---
id: "concept-block-group-resolution"
type: "concept"
source_timestamps: ["¶3", "§ A New Strategy for Location Targeting", "¶17"]
tags: ["data-granularity", "census-data", "targeting-precision"]
related: ["claim-broad-data-obscures", "prereq-programmatic-ip-targeting"]
definition: "Geographic targeting at the level of a census block group (typically 600 to 3,000 people), which provides the necessary precision for relative proximity calculations."
sources: ["tail1"]
sourceVaultSlug: "hbr-seg-tail1"
originDay: 1
articleStem: "hbr-tail-115-location-based-advertising"
sourceUrl: "https://hbr.org/2026/03/a-better-strategy-for-location-based-advertising"
sourceTitle: "A Better Strategy for Location-Based Advertising"
---
# Block Group-Level Resolution

The precision of location data is **paramount** for executing advanced spatial strategies. The authors mapped retail store visits to consumers' **home block groups**, which typically contain **600 to 3,000 people**.

## Why granularity is decisive
When they re-ran the same [[concept-relative-proximity]] models using broader approximations — **zip codes or county-level data** — the correlations **dropped substantially** (formalized as [[claim-broad-data-obscures]]). Broader data aggregates away the specific, granular patterns that make relative proximity work — for example, *exactly which side of a highway a neighborhood is on relative to two competing stores*.

**Connected TV and IP-based ad delivery** now support this household/block-group level of precision (the infrastructure prerequisite is [[prereq-programmatic-ip-targeting]]).

## Enrichment context
This is **methodologically well supported**. Census block groups (600–3,000 people) are a standard fine-grained analytic unit, and the failure mode described is a textbook case of the **Modifiable Areal Unit Problem (MAUP)** in spatial econometrics/GIS: coarser areal units distort relationships. The exact *magnitude* of the correlation drop is specific to the authors' data. Counter-consideration: household/block-group targeting raises **privacy and regulatory constraints** (GDPR, CCPA, platform policy) that can limit real-world deployment even when it is technically feasible.


## Related across articles
- [[concept-broken-data-foundation]]
- [[prereq-data-infrastructure]]
- [[concept-lasso-regression-workforce]]