Case Studies

Hands-Free Data Pipelines

Problem: Teams spend significant portions of their week manually extracting data from legacy systems, third-party platforms, and disparate Excel files. This process is slow, prone to human error, and creates "data lag" where decisions are made on outdated information.

Solution: We build automated pipelines that connect these sources directly. The system performs all necessary calculations and transformations, delivering data into the required format without manual intervention.

Impact (time): Eliminates manual data prep entirely, reclaiming hours of team capacity and ensuring 100% data accuracy for downstream reporting.

Product Catalogue Maintenance

Product Catalogue Maintenance

Product Catalogue Maintenance

Problem: Managing thousands of D2C SKUs across multiple platforms is a massive administrative bottleneck, often resulting in inconsistent product data and slow speed-to-market.

Solution: A centralised user interface that aggregates information from across the business, allowing for bulk edits and real-time monitoring of the entire catalogue.

Impact (time and revenue): Significantly reduces the "time-to-live" for new products and allows a lean team to manage a massive catalogue with high precision.

Inbound stock reporting

Product Catalogue Maintenance

Product Catalogue Maintenance

Problem: Misalignment between Procurement and Operations often stems from fragmented data.

Solution: We developed a collaborative front-end interface that allows multiple teams to view and edit inbound stock projections in one place.

Impact (time): This creates a single source of truth for planning upcoming inventory while providing a clear feedback loop for reviewing previous weeks' performance.

Auto slides

Product Catalogue Maintenance

Auto slides

Problem: High-frequency reporting often leads to teams spending more time building slide decks than analysing the data within them.

Solution: By dynamically linking presentation layers directly to your data sources, we enable teams to generate recurring reports instantly.

Impact (time): This removes the "copy-paste" cycle and ensures that meetings are always based on the most current figures.

Multi-team collaboration interface

Problem: Critical data is often scattered across different departments, leading to disputes over which figures are correct. Without a shared workspace, teams lack visibility into each other's changes, leading to manual errors and misaligned strategies.

Solution: We build a central interface that aggregates data from all sources into a single, agreed-upon "source of truth." By adding controlled edit capabilities and automated validation rules, we ensure that team inputs are standardised and mistakes are flagged before they cause issues.

Impact (time and revenue): Removes the friction of cross-departmental coordination. Teams stay synchronised in real-time, reducing administrative "check-ins" and ensuring everyone is executing against the same data.

AI categorisation tool

Customer/Product life time value

Customer/Product life time value

Problem: Tasks like categorising thousands of keywords or products are traditionally handled by complex, brittle logic gates or expensive manual labour. These manual methods are slow and often plateau at around 80% accuracy.

Solution: We use AI as a "probability engine" to handle classification at scale. By giving the model a strict set of rules and a defined logical outline, we enable it to analyse, suggest, and execute categorisations in minutes rather than hours.

Impact (time): An Ads team that previously spent hours on manual keyword categorisation transitioned to an automated workflow. The system completed the task in a fraction of the time with higher consistency and scale.

Customer/Product life time value

Customer/Product life time value

Customer/Product life time value

Problem: LTV is a critical metric for growth, but it is notoriously difficult to calculate because it varies wildly between business models. Many companies rely on off-the-shelf formulas that don't reflect their actual customer behavior, leading to poor investment decisions.

Solution: We develop custom LTV models tailored to your specific business logic. By identifying the specific product drivers that indicate long-term value, we help teams shift their focus from low-margin transactions to high-value growth.

Impact (revenue): One client shifted their primary product focus based on these insights, resulting in a 60% increase in monthly revenue by prioritising high-LTV customers.

AI translation tool

Customer/Product life time value

AI translation tool

Problem: Expanding into new markets traditionally required specialised translation teams or expensive agencies, making localised content difficult to scale quickly or cost-effectively.

Solution: We implement AI-driven translation workflows that adhere to specific brand rules, such as tone of voice and protected product names. This allows for high-quality, localised content generation across multiple languages simultaneously.

Impact (time and revenue): Enables teams to localise large volumes of content at scale with 90%+ accuracy, significantly reducing the lead time and cost associated with international expansion.

