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AI Product Attribution Services

Turn your approved product information into details buyers can actually use.

We'll reply with a written proposal: the work, what we need from you and an estimated timeline.

The hidden problems

The costly AI product attribution problems hide below the work you can see.

Before we suggest adding more AI-created product details, we check how each product and version is identified, what proof exists, the rules for each field, where the details will be used, how much review work is needed and how your business will use them.

  • Product details are too basic

    Basic size and color fields miss how people really filter, compare and search. But adding values the AI has guessed without proof can create a different and more serious data problem.

  • Site search shows poor results

    Site search and product recommendations need richer product details to match what shoppers are really looking for.

  • Product feeds leave out useful facts

    Shopping channels and AI shopping tools may accept richer product details, but every value still needs a rule for where it goes, a type of proof and someone who owns the product or version it describes.

  • Grouping products is done by hand

    Your team spends time grouping products by hand because the catalog doesn't have product details stored in a consistent, organized way.

  • Nobody controls what each product detail means or is for

    Before creating anything, decide where the detail will be used, which group of products it covers, what it means, which values are allowed and how the business will use it. Without an owner, a rule for proof and a check that it works, having more product details is not progress.

  • The proof behind each value, and how sure we are, is an afterthought

    Keep the text, image, document, supplier or human evidence behind every value. Label whether it was seen directly, copied out of a source, worked out by the AI or written by an editor, and let the system say "unknown" when the proof is weak.

Scope

What our AI product attribution service includes.

Product attributes are the details that describe each product, such as size, color, material, fit, style or what it is used for. Softscotch helps online stores decide which details matter, create them with the help of AI from your approved product text, images and documents, and check their quality. Every suggested value stays linked to a product or a specific version of it (a variant), the source it came from, an approved list of allowed values and a clear decision to publish it, review it or reject it. We can prepare approved details for your site search, filters, product pages, product feeds and the AI shopping tools that support them. We never claim that AI-created data is automatically true or that it will increase sales.

The first eight cards are part of this service. The last two are related services, shown here so it is clear who does what and nothing overlaps without anyone noticing.

Checking your product details

We review how each product and version is identified, your current fields, filters, feed data, how people search your site, how product details are organized, the quality of your sources and what your merchandising team needs.

The words buyers actually use

We map how shoppers describe products: what they use it for, style, fit, occasion, material, benefit and who it is for.

Plan for adding product details

We define which details are required, optional, worked out from other details, not allowed or not relevant, which approved text, image or document sources can be used, when a person must review and the rules for publishing.

Matching details to feeds and search

We create a separate map for each place the details are used: site search, filters, product detail pages, product feeds (the data files sent to Google and other shopping channels), recommendations and supported AI shopping fields, instead of copying one record everywhere.

Quality checks and upkeep

We check how products are identified, the type of proof, accuracy, consistency, duplicates, unknown values, high-risk claims, reviewer decisions, versions and who owns what.

Defining each product detail and its purpose

This work includes defining each product detail and its purpose. We set its meaning, which categories it applies to, whether it belongs to the whole product or each version, the allowed values, the type of proof needed and the shopping or business decision it supports.

Proof and confidence for each value

This work includes keeping proof and confidence for each value. We keep the source and how the value was taken from it, tell apart values seen directly from values the AI worked out, set publish or review limits based on risk and keep clear "unknown" or "not sure" states.

Training for your catalog team

We show your merchandising and catalog staff how to review suggested values, add new allowed values, handle "unknown" or "not sure" states and publish updates safely, with a short written guide they can keep and come back to.

Why Softscotch

We connect your product facts to the places buyers actually use them.

The work links agreed meanings for each product detail, the proof behind it, setup, review checks and later reporting, instead of stopping at a spreadsheet full of AI-created values.

We agree the starting point before doing more

We write down where your AI product attribution stands today, its limits and the meaning of each term, so the plan starts from evidence.

Expert inputs stay connected to the actual work

We define where details will be used, which group of products they cover, the proof needed and how we'll check them before creating any values. The same plan carries approved details through saving them back to your systems in numbered versions, checking the final output, measuring and undoing changes if needed.

Safeguards and failure plans are designed early

Permissions, platform rules, bad data, duplicate actions and undoing changes are dealt with before they turn into problems on your live store.

Platform automation stays visible

Any automatic expansion, AI generation or optimization comes with written inputs, exclusions, quality checks and a clear point where a person must review.

Quality is measured beyond the surface number

Reports include contribution margin (profit after direct costs), how easily shoppers find products and the effect further down the line on your business, where the data allows.

The system keeps working after we hand it over

Owners, definitions, source records, notes on each change, re-checks and triggers for updating everything stay usable after launch.

The full working scope

The decisions behind product details you can rely on.

These 36 factors cover what the buyer is trying to do, facts about each product and its versions, rules for creating values, human review, checks in each place the data is used, measurement and upkeep.

