Digital laggers with cash
Digital laggers with cash
Identify firms with strong finances but lagging digital maturity, benchmarked vs peers.
Identify firms with strong finances but lagging digital maturity, benchmarked vs peers.
Why this matters
Why this matters
Most datasets show either financial robustness or technographic signals, not both. Consultants and SaaS teams need to know who has the money but lacks digital maturity.
This segment blends hard financials with a 5-dimension Digital Maturity Index (incl. AI), benchmarked against sector and country peers, and explained with traceable comments.
How Starzdata solves this
How Starzdata solves this
Collect solvency and turnover as financial anchors.
Score DMI across 5 consultant-inspired dimensions.
Normalize against sector & geography.
This segment is activated with a blend of trusted sources and your own inputs
AI reasoning
User Input
Curated APIs
Web intelligence
What you get:
What you get:
Transparent Laggard Scores with commentary.
Benchmarks vs sector and country peers.
Dimension-by-dimension DMI with evidence.
Finance + digital combined into actionable scoring.
CRM-ready conversion pipeline in 72h.
Sample data for this segment
| # | company name | country iso | website domain | years since incorporation | turnover latest | solvency score | cash reserves est | dmi strategy score | dmi strategy comment | sector dmi strategy avg | country dmi strategy avg | dmi technology score | dmi technology comment | sector dmi technology avg | country dmi technology avg | dmi ai data score | dmi ai data comment | sector dmi ai data avg | country dmi ai data avg | dmi cx sales score | dmi cx sales comment | sector dmi cx sales avg | country dmi cx sales avg | dmi security score | dmi security comment | sector dmi security avg | country dmi security avg | digital maturity index | digital lagger score | custom taxonomy tags | overall comment |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | NordicSteel AB | SE | nordicsteel.se | 35 | 210000000 | 82 | 45000000 | 35 | No digital leadership roles or digital strategy co... | 52 | 48 | 40 | Legacy CMS and ERP, no cloud adoption. | 55 | 50 | 20 | No AI-related job postings or AI language found si... | 45 | 42 | 38 | No self-service portals; site not mobile-friendly. | 50 | 47 | 52 | Basic HTTPS; no ISO or GDPR compliance banner. | 65 | 60 | 37 | 78 | ["Manufacturing","Industrial"] | High solvency and turnover but digitally 15–20 pts... |
| 2 | AgriFoods Italia SRL | IT | agrifoods.it | 28 | 89000000 | 79 | 16000000 | 30 | No formal digital roadmap or leadership role found... | 48 | 44 | 42 | On-prem ERP detected; no cloud migration. | 50 | 46 | 18 | No AI roles or projects mentioned; weak data pract... | 42 | 40 | 25 | Outdated site; no e-commerce or chat features. | 45 | 41 | 45 | HTTPS present, no cookie consent or compliance inf... | 60 | 58 | 32 | 81 | ["AgriTech","Food Processing"] | Cash-rich food SME, digitally 15–20 pts behind pee... |
| 3 | MedSys Diagnostics SA | FR | medsys.fr | 15 | 125000000 | 85 | 30000000 | 42 | Some digital leadership, but limited digital narra... | 55 | 53 | 48 | CMS outdated; basic CRM but no automation. | 58 | 54 | 25 | No AI-related jobs; minimal data practices detecte... | 50 | 48 | 40 | Website functional but no advanced CX tools. | 52 | 49 | 50 | HTTPS; no privacy/compliance certifications. | 62 | 60 | 41 | 74 | ["MedTech","Diagnostics"] | Profitable and solvent; digitally 10 pts below pee... |
| 4 | BalticChem OU | EE | balticchem.ee | 22 | 64000000 | 76 | 12000000 | 28 | No digital strategy or leadership detected. | 45 | 42 | 35 | Legacy systems; no SaaS detected. | 50 | 46 | 15 | No AI or data roles present. | 40 | 38 | 22 | Minimal digital CX; poor site usability. | 48 | 45 | 40 | HTTPS only; no compliance banners. | 58 | 55 | 28 | 84 | ["Chemicals","Industrial"] | Financially viable but 20 pts behind sector averag... |
| 5 | GreenBuild Polska Sp. z o.o. | PL | greenbuild.pl | 18 | 72000000 | 80 | 15000000 | 32 | No visible digital roadmap. | 48 | 46 | 37 | Basic CMS; no cloud adoption. | 50 | 47 | 20 | No AI or predictive analytics detected. | 42 | 40 | 30 | Site has contact form, no advanced CX. | 46 | 44 | 42 | HTTPS only; no GDPR/ISO compliance visible. | 59 | 56 | 32 | 79 | ["Construction","GreenTech"] | Turnover and solvency solid, but 15–20 pts below b... |
Each row represents one company, enriched with financial anchors (turnover, solvency, cash reserves) and a Digital Maturity Index (DMI) across five dimensions: Strategy, Technology, AI/Data, CX/Sales, and Security.
For every company, you see:
Core profile: company name, domain, country, age.
Financial strength: turnover, solvency score, estimated cash reserves.
Digital maturity: scores and short comments per dimension, benchmarked vs sector and country averages.
Composite view: overall Digital Maturity Index and a Digital Laggard Score combining finance and digital lag.
Commentary: plain-language rationale explaining why the company is cash-rich but digitally behind peers.
... or explore the segment structure:
... or explore the segment structure:
... or explore the segment structure:
Your questions on this segment, answered
Do you provide context beyond the score?
Do you provide context beyond the score?
How quickly can we activate a laggard segment?
How quickly can we activate a laggard segment?
Can these laggard scores be plugged into CRM or ABM tools?
Can these laggard scores be plugged into CRM or ABM tools?
How does Starzdata differ from financial or technographic databases?
How does Starzdata differ from financial or technographic databases?
Why does it matter to identify these firms?
Why does it matter to identify these firms?
How are the scores validated and kept reliable?
How are the scores validated and kept reliable?
Which dimensions are used to measure digital maturity?
Which dimensions are used to measure digital maturity?
How do you define a “digital laggard with cash”?
How do you define a “digital laggard with cash”?
