Consumer Intelligence Glossary

The movement of consumers between brands across two comparable time periods. Brand switching analysis answers: where are we gaining buyers from, and where are we losing them to?

Switching is decomposed into six components: switching in (from specific competitors), switching out (to specific competitors), added to repertoire (new to your brand but not new to category), dropped from repertoire (stopped buying your brand but remained in category), gained market purchases (new category occasions you captured), and lost market purchases (fewer category occasions affecting you).

In Saudi Arabia, switching behavior is amplified by the high proportion of promotional purchasing and the growing role of availability-driven choice in convenience channels. A consumer who switches to a competitor at a gas station because your brand is not stocked there looks identical in the data to a consumer who switched by preference. Distinguishing availability-driven switching from preference-driven switching requires channel-level analysis.

The set of brands a consumer purchases within a given category during a defined period. A buyer with a repertoire of 3 brands is purchasing from three different brands. Repertoire size reflects the degree of brand loyalty versus variety-seeking in a category.

Average repertoire size varies by category. In Saudi Arabia, dairy categories typically show repertoire sizes of 2.5-3.5 brands. Carbonated soft drinks: 2.0-2.8. Laundry detergent: 1.8-2.2 (higher loyalty). Snacks: 4.0-5.5 (high variety seeking).

Repertoire size is increasing in most FMCG categories, driven by channel proliferation. Consumers who were sole buyers in supermarkets become multi-brand buyers when convenience and delivery channels offer different assortments. This is not declining loyalty in the traditional sense. It is channel-driven repertoire expansion, a phenomenon that is invisible without multi-channel measurement.

A multiplicative adjustment applied to panel data to account for known systematic biases in diary-based collection. Panelists tend to under-report certain purchase behaviors: small impulse buys, routine staples, and categories perceived as low-status. Correction factors adjust for this under-reporting.

Correction factors are category-specific. Snack categories are typically under-reported by 10-20%. Staples like rice and cooking oil are under-reported by 5-10%. High-engagement categories like baby care are over-reported by 5-8% (diary compliance is higher when the purchase feels important).

The correction factor chain in a mature panel can be multi-layered: region correction, SEC correction, nationality-by-category correction, channel correction, and diary correction. Each layer addresses a specific bias. The total correction is the product of all layers.

The difference between a brand's household penetration and the total category penetration. If a category has 70% household penetration and your brand has 25%, the penetration gap is 45 percentage points. These are the households that buy in your category but not from you.

The penetration gap is the brand's addressable growth ceiling within the existing category buyer base. Closing the gap requires understanding who these non-buyers are (demographics), what they buy instead (competitive switching), and where they buy (channel).

In MENA markets, the penetration gap is often artificially narrowed when measured on in-home data alone. If out-of-home data reveals that the true category penetration is 80% (not 70%), the brand's gap is actually 55 points, not 45. The growth opportunity is larger than the data suggests.

The mathematical breakdown of a brand's value growth into its component drivers: penetration change, frequency change, volume per occasion change, and price change.

The formula: Value Growth = Penetration Change + Buy Rate Change + (Penetration x Buy Rate interaction). Buy Rate itself decomposes into Frequency Change + Volume/Occasion Change + Price/Unit Change.

Growth decomposition answers the most important strategic question: why did we grow (or decline)? A brand that grew 10% through penetration gains has a fundamentally different outlook than one that grew 10% through price increases. The first is gaining buyers. The second is extracting more from existing buyers.

In MENA markets, the price component of growth decomposition requires careful interpretation. Currency effects, VAT changes, and subsidy removals can drive price-led growth that has nothing to do with brand strategy.

The percentage of households in the measured universe that purchased at least once in a given category or brand during a defined time period. This is the most fundamental metric in panel analytics. It answers the question: how many households buy this product?

In Saudi Arabia, household penetration must account for the 68/32 Saudi-to-expat split. A category at 55% penetration nationally might be 65% among Saudi households and 35% among expat households. The distinction matters because the two populations have different basket compositions, brand preferences, and channel behaviors. Reporting a blended penetration number without the nationality break hides more than it reveals.

Penetration also depends entirely on the measurement perimeter. A category measured in-home only may report 42% penetration. The same category measured across all channels, including out-of-home, may report 54%. Both numbers are mathematically correct. Only one reflects reality.

Iterative Proportional Fitting. A statistical technique used to adjust panel data so that the sample matches the known population distribution across multiple demographic dimensions simultaneously. In practice, it assigns a 'weight' to each panelist household that reflects how many households in the national population that panelist represents.

In Saudi Arabia, IPF must balance four dimensions: region (10 strata), socioeconomic class (4 levels), nationality (Saudi, Expat Arab, Expat Non-Arab), and household size (1-3, 4-5, 6+). The 68/32 Saudi-to-expat split is unique to the Gulf region and creates weighting challenges that no Western panel system was designed for. Labor camp populations are excluded from the residential household universe, which shifts the effective ratio from the census 55/45 to approximately 68/32.