Strategy, demand and starting point
Defining each product detail and its purposeDecide which product details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.
Product details are too basicBasic size and color fields miss how people actually filter, compare and search for products.
Site search shows poor resultsSite search and product recommendations need richer product details to match what shoppers are really looking for.
List of the words customers useCollect the words buyers use from site searches, reviews, support conversations and competitors.
Plan for growing the workWork through test groups of products in order of customer value, data risk, review workload and results on a checked sample, before adding details across the whole catalog.
Merchandising and profit briefDefine the part of the catalog, what the buyer is trying to do, the profit margin, stock risk and goal for repeat customers before changing your online store.
Data, signals and inputs
Proof and confidence for each valueKeep where each value came from and the type of proof behind it, then decide whether to publish it, review it or leave it blank using limits that are tested and set by risk.
Checking your product detailsReview how each product and version is identified, current fields, approved sources, missing values, how people search, how details are organized and what each destination requires.
Matching details to feeds and searchMap approved details separately to site search, filters, product detail pages, product feeds, recommendations, Google's conversational attributes (optional feed fields for AI shopping) and Shopify Catalog where you are eligible.
Product feeds leave out useful factsShopping channels and AI shopping tools need product facts that go beyond a short title and description.
Structure and setup
Rules for product versionsKeep facts about the whole product separate from facts about each version, so color, size, material and compatibility don't spill over onto the wrong SKU (the code for each exact item you stock).
The words buyers actually useMap how shoppers describe products: what they use it for, style, fit, occasion, material, benefit and who it is for.
How product details are organizedDefine fields for use, style, fit, material, occasion and benefit, with which categories they apply to, the type of proof, units, how many values are allowed and what the AI is never allowed to guess.
Catalog and page structureOrganize collections, products, versions, bundles, menus and reusable content without creating duplicate paths to the same thing.
Making the changes
Plan for adding product detailsSet rules for copying out and working out values from approved text, images and documents, plus examples, "unknown" states, when a person must review and where the results go.
Products to start withChoose test groups of products and versions that balance likely value, data risk, website traffic and how much your reviewers can handle.
Adding product detailsPrepare suggested values linked to their source, review the differences, approve them based on risk, save them back to your systems in numbered batches and check how each destination actually shows them.
Consistent details from search to returnsCarry approved product and version details consistently through searching, choosing a product, checkout, delivery, customer support and the reasons customers give for returns.
Safety checks
Checking against approved word listsLimit results to approved values, units, formats and category rules, with a clear "unknown" option.
Quality reviewCheck how products are identified, that each value can be traced to its source, accuracy, type of proof, duplicate labels, unknown values, category rules and blocks on high-risk claims.
Feed, rules and app checksBefore launch, check that data matches everywhere, rights to source material, permissions, each destination's rules, notices about AI-created content, high-risk claims and what happens when something fails.
Pre-launch checklistBefore any change goes live, test product identification, use of approved sources, leaving values blank when unsure, sending items for review, undoing changes, destination formatting, platform rules and tracking.
Testing and improving
Testing against a checked sampleUsing a "golden set" (a sample of products whose correct details a person has confirmed), measure for each detail and category how many values are right, how many are missed, how often the AI says "unknown", how often reviewers disagree and how useful the details are, and repeat as things change.
Merchandising testsTest an approved set of new details against a similar group of products, with limits that protect product discovery, profit margin, returns and customer quality.
List of fair testsRank ideas that can be tested and change one clear thing at a time where the evidence allows.
Comparing groups of productsCompare meaningful groups and time periods instead of relying on one blended average.
Measuring results and value
Performance reportingTrack the quality of product details and review workload alongside searches with no results, filter use, how shoppers find products, how destinations process the data, sales and money results.
Setting up reportsTrack how shoppers find products, site search, filter use and revenue results.
Profit and customer group reportingLook at contribution margin (profit after direct costs), stock levels, the value of new customers and repeat buying alongside total revenue.
Early signs and final resultsKeep early signs that the work is on track separate from real, confirmed business results.
Sales adjusted for qualityTake into account whether orders were valid, the right kind of customer, profitable, returned or accepted further down the line.
Upkeep, rules and growth
When a person must reviewSet tested limits for publishing, reviewing or leaving a value blank for each detail and category. Block important claims until there is trusted proof and approval from a qualified person.
Quality checks and upkeepKeep the type of proof, reviewer decisions, corrections, batch and AI model versions, warning signs that quality is slipping, who owns saving data back and how to undo changes.
Managing catalog changesGive an owner and re-checks to every change in definitions, sources, AI models, products, feeds and destinations, so earlier corrections don't quietly disappear.
Named owners for each jobMake named people responsible for inputs, approvals, changes going live, monitoring and handling unusual cases.
History of versions and decisionsKeep a record of what changed, why, when, who changed it and what evidence was used.
Research and implementation tools

Product attribute tools help us decide. They don't decide for us.

The tools we use change with your platform and business, but every tool must give us a useful input, a way to check the setup or a real result, not just another score nobody can explain.

Look under the hood

Seven checks show what must be true before product details are rolled out widely.

Open each tab to see the proof, safeguards and sign-off checks behind each deliverable. Every finding becomes a task with an owner, or a clear decision not to act.

