[E-commerce & B2B Analysis]

We plan and manage advanced analytics projects dedicated to e-commerce and B2B to transform your business’s raw data into measurable decisions on marketing mix, customer segmentation, lifetime value, and funnel optimization. We work on cohort analysis, multi-touch attribution, customer segmentation, LTV/CAC, and predictive analysis — with methodologies built on standard tools (GA4, BigQuery, CRM) and custom models tailored to the real business. One goal only: to transform analytics from descriptive reporting (“what happened”) into predictive decision-making capability (“what should I do now to grow sustainably”).

Main Objectives

01
E-COMMERCE OBJECTIVES — COHORT AND LTV
- Build cohort analysis for acquired customer segments with retention, frequency, and revenue comparison over time
- Calculate actual Customer Lifetime Value (LTV) by acquisition channel, product category, and user segment
- Measure LTV/CAC ratio for paid and organic channels to identify where to scale and where to reduce investment
- Identify high LTV segments on which to build dedicated acquisition and retention strategies
02
E-COMMERCE OBJECTIVES — SEGMENTATION AND RFM
- Build RFM (Recency, Frequency, Monetary) customer segmentation to customize marketing communication
- Identify VIP, churn risk, sleeping, new customer segments with dedicated operational strategies for each
- Build data-driven personas based on actual purchasing behavior rather than qualitative assumptions
- Activate segments on CRM, marketing automation, and paid platforms for cross-channel personalization
03
E-COMMERCE GOALS — FUNNEL AND CONVERSION
- Analyze conversion funnels with drop-off per step, segmented by device, traffic source, and user segment
- Identify specific bottlenecks in the checkout, payment, and abandoned cart management process
- Build prioritized AB tests on improvement hypotheses with a structured evaluation framework
- Measure the impact of CRO optimizations with correct attribution on the measured conversion increase
04
B2B OBJECTIVES — PIPELINE AND SALES ALIGNMENT
- Build an integrated marketing-sales vision with lead attribution throughout the entire B2B sales cycle
- Measure conversion rate per funnel stage — MQL, SQL, opportunity, customer — with channel segmentation
- Identify B2B funnel bottlenecks with intervention priorities based on real impact on closed revenue
- Calculate cost per opportunity and cost per customer for marketing acquisition channel over the complete cycle
05
B2B OBJECTIVES — ACCOUNT-BASED ANALYTICS
- Analyze behavior by target account (not by individual lead) to support structured ABM strategies
- Measure aggregated engagement by account with scoring on marketing, sales, and content consumption touchpoints
- Identify high-potential accounts with behavioral signals (intent signal) and sales prioritization
- Build ABM-friendly dashboards for sales-marketing alignment on strategic accounts in the pipeline
06
MULTI-TOUCH ATTRIBUTION OBJECTIVES
- Implement data-driven, position-based, time-decay attribution models with comparison on actual revenue
- Measure assisted contribution of upper funnel channels (social, video, display) often undervalued by last-click
- Build real customer journeys with touchpoints mapped cross-device, cross-channel, and cross-session
- Allocate marketing budget based on real attribution evidence rather than last click or qualitative beliefs

How do we work?
19ADV’s operational framework
in Performance Marketing

Briefing and definition of requirements

We analyze the business model, target audience, and reference markets — B2C, B2B, and D2C — and define concrete objectives and measurable KPIs: listens, visits, leads, awareness, and cost per acquisition.

Free diagnostic audit

If you are already investing in Spotify Ads, we analyze your account to identify issues and opportunities in campaign structure, segmentation, audiences, and audio and video creatives.

Strategy definition

We build the campaign strategy starting from the objectives not from the available formats and we define the targeting budget, mix of formats and integration with the other active channels

Production and setup

Gestiamo o coordiniamo la produzione degli asset creativi script audio voiceover e video e configuriamo le campagne su Spotify Ad Studio con tracciamento preciso degli obiettivi

Ottimizzazione continua

We monitor performance and constantly optimize exposure frequency, targeting and creativity. No campaign is left running unsupervised.

