When businesses review Google Ads reports and see the "Store Visits" metric, they may notice discrepancies between Google's reported numbers and their own in-store counts. While Google might show an increase in visits, actual people counter data can sometimes reflect a decline.
The main reason for this difference is that Google Store Visits are not direct counts of customers entering stores. Instead, these figures are estimates generated through machine learning models based on partial data. Google only detects a subset of visitors who have location services enabled on their devices. The company then uses statistical modeling to estimate total visits from this sample.
Google states that Store Visits are intended to provide directional accuracy rather than exact numbers. They are not designed to match perfectly with point-of-sale systems or physical people counters. Expecting an exact match can lead to confusion and frustration.
There are several reasons why Google's estimates may diverge from actual store activity. Because Google cannot observe every visitor, it must fill gaps with assumptions, which can vary in accuracy depending on factors like location services adoption rates among different customer groups or regions.
Store Visits estimates become less reliable at smaller scales or in complex environments such as malls, urban areas, strip centers, or multi-tenant buildings. In these scenarios, Google's system may misclassify nearby device activity as store visits due to shared walls or entrances.
Additionally, privacy thresholds enforced by Google mean that if there is insufficient data for a particular store or time period, Store Visits reporting may be withheld or appear inconsistent—especially at low-volume locations where the numbers can seem random.
Platform bugs have also affected Store Visits and related metrics in the past without always issuing alerts to users. This volatility can influence marketing decisions if teams rely too heavily on these modeled metrics for optimization and bidding strategies.
Users do not have access to the underlying raw data used by Google’s models. As a result, while Store Visits can provide helpful trend signals when viewed alongside other internal metrics like sales data or foot traffic counters, they should not be treated as definitive measures of performance.
In summary, Store Visits should be considered as one input among many when evaluating marketing effectiveness. Treating them as precise counts risks undermining trust and decision-making accuracy. Using them directionally and validating against real-world results allows businesses to benefit from the insights they offer without overreliance on modeled data.
