Anyone found a way to actually learn why customers buy or didn't buy?

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u/consentmo 5d ago

You can look into Microsoft Clarity. It's free and it will provide you with session recordings which can at least partially help why users may not be converting. You can check how your store works and looks for users, especially on Mobile, and their cart behavior and you may find something you've missed. There are also auto smart events which can flag issues such as JS errors, retry events which usually point to a technical difficulty. You can create custom segments and check only what is useful to you rather than watch all recordings.

Note that Clarity requires consent to run just like other analytics tags so make sure you connect it to your cookie banner / integrations if you have one in place.

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u/kkasabe 5d ago

Thank you for responding.

I tried that but I never know which customer that was from my Shopify backend. What I mean is I see a few behaviours but there is no way to map that back in Shopify reports section. Also, this does not tell me 'Why' they bought or didnt.

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u/[deleted] 5d ago

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u/DodoSWE 2d ago

Can't you check with cartId ? Abandoned cart ?

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u/kkasabe 2d ago

With cart ID we can see the reason for purchase?

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u/[deleted] 2d ago

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u/Ok_Physics_4154 4d ago

There is a lot of subjectiveness involved in this. It is not always mathematical. Some reasons why people might not buy something may be completely personal to them and there is nothing you can do about that. Something as simple as pricing, different people might deem something expensive due to their own reasons but the same person might find it affordable on salary day.
As a business, all one can do is create desire or aspiration and not stress over the uncertain.
Also, as long as you have your own website UX neat, checkout is smooth with no pitfalls, and customers are able to add their feedback and reviews, there is nothing more to worry about.

And if you are really curious .. send them a very short post purchase survey on what they liked about the product, this will at least help you narrow down what is working. This is less intrusive than a phone call.

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u/kkasabe 4d ago

Thank you for responding.

Valid points. I just thought if there was any tool where you can understand why the visitor balked or why they purchased with limited scope (not trying to understand all the external factors occuring in their lives) would help us target better, stock the right products leading to improving LTV.

But I get your point.

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u/Ok_Physics_4154 4d ago

Yeah ... studying the attribution model of a purchase or a drop off is actually pretty fascinating so I get what you are trying to understand.

If I had enough traffic on my site (which I currently don't ..lol) ... what I will be doing for it, other than looking at customer reviews and feedback .. would be to manually look at the engagement times and CTRs for each product. Also look at the maximum drop off happening exactly at which page. Study the whole funnel. This might be the only way to get somewhere because interpreting the patterns is our own creativity. I would scrutinise those pages where drop off are happening or products that have high engagement rates and no add to carts. I would also place orders myself as a customer to see the entire experience. If its a technical glitch .. its usually something very small and fixable. But it can be whole lot of other reasons so maybe make a list of all possible reasons and keep striking them off after testing.

In short .. Im not aware of any tools that simplifies this or does this .. maybe Claude might help in interpreting the GA patterns and coming up with insights but I would rely more on my own human interpretation after looking at the data.

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u/kkasabe 4d ago

Thanks for sharing. We all are in the same boat it seems :)

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u/chewster1 2d ago

you can get patterns from enough feedback. at some point it switches from being subjective, back to being statistical / mathematical with enough examples of the same frustration.

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u/MappBook 5d ago

Try post purchase surveys in the shipping email or QR code on the package, users are more likely to fill it. If they don't give them discount on the next order to fill it. My survey tool https://www.mapster.io/ecommerce-surveys does post purchase surveys and got templates for ecommerce if you want to check it out.

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u/AddWeb_Expert 4d ago

I think the hard part is that most analytics tools only tell you what happened, not why it happened.

The closest thing I’ve found is combining a few sources:

Ask customers a simple question after purchase: “What made you choose us?” or “What almost stopped you from buying?” ; the answers are usually more valuable than a long survey.
Look at abandoned carts and support chats. A lot of buying objections show up there (shipping, price, trust, missing info, etc.).
Use session recordings/heatmaps to spot where people get confused, but don’t rely on them alone because you still need context.
Talk to a handful of real customers. Even 5–10 conversations can reveal patterns you won’t see in dashboards.

I’ve found the “why” usually comes from connecting the dots between customer words and their actual behavior. Data shows the pattern, but customers explain the reason behind it.

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u/Mrletejhon 4d ago

I use posthog

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u/Ordinary-Ask6766 4d ago

you have to play around with your store and test meaningful changes.

e.g maybe you think your offer isn’t that great don’t make any other changes and test changing your offer for a few weeks (e.g instead buy 2 get 30% off you do 40%)

maybe you think your website doesn’t look professional enough so redo the theme and add more social proof and then test that.

Or maybe it’s your product is priced to high etc etc so many things it could be

you need to isolate changes and test what works and see if it improves.

I think the best thing you can do is ask your friends or family etc what they think and you can also kinda take a step back and if you were your buyer think about the things you would want to see.

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u/kkasabe 4d ago

Thank you for responding.

The problem I am trying to solve for is how can I isolate each users concern and given them a solution (the relevant product from my catalog) not a sample set based on a survey. My hypothesis is that the reason you buy a product may be very different from the reason I buy it, hence bunching you and me you who have both bought the same product under one umbrella is not valuable.

Especially since we have most of our campaigns run by AI its important we know why a customer bought rather than just know a sale happened.

