limitations of only looking at the past. An AI platform that makes predictions based only on historical data can’t add much to the conversation for the humans on the team.
“Anything that mimics how a human would think, or what looks like intelligence, falls under AI,” explains Steven Messer, co-founder of Collective[i]. But what distinguishes true AI from AI-lite is the difference between prescription and automation. Basic AI models can automate simple repetitive tasks for a sales team, but AI-lite models can’t learn from these events without being trained by developers or being fed more historical data.
By comparison, the more advanced AI technology that leverages deep learning and neural networks (the kind of AI used for self-driving cars, reading MRIs, and basically all the innovation that has gotten people excited about AI again) can automatically learn from real-time inputs. Leveraging larger data sets beyond a single company’s sales data lets Collective[i]’s technology learn faster and provide better insights to selling teams. Based on recognized patterns achieved through analyzing large amounts of internal and external data, the AI accelerates the work of any team of sellers, quickly informing the team on the ground with insights and intelligence that can arm them for better, more convincing calls and interactions with buyers.
Messer says that much like humans, AI and neural networks learn best through observation: “There are certain types of behavior that improve or reduce your risk. The more information you have, the more able you are to make better decisions. With deep learning, I want as much information as possible because it will help the AI to decide what is most important for each task.”
That’s why more is more when it comes to networked data. When an organization only relies on its own historical data to make predictions, there isn’t enough information for either a neural network or the sales team to make predictive assessments or create high-quality sales forecasts. Present-day information about the market, buyer behaviors, and seller activity can and should be leveraged to achieve more perspective and accuracy in business decisions. Collecting and inputting all that information to achieve these results with an AI-lite solution is a job in itself, and by the time the data can be added, it may no longer be deeply relevant. Alternatively, true AI such as a neural network seeks out data from a wide variety of sources and learns independently, in real-time. This lets sellers make more of the influx of information with less effort.
The value of a neural network is its ability to learn, recognize patterns, and make predictions based on more data than a human could ever access or analyze in time for it to be useful. The more varied and robust the networked data sources, the better and more useful the neural network. At Collective[i] we’ve been training our neural network for eight years, using sources such as holistic market insights, changing buyer preferences, and successful revenue-growing strategies from across industries and business models.
Relying on the Collective[i] neural network and extensive networked data manifests many benefits for sales teams, including:
- Accurate daily sales forecasts that are updated daily based on dynamic market data as well as traditional signals such as the stages of a sale and activity levels.
- An optimized daily to-do list recommending the next and best actions for sellers to achieve maximum impact, based on learning from the activity of top sales performers.
- Automated CRM capture from inside and outside sales, which updates CRM, minimizes duplicates, and has led to as much as 20% productivity gains.
- Better-enabled social selling with AI-enabled recommendations to leverage seller networks and grow targeted, meaningful relationships.
“Our goal is to really enable companies to better predict, manage, and grow their revenue,” says Messer. Collective[i] is a turnkey digital sales transformation solution allowing sales organizations to grow revenue by leveraging the latest learnings and algorithms of AI technology. The support of real-time networked data pooled from various sources ultimately creates a superpowered, agile, and adaptive sales team.
Explore Collective[i] and learn about the benefits our neural network could brainstorm for you.Explore Collective[i]