A revolution is happening right before our eyes. It’s groundbreaking, and yet its disruptive power is only fractionally visible. With each passing second and each bit of new data, computers are getting smarter and smarter. Ubiquitous and quiet, machine learning algorithms are watching us, gathering information about our preferences, lifestyle, and habits. Consciously or unconsciously, we feel the benefits of these algorithms while lazing on our couches, clicking away on insightful recommendations for which movies to watch, books to read, and products to buy. But machine learning extends much further. It’s transforming our entire existence.
In fact, Gartner estimates that smart machine technologies will be the most disruptive class of technologies over the next 10 years. Using massive amounts of data, radical computational power, and remarkable advances in deep neural networks, smart machine technologies will allow organizations to harness data in order to solve problems that no one has encountered previously.
Early trials unveil the great business potential these smart technologies have for sales organizations. A study conducted by Accenture and published in MIT Sloan Management Review reveals that 38% of early business adopters “credited machine learning for improvements in their key performance indicators for sales — such as new leads, upsells, and sales cycle times — by a factor of 2 or more, while another 41% created improvements by a factor of 5 or more.”
These brilliant machines and their artificial neural networks allow companies to predict the likelihood of future outcomes. They also provide employees with actionable guidance that helps them achieve better sales results, faster than ever. If you want to grow your company and get ahead of the game in our highly competitive business environment, you can’t overlook the revolution that machine learning has put in motion. But let’s start at the beginning.
What is machine learning and how does it work?
Machine learning is the subfield of computer science that enables computers to use algorithms to analyze large, diverse data sets and automatically find patterns within them. From there, computers can make predictions based on those findings. And with each new data they receive, these smart machines learn even more, updating information, looking for new patterns and optimizing recommendations according to their discoveries. Every time Amazon misses the mark with a personal book recommendation, for example, the algorithm will learn from this new data and alter its process. And adapt to feed you better recommendations the next time around.
For sales leaders, machine learning is the sidekick helping them quickly test hypotheses and make better decisions based on accurate data analysis and computer-generated predictions. For instance, with the help of machine learning, you can accurately forecast which products are most likely to sell – to which clients. Also, you can anticipate who among your customers are most likely to churn in the near future. Based on this information, you can take corrective action before it’s too late.
Machine learning improves sales performance and effectiveness
1. Increased productivity and sales efficiency
Machine learning algorithms automate the administrative tasks that salespeople spend many of their working hours completing, like reporting and gathering sales data. They can even automate the opportunity prioritization process, enabling reps to focus their attention on those opportunities most likely to close. The list goes on. For instance, the algorithms can deliver actionable guidelines that help salespeople achieve their quotas.
When you consider that currently salespeople devote only 35.9% of their time to actual selling, the big picture machine learning creates becomes clear: letting machines take over routine, easily learned tasks leads to higher productivity, shorter sales cycles, happier reps, and more revenue.
As a matter of fact, more than nine out of ten companies surveyed by Accenture agree that machine learning is improving processes in real time without human intervention. A recent McKinsey study estimates that at a macroeconomic level, automation alone could raise productivity growth on a global basis by 0.8% to 1.4% annually.
2. Improved overall team performance
For sales managers, machine learning has the potential to transform their sales teams into high-performing sales squads. By analyzing your team’s performance, machine learning algorithms can generate a profile for your ideal sales rep. Next, the algorithms can benchmark all salespeople against this profile and use it to determine the activities and behaviors your people should adopt to boost their sales performance. Machine learning can also help you identify the skill and competency gaps that prevent certain reps from achieving their potential. Based on this information, you can easily individualize and tailor your coaching and training efforts.
The scenario above was played out by a big pharma company. With the help of machine learning, the company was able to accurately identify the number of calls that sales reps should make to specific types of doctors for optimal results. The technology has also delineated the skills and competencies that most powerfully impact sales success. After comparing these findings with the actual performance of each rep, the algorithms made specific course-correction recommendations, equipping sales managers with information that has proved crucial to helping their salespeople improve performance.
In a Harvard Business Review article, Accenture’s researchers state that some early adopters of machine learning have seen a tenfold improvement in workforce effectiveness or value creation.
3. Optimal compensation and more motivated salespeople
The pharma company’s success in improving salesperson competencies through machine learning is impressive, but top-performing salespeople need more than skills. If you want your reps to bring new clients, retain the existing ones, and build brand trust, you have to keep them motivated and invested in achieving sales objectives.
It’s widely known that compensation is the best tool to motivate salespeople and align their selling behaviors with company objectives. But how do you design a sure-fire compensation plan that matches every sales rep’s expectations?
