Setting Sales Objectives: The Foundation of Retail Merchandise Planning

Every successful retail season begins with a number. Before a single product is ordered, before a rack is filled or a shelf is stocked, a retailer must answer one question: how much do we plan to sell? Setting sales objectives is the financial foundation of merchandise planning. Get it right, and inventory flows smoothly while cash stays healthy. Get it wrong, and you either run out of stock during peak demand or end up with unsold goods tying up working capital. This is the discipline of turning past data and market judgment into a concrete sales target.

Why past sales performance is the starting point 🔗

The most reliable place to begin is your own history. Retailers analyse last year’s sales for the same period before estimating anything for the season ahead. This historical record reveals growth rates, seasonal patterns, and the natural rhythm of demand across the calendar.

This approach is known as time-series forecasting, which examines past sales data to identify growth rates, cycles, trends, and seasonality. The merchandise planner uses data from the previous season to build a sales plan that aligns with the company’s strategy. For example, a planner for women’s ethnic wear might decide to plan a 10% increase over the prior year if the previous season missed potential sales due to stock shortages.

Historical data gives you a baseline, but it has a blind spot. It only records what you actually sold, not what you could have sold. If a popular kurta sold out in week two and you had stocked 200 units, your records show 200 sales even if true demand was 350. This is why past performance is a foundation, not the full answer.

Factors that change your sales numbers 🔗

A forecast built on history alone assumes the future will mirror the past. It rarely does. Accurate sales objectives require adjusting for forces that push numbers up or down. Several conditions can distort a simple year-on-year comparison: a one-time product launch that won’t repeat, an unusually long seasonal peak, price changes on best-sellers, or a wildly successful promotion that inflated the baseline.

Beyond these internal events, external conditions matter just as much. Consider the following when adjusting your forecast:

• Current sales trends: Is the category gaining or losing momentum right now, regardless of last year?
• Economic conditions: Inflation, festival-season spending power, and consumer confidence all shape household budgets.
• Local business climate: A new IT park or a closed factory near your store changes footfall and spending.
• Fashion influences: A colour, silhouette, or fabric going viral can lift or sink demand quickly.
• Competitor actions: A rival’s new outlet, aggressive discounting, or fresh range directly affects your pull.

Changes in your own store concept or market direction also play a role. A store repositioning from value to premium will see its sales mix shift entirely. Over-reliance on intuition produces forecasts that miss the mark, so each of these factors should be weighed with evidence rather than gut feeling.

Calculating planned sales for the season 🔗

Once you have your baseline and your judgment on influencing factors, the calculation itself is straightforward. The formula for seasonal planned sales is:

Planned sales = Last year’s sales + (Last year’s sales × planned increase percentage)

Suppose your menswear section sold ₹40,00,000 last festive season. After reviewing strong current demand and a healthy local economy, you settle on a planned increase of 12%. Your calculation becomes ₹40,00,000 + (₹40,00,000 × 0.12) = ₹44,80,000. This figure is your total rupee sales volume objective for the period.

The arithmetic is simple, but the judgment behind the planned increase percentage is where skill shows. That number is not pulled from the air. It is the product of analysing past performance, current trends, and every external factor discussed above. A 12% increase in a booming market may be conservative, while the same figure in a downturn might be dangerously optimistic.

Knowing your merchandise type before you forecast 🔗

Not every product behaves the same way, so a single forecasting method cannot serve your whole store. Retailers classify merchandise by its sales lifecycle, and each type demands a different approach to objectives and inventory.

Fad merchandise 🔗

Fads generate high sales for a very short period before fading quickly. Think of a toy or accessory tied to a hit film. The objective here is to capture the spike fast and exit before demand collapses, because leftover fad stock is almost impossible to sell.

Fashion merchandise 🔗

Fashion items have cyclical sales driven by changing tastes and lifestyles. They are volatile across seasons and difficult to forecast, often with limited selling history. Retailers may estimate sales by extrapolating from a similar item in a previous year. Buying fashion in the right quantity is both a science and an art, since excess inventory leads to unprofitable markdowns.

Staple merchandise 🔗

Staples are basic, essential items in continuous demand, largely unaffected by trends or seasons, such as everyday white shirts, socks, or grocery basics like rice and flour. Their predictable, stable demand makes forecasting relatively easy, and they are usually managed through a continuous replenishment system with safety stock held as back-up.

Seasonal merchandise 🔗

Seasonal goods fluctuate dramatically with the time of year, like woollens in winter, umbrellas in the monsoon, or diyas around Diwali. A product can be both seasonal and fashionable, which raises the forecasting challenge. The objective must concentrate sales within the climatic window when demand actually exists.

Building forecasts from multiple data sources 🔗

A single data source is a single point of failure. The strongest sales objectives draw on several streams of information so that the weakness of one is covered by the strength of another. Three sources work together effectively.

Past sales volume provides your internal baseline. Published secondary data, such as industry reports and market research, places your numbers in a wider context and signals where the whole market is heading. Customer surveys capture intent and preference that historical sales cannot, especially for new or fashion items with no track record. Combining quantitative history with market research and consumer feedback produces forecasts that are more accurate over time.

To organise this forecasting, retailers group products into logical categories. The widely used Nielsen definition of a category states that products should meet a similar consumer need, or be interrelated or substitutable, with the added condition that they remain logistically manageable in the store. A category is a distinct, manageable group of interrelated products built around consumer needs rather than mere product similarity. Forecasting at this category level, for instance treating all cooking oils as one unit, is far more meaningful than forecasting thousands of individual items in isolation.

Balancing variety, assortment, and availability 🔗

The final piece of setting sales objectives is deciding the shape of what you sell. This involves a constant trade-off between three things. Variety is the breadth of categories you carry. Assortment is the depth of choices within a single category. Product availability is ensuring stock is actually on the shelf when a customer wants it.

You cannot maximise all three at once, because space and capital are limited. A wide assortment strategy offers many product lines but less depth in each, the model a general store or supermarket follows. A deep assortment strategy offers a large number of options within fewer categories, the path a specialty store takes. Smaller stores in particular tend to choose one direction or the other because they lack the room for both.

This choice directly defines your store’s identity and its sales targets. A specialty footwear store stocking a deep range of athletic shoes in every size and colour sets very different objectives from a neighbourhood general store carrying a little of everything. The trade-off you make between breadth and depth is, in effect, a decision about who your customer is and how much they will buy.

What do you think? Looking at a store you shop at regularly, would you describe it as a specialty store with deep assortment or a general store with wide variety, and how does that shape what you buy there? And if you were setting a planned increase percentage for the upcoming festive season, which external factor would you weigh most heavily this year?

References

1. https://www.8thandwalton.com/blog/retail-forecasting/
2. https://courses.lumenlearning.com/wm-retailmanagement/chapter/forecasting-sales-numbers-for-merchandise-categories/
3. https://retalon.com/blog/sales-forecast-vs-demand-forecast
4. https://www.gooddata.com/blog/sales-forecasting-complete-guide-for-businesses/
5. https://courses.lumenlearning.com/wm-retailmanagement/chapter/merchandise-buying-systems/
6. https://www.eposnow.com/us/resources/retail-sales-forecasting/
7. https://en.wikipedia.org/wiki/Category_management
8. https://nielseniq.com/global/en/insights/analysis/2024/exploring-category-management-processes-steps-and-business-benefits-for-a-win-win-win-approach/
9. https://corporatefinanceinstitute.com/resources/management/assortment-strategies/
10. https://en.wikipedia.org/wiki/Retail_assortment_strategies