Technology investments continue to rank at the top of the planned spending list each year for support leaders, and AI and machine learning are among company’s biggest priorities. Coveo has been in this space for a very long time and we’ve seen the power and sophistication of machine learning technology evolve over the last couple of years to be a serious game-changer for early adopters. As our CTO, Laurent Simoneau predicted in 2016, the power of AI is indeed transforming enterprise search to intelligent search and it’s having a significant impact on company’s customer experience, and subsequently their ROI.
Whether your customers are self-serving or in need of agent-assisted support, instant access to relevant knowledge is needed to deflect and solve cases quickly to avoid customer frustration. Intelligent search unifies your company knowledge, finds what is most likely to help your customers and agents succeed based on what has helped others with similar queries and proactively recommends the information they’re looking for. This is made possible by creating a knowledge sharing culture, implementing a knowledge management strategy and leveraging machine learning. Or as we like to say, aligning your people, processes and technology to become more relevant to your customers.
What is machine learning?
Machine learning learns from and makes predictions on data. Applied to search, every time a user performs an action on your website or support portal, he or she provides data about what’s useful. Did they submit a support ticket? That means the articles they just read did not help. Do most people spend only one minute with a document that would normally take 10 minutes to read? That’s a sign that the content isn’t useful, or perhaps it’s too difficult to understand. With machine learning, all of that information and more can be used to make data-driven predictions and decisions without manual intervention, that will delight your customers.
How will machine learning make search intelligent?
When someone submits a search query or clicks on the third search result, they are implicitly telling you what is most relevant. As your online community members download content, visit various web pages, watch videos, start an online chat with your support agents or submit support tickets, their behavior provides information on the relevance of the content they come across. This behavioral data as well as search behavior – which signals intent – are captured by search usage analytics.
Search engines powered by machine learning can leverage such usage analytics data to continuously self-learn. This improves search relevance and hence, the self-service experience on your community in many ways. For example, automatic fine-tuning and ranking of search results based on machine-generated predictions about what’s most useful improves the experience of all community members.
Without machine learning and usage analytics data, administrators need to fine-tune search rankings manually: Create boosting rules, add synonyms, promote documents, etc. Because relevance is an ever-evolving process – the document that was the most relevant last week may no longer be relevant today – it is almost impossible for administrators, especially at large organizations or those with multiple product lines, to keep pace with the rate of change.
With machine learning, highly manual and complex enterprise search can be transformed into intelligent, self-learning and self-tuning search.
What is the impact of cloud-based, self-learning search?
With cloud-based, self-learning search, all the required components are hosted and managed by the vendor, such as Coveo. Because of its scalability, it has the potential to change the customer service industry the same way machine learning has impacted e-commerce and social networks. In the past, the high cost of using and managing machine learning systems meant that machine learning was rarely used for traditional enterprise search or self-service support sites. The cloud makes that affordable to all customers and to all departments, especially when deploying self-learning search on self-service support sites and on communities, because of its ability to scale and handle large volumes of data.
Machine learning has been around for a long time. It used to be very complex to deploy and manage. Collecting usage data, managing databases, provisioning servers, developing and maintaining machine learning algorithms and using machine learning predictions in the search system has traditionally been complicated and hard to achieve. This required data scientists, database experts and developers. But the fast adoption of cloud solutions has made the use of machine learning much easier, cheaper and more attainable. Intelligent search is no longer a nice-to-have. It is becoming the new normal and without it, you will not be able to meet the growing demands of your customers.
Don’t wait, effectively leveraging your company knowledge and making it accessible to your customers and agents alike is the only way to keep your customers from jumping ship to one of your competitors… who are likely already delivering personalized and relevant experiences though intelligent search. Learn more about the common benefits and measurable returns of adopting a strong KM strategy from some of the strongest knowledge-sharing cultures in our latest report TSIA 2017 State of Knowledge Management.