See where customers drop off, from sign-up to repeat purchase.
Laplace combines purchases from every store, recognizes the same customer across them, and calculates accurate first-purchase and repeat metrics. Then pull a target list in a few clicks.
Hover a funnel step to explore conversion, identity merge and repeat rate. (All values are sample data)
Three signs your conversion numbers are off.
Count customers separately in each store and every conversion metric is either inflated or hidden.
One buyer, counted as new in every store
A customer who buys on Shopify, your D2C store and Amazon shows up as three new customers. Acquisition looks bigger than it is, and real retention disappears.
Repeat purchases logged as first orders
When stores don't share customer identity, a loyal customer's second purchase looks like a brand-new first order, so your repeat rate looks lower than it is.
No clear list of who to target
Without each customer's full purchase history in one place, building a win-back list means guesswork in spreadsheets instead of a few filters.
One customer, three stores. One profile.
Laplace matches email, phone, address and platform ID across connected stores, so the same buyer is never counted twice.
Personal info is masked · hover to pause
Follow every customer from sign-up to their Nth order.
See the conversion rate and median time to convert at each purchase stage. Filter by product, channel or period to find where buyers drop off.
See what first-time buyers buy next.
A Sankey chart shows which product first-time buyers pick for their second purchase, and how many never come back. Use it to find your strongest cross-sell paths.
Hover a flowTap a flow to see its count and share. Select a first-purchase product to highlight its flows.
Filter once. Get a list you can act on.
Pick a base condition, add filters, then export a CSV or start an email campaign from the same screen.
Frequently asked questions, answered up front.
Still have questions? Contact us anytime — we will tailor the guidance to how you plan to adopt Laplace.
Laplace merges them into one customer profile by cross-matching identifiers such as email, phone number, platform ID and shipping address across every connected store. A customer who buys on Shopify and later on Amazon counts as one person, not two new customers. The result is a true unique customer count and more accurate conversion attribution by channel.
Laplace classifies each order against the unified customer, not the store account. A store admin only recognizes purchases from the same account on the same platform, so a customer who returns through another channel or account looks like a first-time buyer. Because Laplace links purchase history across channels, its first and repeat purchase classification is far more accurate than any single store's.
Yes. Conversion Analysis measures first-purchase conversion for new customers and repeat conversion for existing customers as separate funnels. You can break both down by channel, product and period, which shows which channels win new customers and which ones drive repeat purchases.
Laplace connects ad channel data from Google Ads, Meta and TikTok Ads to your actual orders and calculates real ROAS for each channel. ROAS in an ad platform's own report can include double-counted conversions; Laplace's ROAS is calculated after deduplication and attribution recalculation. That tells you which channel deserves more of your budget.
Conversion rates can be adjusted retroactively because some processing runs after data is collected: bot traffic filtering, attribution recalculation and session deduplication. If yesterday's rate looks slightly different today, sessions were likely removed as bot traffic or attribution was redistributed. This is a normal step toward more accurate numbers, and the reason for each adjustment is shown in the metric details.