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Retail & Consumer

Static pricing leaves margin on the table. Disconnected channels lose customers mid-purchase. Fix both before the next quarter closes.

We build dynamic pricing engines, omnichannel unification platforms, personalization systems, and demand planning tools for retailers and consumer brands. Our software turns fragmented data into buying decisions.

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25%

Revenue lift

45%

Better demand accuracy

Overview

Disconnected channels and static pricing are bleeding your margin

Retail software development at 1Raft targets the four gaps that cost retailers the most: static pricing that ignores real-time demand, disconnected channels that lose customers mid-journey, generic experiences that fail to convert, and demand forecasts that drive overstock and stockouts. We bring patterns from 100+ products across adjacent industries to build pricing engines, omnichannel platforms, recommendation systems, and planning tools - each engineered to protect margin and increase conversion.

Retailers face customers who expect consistent experiences across channels, personalized recommendations at every touchpoint, and prices that feel fair and competitive. Most retail tech stacks were built for a single-channel world and can't deliver any of this without massive manual effort.

The data to solve these problems already exists - transaction history, browsing behavior, inventory levels, competitor pricing, weather, and local events. But it sits in disconnected systems that nobody can query fast enough to act on. By the time a pricing analyst spots an opportunity, it's already gone.

We build software that connects these signals and acts on them in real time. Pricing engines that adjust thousands of SKUs based on demand, competition, and margin targets. Recommendation systems that personalize every touchpoint. Demand models that tell you what to stock, where, and when. Every product integrates with your existing POS, e-commerce platform, and ERP.

Experience Signal

1Raft builds dynamic pricing engines, omnichannel platforms, personalization systems, and demand planning tools for retailers and consumer brands. Our engineering draws on recommendation and optimization patterns validated across 100+ products - including product discovery engines first built for digital commerce clients.

25%

Revenue lift

45%

Better demand accuracy

Industry Pain Points

What's broken in retail & consumer

01

Static pricing across 10K-100K SKUs leaves 8-15% of potential margin uncaptured because analysts can't react to demand shifts in real time

02

Customers abandon journeys when online inventory doesn't match store availability - 34% of shoppers switch brands after a single bad omnichannel experience

03

Generic product recommendations convert at 1-2% while personalized ones convert at 5-8%, but most retailers lack the data infrastructure to personalize at scale

04

Demand forecasting errors of 30-50% drive $500K-$2M in annual markdowns from overstock and equal losses from stockouts that send customers to competitors

05

Clienteling data - purchase history, preferences, wish lists - lives in associate notebooks or disconnected CRMs, invisible to the rest of the organization

Solutions

Problems we solve in retail & consumer

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Dynamic Pricing Engine

Real-time pricing optimization across thousands of SKUs using demand velocity, competitor pricing, inventory levels, margin targets, and promotional calendars. Adjusts prices within guardrails your merchandising team controls - no pricing surprises, just margin protection.

02

Omnichannel Unification Platform

Single view of inventory, customer, and order data across e-commerce, stores, marketplace, and wholesale channels. Enables buy-online-pick-up-in-store (BOPIS), ship-from-store, endless aisle, and unified returns - without replacing your POS or e-commerce platform.

03

Personalization and Recommendation Engine

AI-driven product recommendations, search ranking, email content, and homepage personalization based on browsing behavior, purchase history, and real-time session signals. Works across web, app, email, and in-store screens.

04

Demand Planning and Inventory Optimization

Forecasting models that predict demand by SKU, location, and week using historical sales, promotions, seasonality, weather, and local events. Generates replenishment recommendations that reduce both overstock markdowns and lost sales from stockouts.

05

Clienteling and Associate Enablement

Mobile tools that put complete customer context - purchase history, preferences, wish lists, and suggested actions - in the hands of store associates. Turns every in-store interaction into a personalized selling opportunity.

Use Cases

Real-world use cases

Dynamic Pricing for a Fashion Retailer

Problem

A mid-market fashion retailer with 180 stores and an e-commerce channel managed pricing through weekly spreadsheet reviews. End-of-season markdowns accounted for 22% of gross revenue, and the merchandising team couldn't react to fast-moving trends.

What we built

We built a dynamic pricing engine that optimized prices across 45K SKUs based on sell-through velocity, inventory depth, competitor pricing, and margin targets. The system recommended daily price adjustments within guardrails set by the merchandising team.

