Reading a channel most data misses

Category growth drivers: penetration, frequency, and spend

Category growth drivers explained: penetration, frequency and spend, which data measures each one, and what store-level scan data can show.

Sep 16, 20264 min read

Competitive analysis with retail data, done responsibly

Competitive analysis with retail data should compare like with like and stay within ethical limits. How to do it fairly and use what you find.

Sep 14, 20264 min read

Trade area analysis: zip codes, density, and reach

Trade area analysis defines where a store's shoppers likely come from. How zip codes, density and reach work as tools, and where they fall short.

Sep 12, 20264 min read

Choosing test stores for a pilot

Test market selection for a retail pilot: how to choose test stores that represent the rollout, match them to controls, and avoid common traps.

Sep 11, 20264 min read

Data storytelling: turning a chart into a decision

Data storytelling starts with the decision a chart should inform. How to build one clear point, add the right context, and present it well.

Sep 9, 20264 min read

Writing a data-backed retail case study that holds up

How to write a retail case study that holds up: a clear setup, a fair comparison, honest limits, and results a skeptical reader can check.

Sep 7, 20264 min read

Reading the Census monthly retail report alongside POS data

The Census retail report estimates monthly sales across US retail. What it measures, how it differs from POS scan data, and how to read both.

Sep 5, 20264 min read

Economic indicators and retail sales: what connects them

Economic indicators and retail sales: which public measures matter, how to line them up with POS data, and where the connection breaks down.

Sep 4, 20264 min read

Trade-down behavior: how to read it in scan data

Trade down shows up in scan data as shifts toward lower price tiers, smaller packs or value brands. How to read it and what else looks similar.

Sep 2, 20264 min read

Shelf space allocation: the data behind a lost facing

Shelf space allocation decisions often come down to one facing. How to read velocity per facing and make a data-backed case to a buyer.

Aug 31, 20264 min read

Building a retail analytics team: roles and skills

Building a retail analytics team: the roles to hire first, the skills that matter beyond SQL, and how to set the team up to be useful.

Aug 29, 20264 min read

Data governance for CPG teams: who owns the numbers

Data governance for CPG teams: deciding who owns each number, who can change a definition, and how data gets used within its agreed limits.

Aug 28, 20264 min read

Sell-in vs. sell-through: reconciling shipments and scans

Sell in vs sell through: why shipments and scan data never match exactly, how to reconcile them, and what the gap says about inventory.

Aug 26, 20264 min read

Data feeds: how retail data reaches your systems

Retail data feeds move scan data from stores to your systems. Common delivery methods, what to check when a file arrives, and questions to ask.

Aug 24, 20264 min read

Charts that tell the truth: visualizing retail data

Retail data visualization done honestly: axis choices, chart types that fit the question, and the common traps that make a small change look big.

Aug 22, 20264 min read

Dashboards vs. reports: when each works for retail data

Dashboards vs reports for retail data: dashboards monitor what you already understand, reports explain what changed. How to choose between them.

Aug 21, 20264 min read

Seasonally adjusted vs. raw sales numbers

Seasonally adjusted data removes the usual calendar pattern to show the trend. When to use it, when raw numbers are better, and why it's revised.

Aug 19, 20264 min read

Confidence intervals: reading a range instead of a point

Confidence intervals give a range of plausible values instead of one number. How to read one in retail data and why the width matters.

Aug 17, 20264 min read

Statistical significance for brand managers

Statistical significance for brand managers: what a p-value says, what it doesn't, and why a significant result isn't always an important one.

Aug 15, 20264 min read

Data dictionaries: agreeing on what a number means

A data dictionary defines every field and metric so teams mean the same thing by the same number. What to include and how to keep it current.

Aug 14, 20264 min read

Data latency: how fresh does retail data need to be?

Data latency is the gap between a sale and when you can analyze it. How fresh retail data needs to be depends on the decision it supports.

