The enterprise software world has seen a phenomenal transformation over the past couple of decades. Not too long ago, enterprise software revolved around “Systems of Record” (SoR), ERP-like database solutions like Workday, Intuit, and Salesforce. Before long, enterprise software met the “Systems of Engagement” (SoE) era, which saw the rise of more decentralized solutions that incorporated peer interactions on platforms like Facebook, Twitter, and LinkedIn. Today, a new renaissance is upon us. We’ve embarked on the “Systems of Intelligence” (SoI) era. SOI are slated to become the future of enterprise software. In a recent tweet, Salesforce CEO Mark Benioff proclaimed, “Systems of Record+Systems of Engagement+Systems of Intelligence=Next Generation of Enterprise Software.”
The SoI era has been welcomed by the massive rise of big data, which has rendered it more and more difficult to manage the massive volumes of data at our disposal. Machine learning, a subset of artificial intelligence, affords computers with the ability to learn. This enables them to uncover insights from massive mazes of big data without being explicitly programmed to do so. The results can be powerful.
Many companies have been quick to jump on the machine learning bandwagon. An MIT study among executives at 168 large companies with >= $500M in annual revenue found that at least 40% had already began to use machine learning to improve sales and marketing performance.
Yet, while many companies have been eager to dabble in machine learning, many have struggled to truly reap rewards. The (few and far between) companies that have been successful tend to focus on using machine learning to advance three core functions:
1. Automating sales processes:
US companies lose $41B annually in the aftermath of a bad customer experience. Machine learning can help automate customer service by routing support issues to the right agents. This not only saves time, but it also improves customer service. Big data companies like Wise.io can help companies mitigate poor customer service experiences by leveraging machine learning to analyze patterns in how support tickets are classified, routed, and resolved. These patterns can dictate which agent incoming tickets are routed to, as well as how agents should optimally respond to tickets.
According to McKinsey, 76% of buyers find it helpful to speak to a human when considering a new purchase, yet only 15% desire to do so when considering repeat purchases. Given this, the potential of machine learning to automate the repeat purchase sales cycle looms large. Tools such as the Einstein Commerce Cloud by Salesforce (a company that has already allegedly spent >$600k on machine learning-driven initiatives) uses machine learning to predict which products customers are more likely to engage with and then provides them with product recommendations.
2. Creating more effective sales interactions:
The need for human interaction at some part throughout the enterprise sales process will never be lost. Machine learning can help predict when customers are most likely to buy, thereby informing where salespeople should focus their attention. Traditionally, this has been difficult to pinpoint. The rise of omni-channel sales and marketing efforts have added increased hardship as customer touchpoints now occur across a multitude of channels. A host of new tools have been developed to analyze customer interactions across all channels using machine learning to identify where customers are throughout the buying cycle (e.g., awareness, consideration, decision, purchase), enabling sales reps to save valuable time. Node takes things a step further by equipping sales reps to take action with personalized emails and unique company and contact insights that power sales intelligence.
3. Crafting more meaningful marketing interactions:
Results will be most effective if machine learning is embedded in the DNA of both sales and marketing departments. Node acts as the brain powering prospect intelligence across sales and marketing action systems by serving up ICP recommendations and targeted advertising tools to optimize demand generation. It’s difficult to imagine the future of marketing without machine learning. Using machine learning, marketers can shy away from traditional “spray and pray” approaches and become more strategic about content distribution and personalization.
The potential of machine learning is enormous. The MIT study cited above found that 38% of respondents credited machine learning for improvements in their sales KPIs (including new leads, upsells, and sales cycle times) by a factor of at least 2. When embarking on the machine learning journey, you’ll do well to focus on the aforementioned three focus areas. A word of caution – don’t ignore the wider enterprise story.Effectively leveraging the potential of machine learning depends on an organizational shift. Machine learning efforts will fail if they are deployed in silos. The true power of machine learning lies in its ability to draw insights across multiple disciplines – sales, marketing, HR, product, etc. Powered by machine learning, organizations can mine the treasure troves of data at their disposal. Seemingly one-off actions will no longer be ignored, but rather assembled to create recognizable patterns that can enhance organizational effectiveness.