If you are an operations manager or growth leader in an on-demand delivery company, this blog is for you. Read this piece to understand how you can use geospatial analytics to acquire and convert more users, improve the utilization of your delivery workforce, and improve your unit economics as a whole.
Online food ordering anytime, anywhere at the tap of a button has become a norm in the modern urban world. At any point in time, we have thousands of meals and a wide variety of cuisines to choose from that can be delivered instantly. With that comes, the need for food delivery companies to be constantly on their toes to give their customers end-to-end visibility and personalized experiences.
However, they also end up burning an immense amount of cash to acquire users due to low repeatability and suffer from low partner retention rates and reduced profits. As per the report by business intelligence company Tofler, Swiggy mounted a loss of Rs 2346 crore while on the FoodPanda's losses stood at Rs 756 crore.
As reported by New York Mag in an article, DoorDash had reported an annual loss of around $450 million in 2019 . All these stats suggest that food-delivery companies all across the globe suffer from losses and cash burn while they strive to deliver food to their customers quick and piping hot.
Food Delievery Use Cases with Location Data
Food delivery is a three-degree marketplace in a hyper-locale setting made of users, delivery partners and restuarants. In a three marketplace, the core operation is to match supply and demands at all times of the day.
For better asset utilization, available riders must spend most of their time doing pickups and deliveries. Idle time must be minimized. Rider productivity is driven by location-based dispatch, task automation in apps, operations visibility in dashboards, and proactive handling of issues that might cause disruption.
Data analytics can indeed go a long way to help food tech companies optimize operations, and improve unit economics through optimizing their delivery fleet , finding bottlenecks in your delivery channel and cutting cost spent in each successful delivery.
When everything is going very well, visibility is a vitamin. It is a painkiller only when things go haywire.
Visibility aslo helps in pruning costs by being more proactive and intervening at times of panic to take the right operational decisions. In this section, we discuss in detail what are some of the most influential metrics and analysis for food-deliveries companies.
The real world is really chaotic and hence demand can abruptly change with changing condition and time of the day. Your user acquisition, conversion and retention efforts have been missing the cotext of location in them up until now.
User Acquisition or Activation
User acquisition mainly deals with finding which areas are your user active or inactive. To expand into the right areas, you can analyze which locations you already have latent demand in terms of app installs and searches. Quite a number of food deliveries companies are opening up cloud kitchens as well.
You can also analyze what the properties of users who need to be activated because they only ordered a couple of times and didn't return back. Is there any pattern in the restaurant or the area or the time or the category? It make sense to do some offline marketing in areas of high density of lost users.
Convertion of Churned Users
Not everyone who opens your app goes on to eventually book an order. Every prospective user that doesn't convert is in a way a loss in revenue. Analyzing which step is causing them to drop off and in which location can be insightful to strategize marketing efforts and contextualize them to different areas.
If price is what makes people go away, can we change our pricing for location sensitive areas? If its the ETAs, can we priortize orders for those particular locations? Do we need to partner with more restuarants in cases where ETAs are very high?
The metrics helpful in this case is analyzing user churn and user cancellations in different locations & dissecting the reasons. Running experiments with some of these hypotheses could lead to some astonishing results.
User retention is the most important metric for consumer metrics as they are the ones who promise a consistent revenue every month. Segmenting users on the basis of their choices, preferences and locations can go a long way to deliver personlaized experiences.
While showing the top recommendations, it is always a great idea to also include not only the favourite resturants, but the ones closest to them. Leveraging cross-selling and up-selling could also help retain your users.
The metrics helpful in this case is analyzing order patterns and slicing that with time, SKUs and locations and then dicing that with different cohorts of users.
Partner Experience & Operations
At every location where a food delivery business operates there needs to be a critical mass of consumers, restaurants, and DEs (delivery executives) at all times to deliver an optimal experience to the end-users.
