By industry

Planning built for your category.

Every industry has its own planning complexity. CauSelf's AI-powered causal modelling adapts to the drivers, channels, and constraints specific to your category — so your forecasts reflect how your market actually works.

🍺 Beverages 🥦 Fresh Food 🏠 Consumer Goods 💊 Pharma & Health
🍺
Beverages

Alcohol, soft drinks, juice & RTD

Beverage planning is driven by weather, occasion, promotional mechanics, and retailer ranging decisions — factors that traditional tools reduce to a seasonal index. CauSelf models the actual drivers of your demand, giving you forecasts that respond to what's really happening in market.

Common planning challenges

🌡️
Weather-dependent demand that's hard to model
Hot-weather categories see 3–5× demand swings around temperature thresholds. Traditional seasonal indices miss the nuance — CauSelf uses actual temperature data as a causal driver.
🎉
Occasion and event-driven spikes
Sporting finals, public holidays, and cultural events create demand patterns that repeat but don't line up neatly with calendar weeks. Manually building event calendars into Excel is error-prone and slow.
📦
Complex promotional mechanics
Multi-pack, scan deals, gondola ends, and catalogue features all drive different levels of uplift. Without structured TPM, you're guessing at trade ROI and over-investing in promotions that don't pay.
🏪
Retailer ranging and distribution changes
New listings, delistings, and ranging resets create step-changes in demand that pure historical models treat as anomalies. CauSelf models distribution as a driver, not a distortion.
💰
Price elasticity across formats and channels
Grocery, on-premise, and food service each have different price sensitivity. Without channel-specific modelling, your pricing strategy is based on blended averages that misrepresent each channel's dynamics.

How CauSelf helps

✓
Weather as a forecast driver
CauSelf automatically ingests temperature and weather data as causal inputs, modelling the non-linear relationship between temperature thresholds and purchase behaviour for your specific products.
✓
Event and occasion modelling
Build structured event calendars for sporting seasons, public holidays, and cultural moments. CauSelf learns the uplift pattern for each occasion type and applies it forward to future events.
✓
Promotion ROI visibility by mechanic
CauSelf separates true promotional uplift from baseline demand by promotion type, channel, and retailer — so you know which mechanics work and which are destroying margin.
✓
Distribution-adjusted forecasting
Model weighted distribution as a demand driver. When a new retailer lists your product or reduces facing count, your forecast updates immediately rather than waiting for three months of error history.
20+
Causal demand drivers modelled simultaneously
8–13w
Typical time from assessment to live
Modules: Demand Planning TPM IBP RGM
🥦
Fresh Food

Dairy, produce, meat & chilled

Short shelf life, volatile supply, and perishability make fresh food the most demanding planning environment in FMCG. Over-forecasting means write-offs; under-forecasting means lost sales and empty shelves. CauSelf's causal models give you the accuracy needed to operate in the margins that fresh food demands.

Common planning challenges

⏱️
Short shelf life — zero tolerance for over-forecast
A 5% over-forecast in ambient can be cleared on promotion. In fresh, it becomes write-off. The cost of a poor forecast is immediate and visible.
🌿
Seasonal and weather-driven supply volatility
Growing seasons, rainfall, and temperature affect both supply volumes and consumer demand simultaneously — creating a two-sided planning challenge that most tools treat as a single variable.
🔄
Weekly and daily demand cycles
Fresh purchases are heavily day-of-week driven. Monthly forecasting models miss the intra-week patterns that determine shelf replenishment and waste.
🏷️
Retailer-driven markdown and clearance mechanics
Yellow sticker markdowns and end-of-life promotions distort historical demand data. Without cleaning this from your model inputs, your baseline is artificially inflated.
📊
New product and range launches with no history
Line extensions, seasonal specials, and format changes have no historical demand data. Analogue-based modelling in spreadsheets is subjective and inconsistently applied.

How CauSelf helps

✓
Daily and weekly granularity
CauSelf models demand at the day-of-week level, capturing the intra-week purchasing patterns that drive fresh food shelf requirements — not just monthly averages.
✓
Weather and seasonality as demand drivers
Temperature, rainfall, and season length are modelled as explicit causal inputs — giving you forward visibility on demand before the season starts, not after it's over.
✓
Markdown and anomaly identification
CauSelf's anomaly detection flags markdown-distorted data and excludes it from baseline calculations, ensuring your base demand model reflects genuine consumer pull rather than clearance activity.
✓
New product forecasting via analogue modelling
Select reference products as analogues for new launches. CauSelf applies the analogue's demand pattern to the new product's distribution profile, giving you a structured starting point rather than a guess.
Day
Minimum forecasting granularity
0%
Hidden data science required
Modules: Demand Planning IBP TPM
🏠
Consumer Goods

Grocery, FMCG, personal care & household

Grocery consumer goods sits at the intersection of complex promotional calendars, multi-retailer trade relationships, and competitive pricing dynamics. This is the environment CauSelf was built for — and where the gap between spreadsheet planning and integrated AI-powered planning is most visible.