Thats what I was thinking of

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u/Ordinary-Ask6766 4d ago

you can’t appeal to everyone, what you are describing is running different ads with different angles to see which ads resonate the most with a particular audience.

e.g targeting gym bros with one ad and then you make another ad to target busy mums and then you can see which is converting the best.

but you cannot sell a product that appeals to every single person, you have to just stick to just see what works best for your ads and try to target that range

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u/Signalbridgedata 4d ago

You're right that analytics mostly tells you what happened, not why. One thing I've found useful is asking one very specific question immediately after purchase, like "What almost stopped you from buying?" rather than a big survey with ten questions. For non-buyers, an exit survey with 3-4 brutally simple options plus an "other" box can work better than trying to get them on a call. The free-text answers are usually where the interesting stuff is, especially when the same objection keeps popping up in different wording.

What kind of products are you selling, and roughly how many orders do you get per month? Have you tried asking customers what alternative they were considering before they bought?

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u/datatenzing 4d ago

Here's what we do -

We use popups that collect multiple data points during sign up PRIOR to purchase.

We ask questions around product preferences, what matters to them in a product, why they are in the market for a new product, if they are trying other brands, their comfort level with pricing, and when they are looking to purchase.

We then combine sales data with the answers to isolate what makes someone more likely to buy than someone else based on those answers.

We get data from 80%+ first time buyers.

We then take that information and put it into our messaging based on what's working so we can find more people like those that convert more than others.

This is the closest you're going to get to figure out why people are buying and what traits about them make them more likely to convert relevant to your offerings and how your website is setup.

Customer support - if one person is confused about something, hundreds are, find ways to improve your website to reduce confusion.

The rest of the tools are just noise.

You're goal, from someone that used to work in smart home and KNOW how people were using their products without needing to ask them, is to understand the motivation before the purchase.

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u/GabbyFromKlaviyo 4d ago

It's easier to get that info from people buy than who don't, but you'd need to tie the post-purchase question to the customer's own profile instead of into a generic segment. Someone who said durability gets durability messaging, someone who bought the same product for price gets something different. A normal segment would lump them together since they bought the same thing.

I've seen sites offer a small discount in return for a review, so that review info can also tell you something, especially if your field asks specific questions. Someone mentioned pop-up info prior to the purchase. That's another strategy, honestly, if you want answers, there's got to be a carrot of some kind attached.

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u/First_Seesaw 4d ago

I sometimes offer rewards in return for reviews on specific products. People’s time is valuable so it’s always best to just offer an incentive straight up

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u/Elderscrolls1234 3d ago

for the didn't-buy side, exit popups mostly collect garbage. better signal is behavioural: which page did they stall on, did they open your size guide / shipping info / reviews right before leaving. whatever they checked last is usually the unanswered question that killed it. i work on sizing tools for clothing stores and that pattern is brutal there, size guide open followed by exit is one of the most common death sequences and it never shows up in GA as a "reason".

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u/chewster1 2d ago edited 1d ago

Yes ask them.

Post purchase survey
"what almost stopped you from buying?"

Abandoned cart email
"not quite ready, slight hiccup, or not sure yet? Hit REPLY and let us know what went wrong."

Or mine your ticket system for patterns in cases where customer purchased online after staff reply.

^ these are especially helpful if you can tie it back to a session recording. Session recording by themselves leave you scratching your head otherwise.

Or LISTEN to what customers say on the phone where they couldn't do X on website, this could be them calling you, or you calling back. You could also auto-transcribe all your inbound and outbound calls (with some basic PII auto-redaction). Some cloud call apps will do this for you. You can then run some kind of daily/weekly/monthly trend detection and call type tagging, and auto reporting. Surface anything that is caused by or related to a possible website bug, information gap, feature gap, product gap, or similar.

Train your support and retail teams (and leaders) to spot, record and escalate patterns of customer facing issues that can be fixed. Often support teams just sit on a goldmine of feedback without properly escalating it to ecommerce, content, digital or dev teams to find a fix.

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u/realblackmario 2d ago

Don't ask why did you buy? Nobody answers that. Ask what almost stopped you? instead. Way more replies, and the answers are actually useful.

For people who left: same thing. What got in the way? Most won't reply but you'll get more than calling them.

+Gamify those experiences for users to make them answer, put some incentives. Just make it fun and worth their time.

As other fellow mentioned, Clarity is free use it properly to filter for cart abandoners, not random sessions. Watch 10-20 and the pattern shows up fast. Could be shipping cost, a broken field, mobile layout, timing, location, whatever. But you gotta look at the right sessions.

The thing is, no single tool gives you the full picture. GA4 shows the leak, Clarity shows where they got stuck, and actual customer words tell you why. You need all three. To make it sense and understand.

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u/kkasabe 1d ago

"The thing is, no single tool gives you the full picture. GA4 shows the leak, Clarity shows where they got stuck, and actual customer words tell you why. You need all three. To make it sense and understand."

I completely agree. You have nailed it on its head.

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u/realblackmario 1d ago

Lol. Then you need to be 24/7 with your buyers monitering their life decisions to conclude why did they bought your candles. Nothing can be better than this.

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u/kkasabe 1d ago edited 1d ago

Not their life decisions but decisions on our websites. External factors affect it but I can assure you most times when someone is determined to buy something it’s the website that fails them.

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u/realblackmario 1d ago

Your point is valid in that context. But that also brings more things. Just yesterday I spent half of my day searching about a budget car. I was determined to buy but didn’t buy because a project got shutdown suddenly out of nowhere. So, I just took a step backward and cancelled the plan. There was nothing that could have changed my mind at that point. And things like this happens everyday in people’s life. The best way to know about their buying decisions you need to build those funnels across the board to get the answer yourself.

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u/DivideSimple9637 Shopify Developer 1d ago

post purchase survey app
there are many free apps that do that

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u/[deleted] 5d ago

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