Aug 12, 20264 min read

Data quality checks every retail analyst should run

Data quality checks to run before any retail analysis: completeness, duplicates, negative units, price sanity and store counts, with fixes.

Aug 10, 20264 min read

Tender mix data: what payment type can and can't tell you

Tender mix data shows how a store's sales split across cash, card and benefits. What it can tell you, what it can't, and how to use it responsibly.

Aug 8, 20264 min read

Why independent-store data doesn't extrapolate to chains

Chain vs independent data: why independent-store scan data describes its own channel and shouldn't be scaled up to stand in for chain retail.

Aug 7, 20264 min read

Store openings and closures: keeping a panel comparable

Store panel changes, like openings and closures, can make totals look like growth or decline. How same-store methods keep comparisons honest.

Aug 5, 20264 min read

Day-of-week and time-of-day patterns in store data

Time of day sales patterns need the right data grain. What day-of-week data shows, what hourly analysis requires, and common traps to avoid.

Aug 3, 20264 min read

Bodega staples: bread, milk, and eggs through a data lens

Bodega staples like bread, milk and eggs give analysts a steady baseline. What staples data can show about a store, and what it can't.

Aug 1, 20264 min read

Functional beverages: how to read a fast-moving category

The functional beverage category moves fast, with frequent launches and shifting definitions. How to read its sales data without being misled.

Jul 31, 20264 min read

Household essentials and the fill-in trip

Household essentials often sell on the fill-in trip, when something runs out at home. What that means for pack sizes, prices and reading the data.

Jul 29, 20264 min read

Refrigerated categories in small-format stores

The refrigerated category in small-format stores runs on a few cooler doors. How to read its sales data when space and out-of-stocks shape results.

Jul 27, 20264 min read

Regional preferences: reading local data without stereotyping

Reading regional preferences in retail data takes the right baseline and a check on store mix. A method that avoids stereotypes and weak stories.

Jul 25, 20264 min read

Immediate consumption categories in small-format retail

Immediate consumption products are bought to use right away. Which categories fit, where they sit in a small store, and how to read their sales.

Jul 24, 20264 min read

Shopping missions: stock-up, fill-in, immediate consumption

Shopping missions explain why a shopper walked in: to stock up, fill in, or buy something to use now. How each mission shows up in store data.

Jul 22, 20264 min read

Convenience vs. grocery: why the shopping trip differs

The convenience store shopping trip is short, specific and often unplanned. How it differs from a grocery trip and what that means for brands.

Jul 20, 20264 min read

Joint business planning with distributors

Joint business planning with distributors works when both sides share data and definitions. What to include, how to set targets and review them.

Jul 18, 20264 min read

Preparing an account review with retail data

How to prepare an account review with retail data: what the buyer needs first, which numbers to lead with, and how to present bad news.

Jul 17, 20264 min read

Sales territory planning with store-level data

Sales territory planning with store-level data: how to balance workload and opportunity across reps, and set visit plans you can defend.

Jul 15, 20264 min read

Sales forecasting basics using POS data

Sales forecasting with POS data: the inputs that matter, simple methods to start with, and how to judge a forecast before anyone relies on it.

Jul 13, 20264 min read

Price thresholds: what happens at round-number prices

Price thresholds are points where a price change seems to move sales more than expected. How to study them in scan data without fooling yourself.

Jul 11, 20264 min read

The post-promotion dip: reading what happens after a deal

The post promotion dip is the sales slump that can follow a deal. How to measure it against a baseline and what it means for promo planning.

Jul 10, 20264 min read

Price gap analysis: your brand against the category

Price gap analysis compares your unit price with the category and key rivals over time. How to set it up and what a widening gap tells you.

Jul 8, 20264 min read

Assortment optimization for small-format stores

Assortment optimization for small-format stores: how to use scan data to choose fewer items that still cover what shoppers come in to buy.

Jul 6, 20264 min read

Line extensions: what to check before adding a flavor

A line extension can grow a brand or just split its sales. The scan data checks to run before adding a new flavor, size or variety to the shelf.

