gtmagency
gtmagency
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Previously ran growth for 500+ SaaS companies through their product launches. Our team has also worked with companies backed by
This playbook maps Accelerated's ideal customer profile across account, prospect, and persona dimensions, then lays out 5 signal-based outbound plays. Each play fires on a specific buying signal so your outreach lands the moment a prospect is most likely to act.
| Dimension | Qualified | Prioritization |
|---|---|---|
| Geography | United States, Canada, UK, Australia | Tier 1: US-headquartered brands with retail presence at Ulta, Sephora, Target, Walmart, CVS, or Nordstrom |
| Employee Count | 10 to 500 employees | Tier 1: 20 to 150 employees (growing brand, under-resourced data team); Tier 2: 150 to 500 (mid-market, replacing legacy BI tools) |
| Industry / Vertical | Beauty, personal care, hair care, fragrance, skincare, cosmetics, wellness CPG sold through retail | Tier 1: Prestige and masstige beauty brands; Tier 2: Hair tools and personal care appliances sold at retail |
| Key Qualifying Signal | Brand sells through 50 or more retail doors and receives EDI 852 or POS data feeds from those retailers | Tier 1: 200 or more doors across 3 or more retail accounts; Tier 2: 50 to 199 doors, single major retail account |
| Tech Stack | Retail Link, SPS Commerce, TrueCommerce, NetSuite, SAP, Shopify (DTC), any EDI provider | Prioritize accounts using manual Excel-based POS reporting or a generic BI tool not built for CPG retail |
| Revenue / Funding | $2M to $200M ARR or equivalent brand revenue; seed through Series C funded, or bootstrapped with retail distribution | Tier 1: $5M to $50M revenue, recently expanded retail distribution; Tier 2: $50M to $200M replacing incumbent tools |
| Intent Signals | Hiring for retail analyst or sales ops roles, new retail partnership announcements, recent funding rounds, attending Cosmoprof or BeautyX Summit | Prioritize brands that announced a new Ulta, Sephora, or Target launch in the past 90 days |
| Dimension | Qualified | Prioritization |
|---|---|---|
| Seniority | VP, Director, Senior Manager, Head of | Tier 1: VP or Director level with direct ownership of retail sales performance and reporting; Tier 2: Senior Manager influencing tool selection |
| Primary Titles | VP of Sales, Director of Retail Sales, VP of Trade Marketing, Director of Sales Analytics, Head of Retail, VP of Commercial | Start with VP of Sales and Director of Retail Sales as primary economic buyers |
| Secondary Influencers | VP of Marketing, CFO, Director of Supply Chain, Director of Demand Planning, VP of Operations, IT Director | Loop in CFO and Demand Planning when deal size exceeds $30K ARR |
| LinkedIn Keywords | retail POS data, EDI 852, sell-through reporting, retail analytics, Ulta buyer, Sephora account management, retail door expansion, trade promotion ROI, inventory replenishment, retail velocity | Prioritize profiles mentioning EDI 852, POS reporting, or sell-through analysis in their bio or recent posts |
| Persona | Level | KPIs | Related Challenges | Intent Signals | Related Benefits |
|---|---|---|---|---|---|
| VP of Retail Sales | VP | Retail door count growth, sell-through rate by door, replenishment order velocity, retail account revenue | Consolidating POS data from 10 or more retailer portals manually, no unified view of which doors are underperforming, slow replenishment cycles due to delayed data | New retail account launch announcement, posting about managing sell-through at scale, hiring a retail sales analyst | Centralized EDI 852 and POS dashboard across all retail accounts, daily sell-through visibility by door, faster replenishment triggers |
| Director of Sales Analytics | Director | Data freshness, report turnaround time, analyst hours saved, accuracy of sell-through forecasts | Spending 60 to 80 percent of time cleaning and normalizing raw POS files from each retailer, no automated reporting layer, manual Excel models break as door count scales | Job post for retail data analyst, posting about Retail Link frustrations, evaluating BI tools on LinkedIn | Automated POS data ingestion and normalization, pre-built retail analytics reports, eliminates manual file processing |
