Artificial intelligence, particularly large language models, have taken the world by storm, emerging in every area of technology and locator software is no exception.
Locator software’s purpose is typically to connect a potential customer with a resource of some kind. The resource might be a local store, an installer, a physician or an agent. In MetaLocator’s world, these resources are often “indirect” in nature, meaning they are independent businesses or entities apart from the company providing the locator.
A common challenge in user experience for locator search is the presumption that the user knows what to look for. Locators might ask a variety of questions regarding the prospective customer’s needs, many of which the customer may not readily understand.
MetaLocator uses conversational AI to extract the customer requirements in a manner similar to a customer service agent. This guides the searcher to the right resource and proactively educates them about the products and services at the same time.
For example, locators for residential construction material installers, for products such as windows, skylights and doors, may ask “Installation or repair”. While this distinction may appear clear-cut, the reality is typically more nuanced. The customer may not know if a window can be fixed, and does repair include the drywall around the window, and what about the siding? If an installer doesn’t provide repair, will they try to sell the customer on a window that could be fixed?
Examples abound in the public health sector. Government agencies use locators to provide “Find a Program” locators which may ask qualifying questions about medicaid, languages spoken, service delivery modalities, all of which can send the typical consumer’s head spinning. Similarly, this forces the providers to curate carefully selected language for choices, while forcing complex decisions and language into a drop-down box or checkbox.
This is akin to the rise of ChatGPT as a growing alternative to Google search. When users already know what they are looking for, Google gets top billing. Conversely, when users are looking to develop an understanding of how to approach a problem, what the possible solutions are, and even recommendations on courses of action, conversational AI is emerging as the clear winner.
Of course, some locator applications are in fact simple: the nearest store, the closest branch, the location that serves my area. This means that AI, and conversational UX, should not be universally applied.
To learn more about how MetaLocator leverages artificial intelligence, large language models and conversational AI in locator applications, book a demo with our Sales Team.
Check out MetaLocator’s software in action by visiting our examples page, featuring demonstrations of our locator software in various configurations.
To learn more about MetaLocator and our products, visit our products page.
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