---
title: "Ecommerce sales forecasting and setting growth targets"
author: "Performetic Ekibi"
url: "https://www.performetic.com/en/blog/ecommerce-sales-forecasting-growth-targets"
published: "2026-03-27T08:00:00.000Z"
updated: "2026-10-05T02:42:04.887Z"
---

# Ecommerce sales forecasting and setting growth targets

> A realistic ecommerce sales forecast is not last year's revenue plus a percentage. Split revenue into its parts: new customer revenue comes from ad budget divided by acquisition cost, returning customer revenue from your customer base and repeat purchase rate. Then layer in seasonality and set the target with pessimistic, base and optimistic scenarios.

## How do you forecast ecommerce sales and set growth targets?

A realistic ecommerce sales forecast splits revenue into new customers and returning customers. New customer revenue comes from ad budget divided by customer acquisition cost; returning customer revenue comes from your active customer base and repeat purchase rate. You then layer seasonality on top and choose the target from three scenarios rather than a single number.

"We did 10 million last year, let's do 13 million this year" is a wish, not a target. If nobody has written down which budget, which acquisition cost and how many customers get you there, the target is either missed or hit through unprofitable growth.

## Should you forecast top-down or bottom-up?

There are two basic approaches to forecasting, and the healthiest result comes from comparing them.

| Approach | How it works | Strength | Weakness |
|---|---|---|---|
| Top-down | Target derived from market size or past growth rate | Fast, easy for leadership to grasp | Weak link to budget and capacity |
| Bottom-up | Revenue built from budget, acquisition cost, customer base and order value | Actionable, shows the levers | Needs more data and effort |

The method: build the bottom-up model first, then compare it with leadership's top-down target. If there is a big gap, write down explicitly which lever will close it: more budget, lower acquisition cost or a higher repeat rate.

## How do you build a sales forecast model?

The bottom-up model rests on one simple split: revenue = new customer revenue + returning customer revenue. The split matters because the two revenue streams have completely different levers.

1. **Estimate new customers.** Planned ad budget / new customer acquisition cost (CAC).
2. **Estimate new customer revenue.** New customers x first-order average order value.
3. **Define your active customer base.** Customers who bought at least once in the last 12 months.
4. **Estimate returning customer revenue.** Active base x repeat purchase rate for the period x average repeat order value.
5. **Add organic and direct.** Forecast new customers from non-paid channels separately, based on your history.

We cover how CAC relates to lifetime value in our [CAC, LTV and ROAS guide](/en/blog/cac-vs-ltv-vs-roas-ecommerce).

### Worked example

Example: a home decor brand is planning its next quarter. All numbers are hypothetical.

| Line | Calculation | Result |
|---|---|---|
| Quarterly ad budget | Planned | $90,000 |
| New customer CAC | Last quarter's average | $60 |
| New customers from ads | 90,000 / 60 | 1,500 |
| First-order AOV | Historical | $140 |
| New customer revenue | 1,500 x 140 | $210,000 |
| Active customer base | Last 12 months | 8,000 |
| Quarterly repeat rate | Historical | 12% |
| Repeat order AOV | Historical | $120 |
| Returning customer revenue | 8,000 x 0.12 x 120 | $115,200 |
| Total (excluding organic) | | $325,200 |

The table gives you more than a number: it shows the levers that move it. Lifting the repeat rate from 12% to 15%, for example, adds about $28,800 in revenue without any extra ad budget.

## Does acquisition cost stay flat as budget grows?

No, and this is the most common modelling mistake. When you double ad budget, new customers rarely double, because the algorithm starts reaching less relevant, more expensive users. So instead of holding CAC flat as budget rises, step it up gradually and use past budget increases in your own account as a reference.

On Google Ads, Performance Planner helps estimate this relationship. According to Google's help page, it simulates auctions from the last 7 to 10 days, accounts for seasonality and refreshes forecasts daily. You can see how conversions and cost might change as you adjust budget on a monthly or quarterly basis. If you are planning a new Search campaign, Keyword Planner also provides performance forecasts for the keywords you select.

