Services

"Alessandro scaled Preply's data function and brought it to maturity. His contribution was vital to the company's success, and I would definitely recommend working with him." — Kirill Bigai, CEO & Founder

    • DATA CULTURE
      Deploy the right infrastructure. Leverage it to achieve Data Reliability and Governance.
      Build solid KPI Frameworks, Analytics, Models, Data Products. Make them available your organization to ultimately foster a culture of data-driven decision making.

    • TEAM'S ORG, HIRING & MENTORING
      Hire the right mix of talent and cultural fit. Make them motivated and empowered. Watch them grow into a spectacular team and drive a data culture across your org.

    • STRATEGIC CONTRIBUTION
      Find your company's next levers of growth by exploring opportunities across the Customer Journey. From Measurement, Attribution & Segmentation to Engagement, Monetization, Predictive Models, Personalization & AI.

    • PROSPECTS EVALUATION
      Receive an impartial, professional assessment on your prospects' product, data literacy, and maturity.

    • SUPPORT TO DUE DILIGENCE
      Ensure your investment decision is backed up by solid numbers and an accurate picture of the company's reality.

    • CONSULTING FOR PORTFOLIO COMPANIES
      Maximise your investments' outlook by backing them up with a solid data strategy and highly effective teams

    • Data Culture

    • Data Strategy

    • Hiring & Leadership

Why work with me?

Six Successful
Scale-up Cycles

A track record of building effective data teams, infrastructures, and processes to address tech companies' needs and make them data-driven

A Proven
Operating System

A fail-proof methodology,
perfected through years of continuous iterations across numerous tech startups and scaleups worldwide.

20 Years
Experience

Gained across multiple
industries and contexts
ensuring my OS will translate successfully to any new situation, even if unprecedented.

Rapid Value
Creation

An iterative approach that shrinks the time to value to a few weeks while creating a solid, scalable foundation for many years to come.

Case Studies

Case Study I: Typeform

  • Meeting the Challenge

    Problem: The typical challenges of a Series A Tech Company

    - Lack of data governance, accessibility, expertise, and business insights.
    - The team was flying blind and felt unable to identify new levers of growth.

    Solution: I was hired as the first data person

    - Built a Datawarehouse
    - Deployed Self-Service Analytics
    - Recruited a team of 20+ professionals
    - Hired and trained Senior Management
    - Defined a KPI framework
    - Introduced an Analytics Framework
    - Enabled Experimentation

  • Outcomes

    1. Produced a Unified Customer View

    Built upon a continuous stream of customer and product insights, proactively produced by the Data Team

    2. Gained Clarity on the Drivers of Retention

    Uncovered the multiple realities of retention, resolved the underlying complexity, segmented the base into customer personas, and identified their Jobs To Be Done.

    3. Unlocked the next phase of growth

    By refocusing the acquisition strategy towards more valuable segments, we unlocked the next phase of growth, and gave reassurance to the investors that the company did not have a retention problem.

  • “Alessandro built the Data Science function at Typeform and continuously improved both the architecture and talent in line with the company's needs. He is an accomplished manager capable of building high-functioning teams”

    ROBERT MUÑOZ, CEO & Co-Founder, TYPEFORM

Case Study II: Preply

  • Meeting the Challenge

    Problem: The typical challenges of a Series B Tech Company

    - Growth decelerating after the pandemic
    - Leaders did not trust the data
    - No Self-Service Analytics and Data Science platform
    - A small team that lacked structure and seniority

    Solution: I was hired as VP Data and implemented my Operating System

    - Scaled the team to 30+ professionals including senior management
    - Established the company KPIs and WBR/MBR
    - Revamped the Tech Stack
    - Deployed Self-Service Analytics
    - Deployed a Data Science Platform
    - Introduced multiple Analytics Frameworks
    - Incorporated B2B into the Data Model
    - Built several customer-facing data products

  • Outcomes

    1. Enabled the next phase of growth

    Increased Retention, ARPU & LTV by managing the transition to a Subscription-based business model and promoting the switch to a new definition of customer.

    2. Improved Unit Economics

    Thanks to the higher LTV and multiple optimizations in CAC fuelled by finer Marketing Measurement, Attribution, and Bidding, we overcame the plateau and restarted growing.

    3. Improved B2B customers utilization

    Integrated CRM data, built a KPI Framework, and built a customer-facing data product to give the clients visibility on the product utilization across their business. Boosting renewals and, ultimately, retention and revenue.

Consulting & Advising for VCs

Case Study III: Market One Capital (VC)

  • Meeting the Challenge

    Problem: Should we invest in company X?

    Evaluating a Data-centric product & start-up

    - Is this yet another LLM wrapper?
    - Is there a uniqueness of their offering? How easy it could be replicated?
    - Have they truly built a top-notch layer that none of their competitors has?
    - How are they positioned vs their competitors in the market? Where do they fit in the overall data landscape?
    - Are there any other ways to solve this problem?

    Solution: my prospects evaluation package!

    - Reviewed the company’s pitchdeck and website
    - Interviewed the founders
    - Collected all the information into a digestible report
    - Presented the conclusions back to the team
    - Conducted a Q&A session

  • Outcomes

    My client made a wise investment decision.

    They fully understood the prospect’s offering from the perspective of their target audience. They obtained a clear picture of product and market fit, based on objective information and expert advice, and felt confident about their final decision.

    Improved knowledge of data-centric products.

    My communication skills and pedagogic approach allowed me to translate complex technical concepts into easily digestible information. My client ended this process with a deeper understanding of this and similar technologies.

    Deeper understanding of the data competitive landscape.

    As a byproduct of this collaboration, we reviewed the prospect's competitors and contributed to building a clearer picture of the market and its players.