Re-allocation & Incentives
Placing your deliver fleet by matching the predictive demand in an area is of utmost importance to reduce their idle time and ensure 100% booking fulfilment. The key to an efficinet re-allocation and deciding the incentives is knowing:
- Fleet Availability: How many total delivery execustives are present and what percentage of them are busy and idle? How often are partners idle in different areas?
- Reason for the Gaps: Is the rate per minute increasing or decreasing? Are delivery partners idle because of less demand and high supply or due to improper order allocation?
- Earnings per Hour: What is the earnings and the incentives of the delivery partners who are not accepting the orders look like?
- Batching of Orders: What percentage of orders are batched? How many of them are giving a good user experience?
We have covered supply-demand gaps in a lot of detail in this piece and highly recommend you to check it out:
Monitoring your fleet utilization across the cities during various time of the day, average wait time of a DE at a particular restaurant, tracking their completion rate and deliveries per hour helps you understand where are the bottlenecks present in the delivery funnel.
- Partner Cancellations: How many orders where cancelled by partners because of wrong location or non prefered destination? Every city has these theoretical “walls” or the combination of origin-destination that drivers don’t prefer to cross.
- Abnormal Behavior: Getting notifications of delays or unexpected behavior while rider is on the way can help the system message the right person or application for recourse is essential.
- Delays: What percentage of orders are delayed orders? Is the waiting time at the restaurant high or is the last-to-last mile delivery taking extra time? Geo-fenced arrivals and exits save time spent by partners in reporting tasks, and by ops managers in tracking progress is very useful for the central ops and city teams.
Restuarants are the third entity in the three degree marketplace that join hands between the users and delivery partners. Here are some use case to analyze their performance in different areas as well as with time:
- Availability & Servicability: What percentage of dishes are available in the order? Which restaurants are operational during which point of time ? Do restaurants serve particular items at a certain time window ?
- Orders Accepted: What is the percentage of orders accepted (Accepted / Received) across all locations? What are the reasons for not accepting?
- Orders Cancellations: What is the percentage of orders canceled?
- Prep time: What is the average prep time across different meals and locations?
- Revenue: Revenue & profitability at each kitchen
At core , the main aim of food delivery companies is to satiate the hunger of their users at the shortest possible time. User demand is highly en-tropic and can easily shift to other offering in case the desired service isn't received. If you are a decision maker, you need to ensure proper utilization of your fleet to keep the efficiency level high along with ensuring outstanding user experience.
- Revenue: How is revenue and APAC (average revenue per customer) distributed in a city? What are the lowest revenue but the highest driver cost areas?
- Partner Utilization: How is driver utilization spread across city and how does that change with different times of the day?
- Orders per hour: How many orders do we get per hour (order velocity) across different SKUs in different areas? How was a sudden dip in orders velocity countered?
- Conversion Rate: What’s the conversion rate of those orders requests in terms of partner acceptance? Where and when do partners don’t accept orders?
- Cancellations Rate: How is cancellation rate distributed between users, partners as well as restaurants?
- Refunds: How many refunds per 1000 orders? In case of damaged/tampered packaging during delivery, identify what bottlenecks are causing such unsuccessful deliveries?
Why Choose Locale.ai Over Building it Iternally?
Locale is an analytics platform which can act as your control centre ingesting your raw spatial, marketing, user demand data to give you real-time insights on your on-ground assets. In a matter of few clicks, we make all the location data accessible seamlessly in one place so that decision makers can make very precise, data-driven decisions about their ground operations.
But the question remains why should you leverage our platform to track and analyze your ground operations when you could build a tool from scratch for your own need/purpose? Building it interally would take 6 months of time with a dedicated team of front end developers, engineers and geo-spatial analysts putting all their efforts to build such a analytics platform along with the added costs of maintaining the system as well. Also, such systems when built internally are not scalable for all use cases.
In an age where companies are using web analytics tools to improve various metrics using click-stream data, it can be extremely detrimental if organisation having moving assets on ground don't look at things geospatially and don't leverage location analytics to get to sustainable growth and operational efficiency.