Common planning challenges

📋
Promotional volume dominates — and is poorly measured
For many FMCG categories, 40–60% of volume moves on promotion. Yet most companies can't accurately separate promotional uplift from baseline demand — making ROI measurement impossible and planning inaccurate.
🤝
Sales and planning operating in silos
Account managers build promotional plans in Excel; demand planners build forecasts in a separate system. The two rarely reconcile until the S&OP meeting — by which time it's too late to change anything.
🏢
Multiple retailers with different promotional windows
Major retailers run non-overlapping promotional windows, each with different mechanics and margin structures. Managing these in spreadsheets creates version chaos and P&L misalignment.
📉
No single source of truth for trade spend
Trade promotion accruals, scan deals, and retrospective deductions sit in different systems. Finance can't reconcile them; sales can't trust them. CauSelf brings them into a single P&L view.
📦
New product and range management at scale
Consumer goods companies typically manage 500–5,000 SKUs across multiple customers. Lifecycle management — launch, growth, maturity, decline — cannot be done manually without major errors.

How CauSelf helps

✓
Integrated promotional planning and forecasting
CauSelf's TPM module feeds directly into the demand forecast — so when a promotional event is planned, the volume impact is immediately reflected in supply requirements and P&L.
✓
Automated promotion review and ROI measurement
CauSelf automatically reviews every post-event promotion, calculates true incremental uplift versus baseline, and flags promotions that are unprofitable — before you repeat them.
✓
Retailer-level P&L visibility
View gross margin, trade spend, and net revenue by retailer in a single screen. Understand which customers are profitable and which are consuming margin through excessive promotional funding.
✓
Sales-integrated S&OP built in
CauSelf's IBP process connects account managers, demand planners, and finance around a single number — eliminating the consensus reconciliation that consumes S&OP meetings.
59%
Of promotions typically unprofitable without measurement
1
Platform replacing 5+ planning spreadsheets
Modules: IBP TPM Demand Planning RGM
💊
Pharma & Health

OTC medicines, supplements & health products

Consumer health and OTC pharma combines the complexity of regulated products with the competitive dynamics of a consumer goods category. Demand is driven by cold and flu seasons, health trends, and promotional mechanics that are harder to model than most categories — but critical to get right.

Common planning challenges

🤧
Illness season volatility is unpredictable but not unknowable
Cold, flu, and allergy seasons vary significantly year to year in timing and severity. Planning on last year's peak is a guaranteed mismatch — CauSelf models leading indicators like reported illness rates and weather.
🧬
Product lifecycle complexity
Reformulations, generic competition, and regulatory changes create demand step-changes that have nothing to do with historical patterns. Traditional models can't distinguish between a trend change and a category disruption.
⚖️
Regulatory and compliance constraints on promotion
Therapeutic claims, advertising restrictions, and pharmacist-dispensed channels create unique promotional constraints. Trade planning must work within compliance boundaries that don't apply in other categories.
🏥
Multi-channel complexity — pharmacy, grocery, and direct
OTC products increasingly sell across pharmacy, grocery, and DTC channels with very different demand drivers in each. Channel mix shifts are hard to model and easy to miss in siloed planning systems.
📰
Media and PR-driven demand spikes
A news story about vitamin D or a social media trend can double category demand overnight. Traditional forecasting tools have no mechanism to anticipate or respond quickly to media-driven demand events.

How CauSelf helps

✓
Illness season modelling with leading indicators
CauSelf ingests reported illness data, temperature, and humidity as causal drivers — giving you a forward view on cold and flu season intensity weeks before it appears in sell-through data.
✓
Product lifecycle and event management
Model reformulations, generic launches, and competitive entries as structural breaks in demand. CauSelf's AI identifies where a new demand regime has started — and adjusts its forecast accordingly.
✓
Multi-channel demand decomposition
Build separate demand models for pharmacy, grocery, and direct channels — each with its own drivers — then aggregate to an ex-factory supply plan. Understand which channel is growing and why.
✓
Anomaly detection for demand spikes
CauSelf's AI flags unusual demand patterns in real time — allowing you to distinguish a genuine trend shift from a media spike that will normalise, and respond appropriately to each.
20
Causal drivers per SKU including illness rates
Real-time
Anomaly detection and alerting
Modules: Demand Planning IBP RGM
Across all industries

Every vertical gets the same AI-powered core

Industry-specific demand drivers, channel structures, and promotional mechanics — sitting on top of CauSelf's unified IBP, TPM, and RGM platform.

🧠
Causal AI & machine learning models
Up to 20 demand drivers per SKU — weather, pricing, promotions, seasonality, distribution, competitive activity — learned automatically from your sales data.
⚡
Automated anomaly detection
AI identifies unusual demand patterns and data quality issues before they corrupt your forecast — so your team focuses on decisions, not error-hunting.
📊
Automated promotion review
Every promotion is automatically reviewed post-event. CauSelf separates true lift from baseline demand and flags underperforming promotions before you run them again.
🔄
Continuous self-improvement
Models recalibrate as new data arrives. CauSelf identifies where forecast errors are occurring by SKU and customer, surfaces them as prioritised improvement opportunities.
💹
Integrated P&L and financial planning
Commercial, demand, and financial plans connected around a shared P&L. One number, one process — eliminating the consensus reconciliation that consumes S&OP cycles.
🚀
8–13 week implementation
From assessment to live and optimised in 8–13 weeks — not the 6–18 months of traditional enterprise planning tools. Your team runs it independently from day one.

Ready to see CauSelf in your industry?

Start with a free assessment. Our FMCG consultants will map your specific planning challenges to CauSelf's capabilities and show you the ROI opportunity before you commit to anything.

Book a free demo → Free risk assessment
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