1: Supply-Demand Analysis: Analyzing the gaps between demand & supply
Supply-demand analysis helps you decode where, when and why the gap between supply and demand occurs. For every unit of demand which is not met, you have lost an order. For every unit of supply which is idle, you are losing money on your vehicles.
Locale.ai
Analyzing the gaps behind supply and demand helps you optimize for metrics such as utilization rate and the number of unserviced orders.
Decisions that can be taken using this analysis
Depending on the time of the day, day of week and location, you can decide:
- Rider Incentives: The incentive that needs to be given to the rider to make them move to a particular area of higher demand
- Surges and Discounts: The surges and discounts that we can give to the user to incentivize them to use your service in areas of high supply?
Insights required for taking these decisions
You can analyze the following metrics from the demand and supply side across different locations:
- Number of Orders Lost vs Order Frequency: How many orders you are receiving and what percentage of that you are losing?
- Idle Supply vs Total Available Supply: How much total supply you have and what percentage of that is idle?
- Distance of Lost Orders to Available Supply: What is the distance of orders that you are losing to the nearest idle supply?
Read more about these metrics here:
Locale.ai
2: Lifecycle Analysis: Drop-offs & time spent across journeys
Lifecycle analysis helps you understand where and when different events happen and what is the conversion rate across those effects, time taken between those events and distance traveled.
Reducing the drop-offs, time taken and distance traveled in your delivery journeys can help you in attaining efficiency and profitability.
Decisions that can be taken using this analysis
Depending on the time of the day and day of week, you can decide:
- Supply Provisioning / Expansion: Making the supply available depending on where users drop-off
- Delivery Charges: Changing the delivery charges based on the areas are the delivery folks spend the most amount of time
Insights required for taking these decisions
You can analyze the following metrics across different locations:
- Conversion Ratio: The number of users dropped in every step of the ordering funnel
- Time Spent: The amount of time spent in different events of the journey by the rider
- Distance Traveled: The distance traveled in different events of the journey by your vehicle
3: Trip Analysis: Order Profitability and Mobility Patterns
Trip analysis helps you analyze the movement patterns of your users — where they come from and where they go. It is also useful to understand the characteristics of profitable routes so that you can maximize them.
You can improve KPIs such as user acquisitions, conversions by understanding how they travel in the city.
Decisions that can be taken using this analysis
Depending on the time of the day and day of week, you can decide:
- User Acquisition: Depending on where power users come from and where they go, we can focus on those areas to acquire similar users.
- Marketing and Promotions: If we know the frequently traveled routes taken by our users, we can subsidize them for increased adoption.
Insights required for taking these decisions
You can analyze the following metrics across different locations:
- Conversion Ratio: The number of users dropped in every step of the ordering funnel
- Time Spent: The amount of time spent in different events of the journey by the rider
- Distance Traveled: The distance traveled in different events of the journey by your vehicle
Read more about trips here:
Locale.ai
4. Static Location Analysis: Improving Performance of Stations
Stations are the dimensions of your business with a fixed location and time. You can guage how stations perform by analyzing the number of bookings or cancellations or time spent in the delivery journey.
Optimizing the performance of stations can increase the efficiency of your journey as well as reduce the idle time of your assets.
Decisions that can be taken using this analysis
- Station Closing: Which stations should closeout or improve conversions by doing offline marketing?
- Debugging: Why do the orders in these restaurants spend a lot of time in the journey?
Insights required for taking these decisions
You can analyze the following metrics across different locations:
- Events: Total number of bookings, trips, cancellations, start to end trip ratio etc
- Time Spent: Time spent being idle, booking turn around time etc
- Demand within x kms: Searches within 3 kms of a station
You can read more here:
Locale.ai
Monitoring: Being Proactive about events happening on the ground
Monitoring helps to know what’s happening on the ground right now and be proactive about it. The real world is very fickle and chaotic and your model always cannot accommodate these sudden changes.
When demand peaks or troughs beyond the average value (it could be rain, traffic, protests, local events etc.) monitoring helps to understand where is the demand abnormally high and what can you do about it.
Decisions that can be taken using this analysis
- Abnormal behavior: If some aspect of the business is performing abnormally, taking the right interventions at that moment.
- User Safety: User safety is of utmost importance for companies with moving assets on ground.
- Vandalism and Abuse: Cases of vandalism and abuse of their vehicles are quite common for micro-mobility companies.
At Locale, we are building an “operational” analytics platform using location data for supply and operations teams in on-demand companies. If you want to delve further, check our website out or get in touch with me on LinkedIn or Twitter.