Check 01 · Checking your product details

Checking your product details

We review your current product fields, filters, feed data, how people search your site, how details are organized and what your merchandising team needs.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Product details check scopeReview your current product fields, filters, feed data, how people search your site, how details are organized and what merchandising needs.
Customer words list startCollect the words buyers use from site searches, reviews, support conversations and competitors.
Plan for growing inputPlan how to add product details across the wider catalog.
Merchandising and profit brief structureDefine the part of the catalog, what the buyer is trying to do, the profit margin, stock risk and goal for repeat customers before changing your online store.
Proof and confidence go liveKeep the text, image, supplier or human evidence behind each added product detail, and how sure we are of it.
Full product details check controlReview your current product fields, filters, feed data, how people search your site, how details are organized and what merchandising needs.
Check 02 · The words buyers actually use

The words buyers actually use

We map how shoppers describe products: what they use it for, style, fit, occasion, material, benefit and who it is for.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Buyer words map scopeMap how shoppers describe products: what they use it for, style, fit, occasion, material, benefit and who it is for.
Matching details to feeds and search inputPrepare product details for your site search, filters, product feeds, recommendations and AI shopping tools.
Feeds leave out useful facts structureShopping channels and AI shopping tools need product facts that go beyond a short title and description.
Rules for product versions go liveKeep facts about the whole product separate from facts about each version, so color, size, material and compatibility don't spill over onto the wrong SKU (the code for each exact item).
Buyer words in practice controlMap how shoppers describe products: what they use it for, style, fit, occasion, material, benefit and who it is for.
How details are organized testDefine fields for what the product is used for, style, fit, material, occasion and benefit.
Check 03 · Plan for adding product details

Plan for adding product details

We define the required details, optional details, examples, data sources and the rules for when a person reviews.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Plan for adding details scopeDefine the required details, optional details, examples, data sources and the rules for when a person reviews.
Catalog and page structure structureOrganize collections, products, versions, bundles, menus and reusable content without creating duplicate paths to the same thing.
Sources and review rules go liveDefine the required details, optional details, examples, data sources and the rules for when a person reviews.
Products to start with controlChoose the groups of products that need new details first.
Adding product details testAdd or link product details for the products that matter most.
Consistent details from search to returns measureCarry product, promotion, contact permission and customer details through checkout, delivery and the systems used after purchase.
Check 04 · Matching details to feeds and search

Matching details to feeds and search

We prepare product details for your site search, filters, product feeds, recommendations and AI shopping tools.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

#1
Feeds and search ready scopePrepare product details for your site search, filters, product feeds, recommendations and AI shopping tools.
Approved word lists go liveLimit results to approved values, units, formats and category rules, with a clear "unknown" option.
Quality review controlCheck accuracy, duplicate labels, missing values and the rules for each category.
Feed, rules and app checks testBefore launch, check that data matches everywhere, app permissions, tax, shipping, returns, accessibility and what happens when something fails.
Pre-launch checklist measureTest inputs, permissions, platform rules, content, tracking and what happens when something fails, before any change goes live.
Testing against a checked sample ownerMeasure how many values are right, how many are missed, how often reviewers disagree and how useful the details are, on a reviewed sample, as your catalog changes.
Check 05 · Quality checks and upkeep

Quality checks and upkeep

We check that product details are consistent, find duplicates and missing values, decide what needs review and agree who owns what.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Quality and ownership scopeCheck that product details are consistent, find duplicates and missing values, decide what needs review and agree who owns what.
Merchandising tests controlTest changes to how products are found, proof, offers and checkout against profit margin, returns and customer quality, not just sales numbers.
List of fair tests testRank ideas that can be tested and change one clear thing at a time where the evidence allows.
Comparing groups of products measureCompare meaningful groups and time periods instead of relying on one blended average.
Performance reporting ownerTrack people leaving after a search, filter use, how shoppers find products, feed quality, sales and the effect on revenue.
Setting up reports startTrack how shoppers find products, site search, filter use and revenue results.
Check 06 · Defining each product detail

Defining each product detail and its purpose

This work includes defining each product detail and its purpose. We decide which details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Each detail's meaning and purpose scopeThis work includes defining each product detail and its purpose. Decide which details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.
Profit and customer group reporting testLook at contribution margin (profit after direct costs), stock levels, the value of new customers and repeat buying alongside total revenue.
Early signs and final results measureKeep early signs that the work is on track separate from real, confirmed business results.
Sales adjusted for quality ownerTake into account whether orders were valid, the right kind of customer, profitable, returned or accepted further down the line.
When a person must review startSend uncertain, high-risk and new kinds of values to category experts, and keep their corrections so the system learns from them.
Quality checks and upkeep inputCheck that product details are consistent, find duplicates and missing values, decide what needs review and agree who owns what.
Check 07 · Proof and confidence

Proof and confidence for each value

This work includes keeping proof and confidence for each value. We keep the text, image, supplier or human evidence behind each added product detail, and how sure we are of it.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Proof for every value scopeThis work includes keeping proof and confidence for each value. Keep the text, image, supplier or human evidence behind each added product detail, and how sure we are of it.
Managing catalog changes measureGive an owner and re-checks to every change in your store theme, apps, products, feeds, promotions and policies.
Named owners for each job ownerMake named people responsible for inputs, approvals, changes going live, monitoring and handling unusual cases.
History of versions and decisions startKeep a record of what changed, why, when, who changed it and what evidence was used.
Each detail's purpose inputDecide which product details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.
Product details are too basic structureBasic size and color fields miss how people actually filter, compare and search for products.
What changes

From scattered product details to a well-run attribute system with clear rules.

Before

Work on product details is happening, but its inputs, safeguards and business decisions are scattered and disconnected.

  • Product details are too basicBasic size and color fields miss how people actually filter, compare and search for products.
  • Site search shows poor resultsSite search and product recommendations need richer product details to match what shoppers are really looking for.
  • Product feeds leave out useful factsShopping channels and AI shopping tools need product facts that go beyond a short title and description.
After the first round of work

The most important changes are live, the key safeguards have been checked and reports use definitions everyone agreed on.

