DATA MINING

Data mining for food and drink, structured around the decision

Forensic, hypothesis-led analysis of syndicated panel data, retailer data, internal data and proprietary sources, scoped specifically around the commercial question the work has to answer. Senior food and drink analysts on every project. Multi-source synthesis is the value, not single-source reporting.

Scope a data mining project

When the answer is in the data you already have, but no one has asked the right question of it

Most food and drink businesses have access to more data than they use. Syndicated panel reports landing every period (Kantar, Nielsen, Circana, retailer data, internal CRM, EPOS, loyalty, sales data). The standard outputs cover the standard questions. But the commercial decisions on the table are rarely standard questions, and the answer often lives in the cross-source signal that nobody has the time, the seniority or the food and drink specialism to surface.
Data mining is the structured, hypothesis-led interrogation of that data, run by senior food and drink specialists who know what to ask. The work is built around a specific commercial question, pulls from whichever sources answer it best (syndicated, retailer, internal, proprietary, public), and lands as a commercial recommendation rather than as a chart pack the team has to mine for itself.

It is not the right tool for every brief. If the question needs primary consumer evidence, you want U&A, segmentation or qualitative work. If the question is about cultural signal that has not yet landed in the data, social scraping is the better fit. If your team is already running structured analysis on the question and the issue is interpretation, an immersion may be the right move. We will tell you straight on the scoping call.

What we do differently

  1. Hypothesis-led, not exploratory dump

    Every project starts with a specific commercial question. The data work is structured around answering it, not around running every possible analysis to see what surfaces. This is the single biggest gap between us and exploratory data work, and the reason the deliverable lands as a recommendation rather than as a 200-slide chart pack the team has to mine for itself.

  2. Senior food and drink specialists, not generalist analysts

    The analysis is run by senior people who have spent time in the food and drink categories you operate in. The interpretive layer reads the data through what actually matters in this sector (occasion behaviour, category dynamics, channel realities, retailer relationships) rather than through a generic analytical framework that misses the sector-specific signal.

  3. Multi-source synthesis, not single-source reporting

    We pull from whichever data sources answer the question best: syndicated panel, retailer data, internal CRM and sales data, proprietary FIS Group sources, public filings, financial trade data. The synthesis across sources is where the value sits, because most commercial questions in food and drink cannot be answered defensibly from one source alone. Single-source reporting is what your syndicated panel already gives you.

  4. A commercial recommendation, not a chart pack

    Every project closes with a clear, commercially framed recommendation against the original brief. What we found, what it means for the decision, what we recommend you do next. Not 200 slides of charts for you to interpret yourself. The job is to help you make a better call, not to hand you the raw analytical material.

Six commercial use cases, written as scenarios a buyer will recognise from their own brief. The aim is for the reader to see their question on this list and self-select.

Pre-strategy fact-base building

Before a major strategic decision (range review, brand stretch, channel expansion, category entry), build a rigorous, multi-source data foundation that survives the scrutiny that comes from board, finance, retail buyer or investment committee challenge.

Hypothesis testing on category dynamics

You have a specific commercial hypothesis about what is happening in your category, your brand or your competitive set. Test it forensically against the existing data before committing to primary research or before making the commercial move.

Competitor performance interrogation

A forensic read of what competitors are actually doing in the data: penetration shifts, frequency changes, switching patterns, channel performance, regional variance. The structured analytical version of competitive review, built on what the panel and retailer data actually shows.

Internal data audit and activation

Make use of internal data (CRM, EPOS, loyalty, sales data) that has been sitting unused or under-interrogated. Senior specialists work across your internal sources alongside the external panel data, finding the patterns the in-house team has not had the time or specialist resource to surface.

Cross-source triangulation on a decision

A commercial question that no single data source can answer defensibly. Pull together the signal from syndicated, retailer, internal and proprietary data, triangulate the cross-source view, and land a defensible recommendation. The work that earns the seniority of the team running it.

