Scanner data — POS transaction data collected from retail checkout systems — is the foundation of retail intelligence in developed markets. A category manager at an FMCG company operating in Western Europe or North America builds their category review on scanner data. They know which SKUs moved, at what price, in what volume, across which store format, week by week. The data is comprehensive within its scope, it is structured, and it is available from established panel providers who aggregate across retailers.
In MENA, and particularly in Egypt, Jordan, Lebanon, and lower-income tiers of the Gulf, the scanner data premise breaks down in a specific and important way: the majority of FMCG volume does not flow through POS systems that generate scannable, aggregated transaction data. It flows through traditional trade — stores that are often not fully computerized, managed by the owner with a mental inventory, priced by judgment, and visited by a van sales route once or twice a week.
The Coverage Gap Is Not a Technicality
Industry estimates for traditional trade's share of FMCG volume in MENA markets vary, but across Egypt and Jordan the traditional trade channel accounts for somewhere in the range of 55–70% of FMCG unit volume across most categories. Personal care may sit closer to the lower end in Egypt given the growth of organized modern trade in Cairo. Ambient food, beverages, and household cleaning typically sit at the higher end.
A scanner data feed from modern trade chains — even the largest ones in a market — is therefore capturing, at best, 30–45% of the market by volume. It may capture a higher share of value because modern trade tends to index higher on premium SKUs and promoted items. But any volume or velocity analysis built primarily on scanner data is a partial picture. Calling it incomplete is not harsh; it is accurate.
The practical consequence for category managers: a brand that appears to be holding share, or even growing share, in scanner data may be losing ground in traditional trade without knowing it. A competitor that has invested in aggressive van sales pricing or improved distribution depth in traditional trade will not show up clearly in a modern trade scanner panel until the effect is large enough to appear in consumer survey data or basket studies — by which point the competitive position has already shifted.
What Field Data Captures That Scanner Data Cannot
Field data, as the term is used in MENA retail intelligence, refers to data collected by trained auditors who physically visit stores, observe and record what is on the shelf, and capture specific attributes according to a structured form. The attributes a field audit captures typically include: which SKUs are present and facings per SKU (for shelf share), shelf price per SKU (for pricing intelligence), promotional materials or price tickets present, out-of-stock flags for listed SKUs, and in some methodologies, estimated stock depth (visible units).
This captures what scanner data cannot. A field auditor in a traditional trade store sees that a competitor's SKU is present in 80% of visited outlets in a neighbourhood cluster. Scanner data for that cluster does not exist. The field data is the only systematic source of that competitive intelligence.
Field data also captures the context around a price. A scanner record shows a transaction at a given price point. A field audit shows that the price was supported by a hanging promotional ticket, that the SKU was at end-of-aisle, and that the competing brand directly adjacent was OOS. That context changes how you interpret the price data — and it is not available from any scanner source.
The Honest Limitations of Field Data
Field data has real limitations that practitioners should understand before treating it as a replacement for scanner data in contexts where scanner data exists and is comprehensive.
Coverage is sampled, not comprehensive. A field team visiting 50 traditional trade stores in a city of thousands of such stores is generating a representative estimate, not a census. The sample design matters enormously — which stores, which neighbourhoods, which store tiers — and a poorly designed sample generates confident-looking data that is systematically biased.
Timing is coarser. A scanner system captures every transaction. A bi-weekly field visit captures a single snapshot. A promotional price that ran for four days between field visits is invisible. For highly dynamic categories where price changes frequently — cooking oil in Egypt is an example — a fortnightly field visit cadence may miss meaningful price movements.
Observer consistency is a quality control challenge. Two auditors visiting the same store on the same day can return different facing counts if their training or interpretation of what constitutes a facing is not strictly aligned. Panel providers invest heavily in inter-rater reliability training; brands using informal field data collection through their own sales forces tend to get optimistic and variable results.
We're not saying field data is inferior to scanner data. The comparison is not meaningful when scanner data does not exist for the channel you care about. We're saying that field data has its own quality requirements that need to be met for the data to be decision-grade.
When to Use Each — and When to Combine
In markets where both data types are available — modern trade with scanner feeds and meaningful traditional trade coverage from a field panel — the most complete picture comes from layering them. Scanner data gives you the volume velocity signal in organized retail with temporal precision. Field data gives you the competitive shelf context, the traditional trade pricing landscape, and the distribution reach in channels the scanner does not touch.
A category manager at a regional FMCG brand operating across Egypt and the Gulf faces a practical data architecture question: which sources to commission for which decisions. For a quarterly category review at a major hypermarket chain, scanner data from that chain (if available) is the primary analytical foundation, supplemented by field-collected shelf share data to show the competitive facing context. For a distribution expansion decision in traditional trade, field data is the primary source, and scanner data from modern trade is at best a reference point for category growth direction.
The Syndicated Panel Caveat
Major retail panel providers operating in MENA — including established names with long regional histories — have expanded their traditional trade coverage over the years, and their FMCG volume estimates increasingly incorporate traditional trade estimation methodologies. These estimates use combination approaches: some direct field observation, some statistical projection from modern trade to traditional trade relationships calibrated by category.
The projection component is the honest caveat. In a market as structurally complex as Lebanon (where the store universe has changed significantly in recent years) or as large and diverse as Egypt (where Greater Cairo, Alexandria, Delta cities, and Upper Egypt have very different traditional trade structures), the projection assumptions embedded in syndicated estimates can be wide of the mark in specific markets or categories. A brand manager relying on national syndicated traditional trade estimates to make a Nile Delta distribution decision is using a blunt instrument for a specific task.
Direct field data for defined geographies, for specific category SKU sets, collected on a defined cadence — that is what answers a specific distribution or pricing question. The scope is narrower than a national panel; the relevance to the specific decision is higher. Both types of data serve legitimate purposes, and the choice between them should be driven by the decision being made, not by habit or availability.