Weight capping is critical: no single household should represent more than 3-5 times the average weight. Uncapped weights produce volatile KPIs dominated by a handful of over-represented panelists.

The classification of a brand's buyers into four tiers based on share of category spend allocated to that brand.

Sole buyers (100% of category spend): They buy only this brand. The most valuable and defensible segment.

Primary buyers (50-99%): This brand gets the majority of their category spend. They are loyal but not exclusive.

Secondary buyers (25-49%): This brand is part of their repertoire but not the dominant choice.

Light buyers (<25%): Occasional purchasers. May be trialists, deal-driven, or filling availability gaps.

In MENA markets, sole buyer rates are generally lower than in Western markets. The combination of high brand availability, frequent promotions, and channel fragmentation encourages repertoire buying. A brand with 20% sole buyers in Saudi Arabia is performing at the top of its category.

Any FMCG purchase that is consumed outside the household. This includes gas station convenience stores, cafes, restaurants, vending machines, workplace purchases, and delivery app orders consumed at the office or on the go.

Out-of-home consumption accounts for 25-35% of total FMCG value in Saudi Arabia, yet no legacy panel in the region captures it at the item level. The gap is not uniform: carbonated soft drinks have 35-45% OOH exposure, while cooking oil has less than 5%. The strategic significance is that OOH channels are growing at 2-3 times the rate of in-home channels, meaning the unmeasured share of consumption is increasing every year.

The average number of purchase occasions per buying household in a defined time period. Frequency separates habitual consumption from occasional trial. A brand with 30% penetration and frequency of 8 per year has a fundamentally different commercial profile than a brand with 30% penetration and frequency of 2.

In MENA markets, frequency is heavily influenced by channel. A consumer who buys bottled water weekly from a supermarket and daily from a gas station has a true frequency 5-7 times higher than what an in-home panel would report. Traditional trade (baqala) also drives high-frequency, low-basket purchases that are systematically undercounted in diary-based panels.

The total population that the panel is designed to represent. Panel universe is not the number of panelists. It is the number of households or individuals in the country that the panelists' weighted behavior is projected onto.

In Saudi Arabia, the residential household universe is approximately 8 million. This excludes labor camps, diplomatic compounds, and institutional housing. The panel universe defines the denominator in every penetration calculation. If the universe is wrong, every KPI built on it is wrong.

The distinction between household universe and individual universe matters. Household panels project to ~8M households. Individual panels can project to ~35M residents. The weighting methodology and the interpretation of results differ fundamentally depending on which universe is used.

A framework for measuring new product launch performance. Trial rate is the percentage of households that bought the product at least once since launch. Repeat rate is the percentage of trialists who bought again.

The critical metric is not trial rate alone. It is the trial-to-repeat conversion funnel: 1st purchase to 2nd, 2nd to 3rd, 3rd to 4th+. Consecutive repeat rate (was the brand's most recent purchase a repeat of the immediately prior brand choice?) is a stronger predictor of long-term share than simple repeat rate.

In Saudi Arabia, trial rates for new FMCG products are typically 8-15% in the first six months. Of those trialists, 25-40% repeat. Strong launches exceed 40% repeat. Failed launches fall below 20%. The first repeat purchase is the make-or-break moment: if a trialist does not return within the first eight weeks, the probability of future purchase drops below 10%.

A brand's share of total category spend, expressed as a percentage. Value share answers the question: of every riyal spent in this category, how much goes to this brand?

Value share and volume share can diverge significantly. A premium brand may hold 15% volume share but 25% value share, reflecting higher price points. Conversely, a value brand may lead on volume but trail on value. In MENA markets, where price architecture spans from SAR 1 baqala sachets to SAR 50 premium formats, the gap between volume and value share is often wider than in Western markets.

Shares must always sum to 100%. In practice, the long tail of small brands is grouped into an 'Other' bucket to maintain clarity.

A brand's share of total category volume, expressed as a percentage. Volume is measured in standardized units: liters for liquids, kilograms for solids, units for count-based categories. Volume share strips out price effects to reveal physical consumption patterns.

In Saudi Arabia, volume share is particularly informative in categories where pack size proliferation is high. A brand may hold dominant volume share in 500ml formats but be absent in single-serve 200ml. Since OOH consumption skews heavily toward single-serve, brands with strong volume share in traditional pack sizes may have weak share in the fastest-growing format tier.

The percentage of category sales value accounted for by stores that stock a given brand. Unlike numeric distribution (which counts stores), weighted distribution reflects the commercial importance of the outlets carrying the brand. A brand stocked in 30% of stores that account for 70% of category sales has 70% weighted distribution.

In Saudi Arabia, weighted distribution must account for the traditional trade network. A brand with 95% weighted distribution in modern trade but 20% in traditional trade has a significant exposure gap. Since traditional trade accounts for 35-40% of FMCG value nationally, low traditional trade distribution leaves a substantial share of consumption unreachable.