Which local government areas should an off-grid energy operator enter? A SQL-first market segmentation of Nigeria's 774 LGAs
# Market Segmentation for Off-Grid Energy Expansion in Nigeria
**Which local government areas should an off-grid energy operator prioritise for
expansion, and what does each type of market need?**
A pay-as-you-go solar company deciding where to expand faces a problem that looks
like customer targeting but sits a level above it. Before asking which household
to approach, the company must decide which *market* to enter — where to place
agents, stock, service infrastructure and credit exposure. Enter the wrong local
government area and no amount of household-level targeting will recover the cost.
Nigeria has 774 LGAs and they differ enormously in the things that determine
whether an off-grid energy business succeeds: how many people live there, how
many lack electricity, whether they can pay, and how expensive they are to serve.
Treating that variation as a ranked list loses information. LGAs are not better
and worse versions of each other — they are *different kinds of market*, and each
kind calls for a different operating model.
The output is therefore a **typology**, not a ranking: a small number of market
types, each named, profiled, and paired with what an operator should do
differently there.
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## What this project has found so far
**Nigeria's electrification data cannot speak for a quarter of the country, and
until now nobody had measured by how much.**
The settlement-level electrification model this project depends on (World Bank
GEP) publishes 708,536 settlement clusters with coordinates but no LGA. Assigning
each to an LGA by its single representative coordinate misattributes population
wholesale wherever a cluster is large and the LGAs beneath it are small.
Two independent tests measure the damage
(`sql/04_gep_quality.sql`):
| | result |
|---|---|
| LGAs whose indicators are usable without a caveat | 600 of 774 (73.4% of population) |
| LGAs flagged `suspect` | 169 |
| LGAs with no settlement clusters at all | 4 — Agege, Ajeromi-Ifelodun, Mushin, …