Key Takeaways
- • Advent International has embedded data science across its entire investment lifecycle—from deal sourcing through portfolio value creation—treating data as a core competitive asset rather than a peripheral function.
- • Jesse Thomas, Head of Data Science at Advent, leads a dedicated team applying analytical capabilities to unstructured datasets (digital footprint data, hiring trends, consumer reviews) to identify investment opportunities faster and earlier in the diligence process.
- • Portfolio companies including Planet, Xplor, and myPOS have deployed AI-driven customer engagement models built by Advent's data science team, flagging declining customer engagement to improve retention rates and drive operational performance improvements in pricing, retention, and efficiency.
- • Advent leverages data monetization strategies across portfolio companies, helping them structure and analyze internal data assets—historically untapped—to unlock measurable value creation in customer analytics and business intelligence.
- • The firm's 40-year track record in investment expertise is now augmented by systematic data science capabilities, positioning differentiated insights as a core driver of performance across deal sourcing, evaluation, and scaling phases.
Summary
Advent International has positioned data science as a strategic priority across its investment platform, moving beyond treating data as a byproduct to embedding analytical capabilities into every stage of the deal cycle. Jesse Thomas, who joined Advent in 2024 as Head of the global Data Science team based in London, leads this effort to systematize how the firm sources, evaluates, and scales businesses using advanced analytics.
The rationale for this organizational shift reflects a fundamental market observation: while data availability has exploded, institutional investors struggle to convert raw information into actionable insight. According to Thomas, "we live in a data rich world, but there is a drought of insight." Advent's approach involves complementing its 40 years of accumulated investment expertise with dedicated mathematical and analytical capabilities, applying precision to areas where human judgment alone has historically prevailed.
The most visible impact appears in the diligence phase, where Advent's data science team analyzes large, unstructured datasets—including digital footprint signals, hiring trends, and consumer reviews—to uncover opportunities faster and with greater depth than traditional approaches permit. This capability extends into post-acquisition value creation, where many portfolio companies sit on untapped data assets. By helping these businesses structure, analyze, and act on internal data, Advent identifies performance levers in pricing optimization, customer retention, and operational efficiency.
A concrete portfolio application illustrates this approach: across companies including Planet, Xplor, and myPOS, Advent's team analyzed customer usage patterns to identify engagement decay, then built AI-driven models that flag when customer engagement trends downward. This capability enables portfolio company management to intervene before churn occurs, directly improving financial metrics. This pattern—identifying hidden data assets within portfolio companies and converting them to measurable business outcomes—represents a systematic approach to value creation that extends beyond traditional operational improvement playbooks.
The strategic implication for institutional investors is clear: firms investing in data science infrastructure during sourcing and diligence phases may identify opportunities earlier and with greater conviction, while simultaneously creating differentiated value creation levers at the portfolio level that competitors relying solely on operational expertise cannot match.
The rationale for this organizational shift reflects a fundamental market observation: while data availability has exploded, institutional investors struggle to convert raw information into actionable insight. According to Thomas, "we live in a data rich world, but there is a drought of insight." Advent's approach involves complementing its 40 years of accumulated investment expertise with dedicated mathematical and analytical capabilities, applying precision to areas where human judgment alone has historically prevailed.
The most visible impact appears in the diligence phase, where Advent's data science team analyzes large, unstructured datasets—including digital footprint signals, hiring trends, and consumer reviews—to uncover opportunities faster and with greater depth than traditional approaches permit. This capability extends into post-acquisition value creation, where many portfolio companies sit on untapped data assets. By helping these businesses structure, analyze, and act on internal data, Advent identifies performance levers in pricing optimization, customer retention, and operational efficiency.
A concrete portfolio application illustrates this approach: across companies including Planet, Xplor, and myPOS, Advent's team analyzed customer usage patterns to identify engagement decay, then built AI-driven models that flag when customer engagement trends downward. This capability enables portfolio company management to intervene before churn occurs, directly improving financial metrics. This pattern—identifying hidden data assets within portfolio companies and converting them to measurable business outcomes—represents a systematic approach to value creation that extends beyond traditional operational improvement playbooks.
The strategic implication for institutional investors is clear: firms investing in data science infrastructure during sourcing and diligence phases may identify opportunities earlier and with greater conviction, while simultaneously creating differentiated value creation levers at the portfolio level that competitors relying solely on operational expertise cannot match.
Go deeper than the headline
Research every firm in this story, track the ones that matter and get the daily briefing — free for 7 days.