These forecasts are guidance, not guarantees. Comparing your own model with platform forecasts is a good way to stress-test the assumptions on both sides.

## How do you add seasonality to a forecast?

Splitting an annual target into twelve equal parts is wrong for almost every ecommerce brand. The better method uses historical monthly revenue shares.

1. Pull monthly revenue for the last two or three years.
2. Calculate each month's share of annual revenue.
3. Flag and adjust unusual months (a one-off big promotion, a stock crisis, a site outage).
4. Distribute the annual target across months using those shares.

If your history is thin, Google Trends can hint at how interest in your category is distributed through the year. But according to Google, Trends shows normalized relative interest, not absolute search counts, so do not try to convert it into revenue; use it only to compare periods.

## Why build scenarios instead of a single target?

A single number assumes everything goes to plan. Three scenarios manage expectations and let you decide in advance what you will do in each situation.

| Scenario (example) | CAC assumption | Repeat rate | Used for |
|---|---|---|---|
| Pessimistic | 20% higher | 10% | Floor for cash flow and stock planning |
| Base | Last quarter's average | 12% | The official target |
| Optimistic | 10% lower | 15% | Ceiling for stock and operational capacity |

Ordering stock against the base case and planning cash against the pessimistic case lets you grow without betting the business.

## How do you track progress against the target?

A forecast should not be made once and forgotten. Compare actuals with forecast every week and read any gap through the parts of the model: did new customers fall, did CAC rise, did repeat purchases weaken or did order value shrink?

If you use GA4, predictive metrics can support this tracking. According to Google, GA4 can estimate the probability that a user active in the last 28 days will purchase in the next 7 days, and the revenue expected from that user in the next 28 days. It requires thresholds, though: in the last 28 days, over a seven-day period, at least 1,000 returning users must have purchased and at least 1,000 must not have, so not every store will qualify.

If the target is missed systematically, rethink which lever growth should come from. We cover ways to restart flat revenue in our [guide to growing a stagnant store](/en/blog/how-to-grow-stagnant-ecommerce-revenue).

## Key takeaways

- Forecast revenue as new customer revenue plus returning customer revenue.
- Estimate new customers as budget / CAC, but assume CAC rises as budget grows.
- Spread the annual target across months using historical monthly shares.
- Build pessimistic, base and optimistic scenarios; plan stock on base and cash on pessimistic.
- Track the gap weekly and find which part of the model it comes from.

If you would like a growth model built on your own data, the Performetic team can prepare a [free growth analysis](/en/contact).

## FAQ

### What data do I need for an ecommerce sales forecast?

At minimum: monthly revenue history, new and returning customer counts, new customer acquisition cost, first and repeat order values and repeat purchase rate. Add your planned ad budget and you can build a bottom-up model that shows which levers move revenue.

### How can a new store with no history forecast sales?

Without history, the first forecast is built from budget and an assumed acquisition cost, then updated quickly with the first weeks of real data. On Google Ads, Keyword Planner can give a rough idea of demand and cost for Search campaigns. In the early months, updating the forecast often matters more than committing to one number.

### If I double my ad budget, will sales double?

Usually not. As budget grows, ads reach less relevant and more expensive users, so acquisition cost rises. In your model, increase CAC gradually for large budget increases, and check the relationship with tools such as Performance Planner on Google Ads.

### How often should I update a sales forecast?

The annual target can be set once, but compare actuals with forecast weekly and refresh the model at least monthly. Rerun the forecast immediately after a major cost change, a new channel launch or an unexpected shift in demand.

## Sources

- [About Performance Planner (Google Ads Help)](https://support.google.com/google-ads/answer/9230124?hl=en)
- [Use Keyword Planner (Google Ads Help)](https://support.google.com/google-ads/answer/7337243?hl=en)
- [[GA4] Predictive metrics (Analytics Help)](https://support.google.com/analytics/answer/9846734?hl=en)
- [FAQ about Google Trends data (Trends Help)](https://support.google.com/trends/answer/4365533?hl=en)