Result

Full-price sell-through improved 18%. End-of-season markdowns dropped from 22% to 14% of revenue. Gross margin expanded 3.2 points in the first two seasons.

Omnichannel Unification for a Specialty Retailer

Problem

A 90-store specialty retailer had separate inventory systems for stores and e-commerce. Online orders couldn't see store stock. BOPIS was manual - staff checked shelves and called customers. 12% of online orders were cancelled due to inventory errors.

What we built

We built a unified inventory layer that aggregated stock from all locations in near-real-time. Added BOPIS with automated store fulfillment workflows, ship-from-store routing, and endless aisle ordering for out-of-stock items. The customer saw one inventory pool regardless of channel.

Result

Order cancellation rate fell from 12% to 1.8%. BOPIS adoption reached 28% of online orders within 3 months. Ship-from-store recovered $2.1M in previously lost sales from online stockouts.

Personalization Engine for a Consumer Brand

Problem

A direct-to-consumer brand's website served the same homepage, category pages, and email campaigns to all visitors. Conversion rate averaged 2.1% and email click-through had plateaued at 3.4%.

What we built

We built a personalization engine that customized product recommendations, search results, homepage content, and email product blocks based on each customer's browsing and purchase behavior. The system learned in real time - a session that started in athletic wear surfaced related accessories within clicks.

Result

Site conversion rate rose from 2.1% to 3.6%. Email click-through improved from 3.4% to 6.1%. Average order value increased 14% driven by personalized cross-sell recommendations.

Our Approach

How we approach retail & consumer projects

1
Phase 1· Weeks 1-2

Retail Data and Channel Audit

We audit your transaction data, inventory systems, customer data quality, and channel integration gaps. We quantify margin leakage from pricing, stockouts, markdowns, and channel disconnects using your actual numbers.

Deliverables

  • Margin leakage analysis across pricing, inventory, and channel gaps
  • Customer data quality and unification readiness assessment
  • Prioritized opportunity list ranked by margin impact and implementation speed
2
Phase 2· Weeks 3-4

Architecture and Integration Design

We design the product architecture and map every integration point - POS, e-commerce platform, ERP, OMS, WMS, and marketing tools. Data flows are specified for real-time pricing, inventory sync, and customer signals.

Deliverables

  • Technical architecture with POS/ERP/e-commerce integration plan
  • Data pipeline design for real-time inventory and pricing signals
  • Phased delivery roadmap with pilot category or store selection
3
Phase 3· Weeks 5-10

Build and Category Pilot

We build in sprints and deploy to a pilot category, store cluster, or customer segment first. Real transaction data validates model accuracy and business impact before broader rollout.

Deliverables

  • Working product deployed to pilot scope
  • Performance metrics from real transaction and customer data
  • Iteration backlog based on merchandising team and associate feedback
4
Phase 4· Weeks 11-14

Full Rollout and Seasonal Optimization

We roll out across all categories, stores, and channels with segment-specific tuning. Models adapt to seasonal patterns and promotional calendars. Ongoing optimization captures new opportunities as customer behavior evolves.

Deliverables

  • Full deployment across targeted categories, stores, and channels
  • Merchandising dashboards for pricing, inventory, and personalization KPIs
  • Seasonal model tuning schedule and optimization plan

Outcomes

Measurable outcomes

12-25% revenue lift from dynamic pricing and personalized recommendations
30-45% improvement in demand forecast accuracy reducing markdowns and stockouts
40-60% increase in online conversion from personalization and omnichannel enablement
15-30% reduction in end-of-season markdowns through optimized pricing and inventory allocation

Pattern Transfer

1Raft first built product recommendation and search relevance engines for digital commerce clients - learning which signals predict purchase intent across millions of SKUs. That same pattern - behavioral scoring, collaborative filtering, real-time re-ranking - is exactly what powers our retail personalization and clienteling tools. When you've optimized discovery in pure e-commerce, you bring sharper models to omnichannel retail.

Services

Services for retail & consumer

Frequently asked questions

We set pricing guardrails with your merchandising team - maximum adjustment ranges, competitive floor prices, and loyalty member price protections. Prices adjust gradually based on demand signals, not volatile swings. The system optimizes margin within boundaries that preserve customer trust.

Next Step

Every season with static pricing and generic experiences costs you 8-15% in uncaptured margin.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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