Conversational analytics for omnichannel retailers, DTC brands, and marketplaces — turning POS, e-commerce, marketing, inventory, and customer data into the answers your merchandising, marketing, and operations teams need before the next promo window closes.
Retailers and e-commerce operators sit on rich data — POS, e-commerce, marketing platforms, loyalty, inventory, returns. The trouble isn't the data; it's getting a coherent answer fast enough to act on the next campaign, the next markdown, the next reorder.
Paid search says it drove the sale. Affiliate says the same. So does email. Three platforms, three numbers — no system has the actual journey-level truth.
By the time the weekly sell-through report lands, the SKU is either out of stock in three stores or sitting in the back room of fifty.
"What's the LTV of customers acquired through Black Friday paid social in 2024?" — a real question. The answer requires three analyst weeks and a CDP query.
The team learns whether the promo paid for itself two weeks after it ended — when the margin impact is already locked in.
A SKU shows strong sales and a 40% return rate. Net contribution is negative — but the merchandising dashboard shows green.
The same SKU sold through marketplace, DTC, and wholesale yields very different contribution. Most teams compare revenue, not landed margin.
Data Dialogix sits over your existing commerce, marketing, inventory, and customer systems. We collapse the cross-channel mess into a single conversational layer — merchandising, marketing, ops, and finance ask the same questions and get the same numbers, with the underlying data lineage visible.
For retail and e-commerce, we model the way operators actually think: by SKU, by channel, by cohort, by store, by promotion, by season. Not by Shopify report name, not by NetSuite GL line. When a merchandiser asks "what's our true contribution on SKU X across channels last month, net of returns and ad spend," the platform combines the data sources and shows it in fifteen seconds — with the SQL and the breakdown one click away.
Every metric is computed the same way every time, regardless of which team asks. The fight over "whose number is right" ends, because there is only one number — with the same definition, traceable to the same source rows.
POS, web, mobile, marketplace, and loyalty stitched into one customer view.
True contribution by SKU and channel, net of returns, freight, and acquisition cost.
Stock-out and overstock risk by SKU, by store, with replenishment cadence built in.
Cohort behavior by acquisition source, season, and product entry point.
Live promo P&L, markdown effectiveness, and price elasticity by category.
Real questions from retail merchandising, marketing, and ops teams, answered in seconds rather than days.
Every metric below is computed live from source systems, available as a conversational query, and pinnable as an automated monitor.
An anonymized engagement profile drawn from a typical mid-market omnichannel brand. Names and specifics generalized — directionally representative of what a four-month engagement looks like.
The starting point. Marketing and merchandising used different definitions for "performing SKU." Paid media reported 4.2x blended ROAS; finance, working from net contribution, calculated 1.6x. The CFO and CMO had had the same disagreement every Monday for two years. Returns data lived in a separate tool that nobody trusted.
What we did. Connected Shopify, Lightspeed POS, Klaviyo, the two ad platforms, NetSuite, and the returns system. Built a unified customer-journey model and a SKU contribution model that included acquisition cost, returns, and freight. Defined every metric once, with a shared business glossary. Stood up workspaces for merchandising, marketing, and finance — all reading from the same underlying definitions.
What changed. The Monday meeting stopped being a debate about whose number was right. Marketing began running campaigns optimized to contribution-weighted ROAS rather than first-touch revenue. Merchandising killed two underperforming product lines that had looked profitable on revenue alone but were negative on net contribution.
Four months later. Inventory turn improved measurably as buying decisions started weighting net contribution and return rate. The marketing team's reported ROAS dropped (because the new number was honest) but blended margin improved.
In retail, ROI on conversational analytics shows up in three specific places. We instrument each so it's defensible to finance and to the board.
Honest, contribution-weighted analytics typically lifts blended gross margin 1–4 percentage points within a year. The lever isn't price — it's killing or repricing the SKUs and channels that look healthy on revenue but bleed margin.
Live stock-out and overstock visibility tightens allocation. Operators typically reduce inventory carrying cost by 8–15% while improving in-stock rates.
Journey-weighted attribution reveals which channels actually drive incremental contribution. Reallocation away from "ghost" performance typically lifts effective ROAS 20–35% within a quarter.
Native connectors for the systems retail and e-commerce operators already depend on. No replatform required.
Retail handles increasing volumes of customer PII and payment-adjacent data under tightening regulations. The platform is designed to honor that posture from day one.
Card data never enters the analytics environment. Tokenized references only, scoped to the merchant of record.
Row-level masking, right-to-erasure workflows, and consent-aware customer modeling for global brands.
Encryption at rest and in transit, least-privilege access, full audit logging.
Embedded dashboards meet WCAG 2.2 AA color contrast and keyboard navigation requirements.
Book a 30-minute working session with our team. Bring one merchandising or marketing question your current tools answer slowly — contribution, cohort, channel margin, or inventory. We'll show you what conversational analytics looks like against your kind of data.
Book an industry demo