Models and forecasts

Alex Tutor

Morning, Sofia. Before we forecast anything for CatChow, we need to talk about models. Everyone throws the word around, but here it means something specific.

You

A model... like a 3D model of the warehouse? Or you mean a spreadsheet?

Alex Tutor

Closer to the spreadsheet. A mathematical model is a simplified stand-in for something real — it lets us test ideas without running the actual experiment on customers.

Alex Tutor

Models come in two flavours. A functional model links inputs to one outcome, like an equation predicting churn from price and delivery speed.

A structural model instead breaks one big concept into its components — say, splitting 'customer loyalty' into trust, habit and advocacy.

You

So a churn equation is functional, and a loyalty breakdown is structural. Got it — can I build one in Excel by lunch?

Alex Tutor

Slow down. First we decide what might matter — that's the content model. Only then do we collect data and fit the formal model.

Alex Tutor

Question 1

Which of these is an example of a functional model at CatChow?

Choose one answerAn equation predicting next month's churn from price, delivery speed and support ticketsA breakdown of 'customer loyalty' into trust, habit and advocacyA table of every cat food SKU in the warehouseAn org chart of the analytics team

Alex Tutor

Models also split into static vs dynamic, and continuous vs discrete. Static describes one moment; dynamic describes change over time.

You

And continuous vs discrete?

Alex Tutor

Continuous variables take any value — order value in hryvnias. Discrete ones take a limited set — small, medium, large box.

You

So this week's segment mix is static, and 'orders are growing 5% a week' is dynamic?

Alex Tutor

Exactly. And most real CatChow models mix types — a dynamic model with both continuous revenue and discrete plan tiers.

Alex Tutor

Question 2

Match each modelling term to its description.

Tap an answer, then tap the row it belongs to. You can also drag.

Functional model
Structural model
Static model
Dynamic model

Answers left to place

Alex Tutor

Let's forecast something real: weekly orders. For four weeks CatChow logged 200, 215, 230, 245 orders — a clean upward trend.

Alex Tutor

Weekly orders: actual vs forecastThe trend fitted on weeks 1–4 predicted 260 orders for week 5; the actual came in at 258. It forecasts 275 for week 6.

Chart data

Week	Actual	Forecast
W1	200
W2	215
W3	230
W4	245
W5	258	260

Show 1 more row

W6

275

Alex Tutor

y = b_0 + b_1 \cdot \text{week}, \quad b_0 = 185,\ b_1 = 15

The fitted trend line through weeks 1–4: 15 more orders every week, starting from a base of 185.

Plug in week 6: 185 + 15 × 6 = 275 orders. That's the forecast — a straight line extended one step further.

You

Nice, so I just extend the line forever and I'm the office Nostradamus?

Alex Tutor

Not forever — only a little past your data. Trends bend: seasonality, saturation, a competitor's discount. Forecasts need an honest interval, not blind faith.

Alex Tutor

Question 3

CatChow's weekly orders for weeks 1–4 were 200, 215, 230, 245 — a straight line. Using the fitted trend y = 185 + 15 × week, what does it forecast for week 6?

Your answer

Units: orders (e.g. 275)

Alex Tutor

Week 5 actually landed at 258 orders. Our model predicted 260. That gap is called the residual: actual minus predicted.

You

258 − 260 = −2. Basically nailed it?

Alex Tutor

Right — a 2-order miss on 258 is under 1%. Compare that to a lazy model that just repeats last week's number: 245.

That naive model misses by 258 − 245 = 13 — over six times the trend model's error. The trend model is clearly better here.

You

So more error = worse model, obviously. Can I just add ten more variables and get the error to zero?

Alex Tutor

You could — and get a model that memorised the past perfectly and predicts the future terribly. That's overfitting.

Alex Tutor

Question 4

The model forecast 260 orders for week 5; the actual came in at 258. What is the residual (actual − predicted)?

Your answer

Units: orders (e.g. -2)

The fix: test the model on data it hasn't seen — a holdout period — before trusting its forecasts.

Alex Tutor

You

So a perfect fit on old data can still be a bad forecaster?

Alex Tutor

Exactly — and it's the single most common mistake I see in growth decks.

You

Noted. I'll stop bragging about my R² in stand-up.

Alex Tutor

Question 5

Once a forecasting model fits the historical data perfectly, you can trust it to predict the future just as well.

TrueFalse

One more habit worth building: state forecasts as an interval, like '260 ± 15', not a bare number.

You

Because week 5 could've easily been 250 or 270, not exactly 260?

Alex Tutor

Exactly — an interval tells everyone how much to trust the number, and keeps you honest when reality wobbles.

Last habit: re-check the model whenever the business context shifts — new pricing, new season, new competitor.

Alex Tutor

Question 6

Which of these are good practices when building a forecasting model? Select all that apply.

Choose all that applyCheck the forecast against data the model has not seen yet (a holdout period)Add every available variable so the model fits the training data as closely as possibleState the forecast as an interval, not a single exact numberRe-check the model whenever the underlying business context changesPrefer the model with the lowest error on the data it was trained on, ignoring new data

Alex Tutor

Quick recap before the harder questions: models simplify reality, forecasts extend a fitted trend, and every forecast needs an honesty check against new data.

You

And I promise not to extrapolate four weeks into a five-year plan.

Alex Tutor

That's the spirit. Now — explain it in your own words, then handle a real budget request.

You

Deal. Though I reserve the right to blame seasonality when I'm wrong.

Alex Tutor

Question 7Short answer · AI-checked task

In 2–3 sentences, explain to a colleague why a model that perfectly matches every historical data point can still be a bad forecasting tool.

Write your answer and get a score with feedback from our AI reviewer.

Log in to get AI feedback

Nice explanation. Now let's put it to work: marketing wants a decision, not a definition.

You

Uh oh. Real stakes.

Alex Tutor

Real stakes, real ad budget. Here's the situation.

You

Give me the scenario — I've got my holdout hat on.

Alex Tutor

Question 8Case · AI-checked task

Write your recommendation for the ad budget decision below.

CatChow's marketing lead fitted a straight line through the last 4 weeks of orders (200, 215, 230, 245) and wants to double next quarter's ad budget, arguing 'the trend clearly keeps going up at the same rate forever.' She asks you, the analyst, to sign off before she submits the budget request.

Write your answer and get a score with feedback from our AI reviewer.

Log in to get AI feedback

Solid reasoning. That's models and forecasts — next stop, clustering, where we let the data pick the segments for you.

Alex Tutor

That’s the lesson. You answered every task — nicely done.

Next lesson: Clustering for user segmentation