Sustaining the Gig Economy, Part I: The Problem with Hyperlocal
Long gone are days when the word gig was only relatable to musicians because the gig economy has arrived. A Google search for the gig economy throws up the following definition: “a labor market characterized by the prevalence of short-term contracts or freelance work as opposed to permanent jobs.” A number of professionals ranging from designers, developers to SEO experts have started leaving organized work life to take up on-demand jobs or what we call a gig.
However, the emergence of ride sharing, e-commerce and hyperlocal delivery startups like Uber, Ola, Swiggy, Zomato, Dunzo, Rapido, Amazon, Grofers, etc. have led to the rapid growth of a new class of urban gig workers who are the operational backbone of these apps. They are drivers and runners who transport people, food, medical supplies, forgotten keys and a plethora of other items that are available on-demand through apps. This new workforce helps city dwellers save time and live hassle-free by getting them what they want and when they want it, and customers happily pay a premium for the service. Sounds like a win-win deal, right?
Not exactly! Hyperlocal startups are executing customer tasks on-demand through a fleet of drivers and runners that are paid per task. These startups unsustainably incentivized drivers and runners to acquire and retain them, but once the incentivization decreased, these startups ran into big problems with their drivers and runners.
Let me give you some historical context to help you understand the problem with the gig economy in India. 2014 was the year of the Tiger. Tiger Global had entered India and was placing big bets on the new gig economy startups based on the success of similar startups in the US. Flipkart and Ola were raising large amounts of capital to survive against the newly launched Indian counterparts of their originals Amazon and Uber, respectively. Unicorn was the new buzzword in the startup and VC circle. Other VCs started feeling the FOMO (Fear of Missing Out) and latched on to the trend leading to a huge bubble of funding. This led to a vicious cycle and various other gig economy startups started emerging to participate in the gold rush.
However, all the money pumped into these startups sent them on a customer shopping spree, in an attempt to hack their growth and quash the competition. Heavy discounts were given to win customers and tagged as “Customer Acquisition Cost”. Free rides and referral cashbacks became the norm. These strategies worked but there was a caveat. The rapid growth in customers had to be supported by a rapid growth in drivers and runners to drive customers and deliver products. Just like the heavy discounting to acquire and retain customers, there was heavy incentivization to acquire and retain drivers and runners and delivery executives as well. They are paid per ride or per order and in most of the cases, companies would pay them more than the order price. Thousands of college students, unemployed youth or in some cases, even employed youth left their jobs to become drivers and runners as it became a lucrative job option that promised to pay more than a software engineer’s job with the flexibility to work at their own time.
The incentivization was definitely unsustainable and investors started raising their eyebrows as heavy cash burn had already killed hyperlocal delivery startups like PepperTap and TinyOwl. To show the potential of becoming profitable businesses, these startups had to choose between stopping the heavy discounts for customers or decreasing the incentives for drivers and runners. As the competition for customers was fierce, they chose the latter. They toned down the incentives even though the order volume every driver and runner had to service each day kept increasing. The dreams of flexible, carefree, high paying gigs were shattered and the drivers and runners started to look for ways to game the system and keep their earnings intact.
Loyalty is now a thing of the past for these drivers and runners and most of them work for multiple platforms at the same time. If an order is ongoing for one platform, there is bound to be a delay or order rejection on the other platforms. There are numerous stories of drivers and runners asking their friends or even their customers to create fake rides or orders to get their incentive for the day which is activated at a particular number of minimum rides or orders. If an order originated from Swiggy, a delivery executive would order a pick up for that same order on Dunzo and then get paid by both startups for delivering the same order. Some even get paid for delivering their own food. Adding fuel to the fire, there is a very limited control that startups have over their drivers and runners as most of the hiring is outsourced and they neither fit the definition of employees or contractors.
Today, gig economy based Indian startups are laden with these problems thinking how would they ever run a profitable business if heavy discounting keeps customers away and decreasing incentivization repels & gruntles drivers and runners who are the backbone of their service.
If that is the case, is there even a solution to this problem? If you are starting a new hyperlocal delivery startup, should you discount and incentivize to build a scaled marketplace or focus on profitability and get quashed by competition?
Stay tuned: in the next part of this blog series, we are going to diagnose the problem using VentureBasecamp’s Critical Success Elements of a Business and offer possible solutions.
Updated: Part-II of the series is now available here.
This article was originally published here: https://venturebasecamp.co/updates/sustaining-the-gig-economy-part-i-the-problem-with-hyperlocal/