  • Checking your product detailsReview your current product fields, filters, feed data, how people search your site, how details are organized and what your merchandising team needs.
  • The words buyers actually useMap how shoppers describe products: what they use it for, style, fit, occasion, material, benefit and who it is for.
  • Plan for adding product detailsDefine the required details, optional details, examples, data sources and the rules for when a person reviews.
Ongoing upkeep

Named owners, regular re-checks and clear triggers for updates keep the work useful as platforms and your business change.

  • When a person must reviewSend uncertain, high-risk and new kinds of values to category experts, and keep their corrections so the system learns from them.
  • Quality checks and upkeepCheck that product details are consistent, find duplicates and missing values, decide what needs review and agree who owns what.
  • Managing catalog changesGive an owner and re-checks to every change in your store theme, apps, products, feeds, promotions and policies.
Your type of business

AI product attribution changes with your customers, risks and costs.

Our method stays the same, but the data, proof, safeguards, buying cycle and what counts as a good result change from market to market.

Local

Local and multi-location

For local and multi-location businesses, AI product attribution has to take into account location details, what local shoppers search for, how many inquiries each location can handle and results for each branch.

FocusDefining each product detail, product details that are too basic, site search that shows poor results
MeasureProfit after direct costs, how shoppers find products, finished checkouts
Online sales

Ecommerce and retail

For online stores and retail, AI product attribution has to take into account the size of your catalog, accurate product facts, stock levels, profit margin, returns and repeat purchases.

FocusSite search that shows poor results, list of the words customers use, plan for growing the work
MeasureHow shoppers find products, finished checkouts, return rate
Software

Software companies (SaaS)

For SaaS (software sold by subscription) and software companies, AI product attribution has to take into account self-serve product journeys, integrations, ongoing value, getting new users active and keeping customers.

FocusPlan for growing the work, merchandising and profit brief, proof and confidence for each value
MeasureFinished checkouts, return rate, value of new customers
B2B

Business-to-business (B2B) and professional services

For B2B (business-to-business) and professional services firms, AI product attribution has to take into account groups of buyers who decide together, long sales cycles, expert proof and real sales opportunities.

FocusProof and confidence for each value, checking your product details, matching details to feeds and search
MeasureReturn rate, value of new customers, accuracy of the catalog
Health

Healthcare and wellness

For healthcare and wellness businesses, AI product attribution has to take into account accuracy, who reviews what, privacy, safety limits and the quality of appointments.

FocusMatching details to feeds and search, feeds that leave out useful facts, rules for product versions
MeasureValue of new customers, accuracy of the catalog, profit after direct costs
Regulated

Finance, insurance and legal

For finance, insurance and legal firms, AI product attribution has to take into account the location and laws that apply, approvals, evidence, sensitive data and terms that change.

FocusRules for product versions, the words buyers actually use, how product details are organized
MeasureAccuracy of the catalog, profit after direct costs, how shoppers find products
Education

Education and training

For education and training providers, AI product attribution has to take into account course details, deadlines, enrollment decisions, student outcomes and accessibility.

FocusHow product details are organized, catalog and page structure, plan for adding product details
MeasureProfit after direct costs, how shoppers find products, finished checkouts
Media

Publishers and media

For publishers and media companies, AI product attribution has to take into account original evidence, who wrote what, subscriptions, content rights and fast publishing cycles.

FocusPlan for adding product details, products to start with, adding product details
MeasureHow shoppers find products, finished checkouts, return rate
Marketplaces

Marketplaces and directories

For marketplaces and directories, AI product attribution has to take into account very large numbers of listings, matching duplicate records, data quality and making sales work for both buyers and sellers.

FocusAdding product details, consistent details from search to returns, checking against approved word lists
MeasureFinished checkouts, return rate, value of new customers
Travel

Travel and hospitality

For travel and hospitality businesses, AI product attribution has to take into account busy and quiet seasons, locations, availability, policies and booking value.

FocusChecking against approved word lists, quality review, feed, rules and app checks
MeasureReturn rate, value of new customers, accuracy of the catalog
Industrial

Industrial and manufacturing

For industrial and manufacturing companies, AI product attribution has to take into account product specifications, compatibility, distributors, long buying decisions and serious inquiries.

FocusFeed, rules and app checks, pre-launch checklist, testing against a checked sample over time
MeasureValue of new customers, accuracy of the catalog, profit after direct costs
Membership

Memberships and communities

For membership and community businesses, AI product attribution has to take into account access levels, ongoing engagement, moderation, renewals and the value members get.

FocusTesting against a checked sample, merchandising tests, list of fair tests to run
MeasureAccuracy of the catalog, profit after direct costs, how shoppers find products
Clear ownership

We do the agreed attribute work. Your team decides what is true about your products.

These columns show what Softscotch delivers, which outside pieces we coordinate and which facts, permissions and business decisions stay with you.

We do the work

The AI product attribution system we agreed in your scope.

  • Set up and check the review of your product details
  • Set up and check the map of words buyers use
  • Set up and check the plan for adding product details
  • Set up and check how details match feeds and search
  • Set up and check quality checks and upkeep
  • Set up and check what each detail means and is for
We coordinate

The outside pieces that decide whether it all works.