| Director of Trade Marketing | Director | Promo lift per door, ROI on field sales activations, in-store event performance, markdown efficiency | Cannot measure promo impact at the door level, promotional spend decisions made without sell-through data, slow post-promo reporting | Posting about beauty retail promotions, attending trade shows, searching for promo analytics tools | Door-level promo lift analysis, post-promotion sell-through reporting, field sales ROI tied to real POS movement |
| VP of Supply Chain / Demand Planning | VP | Forecast accuracy, out-of-stock rate, overstock write-down cost, replenishment cycle time | POS data arrives too late or too fragmented to drive accurate replenishment, leading to lost sales at retail or excess inventory | Hiring demand planner, posting about out-of-stock issues, evaluating supply chain tools | Near-real-time POS and EDI 852 data feeds into demand planning, automated low-inventory alerts by SKU and door |
| CFO / VP of Finance | C-Suite / VP | Revenue recognized from retail channel, trade spend as percent of retail revenue, gross margin by retail account, inventory write-down exposure | No unified view of retail channel profitability, trade spend tracked separately from actual sell-through results, manual reconciliation of EDI invoices | Attending CPG finance conferences, hiring FP&A analyst with retail experience, reviewing BI vendor contracts | Retail P&L visibility by account and SKU, trade spend tied to actual POS performance, single source of truth for retail revenue reporting |
Brands entering a new major retailer immediately face a flood of EDI 852 and POS data they have no system to manage at scale. We target VP of Sales and Director of Sales Analytics at these brands the week the launch is announced, when the pain is most acute.
LinkedIn post monitoring
Press release tracking
Brand news alerts
Enrich company profile
Find VP Sales and Analytics contacts
Verify email addressesIdentify Buying Committee
Email
LinkedInWhen a brand hires for a retail analyst, they are acknowledging a data gap they cannot solve manually anymore. We intercept the VP of Sales and Director of Sales Ops before they hire and make the case that Accelerated Analytics replaces that hire at a fraction of the cost.
Job board scraping
LinkedIn Jobs monitoring
Company hiring signalsEnrich hiring companyFind decision-maker contactsVerify and waterfall emailsEmailLinkedInBrands actively engaging with competitor content are already in market for retail analytics. We scrape followers and engagers of Accelerated's top three competitors and target the relevant personas with a differentiated pitch focused on beauty-specific depth and 20 years of category expertise.
Competitor LinkedIn followersCompetitor page engagers
G2 competitor review visitorsEnrich scraped profilesMatch to beauty brand accountsVerify contact emailsIdentify Buying CommitteeEmail
LinkedInBeauty industry trade shows concentrate the exact ICP in one place. We scrape attendee and exhibitor lists, enrich them against our ICP criteria, and run a time-bound sequence referencing the event to book meetings at or immediately following the conference.
Exhibitor list scrapingLinkedIn event attendeesConference hashtag monitoringEnrich attendee companiesFind VP and Director contactsValidate emails at scaleIdentify Buying CommitteeEmailLinkedInHigh-intent website visitors have already self-selected by researching the platform. We de-anonymize these accounts in real time, match them to known ICP beauty brands, identify the VP of Sales and Sales Analytics contacts, and launch a fast-follow personalized sequence within 24 hours.
Website visitor identification
Account-level de-anonymization
Intent signal enrichmentEnrich visiting accountFind buyer contacts at accountVerify contact emailsBuying CommitteeEmailYou'll have qualified demos booked in your calendar within 45 days: infrastructure, sequences, and live campaigns installed for you.
You don't pay a retainer. Everything we build belongs to you from day one: data, playbooks, and infrastructure.
If by day 45 you aren't seeing qualified demos booked in your calendar, we keep working completely for free until you do.
We build and install a fully automated, signal-based outbound system, outbound, ads, and content, in a 45-day sprint. No retainer, and you own everything. Demos booked in 45 days or we work free until they are.
