Major global companies are using intelligent search techniques to their advantage.
There is a movement for relevance tuning via analytics to improve findability and for applying knowledge discovery techniques for predictive modeling and proactive insights. This will allow for the creation of early warning systems to predict possible customer problems, and can also prescribe training paths and webinars that may be relevant for the customer. Personalizing the customer’s experience is also a major trend.
In addition to these trends, leading organizations often engage in the following:
- They look at the “meta-effects” of search technologies (in this manner, upskilling and improving proficiency through intelligent search can reduce customer churn and increase customer satisfaction through better self-service web portals and knowledgeable agents);
- They treat knowledge management from managing and curating knowledge to crowd curation and access (organizations take advantage of wisdom of the crowd effects and the use of social networking channels for ease of knowledge retention and transfer);
- They create economic value from the information ecosystem, and well as calculate a measurable return on investment through KPI (Key Performance Indicator)-driven analytics and reporting; and
- They take advantage of “learning at the point of need” in the flow of work.
Intelligent search can greatly facilitate the agility of organizations in focusing on customer outcome-based approaches rather than product-based approaches. This trend is aligned with the movement for generating “customer success”.
Sometimes, however, it is difficult to determine these outcome measures. The Spring 2015 MIT Sloan Management Review (“Minding the Analytics Gap” article) found that a gap often exists between the ability to produce analytics results and the ability to apply them effectively in business issues. However, the best-in-practice organizations perform return on investment calculations on their intelligent search capabilities and resulting effects, both internally and externally. For example, one leading company used a query-based method for their ROI calculation whereby there were 62,500 queries annually via this new intelligent search-embedded solution. By putting the user queries into “basic search”, “informational search”, “investigational search”, and “re-creation of information” query buckets, the total hours saved annually amounted to about 51,094 hours for an estimated annual savings of $6.1 million to the organization.
Intelligent search can also have an “accelerator” effect in enhancing decision making on a timely basis. Through intelligent search and predictive analytics, organizations can become more astute in making informed decisions. I have done work in intuition-based decision making (see Liebowitz, J. (ed.)(2014), Bursting the Big Data Bubble: The Case for Intuition-Based Decision Making, Taylor & Francis), and intelligent search and analytics play complementary roles.
In the near future, more organizations should take advantage of intelligent/smart search technologies in order to stay ahead, or at least keep abreast of their competitors. Without doing so, they may be sub-optimizing and will become laggards versus the leaders in their fields.
To learn more, watch the video of our recent webinar with Dr. Liebowitz on “The impact of intelligent search on the digital workplace.” And if you are participating in Harrisburg University’s Data Analytics Summit II, please join our SVP of Marketing Strategy, Diane Berry at her session: “Intelligent Search: Making Unstructured Data Make Sense” on Monday, December 14th from 2:00-2:45pm.