Ecommerce competitive intelligence: what each source can see

Ecommerce competitive intelligence is the dated record of what rival stores show, send and charge their customers. Product pages, ad libraries, tech stack and traffic estimates are covered by many tools, while welcome codes, basket and win back emails, texts and checkout totals reach only a customer, so a lasting programme signs up to each rival as a new customer, watches for months and keeps every email and page as evidence.

A rival store’s list prices, ads and technology are on show to anyone with the right tool. Its welcome code, basket emails, texts and checkout total reach only its customers, and those decide many shoppers’ choices.

In Klaviyo’s 2026 benchmarks, automated flows such as welcome and basket emails were 5.3% of email sends and nearly 41% of email revenue, and those emails go only to subscribers and to shoppers who left a basket.

What a site promises and what a subscriber receives can differ. A test of 362 ecommerce brands that Joy Joya published in July 2026, using new email addresses and phone numbers, found that 158 sent no welcome email and 20 sent an offer that didn’t match the pop up.

What ecommerce competitive intelligence covers

Ecommerce competitive intelligence is the dated record of what rival stores show, send and charge their customers, and how each of those changes over time.

That information sits in 2 kinds of place. Public surfaces, such as product pages, ad libraries, reviews and the technology a site loads, are open to anyone and to software at scale. Private channels, such as an inbox, a phone that opted in to texts, a basket and an account, show their contents only to the customer they belong to.

What public surfaces show

• Ads. Meta’s Ad Library holds every active ad shown across Meta’s apps, searchable without an account. It keeps inactive ads only if they concern social issues, elections or politics (for 7 years) or ran in the EU or the UK (for 1 year after their last impression). Google’s Ads Transparency Center lists advertisers’ ads on Search, Display, Gmail and YouTube, filtered by date and location.
• Technology. BuiltWith says it tracks 127,929 web technologies, and Store Leads lists over 40 attributes for each store, including installed apps, app change history and estimated monthly sales, updated weekly.
• Traffic. Similarweb builds its figures from first party analytics, a contributor network, public data and partners, and says it extrapolates from those signals.
• Pages and prices. Price trackers and page monitors read list prices, stock and page changes across many products, several times a day.

These sources answer questions of reach and position. Their traffic and sales figures are modelled estimates, best used to compare rivals and spot changes.

What only a customer sees

What decides a shopper’s choice between 2 stores often sits in private channels. A rival’s list price can match yours while its first order costs 15% less after the welcome code. Only a customer of the rival sees:

• The welcome code and its terms, by email or text, and whether the code works on sale prices.
• Basket, browse and win back emails, their timing and any extra discount.
• Texts, including any code the pop up promised.
• Subscriber only prices and early access.
• The checkout price after the code, stacking and delivery, which the guide to a competitor’s price after the welcome code covers.
• Delivery and returns, from the dispatch email to the steps to send something back.
• Support answers, what a rival’s chat or support bot tells a shopper about delivery, returns and codes.

Software that reads public pages can’t reach any of these, because each appears only after a sign up, a basket or a conversation. AI assistants’ answers about a rival sit between the two: anyone can ask, and an answer becomes a record only once someone asks and saves it. The guide to what ChatGPT says about a store sets out a method.

History and evidence

Private information is worth most when it has been observed over time and kept in a form someone else can check.

History can’t be recovered later. An inbox holds only mail sent after it subscribed, so a rival’s September welcome series is out of reach for anyone who signed up in October. A competitor view for Black Friday, on Friday 27 November 2026, has to begin weeks before it.

An original email proves more than a screenshot. Since 1 February 2024, Gmail’s sender guidelines have required senders of more than 5,000 messages a day to Gmail to set up SPF, DKIM and DMARC. A DKIM signature lets the signing domain claim responsibility for a message, checked against a public key fetched from that domain (RFC 6376). An email kept with its full header shows which domain signed it and when it arrived. In Gmail, Show original opens that header.

One identity throughout. Keep the same new customer at each rival from the first sign up, so every later email traces back to a known date and offer.

Synthetic customers and digital twins

Synthetic customers, synthetic respondents and digital twins of a customer are models that predict how people might think, answer or behave. Their makers sell them for research.

• Bain (May 2026) describes synthetic customers as AI generated representations of real customers, built as one to one digital twins or segment personas, for testing prices, bundles, concepts and messages. In one backtest its twins replicated about 90% of the key outcomes of a large conjoint study. Bain recommends adding them to existing research first.
• PwC’s Customer Link runs simulated interviews, focus groups and surveys on AI models of customer segments, and says it can field 25 questions across 5,000 synthetic respondents in 30 minutes.
• Qualtrics sells synthetic panels from its own AI model and asks users to say the data comes from generative AI. Ipsos PersonaBot turns a segmentation into personas colleagues can question.
• Gartner defines a digital twin of a customer as “a dynamic virtual mirror representation of a customer that simulates and learns to emulate and anticipate behavior.”
• Synthetic Users sells simulated interviews from $12,500 a year, and Aaru sells simulated populations and publishes its own accuracy benchmark.

