Dizenly for Ecommerce & Retail
Ecommerce buyers move on a retail calendar, not a sales calendar. This playbook raises geography and outsourcing probability, softens the weight on company size, and schedules outreach to land before peak-season budget locks.
Who you're selling to: Head of Growth, VP Marketing, Ecommerce Director, Director of Merchandising. Tone: Fast-moving and seasonal. Budget appears and disappears around Q4, and a good pitch this week can be irrelevant by the next.
Ideal Customer Profile
This playbook- Roles
- Head of Growth, VP Marketing, Ecommerce Director, Director of Merchandising, Founder (DTC)
- Company size
- 10–800 employees
- Maturity
- Selling online at meaningful volume - has a checkout, a marketing budget and at least one paid channel running
- Geography
- US, UK, EU, ANZ - markets with mature card-not-present payment infrastructure
Buying signals
- Hiring seasonal or growth-marketing roles ahead of Q4
- Recently moved to Shopify Plus or a similar platform
- Raised funding for market expansion
- Running visible paid social or affiliate campaigns
Disqualifiers
- Marketplace-only seller with no owned storefront
- Wholesale/B2B only, no direct-to-consumer channel
- Store inactive for over six months
How the nine scoring factors get re-tuned
Default weights come straight from the platform's baseline model and total 100. The Ecommerce tuning keeps the same total and moves points toward whichever factors actually predict a good ecommerce prospect.
Role / decision authority
Default 20 ptsTuned 16 pts▼ -4Ecommerce decisions sit lower in a flatter org - a Head of Growth signs off nearly as often as a VP.
Industry match
Default 15 ptsTuned 14 pts▼ -1Retail-to-retail fit still matters, just marginally less than the default weight implies.
Services-to-needs alignment
Default 15 ptsTuned 18 pts▲ +3The right channel and campaign fit is what actually predicts a reply in this vertical.
Company size fit
Default 10 ptsTuned 6 pts▼ -4Revenue and headcount vary wildly for stores at the same growth stage, so size predicts less.
Geography match
Default 10 ptsTuned 14 pts▲ +4Payment infrastructure and seasonal calendars differ sharply by region, so market fit earns more weight.
Technology relevance
Default 10 ptsTuned 9 pts▼ -1Platform choice still matters, just marginally less than the default weight implies.
Outsourcing probability
Default 10 ptsTuned 12 pts▲ +2Many brands already outsource growth marketing, which correlates with a faster yes.
AI / digital transformation fit
Default 5 ptsTuned 6 pts▲ +1A modest increase - ecommerce teams are increasingly receptive to AI-assisted personalisation.
Contact info completeness
Default 5 ptsTuned 5 pts- no changeStorefront and marketing contacts are usually already easy to verify, so this stays at default.
Default total: 100 pts · Tuned total: 100 pts
The tuned four-day sequence for Ecommerce
Same four-step structure the platform uses everywhere - reviewed profile, engagement, connection, AI opener - paced and worded for this vertical.
- Day 1
Review profile & current campaigns
Note what they are running right now - a live campaign is a stronger hook than a static profile.
- Day 2
Engage with a recent product or campaign post
React to a launch or seasonal push, timed to land before their next campaign, not after it.
- Day 3
Send the connection request
Reference the specific campaign or channel, not the brand in general.
- Day 4
Send the AI-drafted opener
Prospects with a seasonal outcome - timed relevance beats a generic pitch here.
Sample prospects, re-ranked
Illustrative prospects showing how the ecommerce tuning moves a score against the same nine-factor model.
Ecommerce Director at a 90-person Shopify Plus DTC brand, hiring for Q4 growth roles
Default 76%Tuned 90%▲ +14 ptsVP Marketing at a 500-person enterprise retailer, wholesale-only, no DTC channel
Default 82%Tuned 61%▼ -21 ptsHead of Growth at a 25-person boutique brand expanding into the UK market
Default 68%Tuned 83%▲ +15 ptsFounder at a 15-person marketplace-only seller with no owned storefront
Default 70%Tuned 55%▼ -15 pts
PLACEHOLDER data: these companies, roles and scores are invented for illustration and are not real prospects.
Questions about ecommerce & retail
What this playbook weights differently, and why those factors predict better here than the defaults do.
Is this how you sell?
Bring your own list and we will run it against this playbook while you watch. Half an hour, and you will know whether the ranking matches your judgement.
Why does geography carry more weight in retail?
Because retail operations are regional in a way software is not. Fulfilment, regulation, seasonality and platform mix all vary by market, and a strong prospect in the wrong region is often a poor fit for what you actually deliver.
Who signs in a retail business?
Usually further down the org than the default model assumes, which is why seniority is softened here. Heads of ecommerce and operations managers frequently own the budget outright.
Does peak season change how the queue should run?
It changes the timing rather than the scoring. Nobody buys new software in the six weeks before their peak, and the queue is worth pacing around that rather than re-weighting for it.
Start here
Bring one export. Leave with a ranked queue.
Tell us how your team sources and qualifies today, and we will show you the scored, ranked, owner-assigned queue that comes out the other side - and what it replaces.
Not ready to talk? Start the 14-day trial on your own list instead.
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