Case Study #3 - Foodie-Fi

Please note that the case study information provided below has been sourced from the following link: https://8weeksqlchallenge.com/case-study-3/

Table of Contents

Case Study Introduction Problem Statement Database Schema Question and Solution Database Creation & Table Populating Questions Sets A. Customer Journey B. Data Analysis Questions C. Challenge Payment Question D. Outside The Box Questions 2 Cents

Case Study Introduction

Danny spotted a gap in the streaming market, everyone's building Netflix for everything, but nobody had done Netflix for cooking shows specifically. So he found a few smart friends and launched Foodie-Fi in 2020, monthly and annual subscriptions, unlimited access to food videos from around the world.

The thing that makes this case study interesting isn't the videos, it's that Danny wanted every decision behind the business, investments, new features, pricing, backed by actual data instead of assumptions. So really, this is a subscription analytics case study wearing a food-content costume.

Problem Statement

Foodie-Fi needs help understanding its own subscription behaviour before Danny and his team make bigger bets on the business. Customers move through trials, upgrades, downgrades, and churn in ways that aren't always obvious just by looking at the raw subscription table.

The data itself is fairly lean, just two tables, but each needs careful handling. plans holds the five plan types, trial, basic monthly, pro monthly, pro annual, and churn, along with their pricing. subscriptions tracks the exact date each customer's plan_id starts, but that start_date means different things depending on the situation, downgrades don't take effect immediately, upgrades sometimes do, and churned customers keep access until their billing period ends even though their churn record shows up earlier.

Sorting through that correctly is really the whole exercise here, understanding customer journeys, churn patterns, plan transitions, and eventually building out a proper payments table for 2020 based on how each plan type actually gets billed.

Database Schema

(created using ChatGPT x Info on original site)

Question and Solution

I am going to use DuckDB SQL Workbench to solve these queries.

Database Creation & Table Populating

#1 Plans


CREATE SCHEMA IF NOT EXISTS foodie_fi;
DROP TABLE IF EXISTS plans;

--plans
SET search_path = foodie_fi;

create table foodie_fi.plans (
    plan_id integer,
    plan_name varchar(13),
    price decimal(5,2)
);

insert into foodie_fi.plans
    (plan_id, plan_name, price)
values
    (0, 'trial', 0.00),
    (1, 'basic monthly', 9.90),
    (2, 'pro monthly', 19.90),
    (3, 'pro annual', 199.00),
    (4, 'churn', null);

select * from foodie_fi.plans;

#2 Subscriptions


create table foodie_fi.subscriptions (
  customer_id integer,
  plan_id integer,
  start_date date
);

insert into foodie_fi.subscriptions
  (customer_id, plan_id, start_date)
values
  (1, 0, '2020-08-01'),
  (1, 1, '2020-08-08'),
  (2, 0, '2020-09-20'),
  (2, 3, '2020-09-27'),
  (3, 0, '2020-01-13'),
  (3, 1, '2020-01-20'),
  (4, 0, '2020-01-17'),
  (4, 1, '2020-01-24'),
  (4, 4, '2020-04-21'),
  (5, 0, '2020-08-03'),
  (5, 1, '2020-08-10'),
  (6, 0, '2020-12-23'),
  (6, 1, '2020-12-30'),
  (6, 4, '2021-02-26'),
  (7, 0, '2020-02-05'),
  (7, 1, '2020-02-12'),
  (7, 2, '2020-05-22'),
  (8, 0, '2020-06-11'),
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  (8, 2, '2020-08-03'),
  (9, 0, '2020-12-07'),
  (9, 3, '2020-12-14'),
  (10, 0, '2020-09-19'),
  (10, 2, '2020-09-26'),
  (11, 0, '2020-11-19'),
  (11, 4, '2020-11-26'),
  (12, 0, '2020-09-22'),
  (12, 1, '2020-09-29'),
  (13, 0, '2020-12-15'),
  (13, 1, '2020-12-22'),
  (13, 2, '2021-03-29'),
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  (995, 2, '2020-12-06'),
  (996, 0, '2020-11-11'),
  (996, 1, '2020-11-18'),
  (996, 4, '2020-12-07'),
  (997, 0, '2020-07-27'),
  (997, 1, '2020-08-03'),
  (997, 2, '2020-08-26'),
  (997, 4, '2020-11-14'),
  (998, 0, '2020-10-12'),
  (998, 2, '2020-10-19'),
  (999, 0, '2020-10-23'),
  (999, 2, '2020-10-30'),
  (999, 4, '2020-12-01'),
  (1000, 0, '2020-03-19'),
  (1000, 2, '2020-03-26'),
  (1000, 4, '2020-06-04');

  select * from foodie_fi.subscriptions;

Questions Sets

A. Customer Journey

Wanted to actually see what a subscription journey looks like instead of just staring at aggregate churn numbers, so picked the first 8 customers and traced each one from trial to wherever they ended up.