Jul 4, 20264 min read

Product cannibalization: when a new item takes your own sales

Product cannibalization happens when a new item takes sales from your existing line. How to measure it in scan data and decide if it's acceptable.

Jul 3, 20264 min read

Incrementality: measuring sales that wouldn't have happened

Incrementality measures the sales a promotion or campaign actually added. Here's how to set a baseline, pick control stores and read results.

Jul 1, 20264 min read

Weighted distribution and ACV, explained

Weighted distribution and ACV explained: how to measure where your item is sold by store sales, and what to do with the gaps you find.

Jun 29, 20264 min read

Correlation vs. causation in retail data

Correlation vs causation in retail data: why two trends moving together prove little, and what it takes to show one thing caused another.

Jun 27, 20264 min read

Price, volume, and mix: decomposing a sales change

Price volume mix analysis splits a sales change into what came from price, units and product mix. A worked hypothetical shows each step.

Jun 26, 20264 min read

Unit price analysis: reading prices across pack sizes

Unit price analysis converts shelf prices to a common measure so you can compare pack sizes fairly and see how a category's prices are built.

Jun 24, 20264 min read

Index numbers: comparing categories of very different sizes

Index numbers in retail put categories of very different sizes on one scale. How to build an index, pick a base period and avoid misreading it.

Jun 22, 20264 min read

Moving averages and smoothing in sales data

Moving average sales figures smooth out daily noise. How to pick a window, what smoothing hides, and when to look at the raw numbers instead.

Jun 20, 20264 min read

Outliers in sales data: keep, cap, or flag?

Outliers in sales data can be errors or real events. A practical method for deciding when to keep, cap or flag an unusual store-day figure.

Jun 19, 20264 min read

The 4-5-4 retail calendar, explained

The 4-5-4 calendar splits the retail year into 13-week quarters of four, five and four weeks. Learn how it works and how to compare periods.

Jun 17, 20264 min read

Calendar effects: why an extra weekend changes a month

Calendar effects in retail sales explained: how weekday counts, holidays and month length move totals, and how analysts adjust for them.

Jun 15, 20264 min read

Year-over-year vs. month-over-month: choosing the comparison

Year over year vs month over month: what each comparison measures, when each one misleads, and how to report both without confusing readers.

Jun 13, 20264 min read

Normalizing store data so comparisons are fair

Retail data normalization adjusts raw sales for store count, selling days and panel changes so comparisons between stores and periods are fair.

Jun 12, 20264 min read

Product hierarchies: brand, category, subcategory, and item

A product hierarchy groups items into categories, subcategories and brands. Here's how the levels work and why definitions need to be written down.

Jun 10, 20264 min read

What is a UPC, and why is it the unit of retail data?

UPC level data explained: what a Universal Product Code identifies, why analysts build retail reports on it, and where it needs extra care.

Jun 8, 20264 min read

Knowing when a number is good enough

Precision has a cost and a point of diminishing return. How to decide when an estimate is sufficient for the decision.

Jun 6, 20264 min read

Automating a report without automating an error

An automated report repeats a mistake at scale and in silence. The controls that make automation safe.

Jun 5, 20264 min read

The analyst in a commercial argument

Analysis enters rooms where people have positions. How to contribute without becoming an advocate or being ignored.

Jun 3, 20265 min read

Working with a research agency

The agency relationship determines the quality of the output more than the brief does. What good looks like on both sides.

Jun 1, 20265 min read

Confidentiality and competitive sensitivity in shared analysis

Collaboration between a brand and a retailer involves information that matters to third parties. Where the lines sit and why.

May 30, 20265 min read

Sharing analysis with retailers and wholesalers

What you send to a trading partner becomes their view of you. What is appropriate to share, and what should stay inside.

May 29, 20264 min read

Reading a syndicated report critically

A syndicated report is a product with methodological choices inside it. The sections to read first and the questions they should answer.

