Range Planning: How to Identify Your Product Range Needs

Walking into a retail store, the rows of T-shirts, jeans, and jackets feel like they appeared by chance. They didn’t. Every product on the rack is the result of a deliberate exercise called identifying range needs-the very first step of range planning, where a buyer decides exactly what to stock, in which price bands, in what quantity, and in which styles and colours. Get this right, and the store sells through cleanly while customers find what they want. Get it wrong, and you are left with dead stock, markdowns, and lost sales. This guide breaks down the process into clear, repeatable steps, using a fashion retailer’s seasonal plan as the running example.

Why range needs identification is the foundation 🔗

Range needs identification is the systematic process of deciding which products should fill your retail space, based on data rather than gut feeling. It connects past performance with future investment, ensuring every product decision aligns with business goals. As merchandise planners use information from past sales and consumer insights to choose products they are confident will sell, the aim is to minimise outdated stock while still meeting demand for every item the shopper expects.

The core principle is simple: the past predicts the future, but only if you read it correctly. A balanced range needs enough breadth (variety of categories), width (options within a category), and depth (stock per option) to give customers plenty of choice without drowning the store in unsold inventory. The steps below show how to move from a spreadsheet of last season’s numbers to an actual buying plan.

Step 1: Use past sales data to select product sub-groups 🔗

The starting point is always the previous season’s sales. Pull the figures for every product sub-group and convert them into percentage contributions to total sales. This instantly reveals which categories pull their weight.

Suppose a casual-wear retailer reviews last season and finds T-shirts contributed 20.63% of total sales-the single largest share. A category performing at this level becomes a “sure-shot” inclusion. There is no debate about whether to carry T-shirts next season; the data has decided. This is exactly why the base for most assortment planning is “last year,” where planners review past performance by category and subcategory to understand where opportunity was won or missed.

Step 2: Set a threshold for including product lines 🔗

Top performers select themselves, but the long tail needs a rule. The practical method is to set a cutoff percentage for inclusion. For instance, you might decide to carry every sub-group that contributed more than 2% of sales. Applying this threshold brings in blouses, jackets, jeans, and shrugs alongside the obvious T-shirt winner.

The goal of this cutoff is to cover at least 90% of last season’s sales with proven categories. That leaves a small slice-say 7%-deliberately open for experimentation. This reserved portion lets you test new themes, such as introducing a “party wear” capsule, without betting the whole season on an unproven idea. It is a disciplined balance between safety and innovation.

Why the experimentation buffer matters 🔗

Retail is not static. Consumer preferences shift, and a range built purely on last year’s winners will eventually feel stale. The reserved percentage acts as a controlled space for testing trends. If the experiment succeeds, it earns a bigger slice next season; if it fails, the damage is contained to a small share of the budget. This is how range planning stays a key strategic tool as consumer preferences evolve and the retail landscape becomes more competitive.

Step 3: Segment by price range for deeper analysis 🔗

Choosing categories is only the first cut. When price is a major factor in the customer’s decision-as it usually is in apparel-you need to drill into how each category sells across price brackets.

Take the T-shirt category again. Organising its sales by price band might reveal that the Rs 301-500 bracket and the Rs 501-700 bracket each contribute about 47% of T-shirt sales, while higher and lower bands contribute little. This tells you precisely where customer demand concentrates.

Low-performing price brackets now face a decision: drop them entirely, or attempt to revive them through better design, improved sourcing, or sharper pricing. Either way, you avoid pouring money into a price band the customer has already rejected. This price-led analysis is what prevents dead stock and keeps inventory aligned with actual demand. Comparing your stock mix against your sales mix is central here-if 20% of stock sits in a band that only earned 10% of sales, the mix needs cutting, and the reverse signals an opportunity to expand.

Step 4: Allocate the investment budget across price ranges 🔗

With categories and price bands decided, the plan becomes financial. This step links your total seasonal investment to each category and then to each price band, so spending mirrors proven consumer preference.

Start with two numbers: the total season investment and the planned inventory turnover ratio. The turnover ratio measures how many times you expect to sell and replace your stock during the season. As an efficiency metric, inventory turnover is used during merchandise planning while calculating the buying budget, ensuring you buy the right amount to turn stock at the desired rate.