Each row in subscriptions is really just one event, a date someone entered a plan. Joined it against plans to get readable names instead of IDs, ordered everything by start_date, then used string_agg() to collapse each customer's scattered rows into one clean string.


select
    s.customer_id,
    string_agg(
        p.plan_name,
        ' → '
        order by s.start_date
    ) as onboarding_journey
from foodie_fi.subscriptions s
join foodie_fi.plans p
    on s.plan_id = p.plan_id
where s.customer_id <= 8
group by s.customer_id
order by s.customer_id;

Once that ran, the stories were oddly readable.

Customers 1, 3, and 5 all did the same thing, trial straight into Basic Monthly, no drama, no churn recorded.

Customer 2 skipped the ladder entirely, went from trial directly to Pro Annual, which felt like someone who already knew they wanted in before the trial even ended.

Customers 4 and 6 followed the exact same path as the Basic converts, except they eventually churned, a reminder that converting and staying are two different things, and lumping them together would've hidden something important.

Customers 7 and 8 were the interesting ones, started cheap on Basic, then upgraded to Pro Monthly later, the "grew into it" crowd.

Eight customers, and four distinct behaviours already showed up: settlers who convert and stay, one committer who went straight to premium, growers who upgrade over time, and leavers who convert but eventually walk away.

Small sample, but it's already hinting at the real questions worth chasing in the full dataset, what the actual trial-to-paid conversion rate looks like, how many people upgrade and how long that takes, and whether Basic Monthly churns more than the other plans. Eight rows shouldn't be enough to see the shape of a business, but somehow they were.

B. Data Analysis Questions

#1 How many customers has Foodie-Fi ever had?


select
    count(distinct customer_id) as customer_count
from foodie_fi.subscriptions;

#2 What is the monthly distribution of trial plan start_date values for our dataset - use the start of the month as the group by value


select
    date_trunc('month', s.start_date)::date as month,
    count(*) as trial_count
from foodie_fi.subscriptions s
join foodie_fi.plans p
    on s.plan_id = p.plan_id
where p.plan_name = 'trial'
group by month
order by month;

#3 What plan start_date values occur after the year 2020 for our dataset? Show the breakdown by count of events for each plan_name


select
    p.plan_name,
    count(*) as event_count
from foodie_fi.subscriptions s
join foodie_fi.plans p
    on s.plan_id = p.plan_id
where s.start_date >= date '2021-01-01'
group by p.plan_name
order by event_count desc;

#4 What is the customer count and percentage of customers who have churned rounded to 1 decimal place?


select 
count(distinct case when s.plan_id = 4 then s.customer_id end) as churned_pros,
round(count(distinct case when s.plan_id = 4 then s.customer_id end)*100/ count(distinct s.customer_id),1) as chur_perc
from foodie_fi.subscriptions s;

#5 How many customers have churned straight after their initial free trial - what percentage is this rounded to the nearest whole number?


with customer_journey as (
select s.customer_id,
string_agg
( cast(s.plan_id as varchar), 
	' → ' order by s.start_date
) as journey
  from foodie_fi.subscriptions s 
  group by s.customer_id
)

select count(*) as churned_after_trial,
round(
count(*) * 100.0 / 
(select count(*) from customer_journey), 0) 
as churn_perc
from customer_journey
where journey = '0 → 4';

#6 What is the number and percentage of customer plans after their initial free trial?


with next_plan as (
select 
  s.customer_id, s.plan_id, lead(s.plan_id) over ( partition by s.customer_id order by s.start_date) as next_plan_id
from foodie_fi.subscriptions s
)
select
    p.plan_name,
    count(*) as customer_count, round( count(*) * 100.0 / sum(count(*)) over (), 1 ) as percentage
    from next_plan np
    join foodie_fi.plans p on np.next_plan_id = p.plan_id 
    where np.plan_id = 0 
    group by p.plan_name 
    order by customer_count desc;

#7 What is the customer count and percentage breakdown of all 5 plan_name values at 2020-12-31?


with ranked_plans as (
    select
        s.customer_id,
        s.plan_id,
        p.plan_name,
        s.start_date,
        row_number() over (
            partition by s.customer_id
            order by s.start_date desc
        ) as rn
    from foodie_fi.subscriptions s
    left join foodie_fi.plans p
        on s.plan_id = p.plan_id
    where s.start_date <= '2020-12-31'
),

current_plans as (
    select
        customer_id,
        plan_name
    from ranked_plans
    where rn = 1
)

select
    plan_name,
    count(*) as customer_count,
    round(
        count(*) * 100.0 / sum(count(*)) over (),
        1
    ) as percentage
from current_plans
group by plan_name
order by customer_count desc;