May 27, 20265 min read

Sizing a market from a sample

Market sizing multiplies a measured sample by an assumption. Where the assumptions live and how to bound the answer.

May 25, 20265 min read

Assortment breadth against depth

A fixed shelf forces a choice between more lines and more facings. What the data can say about where the balance sits.

May 23, 20265 min read

Turning a finding into a recommendation

Analysis stops at what is true; a recommendation says what to do. The step between them has its own discipline.

May 22, 20265 min read

Building a category review pack

A category review is a retailer conversation, not a brand pitch. What belongs in the pack and what undermines it.

May 20, 20264 min read

Reading a competitor's launch

A competitor's new item is visible in the data before it is visible in the market. What to watch and what to conclude.

May 18, 20265 min read

Reading a regional rollout

A rollout produces data from markets at different stages. Reading it as one trend confuses stage with performance.

May 16, 20264 min read

Shelf share without a shelf audit

Physical shelf share needs observation. What sales data can and cannot say about it, and the proxies that are defensible.

May 15, 20264 min read

Forward buying by the trade

Distributors and stores buy ahead of a price move. What that does to shipment data, to scan data, and to the weeks afterwards.

May 13, 20265 min read

When not to run the analysis

Some questions should not be answered with data, and some should not be asked at all. Recognising both before work starts.

May 11, 20265 min read

Reproducibility in commercial analytics

An analysis that cannot be re-run is a claim nobody can check. What reproducibility requires in a commercial setting.

May 9, 20265 min read

Questions to ask before commissioning research

Commissioned research is expensive and frequently answers a question nobody needed. The questions worth asking first.

May 8, 20265 min read

Writing an analysis brief that gets answered

Most disappointing analyses were badly specified rather than badly executed. What a brief needs to contain before work starts.

May 6, 20264 min read

Visualising retail numbers without misleading anybody

Most chart problems in commercial analytics are honesty problems. The conventions that keep a retail chart readable and fair.

May 4, 20265 min read

Distribution voids and out-of-stocks: telling them apart

Zero sales in a store can mean never listed or temporarily unavailable. Distinguishing them decides whether the answer is sales or supply.

May 2, 20265 min read

Private label in the data

Private label is measured differently and behaves differently. What that does to share, to category totals, and to competitive analysis.

May 1, 20265 min read

Elasticity estimates and the confidence they deserve

An elasticity is a modelled number with a wide range. What it means, what corrupts it, and how to use one responsibly.

Apr 29, 20265 min read

Price gaps and the competitive set

A price gap is only meaningful against a defined competitive set. How gaps are measured, what they predict, and where they mislead.

Apr 27, 20265 min read

Forecast error: measuring it honestly

Every forecast is wrong; the question is how wrong and in which direction. The error measures worth using and the ones that flatter.

Apr 25, 20264 min read

Significance, confidence, and what a p-value does not say

Significance testing answers a narrow question. What it means, what it does not, and how to present uncertainty in a commercial setting.

Apr 24, 20265 min read

Test and control design for retail

Store-level tests are feasible in independent retail and usually badly designed. The decisions that determine whether a test can answer anything.

Apr 22, 20265 min read

Measuring incrementality without a holdout

A proper holdout is the cleanest test and it is often impossible. What the alternatives are and how much confidence each earns.

Apr 20, 20265 min read

Cohort analysis on transaction data

Cohorts group by when something started, not by what it is. How the idea applies to stores and items without shopper identifiers.

Apr 18, 20265 min read

Repeat rate and the repeat window

Repeat purchase is the clearest signal a new product works. Measuring it without shopper identifiers, and choosing the window honestly.

Apr 17, 20265 min read

Daypart analysis in small-format retail

A small store can behave like several different shops across a day. What daypart analysis shows and how to act on it.

Apr 15, 20264 min read

Trip missions: inferring purpose from a basket

Baskets carry evidence of why a shopper came in. What can be inferred, what cannot, and how to use the inference responsibly.

