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Data Science

The prediction layer of your business.

In data, we trust. But data only matters when it changes a decision, and a decision only matters when it moves the business. Not dashboards for their own sake, but the models, predictions and custom systems that forecast demand, group your customers and tell you what to do next.

We turn data into an engine that predicts and acts, delivered with Inner Data, our dedicated data science company, and wired into everything else we do.

Inner Dataa dedicated team of data scientists
Predictforecast, cluster, score, recommend
Shipswired into the channel where it pays
ISO27001 certified company
Data is only worth what you do with it

From data to decisions to revenue.

Data science is not a science project. Its job is to change what you do and what you earn. Insight that never reaches an action is a cost, not an asset.

Work backwards from the question

We start from the business question, build the models and analysis that answer it, and put the result where a decision is made.

The whole chain, not just the models

Strategy, the data engineering that makes data usable, the analytics that make it visible, the machine learning that makes it predictive, and the activation that makes it pay. Most teams do one part. We connect all of them.

Delivered with Inner Data

Our horizontal engine: a Wise Pirates company of data scientists and engineers, under the same contract and security posture, building the models and handing off to CRM & HVA, Cloud and AI Enablement.

Because we are a performance company at heart, we build rigorous models and measure them on business outcomes, revenue, retention, efficiency, as well as on statistical quality. Any figures we share in a project are estimates and benchmarks, not guaranteed outcomes.

What we predict, cluster and model

What will happen next, and the models to act on it.

Analytics & Measurement tells you what happened. Marketing Mix Modeling tells you how your channels work together. Data science tells you what will happen next, at the level of the individual customer, campaign and product.

Plan on a forecast

Forecasting & time series

Demand, sales, revenue, traffic and inventory predicted from your own history and drivers like seasonality, price and promotions.

Spend before results

Predictive performance

Which creative, audience, keyword or channel is likely to win, plus lead scoring that ranks prospects before you spend.

Who to keep and grow

Customer & audience intelligence

Clustering and segmentation, cohort analysis, propensity, churn and customer lifetime value, so you know who to keep, grow and win back.

The next move

Recommendation & next best action

The right product, offer, message and moment for each customer, at scale, feeding personalization across site, CRM and media.

Who you actually moved

Uplift & causal models

Incrementality at the customer and audience level: not just who will buy, but who will buy because you acted. Channel-level incrementality is MMM.

Off the shelf cannot

Custom models & applied AI

Bespoke models for the problem standard tools cannot solve, including generative AI grounded in your own data.

A prediction is only valuable if it changes a decision. Every model here is built to reach one, and wired into the channel where it pays.

Where prediction pays

Same engine, different goal.

Grow the right customers

LTV + acquisition
  • Model lifetime value, cluster the base, define high-value audiences
  • Build acquisition audiences correlated with them, so CAC to LTV improves

Keep and grow customers

Churn + NBA
  • Churn and propensity with next best action
  • Retention driven by the individual, activated in CRM & HVA

Plan with confidence

Forecasting
  • Demand, sales, revenue and traffic you can plan stock, staffing and budgets around
  • Sharper as they learn from each cycle

Protect margin

Price & promotion
  • Price and promotion response models show what a discount actually buys
  • So promotions stop leaking money on customers who would have bought anyway

The model changes; the discipline, a rigorous model wired into the channel where it acts, does not.

From question to production

Good models need good foundations.

Data strategy & consulting

We translate your goals into a data problem worth solving, with a plan, a business case and an honest read of what your data can and cannot support yet.

Data engineering & foundations

Data lake and warehouse on your cloud, integration and pipelines into one trustworthy source, and a first-party foundation, the raw material for every prediction, with our Cloud Services practice.

Generative AI on your data

LLMs grounded in your own information through retrieval, so people can ask their data plain-language questions and get trustworthy answers. Organization-wide AI lives in AI Enablement.

From model to production (MLOps)

Deploying, monitoring and maintaining models so they keep performing rather than degrade in silence, with predictions pushed where they act, into CRM, media and operations.

Governance, quality & privacy

Clean, documented data, clear ownership and definitions, and privacy by design with a first-party focus, backed by our ISO 27001 certification.

How we work. Understand the business then the data; prepare and model; deploy and evolve, measured against the business outcome and improved over time. The method is not bureaucracy, it is what turns clever into useful.

The horizontal engine

Wired into everything else we do.

Data science powers the rest of the agency, and hands off where a capability has its own home.

CRM & HVA →Where the audiences we model get activated. Cloud Services →The foundations the models run on. Analytics & Measurement →What happened, made trustworthy. Marketing Mix Modeling →Channel-level mix and budget. Programmatic & Agentic →Where predictions feed the media. AI Enablement →Organization-wide AI capability.
Why Wise Pirates for data science

Few connect data science to the marketing that turns it into money.