  • Coordinate catalog and merchandising inputs, owners and go-live decisions
  • Coordinate website development inputs, owners and go-live decisions
  • Coordinate payments and delivery inputs, owners and go-live decisions
  • Coordinate product feed and marketplace inputs, owners and go-live decisions
  • Coordinate email and customer marketing inputs, owners and go-live decisions
  • Coordinate analytics and finance inputs, owners and go-live decisions
You review and sign off

Your business keeps control of the facts and the business decisions.

  • Approve the priorities, limits and the written scope
  • Confirm facts about customers, products and how you operate
  • Review sensitive claims, permissions and policy decisions
  • Approve changes customers will see and any major automation
  • Confirm what counts as a good result and how money is measured
  • Keep final control over access, spending and legal compliance
How it fits together

How AI product attribution works: its inputs, safeguards and follow-up.

The first row shows this service in detail. The wider system around it connects winning new customers, the shopping experience, data, operations and reporting.

AI Product Attribution

01
Checking your product details

Review your current product fields, filters, feed data, how people search your site, how details are organized and what your merchandising team needs.

02
The words buyers actually use

Map how shoppers describe products: what they use it for, style, fit, occasion, material, benefit and who it is for.

03
Plan for adding product details

Define the required details, optional details, examples, data sources and the rules for when a person reviews.

04
Matching details to feeds and search

Prepare product details for your site search, filters, product feeds, recommendations and AI shopping tools.

05
Quality checks and upkeep

Check that product details are consistent, find duplicates and missing values, decide what needs review and agree who owns what.

06
Defining each product detail and its purpose

This work includes defining each product detail and its purpose. Decide which details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.

07
Proof and confidence for each value

This work includes keeping proof and confidence for each value. Keep the text, image, supplier or human evidence behind each added product detail, and how sure we are of it.

08
When a person must review

Send uncertain, high-risk and new kinds of values to category experts, and keep their corrections so the system learns from them.

09
Managing catalog changes

Give an owner and re-checks to every change in your store theme, apps, products, feeds, promotions and policies.

The connected growth engine

Catalog and merchandising

Your catalog and merchandising team provides inputs and decisions that AI product attribution can't take on by itself.

Website development

Your website development team provides inputs and decisions that AI product attribution can't take on by itself.

Payments and delivery

Your payments and delivery teams provide inputs and decisions that AI product attribution can't take on by itself.

Product feeds and marketplaces

Your product feed and marketplace owners provide inputs and decisions that AI product attribution can't take on by itself.

Email and customer marketing

Your email and customer marketing team provides inputs and decisions that AI product attribution can't take on by itself.

Analytics and finance

Your analytics and finance teams provide inputs and decisions that AI product attribution can't take on by itself.

One connected scope. AI Product Attribution is coordinated with the strategy, creative, experience, data and reporting it depends on, so responsibilities and handoffs stay visible in one written plan.

What to expect

Your product attribute timeline, and the full growth plan.

AI product attribution moves from approved sources and agreed field rules to a quality-checked test group of products before it is rolled out more widely. How soon changes go live depends on the size of your catalog, how complex the details are, the quality of your sources, connections to your platform and how much human review is needed. This work sits inside the six-month Softscotch growth plan shown below.

AI product attribution, from data check to adding details at scale

Days 1–5

Catalog and goals brief

Agree the test group of products, what the buyer is trying to do, where the details will be used, how products and versions are identified, approved sources, reviewers and how success is measured.

KPI Brief approved
Weeks 1–2

Field and proof rules

Define required and optional fields, allowed values, whether each detail belongs to the product or each version, types of source, "unknown" values and what the AI must never guess.

KPI Rules agreed
Weeks 2–4

Suggested product details

Create or link suggested values, each tied to its source, for a typical test group of products, without publishing them automatically.

KPI Suggestions ready
Weeks 3–6

Review and checks in each destination

Check accuracy, proof, versions, allowed values, unknowns, risky claims and how each included destination actually processes and shows the data.

KPI Test group accepted
After approval

Careful go-live

Save the accepted test group back to your systems with version records, monitoring, a way to handle corrections and a ready plan to undo it.

KPI Go-live checked
Often weeks 6–8

First useful report

Review quality, how people use search and filters, how destinations process the data, reviewer workload and any available sales safeguards before rolling out more widely.

KPI Next group decided

Our full growth plan (all services together), month by month

Month 1

Setup and first launch

Agree priorities, fix the biggest problems and launch the first approved work once we have access, content, budget and your sign-off.

KPI First work live
Month 2

Build and improve

Use the first report to improve the live work and build the next agreed materials, campaigns, pages or follow-up steps.

KPI Improvements live
Month 3

Do more of what works

Do more of the messages, audiences, pages or channels that the results show are working, while keeping budgets and quality checks within the limits we agreed.

KPI What works, expanded
Month 4

Strengthen results

Fix weak handovers between teams, gaps in tracking and anything slowing delivery, then test the next priorities against measures that matter to your business, such as lead quality and sales.

KPI Blockers removed
Month 5

Turn more visitors into customers

Improve the offer, message, page and follow-up steps wherever the results show people getting stuck or leads that aren't a good fit.

KPI Improvements to win more customers live
Month 6

Plan the next round

Review what changed, what the results really show, what should stop and which work deserves budget and attention in the next period.

KPI Next round agreed
How we compare

AI product attribution is careful work on your product data, not guessing in bulk.

The difference is a clear, written link between expert inputs, how the work is set up, the safeguards, the results we actually see and the next business decision.

