Improving productivity is the holy grail of most strategist and business executives. In order to sustain a high standard of living, productivity must be constantly improved.
There are numerous ways to increase productivity, ranging from basic use of modern IT systems such as smart phones, iPads, CRM systems, etc.
A new and more sophisticated way to look at productivity enhancements is using Predictive Analytics or Machine Learning technologies. In this article I bring to your attention two cases where Predictive Analytics has been used to increase productivity. One looks at the generation of algorithms to predict productivity problems before they appear. The other talks how predictive analytics can be used in the sales process to improve the productivity of the sales team.
Predicting upcoming productivity issues – employee productivity is rarely measured and reported in today’s corporations. In the future, an algorithm that statistically determines which factors positively impact employee productivity can add great value. A related algorithm that predicts where and when within the corporation that major individual and team performance/productivity problems will likely arise will also be valuable. Predicting upcoming productivity problems will give leaders sufficient time to develop approaches to mitigate those upcoming problems. Adding a visual trend line showing the trajectory of the productivity curve will make upcoming problems and opportunities easier for managers to spot. A similar algorithm and trend line revealing predicted decreases in innovation will also add value.
The idea of predictive analytics is about taking a massive amount of history, applying statistical analysis to it, and coming up with insight that is actionable. Unfortunately, many people think of it as having machines analyse sales history to come up with new ideas rather than people looking at charts and tables in reports, and this scares many businesspeople. It’s not familiar. Most senior business people got where they are by having good instincts, and predictive analytics might appear to usurp their skills.
That’s not the case (yet, at least). Predictive analytics techniques yield insight in forms that cannot be applied directly to sales operations. These little nuggets of learning need to be synthesized into actionable plans, and for that, sales executives and managers are needed.
Output for Sales Meetings
Imagine that all the pieces are in place. The company has the skills and software to perform predictive analytics and a sales history database to work with. Where does a sales department start with predictive analytics?
Providing actionable insight is the primary goal. Companies need to drop to-do items right into the salesperson’s workflow. As salespeople meet with prospects and customers, an old selling technique is to describe how a current customer in the same situation as the prospect in the meeting benefited from the company’s products or services. Predictive analytics creates customer clustering models to provide salespeople with the names and sales history of customers most like the prospect in the meeting. The salesperson can take the conversation with the prospect from the general customer-type comparisons to the specific by offering an example of a similar customer and what products and services he or she ordered over time.
Source: In-Person Sales Productivity Increases with Predictive Analytics – Inside-CRM ” – Dec 26, 2014
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