#8 How many customers have upgraded to an annual plan in 2020?


select
    count(distinct s.customer_id) as pro_annual_2020_count
from foodie_fi.subscriptions s
where s.plan_id = 3
    and date_trunc('year', s.start_date) = '2020-01-01';

#9 How many days on average does it take for a customer to an annual plan from the day they join Foodie-Fi?


with annual_customers as (
    select
        customer_id,
        min(start_date) filter (where plan_id = 0) as trial_date,
        min(start_date) filter (where plan_id = 3) as annual_date
    from foodie_fi.subscriptions
    group by customer_id
)

select
    round(
        avg(datediff('day', trial_date, annual_date)),
        0
    ) as avg_days_to_annual
from annual_customers
where annual_date is not null;

#10 Can you further breakdown this average value into 30 day periods (i.e. 0-30 days, 31-60 days etc)


with annual_customers as (
    select
        customer_id,
        min(start_date) filter (where plan_id = 0) as trial_date,
        min(start_date) filter (where plan_id = 3) as annual_date
    from foodie_fi.subscriptions
    group by customer_id
),

upgrade_days as (
    select
        customer_id,
        datediff('day', trial_date, annual_date) as days_to_annual
    from annual_customers
    where annual_date is not null
),

bucketed as (
    select
        customer_id,
        floor(days_to_annual / 30.0)::int as bucket_num
    from upgrade_days
)

select
    (bucket_num * 30) || ' - ' || (bucket_num * 30 + 30) as days_bucket,
    count(*) as num_of_customers
from bucketed
group by bucket_num
order by bucket_num;

#11 How many customers downgraded from a pro monthly to a basic monthly plan in 2020?


with plan_changes as (
    select
        s.customer_id,
        s.plan_id,
        s.start_date,
        lead(s.plan_id) over (
            partition by s.customer_id
            order by s.start_date
        ) as next_plan_id,
        lead(s.start_date) over (
            partition by s.customer_id
            order by s.start_date
        ) as next_start_date
    from foodie_fi.subscriptions s
)

select
    count(distinct customer_id) as downgraded_customers
from plan_changes
where plan_id = 2
  and next_plan_id = 1
  and next_start_date < date '2021-01-01';

C. Challenge Payment Question

The Foodie-Fi team wants you to create a new payments table for the year 2020 that includes amounts paid by each customer in the subscriptions table with the following requirements:

monthly payments always occur on the same day of month as the original start_date of any monthly paid plan
upgrades from basic to monthly or pro plans are reduced by the current paid amount in that month and start immediately
upgrades from pro monthly to pro annual are paid at the end of the current billing period and also starts at the end of the month period
once a customer churns they will no longer make payments

SOLUTION

This one was the actual challenge question, not a "fix the query" problem, a from-scratch build with four rules that all interact with each other. Took a while just to think through the logic before writing a single line of SQL.

The four rules, broken down: monthly billing anchors on each plan's own start date and steps forward month by month. Basic-to-Pro upgrades happen immediately but get credited for whatever was already paid that month under Basic. Pro Monthly to Pro Annual switches don't start billing on the recorded switch date, annual billing only kicks in once the current monthly cycle actually finishes. And churn just stops everything, no more payments once a customer hits plan_id = 4.


drop table if exists foodie_fi.payments;

create table foodie_fi.payments as
with recursive

plan_changes as (
    select
        s.customer_id,
        s.plan_id,
        p.plan_name,
        p.price,
        s.start_date,
        lag(s.plan_id) over (
            partition by s.customer_id order by s.start_date
        ) as prev_plan_id,
        lag(s.start_date) over (
            partition by s.customer_id order by s.start_date
        ) as prev_start_date,
        lead(s.start_date) over (
            partition by s.customer_id order by s.start_date
        ) as next_start_date
    from foodie_fi.subscriptions s
    join foodie_fi.plans p
        on s.plan_id = p.plan_id
    where s.start_date <= '2020-12-31'
),

plan_segments as (
    select
        customer_id,
        plan_id,
        plan_name,
        price,
        next_start_date,

        case
            when plan_id = 3 and prev_plan_id = 2
                then prev_start_date
                     + interval (datediff('month', prev_start_date, start_date) + 1) month
            else start_date
        end as effective_start_date,