The risk of using a different agency for everything
  • Creates values before agreeing what the buyer is trying to do
  • Mixes up facts about the whole product and each version
  • Copies one field map to every destination
  • Treats the AI's confidence score as proof
  • Guesses risky facts from how a product looks
  • Publishes guesses instead of saying "unknown"
  • Reviews only a few easy examples
  • Loses corrections from one batch to the next
  • Reports the number of fields filled in as quality
  • Scales up before knowing how much review the team can handle
vs
Softscotch
  • Starts with buyer needs, your products and where details will be used
  • Sets rules for what each product version inherits
  • Maps each destination separately
  • Keeps the source and type of proof for every value
  • Blocks important claims that have no proof
  • Uses clear "unknown" and "needs review" states
  • Tests a typical, reviewed set of products
  • Keeps numbered versions of corrections and saved changes
  • Reports accuracy, coverage and real usefulness
  • Scales up only after the evidence is accepted
The numbers

Results from Softscotch client work.

These totals come from work for Softscotch clients across search, paid ads, websites and lead generation, as published on softscotch.com. Past results don't guarantee future results.

$12M+Ad spend managed for clients
87,200+Leads generated for clients
2,140+Search terms ranked on Google's first page
$28M+Revenue generated for clients
Client case studies

All six Softscotch client stories.

Every Quick Story currently published on Softscotch.com, covering education, ecommerce, healthcare and SaaS. Stock photography represents each client industry; the figures are reported historical results and do not guarantee future performance.

Education consultant speaking with a prospective student Education Ads + local SEO
+183%leads

183% more leads for an education consultancy

WordPressQuora adsLocal SEO
The challenge

An education consulting business in India needed to build its digital presence and lead flow from scratch.

What changed
  • Built a new WordPress website
  • Used Quora ads to grow daily lead volume
  • Added Local SEO and community engagement
+183% leads25+ leads/day-38% bounce rate+273% new users
Bright modern home interior representing the home-solutions client Home solutions Shopify + SEO
+82%Shopify sales

Home-solutions ecommerce growth

ShopifyOrganic searchAmazon ads
The challenge

A home-solutions business was dependent on business-to-business (B2B) marketplaces, losing sales and recovering from a Google penalty.

What changed
  • Resolved the Google penalty
  • Revamped the website and online store
  • Improved organic visibility and Amazon campaign performance
+82% Shopify sales+147% organic+23% Amazon ROAS
Mother and baby representing the lactation-consultant client Health consultant $20/day
5/daybooked calls

Five booked calls a day for a health consultant

Facebook adsGoogle AdsLocal SEO
The challenge

A Houston lactation consultant needed to recover a malware-damaged website and rebuild local patient acquisition.

What changed
  • Rebuilt the hacked site and migrated it to secure hosting
  • Ran targeted Facebook and Google campaigns
  • Reached top-three local Google positions
5 calls/day$20 daily spend+87% local traffic+34% new users
Ecommerce owner preparing customer orders beside a laptop Ecommerce 10 months
+315%organic traffic

315% organic growth for an ecommerce site

Content strategyOn-page SEO
The result

Softscotch reports a 315% increase in ecommerce organic traffic over 10 months through focused on-page SEO.

What changed
  • Optimized product and collection pages
  • Improved the links between pages and the page addresses (URLs)
  • Made the site easier for Google to explore and better matched to what people search for
+315% organic10 monthsProductsCollections
SaaS team reviewing campaign data around a laptop SaaS platform Cold outreach
16%reply rate

9% close rate for a transcription SaaS

ProspectingSMTP
The challenge

A transcription platform needed a qualified prospect list and a reliable email-sending setup for outreach we managed.

What changed
  • Built a comprehensive prospecting list
  • Managed the cold outreach campaigns
  • Set up reliable email-sending servers (SMTP)
9% close rate16% reply rateSaaSManaged outreach
Healthcare professional consulting with a patient Healthcare 6 months
+182%organic traffic

182% organic growth for a healthcare clinic

HealthcareOn-page SEOOff-page SEO
The challenge

A clinic in a competitive location needed to build organic search visibility from the ground up.

What changed
  • Built the clinic's foundation for unpaid (organic) Google search
  • Combined on-page and off-page SEO
  • Reached top-five positions in Google Search
+182% organic6 monthsTop 5 Google
01 / 06
In their words

Teams that stopped leaving clicks on the table.

★★★★★

"The Softscotch team has been a solid partner as our business evolved over the last five years. Unlike most agencies, they have never charged us for work that falls outside the agreement. That is what sets them apart."

IM
Imtiyaz MominHairareus
★★★★★

"Without their knowledge, we would be in a very different place than we are now. Whenever they find something that can be improved, they implement it quickly. I could not be happier with their performance and response time."

AP
Aaron PaulsonBaby Pavilion
★★★★★

"Softscotch goes above and beyond the scope. The time, effort and dedication they put in is like having your own in-house marketing team. They are not just another marketing company."

TM
Tabassum MithaniTRS Care
★★★★★

"We are extremely happy with Softscotch, such an attentive team. No project is too small, and they give their 100%. We are extremely satisfied with their work."

SH
Safiya HafiziSimply Lactation
★★★★★

"Softscotch is a great team to work with: transparent, reliable and genuinely invested in the growth of your company. They are good partners to have by your side."

MJ
Malvika JainCyclop
★★★★★

"The Softscotch team has been a solid partner as our business evolved over the last five years. Unlike most agencies, they have never charged us for work that falls outside the agreement. That is what sets them apart."