Assume a total investment of Rs 1 crore and a turnover ratio of 3x. The total stock value you can support is the investment multiplied by the turnover-but the allocation to each category follows its sales contribution. For T-shirts at 20.63%, you allocate roughly Rs 20,63,000 of the category budget. You then split this T-shirt budget across its price ranges in proportion to each band’s share of sales. Because the Rs 301-500 and Rs 501-700 bands each took 47%, each receives 47% of the T-shirt allocation, with the small remainder spread across minor bands.

This is the logic of open-to-buy planning, where inventory investment must be productive, accurate, and timely-purchasing enough stock to support sales profitably without tying up cash in slow movers.

Step 5: Calculate quantities, sets, and options per price range 🔗

A budget in rupees still cannot be handed to a supplier. It has to become units. This step converts each price-band budget into the number of pieces, sets, and styles you actually need to buy.

First, find the average price of each band, usually the midpoint. For the Rs 301-500 band, the midpoint is roughly Rs 400. Divide the band’s allocated budget by this average price to get the quantity of pieces. If the band’s allocation works out to support around 2,424 pieces at that average price, that is your unit target.

Next, convert pieces into sets and options:

• Sets: A set is a pack of pieces, for example 8 pieces per set. Dividing 2,424 pieces by 8 gives roughly 303 sets.
• Options: An option is a distinct style or design. Decide how many sets you want per option-if you plan a certain number of sets per style, dividing total sets by sets-per-option gives the number of options (styles) to develop or buy.

The number of options is one of the most important outputs of range planning. A range plan determines how many unique products are needed to support the range, which then directs design and sourcing. This single calculation turns an abstract budget into a concrete buying brief: this many styles, in this price band, in this category.

Step 6: Bifurcate options by style and colour features 🔗

Once you know how many options a price band needs, you decide what those options look like. This is where the range is shaped to match real consumer taste, working down a hierarchy of features in order of importance.

After price, the next most influential feature is typically style, followed by colour. Suppose that within the T-shirt Rs 301-500 band, round-neck styles contributed 45% of sales and collared styles 55%. You split the available options in the same proportion-roughly 45% round-neck, 55% collared. Then, within each style, you split again by colour. If dark shades took 45% and medium shades 55%, the colours of your options follow that ratio.

This hierarchical, feature-by-feature approach mirrors how assortment classification is built on hierarchical levels of style features, ordered by the importance customers give each one. For a bridal lehenga the order might be colour, material, and drape; for a basic T-shirt it is style then colour. The principle stays the same: let the customer’s past behaviour decide the proportions.

Adjusting the mix against last year’s performance 🔗

Feature planning is not a one-time split frozen for all time. The smartest planners compare what they stocked against what sold. If you carried 20% of options in one colour but it earned only 10% of sales, you reduce that colour next season. If a colour earned 15% of sales from just 6% of stock, you expand it. This continuous correction is how a range gets sharper each season, fixing last year’s mistakes to build a stronger base. The same matching logic that offers the right styles, in the right sizes and colours, through the right channels sits at the heart of effective assortment planning.

Bringing the steps together 🔗

Identifying range needs is a funnel. You begin broad-total category sales-and progressively narrow down through price bands, budgets, quantities, options, styles, and finally colours. Each step inherits its proportions from the one before, and every proportion traces back to real customer behaviour from the previous season. The result is a range where nothing is arbitrary: each style, in each colour, in each price band exists because the data showed customers want it.

This discipline is what separates a profitable range from a cluttered one. It controls inventory investment, reduces the risk of markdowns, and ensures the store carries depth where demand is real and restraint where it is not. The small experimentation buffer keeps the range fresh, while the data-driven core protects the bulk of the budget. Done well, range needs identification turns a season’s worth of guesswork into a structured, defensible plan.

What do you think? If you were given last season’s sales data for a category you know well-say sneakers or smartphones-which feature would you treat as the most important after price, and why? And how large an experimentation buffer would you be comfortable carving out from a proven budget to test new trends?

References

1. https://www.iwanttobeafashionbuyer.com/merchandise-planning/
2. https://www.firstfriday.biz/blog/what-is-assortment-planning
3. https://ppnsolutions.com/blog/range-planning-in-merchandising/
4. https://www.retaildogma.com/inventory-turnover/
5. https://www.davinciretail.com/resources/what-is-open-to-buy/
6. https://bamboorose.com/blog/the-ultimate-retail-planning-glossary/
7. https://www.fibre2fashion.com/industry-article/9553/fashion-retail-store-types-and-assortment-plan
8. https://www.oracle.com/in/retail/assortment-planning/