Previously , just one problem that some ecommerce brands face was discussed: how excluding product returns exaggerates the performance of paid media especially if it is regarded as the main driver for growth in the business. Looking further at a few more ecommerce data issues below and how to address them will give clarity on how to overcome them.

Problem #2: Apparel and Cosmetics brands misread repeat purchase

Fashion and beauty brands love repeat purchases. While this is a necessity, customers returning should looked at closely and segmented. Digging deeper helps to uncover patterns.

For fashion brands, checking if the repeat purchases are discretionary especially at full price versus driven by special offers thereby attracting bargain hunters should be prioritised. The lifetime value of the former is usually greater than the latter.

For beauty brands, it helps distinguishing between customers replenishing their consumables vs those spending on non-consumables, whether during promotions or at regular price.

When customer repeat purchase behaviour is not segmented appropriately:

• Customer retention is exaggerated.

• Customer Acquisition Cost (CAC) gets measured incorrectly.

• Promotional offers may heavily influence repeat purchases without notice.

For fashion brands, checking if the repeat purchases are discretionary especially at full price versus driven by special offers thereby attracting bargain hunters should be prioritised. The lifetime value of the former is usually greater than the latter.

Problem #3: Promotions skew historical data

In fashion and beauty, promotions are rarely unbiased. They:

Pull demand forward

Discount offers drive customers to make purchases earlier than usual which can lead to a quieter period once the offer ends.

Change customer behaviour

If promotional events occur too often, then customers become trained to anticipate offers and hold back on spending during full price periods.

Create false baselines

When analysing sales performance, marketing reporting could treat promotional periods as “business as usual” which makes it trickier to forecast demand for a similar period in the future.

So, leadership ends up asking:

• Are we growing profitably?
• Or are we just better at discounting than last year?

If you do not filter out revenue spikes from discount events, year‑over‑year comparisons, for example, become a faulty storytelling and not analysis.

Problem #4: Marketing, merchandising, and finance teams optimise different realities

This is where friction creeps in.

Marketing performance is measured on revenue and Return On Ad Spend (ROAS)

The focus of the marketing team is usually on ROAS and products that drive higher conversion are used to optimise subsequent campaigns. This becomes a challenge if there are higher returns of such products and that has been missed during analysis.

Merchandising cares about margin, sell‑through, and stock risk

It leaves a gap of true product performance when metrics like product-specific retention curves and return rates are not tracked appropriately.

Finance wants cash predictability

The team have their priorities on cashflow. There could be an alignment of marketing budget with revenue forecasts and requirements that the gap between stock replenishments and payments from customers be shorter.

What happens when these teams do not operate from a single source of truth?

• Budget decision meetings turn into debates
• Budget decisions are slow
• Senior management end up doing active arbitration as opposed to leading

This results in conflicts between the teams because of a lack of data clarity.

The real cost of the data challenges: leadership stops trusting the numbers

This is the silent harm done.

Once founders stop trusting data:

• Marketing budget decisions revert to instinct which is riskier
• The opinion with the majority wins
• The budget risk increases just when the business can least afford it

Profitable growth may not stop but it becomes unpredictable.

What changes when the problems are fixed?

Brands that break through these problems do not automatically get more data.

Instead, they restructure it around how money is made by:

• Adjusting marketing performance for product returns and net margin
• Separating demand creation from value creation
• Aligning teams around one commercial source of truth
• Make marketing budget decisions with increased confidence

The goal is not better dashboards but decisive leadership and intentional growth in profitability. That’s the difference between a brand that hovers at say £5m and one that scales beyond it applying the principles of the PROFIT Lens ™ framework.