Hello — I'm

HarshSingh

Senior Commercial Analytics Leader

Drop portrait at

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Portrait of Harsh Singh
CurrentlyQXO

Education

Master of Management Analytics · Smith School of Business, Queen's University · 4.07 GPA, 2nd in class.

I lead pricing transformation, customer-tier strategy, forecasting, marketing analytics, and net-revenue rebuilds inside large enterprises — operating across North America (Seattle, Virginia, Toronto) in B2B distribution, retail, telecom, and financial services.

Current

Leading a team of 15 through a multi-year pricing and forecasting transformation at QXO (NYSE: QXO) — Brad Jacobs' $11B North American building-products distribution platform, consolidating a fragmented industry. Scope: pricing strategy, customer-tier profitability, deal-desk governance, post-acquisition pricing-system integration, demand and inventory forecasting — partnering at SVP and VP level across pricing, engineering, applied science, marketing, and FP&A.

Previously

Canadian Tire · Zayo · TD · Bell · Deloitte

Recent track record

Figures are publicly stated on LinkedIn.

  • Canadian Tire

    $15–18M

    Annualised labour savings

    Held payroll-rate targets through tight financial seasons across $350M+ retail payroll covering Sport Chek & Mark's.

  • Bell

    $8–10M

    Incremental annual revenue

    CLV, segmentation, and pricing-elasticity models across 2.4M+ B2B customers on the Customer Value Measurement Platform.

  • TD Canada Trust

    $10M+

    Projected risk savings

    ML-based behavioural-monitoring framework across four retail business lines — Everyday Banking, Credit Cards, RESL, PS&I.

  • Deloitte

    $1M+

    Practice value

    Analytics SME contributing to NLP-based audience-optimisation and large-scale data initiatives for Fortune 500 clients.

Specialism

  • Customer-tier pricing redesign
  • Deal-desk governance
  • Post-acquisition pricing-system integration
  • Net Revenue / Revenue Growth Management frameworks
  • Demand and inventory forecasting
  • Marketing-mix and elasticity modelling
  • Customer intelligence — CLV, segmentation, churn
  • FP&A analytics partnership

01

The work, in five chapters

Most recent first. Each chapter: where the business was, what the work actually was, and what changed.

  1. Mar 2024 — present

    QXO

    Client Partner — Pricing & Commercial Analytics

    Establishing the operating system.

    • Team of 15
    • Tiger Analytics
    • Multi-year transformation
    + View work

    Setup

    QXO assembled an $11B North American building-products distribution platform from a fragmented industry — and a single pricing system to build, under Brad Jacobs' roll-up thesis.

    At this scale, the price the business actually collects is decided by master-data trust, branch autonomy, rebate logic, and override governance — long before any pricing model has a chance to matter.

    The work

    • Leading a team of 15 on a multi-year pricing and forecasting transformation — partnering at SVP and VP level across pricing, engineering, applied science, marketing, and FP&A.

    • Scope: pricing strategy, customer-tier profitability, deal-desk governance, post-acquisition pricing-system integration, demand and inventory forecasting.

    • Designing the post-acquisition pricing-system integration playbook for the roll-up — built against QXO's publicly announced M&A pipeline (Kodiak Building Partners) so each acquisition inherits a working price-realisation engine rather than absorbing the same integration cost on every deal.

    Outcome

    Building the analytics, governance, and decision layer that supports executive pricing decisions across the consolidated platform and the M&A pipeline behind it.

  2. Mar 2022 — Mar 2024

    Canadian Tire Corporation

    Principal Consultant — Workforce & Labour Analytics

    $15–18M

    in annualised labour savings

    • $350M+ payroll
    • Sport Chek · Mark's
    • CEO / VP / SVP partner
    + View work

    Setup

    Canada's largest multi-banner retailer was running $350M+ in retail payroll across Sport Chek and Mark's as a reporting line — variance a quarter-end surprise.

    Payroll moved from a reporting line to a managed strategic lever.

    The work

    • Led labour and workforce analytics across the $350M+ retail payroll.

    • Built forecasting, governance, and decision frameworks deployed directly with CEO Sport Chek, divisional VP Field Operations, SVP Finance, and FP&A.

    • Linked daily sales, traffic, and promotional signals through to department-level staffing decisions.

    Outcome

    Forecast accuracy
    +15–18%
    Payroll variance
    −5–7%

    Held payroll-rate targets through tight financial seasons without disrupting store flow.

  3. 2022 — 2025

    Zayo

    Strategic Advisor — Revenue Intelligence & Customer Analytics

    Unifying revenue and customer intelligence at $4B+ scale.

    • VP-level advisory
    • $4B+ portfolio
    • Allstream integration
    + View work

    Setup

    Zayo Group was running a $4B+ fibre and managed-services portfolio still absorbing the Allstream acquisition — sales enablement and FP&A building the customer-intelligence layer that turns whitespace and retention into named opportunities.

    Post-acquisition customer integration is a sales-enablement problem before it's a data problem.

    The work

    • Designed the customer-intelligence framework linking segmentation, lifetime value, and churn forecasting to sales enablement — targeting upsell and migration plays across newly acquired Allstream accounts.

    • Partnered with FP&A to modernise P&L and dispersion models, lifting forecast accuracy across revenue, COGS, and gross-margin variance by product line.

