Demand Forecasting
Predict what sells, when, and in what quantity.
Typical outcome:
Cut stockouts 15–20%. Free 20–35% of working capital from overstock.
6–8 weeks to first ship
7 operator-grade playbooks
AI that ships — not AI that slides.
Step-by-step AI implementation guides for Shopify and WooCommerce brands doing $1M–$20M. Built on 15+ years of retail AI and validated on 250+ live sellers.
TL;DR
- → These 7 playbooks cover every major AI use case for e-commerce: demand forecasting, recommendation engines, returns prediction, dynamic pricing, AI customer service, reconciliation automation, and AI product content.
- → Each playbook is a production-grade implementation guide with data requirements, a week-by-week timeline, cost ranges, common mistakes, and ROI models. Written for operators, not consultants.
- → TwoDots has built and shipped every use case in this library for real brands. The numbers are drawn from live deployments, not vendor case study marketing.
- 7
- Production AI playbooks
- 15+
- Years of retail AI
- 250+
- Live sellers tested
- 4–12 wks
- Typical first ship
The full library
Pick your highest-ROI starting point.
Each playbook solves a different part of the e-commerce P&L. Start with the one where you are losing the most money right now.
Recommendation Engines
Surface the right product to the right customer, automatically.
Typical outcome:
Lift AOV 8–15%. Increase repeat purchase rate by 20–30%.
6–10 weeks to first ship
Returns Prediction
Predict which orders will be returned before they ship.
Typical outcome:
Reduce net return rate 15–30%. Cut reverse logistics cost by up to 25%.
4–6 weeks to first ship
Dynamic Pricing
Price every SKU at the margin-maximising point, in real time.
Typical outcome:
Improve gross margin 3–8 percentage points. Reduce end-of-season markdowns.
8–12 weeks to first ship
AI Customer Service
GenAI agents that actually resolve tickets, not just deflect them.
Typical outcome:
Resolve 60–80% of tier-1 tickets without human touch. Cut response time from hours to seconds.
4–8 weeks to first ship
Reconciliation Automation
Automated payment reconciliation across Shopify, Amazon, and payment gateways.
Typical outcome:
Save 10–20 hours per week. Catch discrepancies before they become write-offs.
3–5 weeks to first ship
AI Product Content
Product descriptions, SEO copy, and listing content at catalogue scale.
Typical outcome:
Generate 500+ optimised listings per day. Improve organic search conversion by 10–20%.
3–4 weeks to first ship
AI impact by use case
Know what each playbook moves before you commit.
Not every AI use case fits every business at every stage. This matrix helps you identify where AI will move your P&L the most.
Business impact by playbook
Demand
Forecasting
Recommendation
Engine
Returns
Prediction
Dynamic
Pricing
AI Customer
Service
Reconciliation
Auto.
AI Product
Content
Revenue impact
Cost reduction
Data readiness req.
Time to first value
6–8 wk
6–10 wk
4–6 wk
8–12 wk
4–8 wk
3–5 wk
3–4 wk
Source: TwoDots analysis across the HappySellers platform, 250+ active e-commerce businesses. Revenue impact and cost reduction are relative scores, not absolute dollar values. Individual results vary by business size, data quality, and category.
High revenue impact + fast ship time
Demand forecasting and returns prediction. Start here if revenue leakage or working capital is your primary constraint.
High cost reduction + immediate ROI
Reconciliation automation and AI customer service. Start here if operational overhead is consuming margin without growing revenue.
How we ship a playbook
Five steps from kickoff to production.
Every playbook follows the same delivery structure. The use case changes. The rigour does not. We start with your data, not a template.
Most brands that try to build this internally spend 3 to 6 months before the first model reaches staging. We ship the first production milestone in 4 weeks because we have done this 250+ times.
Platforms we integrate with
Shopify WooCommerce Amazon Seller Central Flipkart Magento Custom ERP
01
AI Fit Sprint
Week 1We map your data, identify the right playbook, and deliver a production roadmap. You see the expected accuracy, cost, and ROI before we write a line of model code.
