Photo credit: Igor Omilaev on Unsplash
Author: Adam Tarshis, Business Development Director, Anadue
For several months now, we at analytics firm Anadue have been speaking with shared micromobility operators about how to best harness the power of AI.
The good news for everyone is that none of our conversations have been about how AI can replace the workforce, but rather how it can make people more productive, drive better decisions and build a more loyal customer base.
It’s still early days in the roll-out of AI as a critical component of shared mobility back-end systems, but I wanted to share some of the pain points we’ve identified and how AI can help.
Data Analysts overwhelmed
Even the largest shared mobility operators that boast whole teams of data-analysts are overwhelmed with work.
Whether it’s developing new reports that will improve decision making, or answering one-off questions from C-level executives, analytics teams all have to-do lists, priorities and conflicting demands on their time that mean data analysis is a bottleneck within the business.
Due to the long delays before getting a response, there is a tendency for people to not even ask for answers that would make their work more productive. So there’s the known demand for data analytics, plus an invisible demand that would exist if people had the confidence that their requests would be answered promptly.
One of our AI evaluation projects is to work with an Operator to see how much data analytics load can be shifted onto a virtual AI Agent to perform. Initially, this is likely to be a tool the data analysts use to quickly turn-around the ad-hoc, one-off questions. But the next step will be for end-users to engage directly with the AI Agent themselves without distracting the data analytics teams from their existing development projects.
The value that can be unlocked by removing the analytics bottleneck is huge. Everyone in an organisation, from street operations to the CEO, could access the information they need to improve their productivity and work smarter. Think about data driven decision making across an entire organisation delivered in a cost-effective manner.
Customer Relations
Large operators are flooded with feedback from riders. Sometimes it’s just a standard 1 to 5 star feedback score, but many times customers leave comments, and even ask questions and make demands via the app. Today, the scores are relatively easy to process and use to gain a macro insight into the level of customer satisfaction.
Our AI solutions already categorise comments, so it’s easy to see what percentage of 1 and 2 star feedback comments refer to a fault with a vehicle, pricing, parking, the app or other major causes of unhappiness.
But the problem for operators is that very few of the questions and demands are being responded to due to a lack of bandwidth.
The resources required to provide relevant information or to fulfill a unique request is uneconomic with traditional solutions. But an AI Agent can enable world-class customer relations, personalised responses, and specific actions (e.g. issue a credit for a ride that was charged but not taken). Customer satisfaction and loyalty can be enhanced, leading to long-term revenue growth.
Dynamic Pricing
Anadue has long provided shared mobility operators with Dynamic Pricing solutions. These allow individual vehicles to be priced differently from each other, based on a wide range of factors. This includes predicted demand in the area where the vehicle is parked, predicted duration that the vehicle will be parked, actual duration that a vehicle has been parked, and much more. Dynamic pricing is being used to encourage riders to move vehicles out of low-demand areas, as well as maximise revenue during peak periods in high demand hot-spots.
The pain point today is that all the variables used to set the price change over time. For example, the duration that a vehicle is parked before it is considered for discounting should be very different in the peak of summer compared to a cold rainy winter’s day. With multi-dimensional dynamic pricing algorithms, it’s a challenging task to know which variable to adjust to achieve corporate goals.
AI solutions can tune the variables and monitor the results of Dynamic Pricing algorithms to achieve optimum results. What is the precise level of demand that should trigger a price change (discount or mark-up)? How many price tiers should there be? What level of discount is optimal to encourage a rider to take a trip they might not have made? When does premium pricing start to generate negative emotions from customers? With an AI Agent monitoring dynamic pricing it can be automatically tuned to achieve the best results and meet corporate goals.
We’re listening
So if you’re a major shared mobility operator and you have a pain point, reach out, as we want to harness AI for as many use cases as possible. Together we can explore how AI will change the shared mobility landscape for the better.
