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In today’s data-driven business environment, intuition alone isn’t enough to drive decision making. As investors, board members, and even employees monitor company movements, every decision made must be backed with data, use cases as proof points, and connection to overarching company goals. Data-based decision making must be a cultural shift; it has to start with your leadership before it becomes engrained throughout the company. On top of that, you must provide the tools and training your employees need to make those data-driven decisions.
One area of a business where data-driven decision making can have a particularly meaningful impact is sales. Data can be particularly useful in decision-making for a business’ salesforce – the ones who are driving top-line revenue and engaging with prospects and customers on a daily basis. For example, sales enablement automation platforms empower sales teams to ensure the front line of the revenue stream is making data-driven moves at all times. These platforms leverage customer and prospect information and data on past similar interactions and deals, and bundles complementary information such as meeting prep materials, or a case study that ties to the pain points the prospect has been discussing in a quickly digestible format so sales reps have all the details they need at arm’s reach. With Machine Learning-based algorithms that combine pattern matching, deep usage analytics and predictive modeling for faster, more accurate recommendations, many platforms provide a cohesive approach to sharing information both inside and outside of the organization.
The Power of AI
With the data collected through automation and the application of advanced technology like AI and Machine Learning, it’s possible to entirely remove for the salesforce the mundane tasks that make customer engagements clunky and unnatural. Instead of halting a conversation to dig through an antiquated server for a piece of content, AI pushes the best collateral to the sales reps’ fingertips, supplementing the conversation rather than interrupting it. Just as any assistant to the sales rep would, the AI learns over time based on how the sales rep interacts with the platform, the information they push to prospects, and how those prospects react.
Support AI with UI
The caveat here is, just as that assistant has to be in the room to pick up on those nuances and provide value based on what they learn, the platform must also get a front seat to those engagements – which means a simple, easy-to-use UI that works on any device, on or offline, is critical. It’s necessary to model the UI after consumer tech solutions, which provides a recognizable format for the end user. This attention to user experience puts the sales representative in a virtuous cycle of inputting better and more consistent data into the platform, which leads to better insights from the AI, leading to more reliance and engagement with the platform, and so on.
Certainly, the conceptualization of this technology on the sales playground is important, but it also needs to be applied to other commercially valuable functions, like field service across industries and consumer wellness. For example, powering a consumer-facing yoga studio placed in the heart of the Denver airport needs a very specific application. An on-demand interface that brings stressed out travelers a perfectly curated yoga experience based on their experience level, the amount of time they have available, and the objective of their workout, whether that is meditation, stretch, or core strength.
Maximize the Value
Data is highly sought after and can be extremely powerful, especially in a sales setting – but only if its managed correctly and put into a useable format. Give your teams the tools they need to maximize the data they have, and watch them rise above the competition. This principle can be applied to many other areas of the organization as well. Not only does such technology help with customer relationships, but also this continued application of AI and its virtuous cycle of better data and insights can help improve employee performance and engagement across the enterprise. Imagine how this concept would benefit your finance team as they rectify budgets and expense reports, or how a manufacturing department could streamline operations. In the coming years, AI and Machine Learning will surely infiltrate and enhance the job functions of employees across industries and through enterprises, elevating the standard of service and job satisfaction.