Apr 13, 20264 min read

Basket penetration and its limits

Penetration counts how many baskets contain an item. What it measures well, and the household question it cannot answer.

Apr 11, 20264 min read

Substitution analysis without loyalty cards

Independent retail rarely has shopper identifiers. What can still be learned about switching from transaction data alone.

Apr 10, 20264 min read

Delisting and the tail: reading a decline

A declining item may be losing shoppers or losing shelf. The measures that tell them apart, and what a tail is actually worth.

Apr 8, 20265 min read

New item ramp curves: what "normal" looks like

A launch's early weeks blend distribution build with trial. Reading a ramp correctly means separating the two before judging it.

Apr 6, 20265 min read

Distribution measures: what ACV and TDP actually say

Distribution is measured in several ways and they are not interchangeable. What each measure captures and where each misleads.

Apr 4, 20264 min read

Category definitions: whose category is it?

A category boundary is a choice, and different parties draw it differently. What that does to share, growth and every comparison.

Apr 3, 20265 min read

Attribute tagging and why taxonomies drift

Tags let a brand analyse the market its own way. They also decay, and a decayed taxonomy answers questions incorrectly.

Apr 1, 20264 min read

Item master hygiene: the unglamorous foundation

Attributes decide what every report can say. Why item masters drift, what breaks, and how to keep one usable.

Mar 30, 20265 min read

Reading a data quality report

Quality reports are published and rarely read. The fields that actually matter and what an unusual value implies.

Mar 28, 20265 min read

Reconciling two sources that disagree

When shipment data and scan data tell different stories, the gap is usually structural. A method for finding where it comes from.

Mar 27, 20265 min read

Data latency and what a fresher number costs

Faster data costs more and is worth less than people assume. How to decide what latency a decision actually needs.

Mar 25, 20265 min read

Restatements: when history changes

Published figures get revised. Why history moves, what a restatement policy should say, and how to keep an analysis reproducible.

Mar 23, 20264 min read

Suppression, minimum cell sizes, and small numbers

Small cells are unreliable and sometimes identifying. Why providers suppress them and what a buyer should do with a suppressed figure.

Mar 21, 20265 min read

Outlier handling: rules before results

Every dataset contains values that do not belong. Deciding what to do with them before seeing the answer is what keeps the answer honest.

Mar 20, 20265 min read

Coverage and representativeness are not the same thing

A sample can cover a lot of the market and still misrepresent it. What each term means and why the distinction decides credibility.

Mar 18, 20265 min read

Weighting and projection: turning a sample into a market

A panel measures its own stores. Projecting to a market is a modelling step with assumptions that decide the answer.

Mar 16, 20264 min read

Store churn, mix effects, and the number that is not growth

A panel that gains stores reports growth that is not market growth. How churn distorts totals and what a like-for-like basis fixes.

Mar 14, 20265 min read

Year over year, sequential, or moving total: choosing a comparison

The comparison you choose determines what you see. Three standard options, what each answers, and when each misleads.

Mar 13, 20265 min read

Seasonal adjustment: what it does and what it hides

Adjusted figures make periods comparable and remove information at the same time. When to use them and when to read the raw series.

Mar 11, 20265 min read

Time series decomposition for retail sales

A sales series contains trend, season, calendar effects and noise. Separating them is what makes a movement interpretable.

Mar 9, 20265 min read

Feature and display: inferring support from data

Scan data shows what sold, not what the store did. What can and cannot be inferred about promotional support, and why it matters.

Mar 7, 20264 min read

Halo and pantry effects around a promotion

A promotion moves more than the promoted item. Halo, pantry loading and the post-period dip all belong in the measurement window.

Mar 6, 20265 min read

Cannibalisation: when a launch moves its own volume

A new item that sells is not necessarily a new item that added anything. How cannibalisation is measured and why launches overstate.

Mar 4, 20265 min read

Base and incremental volume: separating the two

A promoted week's sales include what would have sold anyway. Separating base from incremental is what makes a promotion measurable.