Stock forecasting

Product level margin calculation

Product level margin calculation

Problem: Planning inventory for the year often relies on static spreadsheets that don't account for real-time growth targets or historical seasonality. Without a dynamic model, businesses risk either stockouts that lose revenue or overstocks that tie up vital cash flow.

Solution: We build forecasting models that integrate historical data with your specific growth KPIs. These tools are designed for regular planning sessions, allowing you to adjust projections on the fly and retrospectively analyse performance against your original targets.

Impact (time and revenue): Ensures inventory levels remain aligned with actual demand, reducing capital tied up in stock and ensuring you have the capacity to meet growth goals.

Product level margin calculation

Product level margin calculation

Product level margin calculation

Problem: High-level margin reporting often masks "leaks" at the individual SKU level. Without breaking down every variable cost, from shipping to returns, it is difficult to see which products are actually eroding your profits.

Solution: We develop granular calculation engines that attribute all costs to the specific product level. When mapped against Lifetime Value (LTV), this data allows you to pinpoint exactly where you are losing money and where you have untapped growth potential.

Impact (time and revenue): Provides the visibility needed to optimise your product mix, allowing you to prune unprofitable lines and focus resources on your highest-margin contributors.

Finance returns report

Excess inventory analysis

Excess inventory analysis

Problem: Processing and reporting on financial returns is a high-stakes, manual task that requires absolute precision. Slow response times and manual data entry errors in this area can lead to poor customer experiences and strained vendor relations.

Solution: We automate the returns reporting pipeline to ensure data moves through the finance department with elevated accuracy and speed. By removing the manual "middle-man," we create a streamlined flow that adheres to strict financial logic.

Impact (time): Significantly reduces processing times and eliminates reconciliation errors, improving both operational efficiency and customer trust.

Excess inventory analysis

Excess inventory analysis

Excess inventory analysis

Problem: Aged or excess inventory occupies warehouse space and prevents investment in new opportunities. However, without a clear view of the true carrying costs, it is difficult to know how aggressively to discount or promote these items.

Solution: We build analysis tools that assign specific holding costs to aged inventory. This highlights exactly which items are costing the most to keep, allowing your team to identify clear targets for clearance or promotions.

Impact (revenue): Releases tied-up capital and frees up physical capacity for new, higher-performing stock, ensuring your inventory remains fresh and profitable.

A/B Testing

Significance Testing

Significance Testing

Problem: Marketing and Product teams often debate which version of a campaign will perform better. Without a head-to-head test, the decision is usually made by the "loudest voice in the room," which doesn't always align with how customers actually spend.

Solution: We implement controlled experiments to test specific variations. For example, we might split your email database to test two different subject lines or discount structures to see which drives a higher Open Rate and Revenue per Email. Or, we might serve two different versions of a "Checkout" button to see which reduces cart abandonment.

Impact: One client tested a "Free Shipping" prompt versus a "10% Off" discount; by identifying the winner through an A/B test before a full rollout, they maximized their peak-season margins without sacrificing volume.

Significance Testing

Significance Testing

Significance Testing

Problem: A new landing page might show a 5% increase in conversion over a weekend, but is that because the page is better, or because of a specific Sunday promotion? Making permanent site changes based on "random noise" can actually hurt your long-term performance.

Solution: We use Python’s statistical libraries to calculate the mathematical certainty of your results. For example, if you are testing a new Product Detail Page (PDP) layout, we analyze the traffic volume and conversion delta to ensure there is a 95% or 99% probability that the improvement is permanent and not a fluke.

Impact: This prevents the "false start" of rolling out a feature that doesn't actually work, ensuring your development budget is only spent on changes that are mathematically proven to increase your bottom line.

Multivariate Testing

Significance Testing

Multivariate Testing

Problem: A customer's decision to buy is rarely based on just one thing—it’s a combination of the price, the hero image, and the shipping speed. Testing these one by one takes months and misses the "interaction effect" (e.g., a specific image might work great with a discount, but poorly at full price).

Solution: We design experiments that test multiple variables at once. For instance, on a Promotional Landing Page, we might test three different headlines, two different background colors, and two different "Call to Action" buttons simultaneously. Our models then identify which specific combination of those elements creates the highest conversion rate.

Impact: This allows for rapid optimization of complex pages. Instead of running three separate month-long tests, you get the "winning formula" in a single cycle, allowing you to scale your ad spend with confidence much sooner.