Last year, while looking ahead to the future of international relations, several global leaders wondered if “winter is coming”. Well, it has come. It’s the winter of coronavirus. At a time where regional and global solidarity should be the norm, it is the exception. This crisis calls for more (and better) multilateralism; not less. The crucial issue at stake is the state of our global health system.
How geospatial data can address the energy challenge
The off-grid energy sector continues to grow and innovate as companies, governments, and multilateral organisations invest in increasing electrification reliably and sustainably in emerging markets. As new companies enter a crowded off-grid energy sector, and current ones expand into additional markets, the need for data driven solutions to identify new customers is critical.
Solar home system (SHS) companies are increasingly considering four key questions integral to their success:
- ‘Who are the target customers for each of my products?’,
- ‘What kind of products can they afford?’,
- ‘Where exactly are these customers and communities with this ability to pay?, and
- ‘What is the best way to reach my customers?’
Answers to these questions often lie in areas where data has been traditionally hard to access.
However, thanks to recent technological advancements, consumer data is now available across the entire African continent, even down to the neighbourhood level.
Using tech to identify customers
- Geospatial data can address the challenges facing the energy space head-on. Through the use of machine learning models that combine survey, satellite, and remote sensing data we can now identify potential customers for different types of solar energy products, as well as understand the types of SHS products they can afford.
- The resulting profiles identify which customers are likely to have the highest demand and ability to pay for different products. For example, households that own higher-end assets, such as cars, generators, stoves, and finished housing materials are likely to have a higher ability to pay for SHS products, while households with few assets are identified as having limited ability to afford SHS products.
- When reliable spending data is available, it is also possible to analyse spending patterns within consumer profiles, which can give SHS companies an idea of what certain consumers’ are spending money on, and how much they might be able to spend on SHS products.
Tracking which customers will pay
In addition to the ability to afford SHS products, pay-go companies must ensure customers are credit worthy and willing to pay off their products over time. Nithio, a leading credit scoring company in the off-grid sector, is revolutionising this space with standardised and accessible measures and decision tools.
They have built a highly-innovative approach that applies machine learning algorithms to a host of data inputs, including geospatial data and pay-go companies own repayment data, to provide these solutions and actionable insights.
- Once profiles have been developed, it’s possible to identify communities with the highest concentration of potential customers. These communities generally fall into two broad geospatial buckets: rural communities with limited access to electricity, and urban areas where electricity supply is unreliable. More than 140 million people report not having electricity in their homes in Uganda, Tanzania, Kenya, Rwanda, Burundi, South Sudan. This jumps to more than 200 million if we include Nigeria in West Africa.
- Each type of consumer offers unique challenges that geospatial data helps SHS companies solve. In more remote areas, this data helps SHS companies target specific communities with high ability to pay that are far enough from distribution lines that electricity access may not reach for years.
- For example, Nigerian SHS companies needed to identify and expand operations into remote communities far from the electricity transmission network. Maps displaying high-resolution geospatial data can show the percentage of households that have electricity in their homes, overlaid with the electricity transmission network broken down into 1×1 kilometre grids, highlighting potential communities that have a need for SHS products.
In Nigeria’s case, SHS customers can also be found in urban communities where demand for SHS products is high due to unreliable electrical supply. In urban markets, where access to electricity may be available but unreliable, hyper-local data can illustrate the consumer characteristics of specific neighbourhoods. This helps businesses identify the most promising areas for expansion, for example, neighbourhoods that have access to electricity, but which also experience regular power outages.
With customer profiles and high resolution maps to drive go-to-market strategy, the next question is, ‘how do I reach my customers?’
By overlaying physical infrastructure such as a company’s distribution network, the country’s existing infrastructure (road network, mobile coverage), and points of interest like marketplaces, schools, clinics, and government buildings, with media consumption indicators like television ownership, radio listenership, and internet access, you can strategically invest in marketing, activations, and brand awareness campaigns.
Bottom line: Geospatial data brings granular, reliable insights to the complicated and increasingly crowded off-grid energy sector. From site selection to consumer information to credit scoring, this data can help untangle challenges that have made increasing access to energy so difficult. The ability to speed up learnings and empower decisions made by all stakeholders is exciting and worth keeping your eye on.