You can’t deny this. But clearance sales are a fact of life as a fashion retailer. And you might just be wondering how you could ever increase your sell-through rate.
You will always have unsold items. And you wouldn’t want to be completely sold out any time in the season. Since that would mean that you are out of stock and have not estimated the demand well.
In reality, to sell more than 80% of your items at full price, a lot of things have to fall in place perfectly. And the key metric to monitor, evaluate and deep dive is the sell-through rate.
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Well, you will always have broken sizes and unpopular colours. Few leftovers from the last season and a segment of customers whom we call deal seekers would buy only when the sale starts.
And that’s one of the prime reasons why you don’t see a very high full-price sell-through in fashion retail.
But is it possible to increase the sell-through rate?
The answer of course is yes!
But before I show you how, let us first try to understand what sell-through rate is and how you could measure it.
The sell-through rate of a particular period is often expressed as a percentage of the number of units sold in the period against the number of units available.
Mathematically, this can be expressed as below:
Sell Through= Total Quantity Sold/Total GRN Quantity
(GRN - Goods Received Note is a record of goods received from suppliers)
However, the denominator at times can also be calculated as below:
Total Quantity Sold + End of Hand Inventory
The sell-through rate shows retailers how a particular item is actually selling against the expected sales of that item. Additionally, it reveals whether the buyer’s demand estimation for a specific category of merchandise is good or bad.
At this instance, you need to understand another metric that is of great importance - the full-price sell-through.
It can be expressed as below:
Full Price Sell Through=Total Quantity Sold without any markdown/Total GRN Quantity
While striving to augment this full-price sell-through rate, you must be aware of what ageing is. This helps fashion retailers to set sell-through benchmarks for every item post-launch. So, you could track the performance of an item early on instead at the culmination of the season.
Ageing is nothing but the age of an item since the retailer received it. That is, the number of days or weeks since the item was received in the warehouse.
Keeping all these metrics in mind, let’s now explore the possible reasons for poor sell-through rate.
- Poor demand planning
- Random allocation of items across stores and regions
- Absence of real-time measurement
- No store-to-store consolidation
- Lack of insights into customer preferences
1. Poor Demand Planning
The demand estimated by the category planner is based on a hunch and few excel extrapolations and not data-driven.
Hence out of stock or overestimation of demand for certain items/sizes is quite common. And automatically, this reduces the sell-through.
2. Random Allocation of Items Across Stores and Regions
Allocating item quantities to different stores is mostly based on store sizes.
However, this must be carried out on scientific grounds. You should take into account the local preferences, and accordingly allocate the items, to thereby increase the sell-through rate.
3. Absence of Real-Time Measurement
Retailers can fail to measure the real-time performance of an item based on sell-through and ageing benchmarks.
When you do not endorse this real-time measurement, you cannot be proactive enough to alter the prices, facings or positioning in the store or reallocate the items based on local preferences.
4. No Store-to-Store Consolidation
When you fail to read into early indicators or predict the item sell-through rate at the store level, it becomes hard for you to consolidate items across your stores.
Your sell-through rate inevitably drops down.
5. Lack of insights into customer preferences
It is essential to evaluate the performance of past seasons to understand customer preferences. Price and other attributes for different regions must be taken into account here.
While you might claim that all purchases are based on buyers’ hunch, past performances would still help you augment sell-through.
Hopefully, by now you would have gotten clarity as to why you are unable to push your sell-through rate. Let go of all these mistakes and in their place, follow the strategy below.
- Better planning
- Scientific allocation of items across stores
- Real-time measurement
- Effective and proactive consolidation
- Garner insights from previous seasons
1. Better Planning
Use sophisticated forecasting ML algorithms to accurately predict demands based on product attributes and budget constraints to avoid under or over-ordering.
2. Scientific Allocation of Items Across Stores
Allocate quantities based on demand predictions and preferences of regions and stores. Use advanced optimization algorithms that factor in demographics and localized buying patterns.
3. Real-Time Measurement
You should have a real-time inventory and sell-through dashboard where you should be able to identify early indicators of poor sell-through and proactively act on it.
If a product is moving slowly, identify why. Maybe, you have priced the merchandise high. Or, maybe it is not placed properly in-store. Or, it could even be a poor supply chain that’s delaying items in their arrival from the warehouse.
So, determine the reasons as to why your products aren’t moving as expected with real-time root cause analysis dashboards.
4. Effective and Proactive Consolidation
Categorize the items in real-time into segments based on their sell-through, margin and ageing. Then, you could rate them as a star or good or average or bad performers.
Design a real-time consolidation optimizer on top of the segmentation to maximize sell-through and minimize consolidation cost without breaking sizes too early.
5. Garner Insights from Previous Seasons
Thorough deep dive of past seasons assortment and their performance is key to better planning.
Retailers can build predictive sell-through models based on item attributes and order items that are at the right price point and has the right mix of size, colour, fabric etc.
There is a cost for holding inventory or running clearance sales.
While targeting 100% sell-through for all items is not realistic we can definitely minimize these costs by maximising full-price sell-through.
AI & ML based data-driven predictive decision making is the key for fashion retailers to maximise their sell-through and hence the profit margins.