        case
            when prev_plan_id = 1 and plan_id in (2, 3)
                then (select price from foodie_fi.plans where plan_id = 1)
            else 0
        end as credit

    from plan_changes
    where plan_id in (1, 2, 3)
),

payment_calendar as (
    select
        customer_id, plan_id, plan_name, price, credit,
        effective_start_date as payment_date,
        next_start_date
    from plan_segments

    union all

    select
        customer_id, plan_id, plan_name, price,
        0 as credit,
        case
            when plan_id = 3 then payment_date + interval 1 year
            else payment_date + interval 1 month
        end as payment_date,
        next_start_date
    from payment_calendar
    where
        case
            when plan_id = 3 then payment_date + interval 1 year
            else payment_date + interval 1 month
        end < coalesce(next_start_date, date '2021-01-01')
        and payment_date < date '2020-12-31'
)

select
    row_number() over (
        partition by customer_id order by payment_date
    ) as payment_order,
    customer_id,
    plan_id,
    plan_name,
    payment_date::date as payment_date,
    round(price - credit, 2) as amount
from payment_calendar
where payment_date <= date '2020-12-31'
order by customer_id, payment_date;

Didn't trust this the moment it ran, honestly. Logic like this is easy to get subtly wrong at the edges, so spot-checked it against a few known journeys.

Customer 19 goes trial into pro monthly into pro annual, exactly the case the deferred-billing rule was written for, checked their annual payment landed a month after the switch, not on it. Customers 7 and 8 upgrade from basic to pro monthly, so the payment on the switch date should come out to $10.00, not the full $19.90, confirmed the credit logic actually applied. Customers 4 and 6 churn after basic, made sure no payment rows showed up on or after their churn date.

One thing I'm still slightly wary of, if a pro-monthly-to-annual switch happens to land exactly on the billing anniversary instead of mid-cycle, the plus-one-month logic could accidentally push the annual start a full cycle later than it should. Worth running a targeted check for any customers where that's actually the case before trusting this at scale.

D. Outside The Box Questions

The following are open ended questions which might be asked during a technical interview for this case study - there are no right or wrong answers, but answers that make sense from both a technical and a business perspective make an amazing impression!

#1 How would you calculate the rate of growth for Foodie-Fi?

Depends what you mean by growth, honestly. Pick the wrong number and you'll fool yourself.

New trials per month is the easiest one to pull and the easiest one to be wrong about. Trials mean nothing if they don't convert.

Paid customer count matters more. Take each customer's latest plan using row_number ordered by start_date desc, filter to basic, pro monthly, pro annual, count by month.

But the number I'd actually trust is new paid minus churned, per month. Foodie-Fi could sign up 500 trials and still be dying if 600 people are leaving out the back. Learned this one the hard way with request loss. You can onboard Pros all day, doesn't matter if hub level supply gaps are pushing them out faster than you bring them in. Headline number looks fine. Business isn't.

Revenue growth needs its own line too. Sum of price for each customer's current plan, by month. A business can grow in customer count and shrink in revenue at the same time if everyone's drifting to basic. Seen that pattern before, doesn't announce itself.

If someone made me pick one number for a board deck, I wouldn't. I'd show net paid growth and revenue growth together. Either one alone hides something the other one's telling you.

#2 What key metrics would you recommend Foodie-Fi management to track over time to assess performance of their overall business?

Not one dashboard. A few numbers, because different people in the room are worried about different things.

Trial to paid conversion. First checkpoint, nothing after it matters if this is broken.

Plan mix, basic vs pro monthly vs pro annual, tracked monthly. Tells you if the pricing ladder is doing anything or if everyone's just sitting on the cheapest option.

Monthly churn rate. Obvious one. Still the one that decides everything else.

Time to upgrade, using datediff between plan changes. Slow upgrades usually mean the upsell moment is happening at the wrong point in the product, not that customers don't want to upgrade.

Annual plan penetration. Cash upfront, harder to churn mid cycle, worth watching on its own.

Cohort retention, grouped by trial start month, tracked at month 1, 3, 6, 12. A single blended churn number will hide a bad acquisition month sitting right inside the average.

My version of this board at UC is request loss, RL by category, HH adherence, reversal rate. Four different angles on the same question, is supply meeting demand. Any one alone would've had me chasing the wrong fire more than once.

#3 What are some key customer journeys or experiences that you would analyse further to improve customer retention?

A few, in the order I'd actually look at them.

The decision right before or right after trial ends. Some customers go straight to pro, skipping basic entirely. Would want to know if that's just higher intent from day one or something in the trial experience nudging them there. Data shows the pattern exists, doesn't tell you why yet.