The answer is in the data. Machine learning algorithms analyze historical data and past behaviors to predict which stimuli will propel the sales team to modify its behavior. If you’re looking to change the dynamic of your team, maybe uncapped commissions or a different compensation split will help.
Or, of course, you could just ask your compensation chatbot for a solution. A chatbot is a conversational interactive interface that acts as a personal adviser and makes recommendations for a compensation plan that ensures selling behaviors align with business objectives and sales goals. The suggestions are rooted in complex data sets that include industry benchmarks, best practices, your company’s sales and performance data, and analyst observations.
4. Improved forecast accuracy
Accurately forecasting is another challenge that machine learning helps you deal with. When forecasts are off the mark, the entire sales organization is affected. A forecast above actuals carries more expenses than revenue supports, while a forecast that falls below actual results may cause the organization to struggle to produce enough inventory to meet demand, and miss real revenue opportunities. So, the stakes are high.
But with machine learning, producing accurate forecasting is more easily achieved than it is through traditional, descriptive analytics. By drawing data from all sales forecasts, a smart computer can create a model with a typical path for a successful sale, from start to close, and then compare it to current performance. Machine learning algorithms can quickly detect patterns as well as anomalies, notifying you when the forecast goes off track and giving you the opportunity to step in and redirect sales.
5. Better risk management
Smart computers also help you better manage risk. Machine learning algorithms identify nonlinear patterns in large datasets and increase the accuracy of risk models. By trying out different outcomes and gaining a deeply informed and trustworthy view of potential costs, their range, and the likelihood of their occurrence, you can better prepare for anticipated and unforeseen obstacles.
The financial services industry has quickly embraced the power of machine learning for risk management. A Mckinsey report reveals encouraging results from banks that are experimenting with machine learning algorithms in collections and credit card fraud detection. “We expect banks’ risk functions to apply machine learning in multiple areas, such as financial-crime detection, credit underwriting, early-warning systems, and collections in the retail and small-and-middle enterprise (SME) segments,” state the authors.
6. Increased customer acquisition
Instant access to crucial information about clients, their interests and behaviors is another benefit of machine learning. With data in your pocket, you can better target customers and develop more accurate and successful campaigns. Machine learning algorithms can compare customer profiles in your CRM with information on your prospects, finding patterns, and predicting which prospects are most likely to convert in the future. If certain prospects follow similar behavioral patterns relative to existing customers, you can better foretell their needs and be ready to meet them as the sales cycle continues.
The study conducted by Accenture reveals that 76% of companies using machine learning are doing so with the goal of sales growth. And at least 40% of companies surveyed are already using machine learning to improve sales and marketing performance.
7. Higher customer retention
Acquiring new clients is just one side of sales success. Keeping them loyal is the other. Companies can’t exist without buyers. Thus, focusing on customer retention is crucial for all organizations. But as you already know, attempting to read the minds of your customers and predict which ones will stick with your company and which will churn is not only strenuous; it’s rarely successful. Machine learning helps you address this challenge.
By looking at past data, machine learning identifies the key factors that made previous customers churn and indicates which of your current clients fit the profile of a discontent customer. This way, you can easily identify at-risk customers and proactively save an account by offering personalized experiences. And with each customer you keep, you reduce costs and grow your profit.
A recent McKinsey study found that a dozen European banks are replacing statistical modeling techniques with machine learning, and are increasing customer satisfaction scores and customer lifetime value.
The big question: Are machines going to replace humans?
With the rise of machine learning and artificial intelligence, the biggest worry employees have today is that these new technologies will take over human jobs, leaving lots of people unemployed.
It’s true that repetitive activities will be carried out by machines. But people will still be responsible for doing creative, innovative and meaningful work. We’ve created smart computers to help us, not to work against us.
A recent study by Deloitte reveals that 77% of companies believe automation results in “better jobs.” Fifty percent are investing in retraining workers to work side-by-side with machines, and 33% expect people to do “more human tasks,” augmented by robotics and AI. In fact, only 20% of businesses believe automation will result in job loss.
Machines are great at processing massive amounts of data, but they can’t make thoughtful decisions. Intuition, judgment, and creative thinking are still solidly handled by humans. Only the human mind can decide on the essential questions, such as which critical business problems a company is really trying to solve. As a recent Harvard Business Review article reports, machine learning is a complement to human intelligence. “And cheaper prediction,” state the authors, “will generate more demand for decision-making, so there will be more opportunities to exercise human judgment.”
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