Pre-investment or pre-launch evidence build

Build the structured evidence base behind a major commercial investment: an NPD launch, a market entry, an acquisition target, a brand stretch. The data work is structured for the audience that has to sign off (board, deal team, investment committee, retailer buyer) rather than for analytical internal consumption.

  1. Scoping call

    Twenty minutes on a call. You tell us the commercial question, the decision the work has to inform, the data you have access to internally and the timeline you are working to. We tell you whether data mining is the right tool, what data sources should be in scope, what depth makes sense, and roughly what it will cost.

  2. Data audit and source mapping

    We map the data sources available against the commercial question: which sources will carry the answer, where the gaps are, what internal data you would need to share for the analysis to be defensible, and what licensing or access arrangements are required for any external sources. Signed off before the analysis runs.

  3. Structured analysis

    Senior specialists run the analysis against the agreed source set, structured around the hypothesis or question from the scoping call. Not exploratory data mining for its own sake; structured interrogation built around what the work has to answer.

  4. Cross-source synthesis

    The senior team triangulates the analysis across sources, surfaces where the signal aligns and where it diverges, and reads what the integrated picture means for your commercial question. This is the work most single-source analytical providers skip, and the part most commercial decisions depend on.

  5. Debrief and recommendation

    A structured debrief session, in person or by video, where the senior team walks you through what was found and what we recommend. Followed by a clean, shareable deliverable scoped for the audience that has to act on the work (board, leadership, retailer buyer, deal team). No 200-slide chart pack. A defensible commercial recommendation, with the evidence behind it.

Five data layers. One integrated read.

The work draws from whichever data sources answer the commercial question best. The five layers below are the most common sources we pull from, integrated as one analytical view rather than separately reported.

Syndicated panel data and Retailer and EPOS data

Syndicated panel data
Kantar, Nielsen, Circana and equivalent panel sources. Penetration, frequency, switching, occasion data and category dynamics at the level the panel reports cover. The structured baseline most data mining work sits on top of.

Retailer and EPOS data
Retailer-shared data where available, EPOS performance data, retailer panel feeds. The shelf-level reality of what is selling, where, at what price, with what promotional support. The operational reality layer that syndicated panel often misses.

Internal data sources and Proprietary FIS Group sources

Internal data sources
Your CRM, loyalty, sales and operational data. Worked on under appropriate data sharing arrangements, with the analysis returned in a form the in-house team can continue to use beyond the project. The internal data layer most businesses under-interrogate.

Proprietary FIS Group sources
Our own category panels, syndicate studies, meal tracking and benchmarking programmes, where the licensing allows the data to be brought into the analysis. The proprietary layer adds depth on questions where the public and syndicated data has gaps.

Public and trade data

Financial filings, trade press, real estate filings, public consumer data, regulatory data. The structured public layer that complements the panel and internal data, particularly for competitive review and M&A-adjacent work.

Food and drink is all we do

We are not a generalist analytics consultancy that takes the occasional food brief. Food and drink is the only sector we work in. Our senior analysts know the categories, the panel structures, the channels and the retailer dynamics. The analysis reads the data through what actually matters in this sector, and the recommendations come back framed for the people who actually have to make the decision.
That focus is why we work with 11 of the UK’s top 40 food and drink brands.

Other ways to decode the category

Data mining is one tool in the broader Decode toolkit. Depending on the brief, one of these might be a better fit, or a stronger partner alongside the analytical work.

Data mining work that landed in real decisions

Three real data mining projects across different brief types.

FAQs

How is this different from what Kantar, Nielsen or Circana already provide?

Three differences. First, your panel provider gives you the standard reports against their data. Our work is structured around your specific commercial question, which often requires synthesis across multiple sources rather than reporting from one. Second, the interpretive layer: panel reports surface the data, our work reads it through senior food and drink specialism and turns it into a commercial recommendation. Third, the integration: we work across syndicated, retailer, internal and proprietary data together, which is the cross-source view your single-source panel cannot give you.

