While much has been written about how the “bots” are coming and will threaten to automate everyone’s job, the reality is that Artificial Intelligence (AI) is about automating mundane tasks to improve the customer experience and better scale human interactions for solving the complex and unique issues.
Machine Learning (ML) is all about feeding the algorithm “training data” so that the computer can understand the questions, responses, and best paths for a positive experience. But the old adage holds true, “garbage in, garbage out.” This is particularly important in automotive, where you have a vocabulary that doesn’t translate to all industries and thus a horizontal approach frequently fails.
In the automotive space, AI and ML work behind the scenes. Examples like digital retailing, inventory analysis, and lead response tools are examples of how data-gathering and analysis can be automated to understand trends and thresholds. Another technology that is leveraging AI and ML is phone systems and customer experience.
According to the Local Search Association (LSA), 61% of shoppers call a dealership after doing initial research. Thus, it is critical that your phone system and phone-handling skills are top-notch to convert those leads into appointments and sales. This is a great opportunity to apply AI and ML to segment calls for greater prioritization and handling.
Historically, a phone system provider could gather and report volume data, like which campaigns generated the most calls and which calls led to sales appointments. But, AI takes this to whole a new level of capability. Today, with AI, we can move beyond "volumes" to "value," differentiating what trends, actions, insights, and alerts are needed to segment potential buyers from robocalls and noise.
For example, let’s say Beth is your best salesperson. She routinely posts a 40% appointment rate for calls she answers. John, however, is a staff member that’s struggling. His appointment rate hovers around 10%. Without AI, you are constantly running in circles to achieve consistency. But with AI, this can immediately be improved with real-time data where human and computer work in harmony to improve the customer experience.
AI’s strength is finding patterns in what and how staff talks to customers then harnessing what works so insights can be operationalized, ultimately setting more appointments and closing more sales. Now you have the what and the why you can train John to interact on the phone like Beth. AI helps everyone do their job better so they can meet individual and collective sales goals to contribute to the dealership's growth.
The key to ML is training data, the more examples you can “feed” the algorithm, the better the outcomes. Frequently, this means you need at least a half-million automotive industry-specific data points to provide quality outcomes. This quality training data can be the difference between success and failure. You don't know if the algorithm is working correctly or not without the right volume of quality data to feed it. No short-cuts!
AI and ML can be revolutionary in the value they bring to an organization, but there are no short-cuts. It takes quality and quantity to achieve automation and positive customer experiences. Make sure you partner with organizations that can articulate the quality, quantity, and automotive industry-specific inputs that make AI a success.