Basic to churn versus basic to upgrade. Same starting point, completely different ending. Something happens during that basic period, engagement, price, relevance, that splits people one way or the other. Need usage data sitting next to the plan data to find where that split actually happens.

Early churn versus late churn. Someone leaving 10 days into a paid plan is a different problem than someone leaving after 8 months on annual. First one's an onboarding mismatch. Second one's value eroding slowly. Same plan_id 4, completely different fix required.

The renewal window on annual plans, if it existed in this data. Highest stakes moment in the whole lifecycle. One bad experience there costs you a year, not a slow monthly bleed.

Same instinct as the training eligibility fix I did at UC. Rules made sense on paper, blocked good partners in practice. Wouldn't be surprised if Foodie-Fi's plan data has a similar gap between the journey on paper and the one actually happening.

#4 If the Foodie-Fi team were to create an exit survey shown to customers who wish to cancel their subscription, what questions would you include in the survey?

Short. Five questions, one open box at the end. Whoever's cancelling has already spent their patience on the decision, not on your survey.

What's the main reason you're leaving. Single select, too expensive, not using it enough, found something better, technical issues, temporary, will be back, other. This one bucket alone tells you what's actually fixable.
How would you rate the value for what you paid. 1 to 5. Separates a pricing problem from a value perception problem, need different fixes for each.
Would a lower price or different plan change your mind. Yes, no, maybe. Tells you how many of these churns are actually saveable.
Was there a feature or piece of content missing. Open text, optional. Hardest one to quantify, most useful one if someone actually reads them instead of just counting them.
How likely are you to come back. 1 to 5. Separates a pause from a real exit, changes whether you write the customer off or run a win back later.

Wouldn't add anything that feels like we're trying to talk them out of leaving mid survey. Nobody answers honestly when the form feels like a trap.

#5 What business levers could the Foodie-Fi team use to reduce the customer churn rate? How would you validate the effectiveness of your ideas?

In order of how confident I'd be without seeing more data first.

Annual plan nudges for basic and pro monthly customers around the 3 to 6 month mark. Transitions to annual already happen in this data. Find the typical timing, place the offer right before the point where people currently churn instead of upgrade. Same logic as the churn based onboarding model I built at UC, using history to plan ahead instead of reacting after the fact.

A win back flow for early churners specifically. If someone leaves within 30 days of their first paid plan, that's a different population than someone who leaves after a year. Needs a different message. Something closer to here's what you missed, not a generic discount.

Whatever's keeping people stuck on basic without ever moving up or down. That segment isn't growing, isn't leaving, just sitting there until it eventually does leave. Most under looked group in most subscription businesses. Could be price anchoring, could be nobody knows what pro actually offers, could be genuine indifference. Wouldn't guess, would go find out.

None of this gets validated by launching it and hoping. Hold out a control group, run the lever on a treatment group only, track the same churn and revenue numbers from question 2 across both, over 60 to 90 days, not a before and after on the whole base where seasonality takes credit for something it didn't cause. Same reasoning behind rolling out UC MASTER narrow first instead of announcing it company wide. Start small, measure against a real baseline, scale what's actually working.

2 Cents

The churn piece was the part that actually stuck with me. Everywhere else in the dataset you're counting things that happened. Churn is the one place you're counting an ending, and plan_id = 4 by itself tells you nothing about which ending it was. A customer who tries trial and leaves without ever paying is a completely different failure than someone who rides pro annual for eight months and then leaves. Same number in the same column. Took a lag() over the customer's timeline before that difference even showed up in the query, not just in my head.

The conversion funnel questions were the ones that surprised me. Going in, I expected trial to paid to basically be one clean rate. Turned out the more interesting number wasn't whether people converted, it was which plan they converted into first. Some go trial straight to pro annual, no basic in between at all. That's not a slower or faster funnel, it's a different customer making a different bet on day one, and the schema doesn't hand you that insight, you have to go looking for it row by row.

Small gripe, same one as Pizza Runner really. plan_id and start_date tell you what changed and when, never why. Every genuinely interesting question in this case study, why someone upgraded, why someone churned, why someone skipped basic entirely, needed a story the table doesn't carry. Fine for a practice dataset, would be the first thing I'd go fix in a real one.

If there's one real takeaway, it's this. Subscription data looks deceptively simple, one row per plan change, three or four plan types, one churn flag. But the business questions sitting on top of it, growth, retention, which lever actually moves churn, needed the same discipline as Pizza Runner did. Define the grain first, order the timeline before you touch an aggregate, decide what a conversion even means before you count one. The queries were never the hard part. Knowing what one row of subscriptions actually represents in a real customer's life, that's what made the rest of it click.