Reporting and analysis

Periodic reports with the metrics that really matter: real coverage, ad completion frequency, traffic generated and cost per result, no vanity metrics, just useful data

E-commerce and B2B Analysis Campaigns

Methodological approach

Initial audit with data quality assessment, tracking completeness, and gaps compared to analytical objectives
Data architecture with GA4, CRM, e-commerce data, and ERP integrated into BigQuery or dedicated data warehouse
Custom analytical models on priority business questions with validation framework and continuous iteration
Dedicated dashboards for different stakeholders with drill-down on segments, channels, products, and analysis periods

E-commerce and B2B Analysis Services available

Cohort analysis, LTV/CAC analysis and customer segmentation with dedicated executive and operational dashboards
Funnel analysis and CRO with prioritized AB tests, structured evaluation framework and impact measurement
Account-based analytics and pipeline analysis for B2B with sales-marketing alignment and attribution along the cycle
Custom attribution models, marketing mix modeling and predictive analysis on churn, LTV and cross-sell

Integrated technology stack

GA4, BigQuery, GTM, and Looker Studio for basic data architecture and cross-source dashboard visualization
Enterprise CRM (Salesforce, HubSpot, Pipedrive, Microsoft Dynamics) integrated for complete funnel analysis
E-commerce platforms (Shopify, Magento, WooCommerce, BigCommerce) with structured data export and API
Dedicated tools for cohort, segmentation, and attribution: Mixpanel, Amplitude, Heap, Looker, Tableau

Frequently Asked Questions about E-commerce and B2B Analysis

They are critical for scaling decisions. Without cohort analysis, you don’t know if the customers acquired this month are performing better or worse than those acquired six months ago. Without LTV, you don’t know if increasing the CAC to scale a channel is sustainable or will lead you to lose on every acquisition in the medium term. These are the metrics that distinguish e-commerce businesses that scale with margin from those that grow in revenue but burn EBITDA — a huge difference in business sustainability.

Con dedicated data architecture. The classic GA4 attribution model of 30-90 days does not work for long B2B cycles. It is necessary to integrate GA4 with CRM (Salesforce, HubSpot) through offline conversion tracking and Enhanced Conversions for Leads, build real customer journeys on BigQuery with marketing and sales touchpoints, and apply custom attribution models with extended windows (180-365 days). It is more complex but necessary: without it, B2B marketing budget decisions are based on incomplete and misleading signals.

For a basic setup with cohort, LTV, segmentation, and executive dashboard: 4-8 weeks of work on an organization with already structured data. For advanced setups with custom multi-touch attribution, marketing mix modeling, or predictive models: 3-6 months, including data integration, model building, validation, and internal team training. Timelines mainly depend on the quality of the initial tracking: fragmented analytics setups require months of data cleaning before true advanced analysis.

No, but it is necessary to identify the essential data for the priority business questions. The principle is “minimum viable data”: we start with the core (GA4, CRM or e-commerce platform, main paid marketing data) and arrive at actionable insights in the short term. Advanced integrations (ERP, logistics, customer service, finance) are progressively added when business questions require them. Wanting to integrate everything immediately is the main cause of analytics projects that do not produce value in a reasonable time frame.

Yes, and it is the situation for most Italian companies. We work as an external analytics team building models, dashboards, and analyses that management can consult and understand without statistical expertise. For more mature organizations with an internal data team, the model changes: we support the team on specific projects, advanced models, or complex technical setups. Data science is not a prerequisite; clarity on the priority business questions is.

[E-commerce & B2B Analysis]

E-COMMERCE OBJECTIVES — COHORT AND LTV

E-COMMERCE OBJECTIVES — SEGMENTATION AND RFM

E-COMMERCE GOALS — FUNNEL AND CONVERSION

B2B OBJECTIVES — PIPELINE AND SALES ALIGNMENT

B2B OBJECTIVES — ACCOUNT-BASED ANALYTICS

MULTI-TOUCH ATTRIBUTION OBJECTIVES

E-commerce and B2B Analysis Campaigns

Methodological approach

E-commerce and B2B Analysis Services available

Integrated technology stack

Frequently Asked Questions about E-commerce and B2B Analysis

Are cohort analysis and LTV really that important for e-commerce?

How is attribution managed when the B2B sales cycle is 6-12 months?

How long does it take to build an advanced analytics system?

Do I need to integrate every business data for advanced analysis?

Can I perform advanced analysis without an in-house data scientist?