Mar 2, 20265 min read

Why "average price" is three different numbers

Average price can mean price per unit, price per volume, or a weighted market average. They move independently and mean different things.

Feb 28, 20264 min read

Comparing across pack sizes: equivalised volume

Units sold means nothing across different pack sizes. What equivalised volume does, how it is built, and where it goes wrong.

Feb 27, 20265 min read

What a product identifier actually identifies

A UPC identifies a package, not a product. What that distinction does to reporting, and when an item is not the item you think.

Feb 25, 20265 min read

Data dictionaries: why definitions decide the answer

Two providers can report different numbers for the same market and both be right. The difference is usually in the definitions, not the data.

Feb 23, 20265 min read

The future of retail analytics, without the hype

A grounded look at the future of retail analytics: wider independent-channel coverage, faster pipelines, rising privacy expectations. No hype.

Feb 21, 20265 min read

Representative retail data: what it really requires

What representative retail data really requires in the independent channel: genuine coverage, panel stability, and honestly stated limits.

Feb 20, 20265 min read

Retail analytics glossary: plain-English definitions

A retail analytics glossary in plain English: short, skimmable definitions of the scan data, category, and KPI terms analysts use every day.

Feb 18, 20265 min read

Data-driven retail decisions: closing the loop

Data-driven retail decisions require a closed loop: observe, hypothesize, act, re-measure. Why insights stall, and how to keep them moving.

Feb 16, 20265 min read

Leading vs. lagging indicators in retail, explained

Leading vs. lagging indicators in retail: which metrics hint at what's coming, which confirm what happened, and how to put both to work.

Feb 14, 20265 min read

Monthly retail reporting: why cadence matters

Why monthly retail reporting is the industry's working rhythm: fresh enough to act on, stable enough to trust, and how to build the habit.

Feb 13, 20265 min read

Retail data for brokers: turning numbers into conversations

Retail data for brokers: how to build category-first stories, keep claims inside the data, and turn numbers into productive buyer meetings.

Feb 11, 20265 min read

POS network data: the quiet engine of market research

What POS network data is, how point-of-sale networks turn everyday store transactions into research-grade insight, and where the limits sit.

Feb 9, 20265 min read

Bodega retail trends: America's most underestimated format

Bodega retail trends deserve serious attention. Why the corner store endures, why market data long overlooked it, and what brands can learn.

Feb 7, 20265 min read

Choosing a retail data provider: questions worth asking

Choosing a retail data provider? The unglamorous questions about sourcing, sampling, and support that separate real partners from good demos.

Feb 6, 20265 min read

How to buy retail scan data: a getting-started guide

How to buy retail scan data for the first time: define the decision, match channel coverage, check granularity and cadence, then pilot small.

Feb 4, 20265 min read

Retail data privacy: what responsible looks like

What responsible retail data privacy looks like in practice: anonymized scan data, aggregation before publication, and the discipline behind both.

Feb 2, 20265 min read

Independent grocery: a channel distinct from the chains

Independent grocery data tells a different story from chain data. What defines the channel, how it behaves, and why brands should read it separately.

Jan 31, 20265 min read

Why small-format-first launches deserve a look

The case for a small format product launch: faster shelf decisions, earlier real-world reads, and scan data that turns corner stores into a test market.

Jan 30, 20265 min read

Retail media and closed-loop measurement, explained

Retail media measurement explained: what closed-loop means, where POS scan data fits, and what to ask before trusting ad-to-sales claims.

Jan 28, 20265 min read

Sales velocity benchmarks: comparing very different stores

How to build a fair sales velocity benchmark when stores differ in size, traffic, and assortment: normalization, comparable sets, and pitfalls.

Jan 26, 20265 min read

Category adjacencies: what sells together

Product adjacency analysis explained: how transaction data shows what sells together, and how to turn co-purchase patterns into placement decisions.

