Broker Identification Methodology
BKSE's first broker identification methodology — a seat-, event-, and account-level framework that translates ticketing behavior signals into confidence-graded broker buckets, anchoring targeted business actions on pricing, inventory, and fan-first access.
Why it mattered
Broker activity inside the season ticket member base creates a chain of business problems. For one franchise, brokers undercut primary pricing and conditioned demand to wait for cheaper secondary-market inventory — eroding price integrity and shifting purchases closer to game day. For the other, brokers captured upside the business could have monetized directly. Across both, broker activity diluted STM value, drove sales and service inefficiency, and weakened fan-first inventory access. Leaders needed a structured, evidence-based way to distinguish broker behavior from legitimate fan use — not anecdotal flags.
Built across three phases
Led the end-to-end development of BKSE's first broker identification methodology — a seat-, event-, and account-level framework that combined ticketing lifecycle, resale, transfer, attendance, and account-tenure signals into confidence-graded broker buckets. The work moved through three phases: defining the business problem and indicators, refining the population through manual validation and confidence scoring, and translating findings into section- and price-point-specific business actions.
Define the business problem and indicators
Established common definitions, identified the signal categories that distinguish broker behavior from genuine fan use (ticketing lifecycle, account behavior, and resale-market context), and set the initial flagging criteria.
Refine the population through validation
Layered additional metrics, manual cross-checks, and confidence scoring on top of the first-pass flags. The result: confidence-graded broker buckets that separate high-confidence broker activity from lower-confidence or legitimate-use cases.
Translate findings into business actions
Converted the analysis into section- and price-point-specific recommendations leadership could act on — not a leaderboard, but a defensible framework for targeted account decisions in specific seating areas.
The shape of the work
- 3 phases — problem definition, validation, business action
- 2 franchises — Brooklyn Nets and New York Liberty
- Multi-source — ticketing lifecycle, account behavior, and resale-market context combined into a single confidence-graded view
- 300+ industry professionals — methodology presented externally on the Russell-Scibiti call
How leaders use it
Anchors targeted account-level decisions, supports pricing and inventory strategy, and gives sales and service teams a defensible framework rather than anecdotal flags. The methodology was presented to senior leadership and later shared with 300+ sports industry professionals on the Russell-Scibiti call as a best-practice example of analytics-driven ticketing policy.
- Targeted account decisionsAnchors specific actions in specific seating areas and price points rather than blanket policy.
- Pricing and inventory strategyGives leadership a defensible view of where broker behavior concentrates and how that shapes pricing posture.
- Sales and service workflowsReplaces anecdotal flagging with a structured framework that sales and service teams can apply consistently to STM relationship decisions.
What this unlocks
A scalable operating model for ongoing broker detection — SQL-based infrastructure, repeatable definitions, validation steps, confidence buckets, and recommended workflows that extend across future seasons and into ongoing Archtics and Salesforce-integrated review processes. The methodology was shared with the broader industry on the Russell-Scibiti call as a best-practice example of analytics-driven ticketing policy — establishing both internal credibility and external positioning for the work.