Business results, always

Our only motto. Judged on revenue, retention and efficiency, not on a model's accuracy alone.

Prediction that ships

Forecasts, scores and recommendations wired into the channel where they pay, not left on a slide.

A dedicated team, via Inner Data

Data scientists and engineers on a modern, GCP-first stack (BigQuery, Vertex AI, Python), with engineering partners for heavy builds.

Data plus marketing

We speak both statistics and business, so models get used, not admired.

Secure by standard

An ISO 27001 certified company (PT010101, Bureau Veritas), with governance and privacy built in.

Selected work

Modeling craft, wired to a business outcome.

Lifetime value & high-value audiences, retail (Inner Data)

We modeled customer lifetime value, clustered the base into eight groups, defined the High Value Audiences from first-party data, then built marginal acquisition audiences correlated with them. The CAC to LTV ratio improved by around 22 percent over a year.

First-party data foundation, retail (Inner Data)

A retailer's first-party data unified and made activation-ready, the base that feeds prediction, clustering and personalization. See innerdata.ai/cases/first-party-data-retail.

Predictive modeling, retail (Inner Data)

Custom models built with Meta Robyn and Google Meridian, around 250 thousand euros of incremental revenue at zero extra spend. The full service lives on our Marketing Mix Modeling page. Client results reported by Inner Data, indicative not guaranteed.

Frequently asked questions

The questions we hear most.

What is data science, and what does it do for a business?
Data science turns raw data into decisions and revenue. It uses analytics, machine learning and applied AI to answer real business questions, predict what will happen and recommend what to do, so data drives better, faster decisions instead of filling reports nobody reads.
How is data science different from your Analytics & Measurement and MMM services?
They answer different questions. Analytics & Measurement tells you what happened; Marketing Mix Modeling tells you how channels work together; data science predicts what happens next, at customer, campaign and product level, and builds the models to act on it. The three work together.
Can you forecast demand, sales or revenue?
Yes. Forecasting and time series are core to what we do. Using your own history and drivers like seasonality, price and promotions, we predict demand, sales, revenue, traffic and inventory, so planning, budgets and stock rest on a forecast rather than a guess, and get more accurate as they learn.
Can you segment or cluster our customers and audiences?
Yes. We use clustering to find the groups that genuinely exist in your data, not just the ones you assumed, and turn them into segments you can act on across CRM and media. It is one of the fastest ways to make marketing more relevant and spend more efficient.
Can you predict churn, propensity or customer lifetime value?
Yes. Propensity models score how likely a customer is to buy, churn or upgrade, and lifetime value models estimate what each customer is worth over time. Together they tell you who to keep, who to grow, and how much a new customer is worth acquiring.
Can you help acquire more customers without lowering their value?
Yes. We model lifetime value, define your high-value audiences from first-party data, and build acquisition audiences correlated with them. This wins high-value new customers while protecting your average, which improves the CAC to lifetime value ratio.
Can you predict which campaigns, audiences or creatives will perform?
Yes. Predictive performance models rank creatives, audiences, keywords and channels by their likelihood to win, and lead scoring ranks prospects before you spend. It moves budget toward the highest-probability outcome instead of waiting for results to come in.
Can you build a custom model for a problem off-the-shelf tools cannot solve?
Yes, and that is often where we add the most value. When standard platforms cannot answer your question, we build a bespoke model on your data, including generative AI grounded in your information, and wire it into your media, CRM or operations so it actually gets used.
Do you build the data foundation too, or only the models?
Both. Most data problems are actually foundation problems, so we build the data lake, warehouse, integrations and first-party pipelines as well as the models. A model is only as good as the data under it, which is why we cover the whole chain rather than just the clever part.
How do you use generative AI and large language models on our data?
By grounding them in your own information. We connect LLMs to your data through retrieval, so they answer from your reality, not the open internet, and let people ask plain-language questions of it. Organization-wide AI capability lives in our AI Enablement practice.
How do you take a model from prototype to production?
Through MLOps: deploying, monitoring and maintaining models so they keep performing instead of degrading unnoticed, and pushing predictions where they act, into CRM, media and operations. Value comes when a model runs reliably and is measured on the outcome, not when it sits in a notebook.
What is Inner Data?
Inner Data is our dedicated data science company, a Wise Pirates spin-off. It gives projects real data scientists and engineers and a proven method, while the wider Wise Pirates team connects the work to marketing, media and business outcomes, so the data science actually gets used and pays.
Why use you instead of building it with our in-house data team?
Because we add what many in-house teams lack: the marketing and media connection, a proven method, and the capacity to ship and activate, not just model. We work alongside your team via Inner Data, handing models into the channels where they pay, to accelerate and de-risk what you started.
In data, we trust

Turn data into decisions, and decisions into revenue.

Tell us the question you need answered. We will build the model that answers it, and wire it into the channel where it pays.

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