Analytics and business development

Gain complete control over the development of your business with modern data analytics. Monitor key indicators, make informed decisions based on reliable analyses, and respond swiftly to changing market conditions. Leverage the potential of data to enhance operational efficiency and achieve sustainable growth for your company.

Dzia?ania wspieraj?ce analityk? biznesow? i rozwój firmy

Analytics and business development

New approach to analytics and business development

We design comprehensive data architecture and analytics — from data ingestion (ETL/ELT), through data warehouses (DWH/Lakehouse), to intuitive dashboards and advanced decision models. This enables us to digitize the measurement and reporting process, allowing for ongoing optimization of operations and quicker, more accurate decision-making — from precise forecasts to effective customer segmentation. Such an approach helps enhance business efficiency and gain a competitive edge in the market.

Comprehensive data architecture

We are building solid foundations based on modern ETL/ELT solutions and data warehouses that ensure consistency and fast access to the necessary information.

Intelligent tools supporting decision-making

We create clear dashboards and advanced analytical models that shorten analysis time and enable dynamic process optimization and precise customer targeting.

How it works

We design data and analytics architecture: from data ingestion (ETL/ELT), through data warehousing (DWH/Lakehouse), to dashboards and decision models. This allows us to digitize measurement, optimize processes, and reduce decision-making time—from forecasts to customer segmentation.

Business benefits

Below, we present the key benefits that support your growth and optimize your operations.

Better decisions - reliable KPIs and reports in one place.

Time saving – automation of reporting and self-service BI.

Increase in revenue – better segmentation, cross-selling/up-selling, forecasting.

Lower risk - governance, access control, data audit.

Scalability – readiness for increasing volume and new sources.

Implementation process

The process of implementing modern data analytics consists of several key stages that ensure a comprehensive and effective deployment of a solution tailored to your company's needs. Below, we outline the successive steps — from analysis and technology selection, through design and execution, to testing, deployment, and ongoing support and system development. With this approach, we guarantee that the data will be reliable, timely available, and maximally useful for making informed business decisions.

Analysis

Inventory of sources, reporting requirements, and business decisions.
Solution validation

Selection of the stack (e.g., BigQuery/Snowflake, dbt, Looker/Power BI), quality policies.
Design

Data models, dictionaries, governance, KPIs, dashboard design.
Pricing

Implementation, licenses, maintenance (package options).

Execution

Data pipelines, models, reports, alerts.
Tests

Quality validation, access security, query performance.
Deployment

Training, roll-out for teams, monitoring.
Support

SLA, cyclical optimizations, model development (e.g., LTV, churn, propensity).

Frequently asked questions (FAQ)

What is "Analytics and Business Development" and what is its purpose?

This is a combination of data analytics with growth activities (marketing, sales, product). Organized data ? insights ? decisions ? growth initiatives (new channels, conversion improvement, retention, monetization). We work on KPIs/OKRs and close the loop: collect ? analyze ? test ? scale.

What does the collaboration process look like — from diagnosis to scaling?

1) Discovery (goals, ICP/personas, funnel, data sources) ? 2) Data and metrics audit ? 3) Architecture (DWH, ETL/ELT, models) ? 4) Dashboards and KPI definitions ? 5) Experiments/A/B tests and hypotheses ? 6) Automations and implementations ? 7) Review cycle and growth roadmap.

What KPIs and metrics do you recommend?

Depending on the model:
• E-commerce: ROAS/POAS, revenue, margin, AOV, conversion, LTV, returns.
• B2B: MQL/SQL, lead-to-SQL time, win rate, ACV, pipeline velocity.
• Product/SaaS: activations, cohort retention, churn, ARPU, LTV/CAC, NRR/GRR.
All KPIs have dictionary definitions and sources in the data catalog.

What tools and technologies do you use?

- Collection layer:GA4, server-side GTM, application events, CRM (HubSpot/Pipedrive), marketing automation.
- Warehouse (DWH):?BigQuery/Azure SQL/Redshift.
- ETL/ELT and modeling:?Fivetran/Hevo, dbt, Python.
– BI:Power BI, Looker Studio (reviews), sometimes Tableau.
- Experiments/CRO:Alternatives to Optimizely/Google Optimize and custom A/B frameworks.
- Attribution:why data-driven + rule-based with adjustments for the funnel model.
– Governance:data catalog, versioning models in GitHub/GitLab.

How do you integrate data from multiple sources (website, store, CRM, ads)?

We build connectors and streams (API, webhooks, CSV/S3), standardize event schemas (e.g., ecommerce_purchase, lead_submitted), load data into the DWH, and dbt creates consistent models (fact_, dim_). This way, the dashboards (Power BI) have a single "source of truth."

Do you assist with forecasts and strategic analyses (pricing, demand, TAM/SAM/SOM)?

Yes. We are preparing demand and revenue forecasts (ARIMA/Prophet/custom), price/elasticity analyses, customer segmentation (RFM, CLV), as well as market size (TAM/SAM/SOM) and GTM strategy (channels, offerings, funnels).

How do you conduct experiments and A/B tests?

We establish a hypothesis, metrics for success, sample size, and timeframe. Implementation of events, randomization, sanity checks, statistical analysis, decision memo. We implement results in sprints; unsuccessful variants are archived with conclusions.

How do you ensure data quality and compliance (GDPR, Consent Mode)?

Validation layer (dbt tests, anomaly monitoring), data contracts, metrics dictionary. Privacy: CMP, Consent Mode, field minimization, pseudonymization, RBAC/SSO access, audit logs. For elevated risk — DPIA. We combine marketing data in accordance with user consents.

What will I receive at the start of the collaboration (deliverables)?

Data and metrics map, backlog of growth initiatives, initial dashboards (management, marketing, sales), experiment schedule, definitions of KPI/OKR, and source integration plan. In the next phase: forecasts and automations (e.g., lead scoring, KPI alerts).

How long does it take and how to calculate ROI from analytics?

"Quick wins" (audit + core dashboards) usually take 2–4 weeks; integrations and models take 4–8 weeks; ongoing optimization is continuous. We calculate ROI as the increase in revenue/margin and/or the decrease in CAC/operating costs versus the project cost (tools + labor).

What is needed to effectively launch a project?

Access to accounts (GA4, GSC, Ads, Meta/LinkedIn, CRM), data exports (store/ERP), list of goals/KPIs and definitions, funnel structure, key product events, privacy policies/CMP, scope of permissions, and contact information for process owners.

Do you support operational business development (RevOps/enablement)?

Yes. We are standardizing definitions of leads (MQL/SQL), qualification rules, sales SLAs, contact playbooks, email/LinkedIn sequences, lead scoring and enrichment, as well as pipeline reports (velocity, bottlenecks) and sales forecasts.

Contact us

We’re happy to answer your questions and help resolve any doubts you may have!

ADDRESS

Jana Keplera 36, 80-299 Gda?sk

PHONE

+48 501 473 978

office@new.techfinity.pl

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Analytics and business development

Analytics and business development

New approach to analytics and business development

Comprehensive data architecture

Intelligent tools supporting decision-making

How it works

Business benefits

Implementation process

Analysis

Solution validation

Design

Pricing

Execution

Tests

Deployment

Support

Frequently asked questions (FAQ)

Contact us