IM
Imtiyaz MominHairareus
★★★★★

"Without their knowledge, we would be in a very different place than we are now. Whenever they find something that can be improved, they implement it quickly. I could not be happier with their performance and response time."

AP
Aaron PaulsonBaby Pavilion
★★★★★

"Softscotch goes above and beyond the scope. The time, effort and dedication they put in is like having your own in-house marketing team. They are not just another marketing company."

TM
Tabassum MithaniTRS Care
★★★★★

"We are extremely happy with Softscotch, such an attentive team. No project is too small, and they give their 100%. We are extremely satisfied with their work."

SH
Safiya HafiziSimply Lactation
No black box

You'll always know exactly what we changed.

Plenty of agencies go quiet after the audit and you never quite know what you're paying for. We do the opposite. You get a live view of every change and what it moved, in plain language you can act on.

Your delivery, at a glanceSample
Changes shipped12
Pages reviewed28
StartMidpointNow
Delivery activity✓ Up to date
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Live client dashboard

Every change, score and ranking in one place you can open any time, not a monthly PDF you wait around for.

This week, explainedSample
✓Titles updated
Ready for your review
SSSoftscotch weekly update
0:00
2:00
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Weekly video summary

A short screen-recorded video (Loom) each week walking you through exactly what shipped and what it moved, so nothing is a mystery.

The change is clearSample

Before

Titles30 / 100
Structure42 / 100
Content28 / 100

After

Titles84 / 100
Structure79 / 100
Content90 / 100
Same page. Reviewed changes.
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Before/after score per page

See the exact lift on every page we touch, not a vague "it's improving" you have to take on faith.

Your weekly reportSample
01
What shipped

Approved page updates are live.

02
What we learned

Product details need a review.

03
What happens next

Confirm the next priority pages.

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Plain-English reporting

Clicks, rankings and revenue, explained in words you can act on, instead of a wall of charts about how often you were seen (impression charts.

Your data. Your view.Sample
Organic search
StartMidpointNow
Sample report preview↗
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Reports on demand

Ask for any cut of the data whenever you need it and get it, no waiting for the end of the month.

Every step, visibleSample
  1. ✓
    Proposed

    Softscotch prepares the change.

  2. ✓
    Approved

    Your team gives the go-ahead.

  3. ✓
    Published

    Checked live and recorded.

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Ethical, transparent, always on

One accountable team, strictly white-hat (only methods search engines and platforms allow), no shortcuts and no surprises. Support whenever you need it.

One plan, full-stack access

One Softscotch plan gives you access to the full service stack, coordinated around your priorities.

You do not need a separate agency or retainer for every channel. Your plan can draw from search, paid media, web, ecommerce, outreach, automation, lead generation, messaging, content, creative and operations. The agreed scope sets what is active, in what order and at what capacity.

Pricing approach

One price for everything, not a stack of separate retainers.

You get one price for your whole marketing, not a separate retainer for every service. SEO, paid ads, your website, automations, cold email and newsletters, we run all of it as a single scope, so you get the most result for what you pay. Agencies sell each of these on its own, so the invoice climbs whether or not that line drives a single lead.

Bought separately, service by service

A different agency or retainer for each service. The bill stacks up fast, and keeps climbing whether or not each one moves a single lead.

SEO (technical, on-page, content)$2,000–3,500/mo
Local SEO+$750–1,500/mo
Programmatic SEO+$1,000–2,500/mo
CRO & landing pages+$2,000–3,500/mo
Paid ads (Google & Meta)+$2,500–4,000/mo
Marketing automations+$1,500–3,000/mo
Cold email outreach+$1,500–2,500/mo
Email marketing & newsletters+$750–1,500/mo
Website management & care+$750–1,500/mo
WordPress build & setup+$2,500–6,000 once
10 separate services$12,750–23,500/mo

Plus the one-off build, and a fresh contract, kickoff and onboarding delay for every new service you add.

All of it, one scope

We run your whole marketing as one engagement, so you get the most result for what you pay, not a pile of disconnected invoices.

  • SEO: technical, on-page, content, local and AI search
  • Template-built pages (programmatic SEO), links from other websites (backlinks) and authority
  • CRO and landing pages
  • Paid ads across Google and Meta
  • Automations, cold email and newsletters
  • WordPress build, setup and ongoing care
  • One team, one roadmap and one point of contact
  • Reporting tied to qualified leads and revenue
Everything above. One flat monthly fee.A fraction of the cost of the stack on the left
Plus free access to our own platforms
Post InstantlyVerified WidgetsTrending FunnelsBrands for CreatorsDarnItSEOGoHighLevel

Priced to your site after the audit. No sticker price, and no per-service creep.

What shapes your quote
Site sizePage count, templates and how much of the site actually needs work.
Market competitionHow hard the terms you want to win are, and who you are up against.
Content needsHow much new writing, rewriting and production the plan calls for.
Build complexityYour website platform (CMS, the system you edit your site in), your other tools and how involved the changes are to make.
Final pricing comes after the audit, once we have seen the four things above on your actual site. You get a written scope and a fixed quote before any work begins: no lock-in, no surprise line items, and no upfront number that ignores your market.
Request a custom scope
Questions, answered

AI product attribution FAQs, including the questions other agencies often skip.

These answers cover expert safeguards, setup, measurement, what we can and can't guarantee, timing, pricing and who owns what.