Jan 24, 20265 min read

Measuring brand loyalty without loyalty cards

How to measure brand loyalty with scan data when stores run no loyalty program: velocity stability, share durability, and the honest limits.

Jan 23, 20265 min read

Planogram compliance: what the data can flag

What planogram compliance means in independent retail, and the scan data signals that tell a field team which stores to visit first.

Jan 21, 20265 min read

Counter placement: why the register zone behaves differently

Why counter placement near the register behaves unlike any shelf in the store, and how CPG teams can read front-of-store performance in scan data.

Jan 19, 20265 min read

Pack size strategy: the economics of small-format stores

Why pack size strategy works differently in small-format retail: price points, cooler space, and what scan data shows about which sizes move.

Jan 17, 20265 min read

Pricing power: what repeat purchase behavior tells you

How repeat purchase behavior reveals CPG pricing power, and how scan data from independent stores shows whether volume survives a price increase.

Jan 16, 20265 min read

Share of shelf vs. share of sales: reading the gap

Share of shelf meaning, how it differs from share of sales, and what the gap between space and sales tells a brand manager in small-format retail.

Jan 14, 20265 min read

CPG benchmarking: measuring your brand against the channel

What CPG benchmarking means, why channel context beats internal targets, and how to compare your brand fairly using independent retail scan data.

Jan 12, 20265 min read

Retail data pipeline: from raw transactions to a report

How a retail data pipeline turns raw register transactions into a monthly report: capture, cleaning, coding, anonymization, aggregation, and review.

Jan 10, 20265 min read

Retail data sample size: what representativeness requires

Retail data sample size gets the attention, but representativeness decides whether the numbers mean anything. What to check beyond the store count.

Jan 9, 20265 min read

The retail data stack: where scan data fits

What a modern retail data stack includes, layer by layer, and why scan data from the register anchors the rest of the evidence.

Jan 7, 20265 min read

What is syndicated retail data? A working definition

Syndicated data definition in plain English: data collected once and sold to many, what it includes, why brands buy it, and where its blind spots sit.

Jan 5, 20265 min read

Store-level vs. market-level data: choosing your altitude

Store-level data and market-level data answer different questions. How to pick the right altitude for share, execution, voids, and trend reads.

Jan 3, 20265 min read

Neighborhood assortment data: why averages mislead

Why the "average store" is a fiction: how neighborhood assortment data works and how to analyze store-level variation responsibly.

Jan 2, 20265 min read

Urban retail data: why city stores read differently

Urban retail data reads differently because of store format, trip patterns, and density, not shopper mystique. How to read city stores fairly.

Dec 31, 20254 min read

SNAP and EBT retail data: what scan data can responsibly show

What SNAP and EBT retail data can responsibly show: aggregate, anonymized patterns in independent stores, and the ethical lines that shouldn't move.

Dec 29, 20255 min read

Shelf inflation measurement: a methodology primer

Shelf inflation measurement explained: matched items, mix effects, pack-size changes, and why average price is not the same thing as inflation.

Dec 27, 20255 min read

Holiday sales in convenience stores: reading demand

Why holiday sales in convenience stores follow their own pattern, which calendar traps distort comparisons, and how to define a fair holiday window.

Dec 26, 20255 min read

Weather impact on retail sales: signal vs. noise

How weather impact on retail sales actually works in scan data, why it's overused as an explanation, and three checks to run before crediting the forecast.

Dec 24, 20255 min read

Seasonality in convenience stores: reading the calendar

What seasonality in convenience stores really means, how calendar effects distort monthly comparisons, and how analysts separate trend from calendar.

Dec 22, 20255 min read

How to read a same-store sales report

How to read a same-store sales report: what the comp base means, why dollars and units differ, and how much weight one month should carry.

Dec 20, 20255 min read

Unit sales vs. dollar sales: reading the same month twice

Unit sales vs dollar sales: why the two diverge when prices move, and a short checklist for deciding which number to trust for which question.