Why not just use our internal analytics team?

Most in-house analytics teams are already running at capacity on standard reporting cycles, and most are generalist rather than food-and-drink specialist. Our work is project-based, hypothesis-led and run by senior food and drink specialists who can spend the time on a specific commercial question that the internal team cannot afford to take from their standard workload. Often the strongest result comes from commissioning external data mining alongside in-house analytics, with the two working in parallel rather than in competition.

What data sources can you actually access?

Syndicated panel (Kantar, Nielsen, Circana and equivalents) where the licensing allows us to bring the data into the analysis. Retailer data where you have access and the sharing arrangement permits it. Your internal data (CRM, EPOS, loyalty, sales) under appropriate data sharing arrangements. Our proprietary data from category panels, syndicate studies, meal tracking and benchmarking programmes. Public and trade data. We scope the source set at the scoping call so the data audit is honest about what is genuinely accessible for your brief.

Do we have to share our internal data with you?

Only where the question genuinely requires it. Many briefs can be answered entirely from external sources (syndicated, retailer, proprietary, public). Where internal data is needed for cross-source triangulation, we work under formal data sharing arrangements with the appropriate confidentiality protocols. The senior team is small and named on the engagement, and the internal data is handled under the same protocols sophisticated buyers expect from any external analytical partner.

How long does a typical data mining project take?

Four to eight weeks from scoping call to recommendation is the typical window, depending on the depth of analysis, the number of sources in scope and the complexity of the commercial question. Compressed timelines are possible for hypothesis-testing work with a tight scope. Longer engagements are scoped for cross-source triangulation work and for pre-investment evidence builds where the scrutiny of the deliverable is high.

Can you work with messy or incomplete data?

Yes, within reason. Most real-world commercial data is messier than the standard panel report suggests, and a significant part of senior analytical work is making honest sense of incomplete or imperfect data. We will tell you straight at the data audit stage what the data can credibly answer and what it cannot. If the gap is too large to give a defensible recommendation, we will tell you and recommend primary research to fill the gap rather than over-claiming on the data we have.

What if we want to commission this as an ongoing programme?

Possible. Ongoing data mining engagements (typically as a retainer with agreed cadence) are scoped where the brief calls for continuous analytical work rather than one-off projects. Common for businesses going through a strategic transition, for commercial leadership teams who want a continuous external analytical layer alongside the in-house team, or for board-level decision support. Scoped at the start of the engagement.

What does the deliverable look like?

A structured written deliverable and a debrief session, scoped to the audience. For board, leadership and deal-team audiences, the deliverable is typically formatted as a structured commercial recommendation with the analytical evidence behind it. For analytical and insight team audiences, the deliverable includes deeper analytical detail and the working data sets the in-house team can continue to use. Format is agreed at the start.

Can this be used for M&A diligence?

Yes. Data mining work is run regularly for pre-investment and pre-acquisition evidence builds, and is scoped to sit alongside (rather than replace) the financial and legal diligence streams. The work runs under NDA where required. Most M&A and PE buyers commission analytical work as one workstream inside a broader diligence process, often pairing data mining with QSR Operation Review or sector-specific operational diligence.

How much does it cost?

Project-based, scoped around the source set, the analytical depth and the audience for the deliverable. Single-source hypothesis-testing work is the lowest entry point; multi-source pre-investment evidence builds with high audience scrutiny are the highest. We will give you a clear, all-in quote at proposal stage with no hidden extras, and we will tell you straight if your budget will not buy the depth your brief requires.

Got a commercial question the data should answer, but no one has asked yet?

Tell us the question, the decision the work has to inform, the data you have access to internally and the timeline you are working to. We will tell you whether data mining is the right answer, what data sources should be in scope, what depth makes sense and what it will cost. Twenty minutes on a call.

Senior food and drink analysts. Hypothesis-led, not exploratory. Multi-source synthesis. Specialists in food and drink, only.