02
Data pipeline and baseline model
Weeks 2–3We connect to your data sources (Shopify API, database export, ERP), clean and engineer features, and build the first version of the model on your top SKUs or highest-impact segment.
03
Holdout validation
Week 4Before anything goes live, we run the model against a holdout period you have not seen. You see accuracy vs. your current baseline. You decide whether the result justifies production deployment.
04
Production integration
Weeks 5–6The model plugs into the tools your team already uses: Shopify apps, your WMS, your Slack workspace. No new dashboard. Outputs arrive where decisions are made.
05
Monitor, retrain, expand
OngoingWe set up drift detection and weekly retraining. You get a weekly accuracy report. Once accuracy targets are stable on the pilot scope, we expand to the full catalogue or next use case.
Why operators choose us
Every competitor has 1 of these. We have all 4.
Retail DNA
15+ years inside Kohl's and Sears building the AI systems you are trying to buy. We know that Q4 inventory locks in August. We know returns spike the second week of January. You either have that knowledge or you do not.
HappySellers as a live lab
Every AI technique in these playbooks was stress-tested on HappySellers before we put it near a client. 250+ active sellers doing real transactions. Our own risk. Our own data. No competitor has this.
Ship-or-don't-bill
Milestone-based fixed-price delivery. If the first production milestone does not ship in the agreed timeframe, you do not pay it. Big consulting firms cannot do this. Their billing model is based on hours, not outcomes.
AI-native delivery
We do not place data scientists on client headcount. We build the thing and leave it running in your stack, documented, observable, and owned by you. The engagement ends with working AI, not a strategy deck.
Common questions
Frequently asked
If yours is not here, the 30-min call exists for exactly that.
What is an AI playbook and how is it different from an AI strategy document?
A playbook is a step-by-step execution guide, not a strategy deck. It tells you exactly what data you need, how the model works, what it costs, what can go wrong, and how long it takes to ship. Strategy documents describe what to do. Playbooks are how you actually do it.
Do I need to implement all 7 playbooks?
No. Most brands start with one. The right first playbook depends on where your biggest revenue leak or cost drain is today. For brands with stockout problems, that is demand forecasting. For brands losing 3+ hours a day on reconciliation, that is reconciliation automation. We help you identify the highest-ROI starting point during the AI Fit Sprint.
Which platforms do these playbooks work with?
Shopify, WooCommerce, Amazon Seller Central, Flipkart, and custom ERPs. Each playbook specifies the data inputs required. The underlying model logic does not change by platform. The integration layer does.
How long does a typical playbook implementation take?
Between 3 and 12 weeks depending on the use case and your data readiness. Reconciliation automation can be live in 3 weeks if your data is accessible. A full recommendation engine on a 50,000 SKU catalogue takes 8 to 10 weeks. Every playbook page shows a week-by-week timeline.
Are these playbooks validated on real e-commerce businesses?
Yes. Every playbook reflects what we have built and shipped for actual businesses, plus the patterns we see across the 250+ sellers on HappySellers. The numbers in each playbook are ranges drawn from real deployments, not marketing estimates.
What does it cost to implement one of these playbooks?
Costs range from $8,000 for a contained automation (like reconciliation) to $40,000+ for a full recommendation engine. Each playbook page includes a transparent cost range and a breakdown of what you are paying for. We do not do T&M billing. Milestones are fixed-price.
Can I implement these myself or do I need TwoDots?
These playbooks are written to be genuinely useful whether or not you work with us. If you have a strong internal data team, the playbooks give you the blueprint. If you want it shipped in production in 4 to 12 weeks without building that internal capability, that is what we do.
Ready to ship your first AI use case?
Pick a playbook. Ship in 4–12 weeks.
Start with a free AI Fit Score to identify which playbook will move your P&L the most. Or book a 30-min call and we will tell you directly.
No pitch decks. No discovery calls that go nowhere. Just a clear read on whether AI will move your numbers.