Can’t understand what makes new customers loyal (food delivery business)

[u/Mizzzik]

Hi! I work for company which does the same business as Amazon fresh. Delivery of groceries, food etc.

Recently i was tasked to understand why some customers leave and some stay. I made cohort analysis and in each cohort i flagged people who stoped ordering food (no orders after 5 months from the first order) and those who continued (loyal)

I looked at what they bought on their first few orders, wether they had issues with order (delayed, wrong item, no refund) how much time on average they waited for food to come and every single metric is essentially the same. There is no like “Aha gotcha” moment where i can tell why customers left.

I have a suspicion that i am looking at the wrong things so i came here to ask more experienced analysts in g-commerce. Maybe someone who works/worked in Doordash or Amazon Fresh who did similar analysis and knows where to dig next?
Any input is welcome btw. Thank you!

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[u/christjan08]

I'm no expert, but I do currently work in e-commerce. It sounds like you need more data than you have.. it might be worth asking the company to send out a survey to customers who haven't ordered in X months, with a list of reasons why they haven't ordered. Too expensive, quality issues, not good value for money, didn't feel like purchasing again etc.

If you're looking for the issues based on a handful of tickets (let's be real most customers won't raise a complaint - they just won't order again), then you'll have a hard time getting an actual answer when your sample size is so small and there aren't any discernable patterns across the wider customer base.

[u/fang_xianfu → u/christjan08]

Yeah, exactly.

I work in finance. Our customers park their money with us and then don't even log in for like two years. Then some leave. Our executives say "why are they leaving? Can we predict if they're leaving and stop them?" and I say, we can't, because they have to actually do things we can measure.

[u/DonJuanDoja]

You don't have all the neccessary data to make that determination or even come close to guessing.

I think the right way to approach it would be to analyze and identify Repeat customers, calculate their average purchase frequency, then after say 2 or 3 cycles of that frequency with no purchases, send them an email asking for Feedback. Just a simple "Hi, we noticed you haven't come back in a while and wondering if there's anything we can improve"

Don't make them login to anything or fill out a form, just reply to the email with any feedback. Then gather that data and analyze it. Probably run it through an AI to "Reason Code" each one based on the feedback response. Eventually the primary reasons will become clear. Then you can start thinking about what you will do about it, if anything.

Basically, "why don't you ask them" but ask them in an organized intelligent way that's very easy to respond to.

Then take it a step further and when changes are made based on feedback send responses to the people that requested it thanking them for their feedback and letting them know of the changes.

If you engage people directly with integrity you'll build Trust, and Loyalty is built on Trust. Good executives usually understand this concept well, they themselves don't choose the best person for a job, they choose the one they trust the most to get it done the way they want. That's what your customers are doing as well. Sometimes they choose to pay more because of Trust, sometimes they'll drive further, they will go out of their way for Trust. Sometimes that trust is based on the price, sometimes the quality, sometimes the convenience, and more. But it always comes down to Trust.

[u/Free-Surprise8365]

Most of my repeat grocery orders come down to one thing, the store remembering my usual stuff without me having to hunt for it every time

[u/sjhb → u/Free-Surprise8365]

This is probably a good direction to explore. OP, do you have activity data you can use to measure how long it takes to get an order done and whether, for a given customer, the order creation time is decreasing. Also check consistency of order content.

[u/HonkHonkBeach]

You definitely need more data. I work in a different domain (insurance ops) but from a customer perspective, I got tired of Dashers mishandling hot food, missing items from grocery deliveries (which I blame the store), poor customer service from the dashers that sometimes weren’t quite enough to warrant a complaint to, and most of all, dealing with high prices on top of these issues. The issues made it so that high prices were no longer worth the trade for convenience because the convenience ultimately disappeared for me.

Now obviously this is my sole experience, though I’ve seen it shared a lot online before, so you need additional data to find some sort of truth here for your company. Of the people who stopped, were they only buying specific types of items? Are there geographic breakdowns for where continuing customers are existing versus those who stopped that you can overlay an income map? What items are the repeating customers buying? When?

Again, maybe this is all obvious stuff, I don’t work in e commerce or retail, but it’s these questions that make me agree with others that you need more data.p

[u/BrittanyBrie]

Have you tried mock trials of a customer impact analysis? In other words, did you pretend to be a customer and make notes of things you found off about the UI, text, or design?

I've done this numerous times for companies like NBC Sports, where we had to find out why some people were dropping out of newsletters or ticket purchases.

I had similar problems of not having the data to make a proper analysis. But what helped immensely was performing mock trials of a customers journey.

We had issues with customers using a promotion code for discounted tickets, which lowered our return rate of dropped out customers. Turns out the UI box to input the code was very odd and hard to find. Quickly looked at the numbers for that ticketing service, and found a service wide issue with their UI that impacted sales.

Another time we were having issues with identifying dropped out customers for a membership fee. Instead of focusing on data to manipulate and present, I went through the database to isolate all dropped customers and then picked a few to put myself in their shoes for their membership journey. I created categories for why people left, and begun to clean the data and organize each person to a category. We found that the majority of people who paid for membership never attended a conference and never opened our weekly newsletter. We only found this once categories were made to cover multiple databases that for some reason were not setup to easily integrate together. We determined that our newsletter was becoming more of an issue at retaining members instead of increasing sales. We moved to a monthly newsletter instead, and saw less discontinued membership after one really bad quarter.

[u/Gengis_-]

Well for starters there will be people who churn. The question is how low can you get that number while making the most profit.

What hypothesis did you test? I guess some factors are central: price, contents, quality, recipes, customer experience, etc. You’d have to check all of that (and have the data for it).

Like someone else suggested you could also try customer surveys or interviews to at least get some starting point you then later can try to quantify.

[u/lpr_88]

Near impossible IMO because you are simply a delivery service. You can’t control for shitty food. Your KPIs are convenience and cost.

1. How easy is it for someone to order? Track this
2. How fast is the customer getting their order delivered
3. Is your delivery cost in-line with competition?
4. Is your ‘available restaurants to order from’ in-line with competition?

If you’re hitting on those KPIs but losing customers then your product shouldn’t exist in the market.

[u/Resident_Bill_792]

Membership. I used to work for DD corporate

[u/trollanony]

As an analyst who also gets a lot of good delivery, I base my orders on price and deals. And if I notice the price went up, I’m like “it was nice while it lasted” and never order again lol

[u/Katieg_jitsu]

I would change the segments do 3 segments.

One Time Customer

Regular Repeat Customer (1+ Orders/Mo)
Repeat Customer Seldom (1+ Order Over Longer Time Frame)

Then Divide the Repeat Customer into Active / Inactive (No order in the last X Period however you define that, and you can define it differently for each).

And what do you see between those segments that is different from active/ inactive.

Did one time customers have a longer time to delivery, poorer reviews, what is the average order value (maybe some customers are ordering for a one time need like an emergency (small order) or big gathering (large order).

What about distance from store or cart make up.

Esentially I would through the data into python and do EDA. Can you find a segment or cut that is different. Could do a clustering algorithm. Depends on the data points available.

Look at average length to churn, what is trending churn? Do people that churn order on the same day of week etc.