Filter
What is included in AI product attribution?

It can include checking how products and versions are identified, organizing details by category, researching the words buyers use, pulling details out of approved text, images or documents, rules for proof and confidence, approved word lists, human review, mapping details to each destination, releasing changes in numbered versions, testing against a checked sample (a "golden set"), training for your catalog team and reporting on results. The written scope says which groups of products, fields and places they appear are included.

Is this only for fashion catalogs?

No. It can help clothing, beauty, home, electronics, industrial parts and other catalogs where shoppers need good details to find the right product. Important fields such as dosage, safety, compatibility, certification, ingredients or materials, or claims covered by regulations need trusted proof and review by a qualified person. They are never guessed from thin text or from how a product looks.

Can product details improve site search?

They can help filters, search matching, recommendations, product grouping, feeds and merchandising when the fields match something customers really want to do and your search system uses them correctly. We also look at searches with no results, searches with few clicks and how shoppers narrow their searches, because adding more fields doesn't automatically make results more relevant.

How do you measure AI product attribution?

First we measure how products are identified, how much of the data has proof, how many values are right, how many are missed, how often the AI says "unknown", how often reviewers disagree and how destinations process the data, by detail and group of products. Then, where the data allows, we compare searches with no results, filter use, how shoppers find products, sales, returns and money results, without treating things that happen together as proof that our work caused an increase.

Do you include defining each product detail and its purpose?

Yes, where it fits the agreed AI product attribution scope. We decide which product details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values. Our first review confirms the access, proof, person responsible for the work and the check that it works before it is added to the plan.

Do you keep proof and confidence for every value?

Yes. We keep where each value came from and whether it is official, seen directly, copied out of a source, worked out by the AI, written by an editor, unknown or not relevant. We don't treat the AI's raw confidence labels as reliable percentages. Limits for publishing, reviewing or leaving a value blank are tested for each detail, category and level of risk before a wider rollout.

Do you include rules for product versions (variants)?

Yes, where it fits the agreed AI product attribution scope. We keep facts about the whole product separate from facts about each version, so color, size, material and compatibility don't spill over onto the wrong SKU (the code for each exact item). Our first review confirms the access, proof, person responsible for the work and the check that it works before it is added to the plan.

Do you include checking values against approved word lists?

Yes, where it fits the agreed AI product attribution scope. We limit results to approved values, units, formats and category rules, with a clear "unknown" option. Our first review confirms the access, proof, person responsible for the work and the check that it works before it is added to the plan.

Do you include testing against a checked sample over time?

Yes, where it fits the agreed AI product attribution scope. On a sample a person has reviewed, we measure how many values are right, how many are missed, how often reviewers disagree and how useful the details are, and repeat this as your catalog changes so we spot quality slipping. Our first review confirms the access, proof, person responsible for the work and the check that it works before it is added to the plan.

Do you set rules for when a person must review?

Yes. Uncertain, new and high-risk values can be sent to category experts, while important claims can be blocked from automatic publishing completely. We keep each decision and correction so the next batch can be tested against mistakes we already know about.

What do you change first?

We start with the biggest blockers found when we check your product details, then set priorities by business impact, how strong the evidence is, risk, what has to happen first and the effort involved.

Do you carry out the recommendations?

Yes, within the written scope. We build, set up or rewrite the approved AI product attribution work, coordinate the outside pieces and check the live result. Any task that needs a separate specialist or someone on your team is named before work starts.

How do you test whether new product details really improve results?

Where there is enough traffic and the platform allows it, we agree before launch which products get the change, a similar comparison group, the starting point, safety limits and when to stop. Methods such as holding some products back or comparing matched groups before and after can be used when they are valid. But stock levels, seasons, ad spend and other catalog changes happening at the same time can limit what we can say about cause and effect.

Can you guarantee a specific result?

No. Platforms, markets, customer behavior and outside conditions are beyond any agency's control. We are responsible for the agreed setup, quality checks and reporting, and we will show you what changed and what the evidence supports.

How long does the first round of work take?

A catalog and goals brief usually takes 3 to 5 business days once the test group of products, sources, reviewers, destinations and access are ready. Adding details to a typical test group is often planned over 4 to 8 weeks. Catalog clean-up, many fields or destinations, custom system connections, risky claims, specialist review and slow approvals can make it take longer. These are planning estimates, not a promised go-live or results date.

How is AI product attribution priced?

Pricing depends on the number of products and versions, how many details are needed and how risky they are, the types of source, system connections, how much review is needed, destinations, testing, releasing changes and upkeep. Platform licenses, paid tools, source clean-up, buying data and specialist review are separate unless listed in the proposal. Before work begins, the proposal states what you get, our assumptions, who does what, timing, our fee, the contract length and how either side can end it.

Can you work out product details from images and documents?

We can pull suggested values from approved images, manuals, specification sheets and product text when you have the rights to use the source, it is matched to the right product and it suits that field. Every suggestion can be traced back to its source. How a product looks is never accepted as proof of hidden material, compatibility, dosage, safety, country of origin or certification.

Can you prepare product details for AI shopping tools and assistants?

Yes, where a destination supports the fields and your store is eligible. Work can include Google Merchant Center's conversational attributes (optional extra fields that help Google's AI shopping features understand your products) or Shopify Catalog mappings for titles, descriptions, categories, grouping and options. Each destination gets its own approved field map. Better data can't guarantee your products will be included, ranked, recommended or bought.

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