Stores like yours have already tested thousands of apps. The recommender learned from their choices. Enter your store, or the tools you use, and see which technologies would most likely make a difference for you.
The best-rated app overall may be wrong for your size, category or setup. Stores that already look like yours are a far better guide, because they faced the same choices you face now.
Recommendations come from what stores actually installed and kept, not from reviews or ads.
Picks depend on what you already run, so they fit together instead of overlapping.
Each pick names the tool you already use that led to it, so you can judge it quickly.
Whether your store is in our database, brand new, or one of many you manage, there is a way to get recommendations in minutes.
If your store is among the 1M+ in the database, open its record.
New store or not listed yet? Enter the technologies you use.
Run several stores or brands? Send us up to 500 domains.
A list of tools is easy to ignore. A pick that says which of your current tools led to it gives you a reason to look.
A good recommendation is a starting point. These questions turn it into a sound decision that you will not regret in three months.
Name the specific issue in your store it would fix, in one sentence.
Pick one number to watch for a month: conversion, repeat rate or tickets per order.
Every script has a cost. Check page speed before and after installing it.
Confirm it works with the driver tool and your store platform out of the box.
A tool nobody configures is money wasted. Name the owner before you install.
Look at similar stores' stacks to see which product in the category they chose and kept.
Start from the problem, then check whether the matching tool type appears in your recommendations. If it does, peers already solved it that way.
| Problem | Kind of tool | Number to watch |
|---|---|---|
| Visitors leave without buying | Reviews, live chat, trust badges | Conversion rate |
| Carts abandoned at checkout | Abandoned cart emails, pay-later options | Checkout completion |
| Customers buy once and vanish | Email automation, loyalty | Repeat purchase rate |
| Same questions asked daily | Helpdesk, FAQ widget, chatbot | Tickets per order |
| Shoppers cannot find products | Site search, better categories | Search exits |
| Average order is too small | Upsell, bundles, free-shipping bar | Average order value |
| Returns eat margin | Size guides, returns portal | Return rate |
| Ads are expensive | Better tracking, email and SMS capture | Cost per order |
| International visitors bounce | Translation, currency switcher | Foreign conversion rate |
| Unsure what is working | Analytics, session recording, testing | Tests run per month |
A lean stack is a fast stack. Adding the right tool matters, and so does removing the wrong one before it costs more.
Two apps doing one job slow the store and confuse the team.
If it runs unattended, it is probably not helping.
A month of measurement with no change is a clear answer.
Product and checkout pages deserve the fastest load times.
If similar stores no longer run it, ask why.
Starter tools often hold growing stores back.
Most apps add scripts to your pages. One is fine, ten add up. Treat speed as a budget you spend carefully.
Recommendations adapt to where you are. This is the typical path we see across stores as they move from first sale to established brand.
| Stage | Usually already running | Commonly added next |
|---|---|---|
| Launch | Store platform, theme, payments, basic analytics | Email capture, reviews |
| First traction | Email marketing, reviews, ad pixels | Live chat, upsell, abandoned cart flows |
| Growth | Chat, upsell, more ad channels | Helpdesk, SMS, loyalty, subscriptions |
| Scale | Helpdesk, loyalty, several channels | Testing, personalization, site search, returns portal |
| Mature | Full marketing and support stack | Advanced analytics, international tools, integrations |
General pattern only. Your own recommendations reflect your actual stack and peers.
General starting points before you personalize with your own recommendations. Each store type leans on different tools, because its customers need different things before they buy.
Do it once a quarter. It usually finds one tool to add and one to remove, and keeps your store fast and focused.
Open your store record, or list your apps and scripts by hand.
Note the top ten picks and the driver tool behind each one.
Open five similar stores and compare their stacks with yours.
Two tools doing one job? Keep the better one and cancel the other.
The recommendation with the clearest problem to solve this quarter.
Keep it if the number moves, remove it if not. No exceptions.
Another angle: pick your store category, for example Baby Health, and see which technologies the stores in it use most. It is the quickest way to spot what your niche treats as standard.
Most stores end up with a stack that grew by accident. These are the patterns we see most, and the habit that prevents each one.
An app gets added after one webinar. Habit: write the problem down first.
Old tools stay forever. Habit: one out for every one in.
Enterprise stacks rarely suit small stores. Habit: compare with peers your size.
Tools without an owner drift. Habit: name one person per tool.
Nobody knows what works. Habit: one number per tool, checked monthly.
Pages slow down quietly. Habit: test speed after every install.
Once a recommendation makes the shortlist, these questions help you compare the actual products and avoid expensive surprises later.
Hours or weeks, and who on your side does the work during a busy month.
By orders, contacts, sessions or seats, and what happens to the bill at your next stage of growth.
Especially the driver tool behind the recommendation, and your store platform.
Ask how scripts load, on which pages, and whether they can be limited.
Data export and contract terms matter if it does not work out after the trial month.
Ask for examples in your category and size band, and check them in the store database.
Recommendations are one part of a toolkit built for running a better store. These four tools sit in the same login.
An illustrative quarter for a mid-size pet food store with a growing repeat customer base.
From the technology choices of millions of websites. The recommender suggests the tools most often found alongside the ones you already use, ranked by how strongly they go together.
Up to 1,000 when you type your tools, ranked from most to least likely. The top 20 are where to focus.
Both plans, Advanced and Enterprise, as part of the AI tech recommender, which also powers lead lists for software vendors.
Type the technologies you use. Recommendations work for any combination of tools, even for a store that launched last week.
No. Recommendations are technologies you do not run yet, so every row is a genuine option.
Yes. Send up to 500 domains to [email protected] and we return recommendations for each, which suits store groups and agencies.
No. Recommendations come from usage patterns across millions of sites, not from partnerships or paid placements.
No. Pick one tool that solves a clear problem, measure it for a month, then decide on the next one.
Yes. The same engine covers the five million most popular domains, not only stores, so publishers and service sites can use it too.
Yes. Agencies use it to show clients the tools their closest peers run, which makes recommendations easier to accept. See the agency page.
Quarterly is enough for most stores, and after any big change such as a new platform, a new market or a new sales channel.
Peers sometimes rely on smaller tools that rarely advertise. Check the driver tool and similar stores to see why it appears; hidden gems are common.
Recommendations are specific technologies, so yes. Compare the top few in a category by looking at which ones your closest peers chose.
Very. Type your platform and the few tools you have chosen, and see what stores starting the same way usually add next as they find their first customers.
Store records show each store's technologies. Use the similar store finder to locate your closest peers first, then open their records.
No. The right tool for a real problem helps; extra tools slow the store and cost money. Add deliberately, measure honestly, and remove what does not earn its place.
The technology you already run that contributed most to a recommendation. It explains why the pick appears and often hints at an integration.
Yes. Pick your category, sort stores by popularity or authority, and open the leaders' records to compare their technologies with yours.
Yes, as peers adopt and drop tools and as your own stack changes. That is why a quarterly check is worthwhile for every store.
Every plan includes recommendations. Advanced at $999 per year is the starting point.
Up to 1,000 explained recommendations, plus store, product and catalog tools. From $999 per year.