What they’re missing is insights.
Although CIOs have focused on digital transformation initiatives, such as cloud computing and mobile applications, they are not seeing the full value from these deployments. These initiatives are trapping all of this information into silos, making it difficult to capitalize on all of this information to meet KPIs. The solution: an insight engine.
According to Gartner, “insight engines apply relevancy methods to describe, discover, organize and analyze data. This allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers or constituents at timely business moments.”1
Sound familiar? Insight engines are the latest evolution of enterprise search, a change that we believe reflects the pain points today’s businesses are encountering with digital transformation. Employees cannot find the information they need, and are struggling to become proficient at their positions. Coveo™ has been positioned furthest in the Leader Quadrant for Completeness of Vision and Ability to Execute in the Magic Quadrant for Insight Engines and has seen how insights can transform a workplace by solving pervasive employee proficiency and skills shortage issues.
The Power of an Insight Engine
A major manufacturing company is expanding into new markets, but new field service reps are struggling to answer customer questions and efficiently navigate support software for complex tasks compared to more experienced counterparts. The time to proficiency is eating into the bottom line and customer satisfaction scores are plummeting. As more of the older employees retire, their tribal knowledge is not transferring to the new employees.
By deploying an insight engine to their intranet solution, this situation is completely different. With insights, employees are empowered with relevant information to do their jobs and make better, more informed choices, and handle more complex tasks. Enterprises are able to do more with less when they use an insight engine to leverage the valuable resources they currently have, which includes both knowledge and employees.
A similar transformation occurs when an insight engine is deployed to a contact center. When employees are unsure of a customer question and turn to the knowledge base for help, an insight engine uses machine learning to understand the intent and context of the question, and then recommends content that will lead to a successful outcome.
Customers are more satisfied when their support agent can quickly and efficiently provide support. It also reiterates that the company values its customers’ time; in fact, for 73 percent of customers, valuing their time is the most important thing a company can do for good customer service, according to a 2016 survey.
“Valuing their time” is also important to the partners and dealers enterprises use to grow their business quickly. For many, insufficient communications tools lead to these groups misrepresenting the brand and missing opportunities for better service and sales. Too often, “portals” fail to do enough to correct the situation because partners and dealers struggle to find the right content that they need.
Partners and dealers need up-to-date and relevant content. Deploying an insight engine to the portal meets those needs and when machine learning is applied to usage analytics to understand users’ intent and context, the portal experience continuously improves. Partner management teams can also look at the same analytics to understand how well the portal content is able to meet the needs of its users.
If you’re unclear on how an insight engine will make an impact on your business, watch our on-demand webinar: “Takeaways from the Gartner Magic Quadrant: How AI-Powered Search Can Impact Your Business.”
How to Evaluate an Insight Engine for the Workplace
Not all insight engines are created equal. In order to maximize ROI from this investment, consider how you prioritize these capabilities to meet your organization’s needs:
- Complete connectivity. Access information from across your organization’s disparate information silos with a solution that has a full set of connectors.
- Intelligent search. Use the context of the user to personalize the results and recommend content to create a consumer search-like experience.
- User-driven insights. Analytics will help you understand how to create content that will help users’ needs and provide a more robust picture of company knowledge.
- Automated machine learning. Deploy an insight engine that uses machine learning to continuously improve its ability to provide relevant results to users.
Prioritizing the capabilities that are most important to your organization should be the first step in your search for an insight engine and realize the full impact of solving the relevance problem. Not sure where to start? Access your copy of the 2018 Gartner Magic Quadrant for Insight Engines.
1. Gartner Magic Quadrant for Insight Engines, Whit Andrews, Guido De Simoni, Jim Murphy, Stephen Emmott, 20 March 2017.↩