Dec 19, 20255 min read

Retail KPIs explained: AUR, UPT, and basket size

Retail KPIs explained in plain English: what AUR, UPT, and basket size measure, how the three connect, and how to read them without overreacting.

Dec 17, 20255 min read

Distributor scan data: how wholesalers put it to work

Distributor scan data closes the gap between sell-in and sell-through: account priorities, distribution voids, catalog decisions, and sales conversations.

Dec 15, 20255 min read

Direct store delivery explained: how DSD works

Direct store delivery explained: how DSD routes work, which categories use them, and where retail scan data fills the gap between truck visits.

Dec 13, 20255 min read

Trade promotion in independent stores vs. chains

Trade promotion in independent stores works differently than in chains: funds travel through distributors, execution varies, and scan data shows both.

Dec 12, 20255 min read

Snack category management in small-format stores

Snack category management for small-format stores: placement zones, assortment width, price points, and a simple review cadence an owner can run.

Dec 10, 20255 min read

Beverage category decisions in small-format stores

How beverage category decisions get made in small-format stores: door allocation, single-serve packs, and velocity per facing, explained step by step.

Dec 8, 20255 min read

Category management basics for small-format retail

Category management basics for small-format retail: a five-step cycle for assortment, pricing, and placement that fits stores without a planogram team.

Dec 6, 20255 min read

Promo vs. everyday pricing: separating the two in data

Promo vs everyday pricing: why blended average price misleads, and how to separate base price from deal price in retail scan data.

Dec 5, 20255 min read

Measuring promotion effectiveness: did the deal work?

A practical guide to measuring promotion effectiveness: build a clean baseline, measure lift in units and dollars, and check the weeks around the deal.

Dec 3, 20255 min read

New product launch tracking: the first 90 days

New product launch tracking, step by step: what to watch in the first 90 days, from distribution build to velocity and early repeat signals.

Dec 1, 20255 min read

Out-of-stocks: measuring the sales you never saw

Out of stock measurement with scan data: how zero-scan gaps reveal missed sales, why averages hide the problem, and how to estimate the loss.

Nov 29, 20255 min read

Distribution voids: finding stores where your product isn't

Distribution voids explained for CPG teams: how scan data finds stores that sell your category but not your brand, and how to size the gap.

Nov 28, 20255 min read

SKU velocity: reading the speed of the shelf

What is SKU velocity? How units per store per week measures the speed of the shelf, and how to use velocity without misreading it.

Nov 26, 20255 min read

Using scan data to understand price sensitivity

Price elasticity from scan data: how unit movement at different price points reveals sensitivity, and the confounders that ruin a careless read.

Nov 24, 20255 min read

What CPG brands can learn from corner stores

What corner stores teach CPG brands about independent retail: early demand signals, single-unit price behavior, and neighborhood-level variation.

Nov 22, 20255 min read

Market basket analysis: what baskets show that totals don't

Market basket analysis explained: how studying which items sell together reveals shopping occasions, placement clues, and promotion pairings.

Nov 21, 20255 min read

How point-of-sale scan data is collected and anonymized

How is scan data collected? Follow the path from a barcode beep at the register through cleaning, anonymization, and aggregation into reports.

Nov 19, 20255 min read

Why independent stores are a market data blind spot

Independent retail data is scarce in traditional datasets built on chain feeds. Here's why the blind spot exists and how scan data is closing it.

Nov 17, 20255 min read

The independent retail channel: what it is and why it matters

The independent retail channel explained: what counts as an independent store, how the channel differs from chains, and why brands should watch it.

Nov 15, 20255 min read

Same-store sales, explained

What is same store sales? A plain definition, how the comparable-store base is built, and why analysts trust it as a measure of real retail growth.

Nov 14, 20255 min read

POS scan data vs. consumer panel data: what each can tell you

POS data vs panel data: how scan data and consumer panels differ, what each measures well, and when an analyst should reach for one or both.

Nov 12, 20255 min read