    • Advised the VP of Strategic Sales Enablement and senior leadership on pricing, product-migration, and whitespace-capture strategy — translating analytical findings into committed go-to-market actions.

    Outcome

    A unified customer-intelligence and revenue-forecasting layer giving executive leadership a single view of profitability, retention risk, and named-account upsell across the post-Allstream portfolio.

  4. Jan 2021 — Feb 2022

    TD

    Analytics Lead — Regulatory & Behavioural Risk

    $10M+

    in projected risk savings

    • Four retail lines
    • ML behavioural-monitoring
    • Compliance · Risk partner
    + View work

    Setup

    TD Canada Trust's Personal Banking — four retail business lines, high-stakes mandate to catch non-compliant behaviour before it became a regulatory event.

    Catching non-compliant behaviour before it became a regulatory event.

    The work

    • Led the regulatory analytics function across the Personal Banking network.

    • Built an ML-based behavioural-monitoring framework scoring employee and transactional patterns across four lines.

    • Surfaced anomalies rules-based controls missed.

    Outcome

    Business lines
    4
    Approach
    ML monitoring

    Surfaced risk before it materialised across Everyday Banking, Cards, RESL, and PS&I.

  5. Jul 2019 — Jan 2021

    Bell

    Marketing Analytics Specialist — Advanced Analytics

    $8–10M

    in incremental annual revenue

    • 2.4M+ customers
    • Salesforce + AWS
    • Standing platform
    + View work

    Setup

    Bell Business Markets managed 2.4M+ B2B customers on partial customer pictures — no unified view of value, churn risk, or pricing sensitivity.

    The platform became the standing analytical layer for B2B commercial decision-making.

    The work

    • Built Bell Business Markets' Customer Value Measurement Platform on Salesforce and AWS.

    • Designed CLV, segmentation, churn-propensity, and pricing-elasticity models running on the full 2.4M+ customer base.

    • Platform became the standing analytical layer for B2B commercial decision-making.

    Outcome

    Customer base
    2.4M+
    Models
    CLV · Churn · Elasticity

    Targeted marketing and pricing optimisation grounded in customer-level economics.

  6. May 2015 — May 2018

    Deloitte

    Management Consultant

    $1M+

    in annual practice value

    • Fortune 500 clients
    • Pricing · Risk · Marketing
    • Analytics SME
    + View work

    Setup

    Deloitte's analytics practice across pricing, risk, and marketing — Fortune 500 clients.

    Build the analytical fluency that translates business questions into models that actually run.

    The work

    • Delivered predictive-modelling, NLP, and BI solutions across pricing, risk, and marketing.

    • Recognised as analytics SME within the practice.

    • Contributed to NLP-based audience-optimisation and large-scale data initiatives that became reusable IP.

    Outcome

    Contributed as the analytics SME through NLP and large-scale data initiatives that became reusable IP.

Concurrent · Apr 2019 — present

Smith School of Business · Queen's University

Industry Advisor — Master of Management Analytics

Capstone project advisor, alumni mentor, and case-study contributor on pricing analytics, customer intelligence, and ML applications in commercial decision-making.

02

How I think about pricing

In complex B2B and multi-site businesses, price realization is not the output of a model — it is the output of the entire commercial system. Rep behavior, branch autonomy, customer-specific contracts, local market competitiveness, product and customer mix, rebates and filebacks, ERP and order-entry logic, master-data trust, and pricing governance all feed into the price the business actually collects.

The real work of commercial analytics is not building a better price recommendation. It is building the analytics, governance, and decision infrastructure that lets a business set, execute, measure, and improve pricing at scale.

What actually changes at scale

  1. Data fragmentation is the silent margin tax.

    When a distributor scales through acquisition, the cost basis a price is set against, the customer master prices are quoted to, and the rebate logic that lands at quarter-end no longer share a common shape across the platform. Margin variance gets blamed on pricing decisions when it actually originates in master-data divergence. Before pricing-model investment earns its keep, the platform needs a governed view of what 'the same customer, the same product, the same branch' actually mean.

  2. Customer pyramid leakage is structural, not behavioural.

    In B2B distribution, the largest-revenue customers tend to operate at the thinnest realised margin — not because reps are bad at pricing, but because the architecture rewards volume capture over price discipline at the top of the pyramid. Tier-based price floors and governed deal-desk overrides matter more than elasticity modelling when the dilution sits inside the rules the business already chose.

  3. Branch autonomy creates override drift.

    With hundreds of branches and the autonomy to set local price, the question is not whether overrides happen — they will — but whether the platform can see the pattern. Realised price becomes a function of override discipline more than list price, and pricing governance is where the leakage stops or doesn't.

  4. M&A integration is the recurring shape of the work.

    A roll-up business pays the data-fragmentation cost on every acquisition. The job is not to integrate one company but to build the playbook that absorbs the next, the one after that, and the one after that — so the pricing-analytics layer doesn't get rebuilt at each deal close.

Pricing is a commercial operating system. Operating systems are tuned, not modeled.

03

Let's talk

Open to conversations with operators, pricing leaders, analytics executives, recruiters, and investors working on commercial transformation in B2B distribution, industrials, retail, telecom, and PE-backed environments.