Research on accuracy is mixed. Argyle and colleagues conditioned an OpenAI language model on the backgrounds of real survey respondents and found it reproduced the answer patterns of many groups (Political Analysis, 2023). Bisbee and colleagues found that about 48% of regression coefficients from ChatGPT’s answers differed significantly from a national election survey, that its answers varied less than people’s, and that the same prompt gave different results in April and July 2023 (Political Analysis, 2024).

Synthetic research suits concept, message and price tests before fieldwork. No model received the email a rival sent its new subscribers on 3 November, so a synthetic customer can’t report what a company did.

Human and AI mystery shoppers

Mystery shopping observes a company as a customer, and human programmes are priced per visit.

BestMark works on site, online, by call centre and in app, and Shepper says it has over 300,000 shoppers in the UK and Ireland. T-ROC’s guide puts basic retail shops at $40 to $65. At $50 a shop, a monthly visit to 10 rivals costs $6,000 a year, and each visit reports 1 moment.

AI mystery shoppers use software agents for the same job. S-PRO’s suite runs shopper agents across the site, email, chat and phone for CX teams in banking, insurance and retail, and publishes no price. Like a human shopper, it reports what a real company did.

What AI assistants can do

ChatGPT and Claude are good at the reasoning side of this work: researching rivals, comparing pages and summarising the emails in an inbox you connect. ChatGPT allows 3 to 15 active scheduled tasks, by plan, and Claude’s Gmail connector reads only the account you connect and can’t see images inside emails. An assistant can say what a rival sent in the last 30 days only if that mail already sits in a connected inbox. The guide Can ChatGPT or Claude track your competitors’ emails? compares 5 assistants job by job.

A durable competitive intelligence programme

A lasting programme watches the channels behind your offers at a steady rhythm, keeps every item as evidence and reports each change. It can start this week with free tools.

1. Choose the rivals and the decisions

List the 3 to 10 rivals your customers compare you with, then the decisions the programme feeds, such as your Black Friday offer, your welcome code and your delivery threshold.

2. Give each rival a new customer

For each rival, set up a new Gmail address, a phone number that can receive texts and a private browser window. Sign up through the public pop up, record the offer on screen with the time, and confirm the first email the same day. Read each site’s terms and never place an order you won’t take delivery of. This guide is not legal advice.

3. Set what to watch and how often

• Daily in sale weeks, weekly otherwise: list prices, sitewide banners, delivery thresholds and the pop up offer.
• As it arrives: every email and text, logged within a day with its time, subject, offer, code and Gmail tab.
• Weekly: new ads in the Meta and Google libraries, each opened to its page.
• Monthly, and before each sale: a basket and checkout at each rival for the price after the code and delivery, then the basket email, plus 3 questions about prices, delivery and returns, put to its support bot and to 2 AI assistants.
• Quarterly: tech stack and traffic estimates.

4. Keep every item as evidence

Keep the original of every email with its full header, and a timed screenshot of every page and checkout. Log each item in a sheet with the rival, channel, time, what you saw, the offer and a link to the original, so anyone can check a row against its source.

5. Report each change with its evidence

Send 1 page a week: what changed at each rival, when, the evidence link and the decision it affects. In sale weeks, send changes the same morning. The Black Friday rival report template gives a layout for clients.

6. Work out the cost by hand

Use your own numbers: rivals × channels × minutes a week on each ÷ 60 = hours a week. Multiply by your team’s cost an hour and by 52.

For comparison, a store owner on r/smallbusiness wrote in January 2026 of spending 1 to 2 hours every day on competitor prices alone.

By hand, the programme holds for 1 to 3 rivals. Past that, it needs an inbox and phone number per rival for months, with every original kept. Obsession’s competitor tracking signs an AI agent up to each rival you name and logs every email, text, offer, price change and public ad on 1 timeline, and price watch checks rivals’ prices, offers and delivery each morning, plus the codes they only send subscribers.

Competitive intelligence tools compared

The tools group by where their data comes from, which sets what each can see. We make Obsession, which does some of this, so every line about another tool is taken from that tool’s own pages, checked on 23 October 2026.

Crayon and Klue are built for B2B sales teams, with battlecards and answers for deals in flight. None of the page, ad, stack or traffic tools signs up as a customer, so welcome codes, basket emails, texts and checkout totals fall outside them.

Limits and next steps

• 1 identity sees 1 customer’s view. A new subscriber sees what rivals send new subscribers. Offers for loyal customers, other markets and other devices need identities of their own, so record the market and device with every item.
• Third party figures are estimates. Use traffic and sales figures from these tools for direction and relative size.

Start the sign ups this week. Rivals’ Black Friday teasers begin in late October, and an inbox that signs up by Friday 30 October holds 4 weeks of a rival’s mail by Black Friday on 27 November. Before code freezes, about 20 November, run the same new customer tests on your own store.

Frequently asked questions