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Data will fuel ride hailing companies’ race to the top

Ben Leo
By Ben Leo
Chief Executive Officer, Fraym

Fraym is an Africa-focused data platform offering hyper-local insight into communities across the continent.

Posted on Wednesday, 3 July 2019 13:47

Suzuki cars branded with Uber stickers are seen in a showroom in Nairobi, Kenya, October 30, 2018. Reuters/Baz Ratner

Ride-hailing companies are racing to capture market share across the African continent.

Investors have recognised the massive opportunity and are quickly selecting companies to back as the competition increases.

  • In Nigeria, there are roughly 20 players vying for market dominance. Start-ups can differentiate themselves based on business models and branding but, ultimately, the winner will be the one that establishes itself in the most lucrative markets first and expands as rapidly as possible.

Like mature ride-sharing companies, the pitch for business on the continent includes a model that extends beyond transporting just people. As Uber has proven, an operational network of vehicles and drivers can be leveraged to service multiple transportation-based markets—whether that’s moving people and products or delivering services.

Building the capacity and infrastructure to do this is a challenge and is dependent on creating a strong business foundation fast.

To make calculated decisions while setting up shop quickly, these teams need reliable, geographically specific data. Insufficient or incorrect inputs can lead to momentum-grinding mistakes. Now, the type of hyper-local data that can optimise driver recruitment networks, consumer sales, marketing campaigns and product launches is available and accessible.

  • For example, after receiving an injection of capital, a motorcycle ride-hailing company had to develop and implement an expansion plan in Nigeria. Using localised geospatial data, they were able to rapidly answer questions like, “Where are concentrations of our target consumers?” and, “What services might they be interested in?”.

Often, it takes months of costly trial and error before one can glean whether or not a market is viable but by combining human-centric and geographic data, solving product-market fit becomes a more focused and streamlined exercise.

Granular insights about people in specific communities are an informational advantage that can turn cash-strapped businesses into market contenders. What’s more, the type and amount of data that is available at a resolution as high as 1km² are vast. In fact, it’s possible to dive deeper into categories like demographics, spending and asset ownership to identify specific consumption patterns.

  • For instance, for ride-hailing services, we can create profiles based on high and low thresholds for household spending on transportation and speed up driver network rollouts by identifying neighbourhoods with high scooter ownership rates.

To meet the pace of these start-ups, customised hyper-local data can be made available via an interactive platform. Meaning, the tool works with, and not against, the changing needs of the business. Pre-loaded profile characteristics, such as middle-market consumers, can be surfaced across countries, cities and communities as needed. With short runways, demanding key performance indicators and weighty goals, easy access to actionable data is paramount to success.

Bottom line: Emerging markets are ripe with business opportunities. By 2050, Africa will have the largest youth population on the planet, the consumer class has already reached 330 million, and regional household income was $1.6trn in 2017. The type of strategic agility delivered by geospatial data is unprecedented in these markets and will deliver the necessary speed to outmanoeuvre the competition and capture opportunities.

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