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In the jewellery industry, festival seasons — Diwali, Eid, Christmas, Akshaya Tritiya, wedding season peaks — can represent 40 to 60 per cent of annual revenue. Getting the stock right for these periods is one of the most consequential decisions a jewellery business owner makes each year. Too much stock ties up capital in gold that does not sell and sits in the safe depreciating if the market moves. Too little means turning customers away during the most lucrative weeks of the year. This article is about making the decision with data rather than instinct.
Festival stock decisions in most jewellery businesses are made on a combination of last year's general impression, the owner's sense of what is fashionable, and supplier suggestions. The result is almost always the same: certain categories sell out early and certain categories barely move. The sell-out items represent lost revenue — customers who came, wanted to buy, and left empty-handed because the right stock was not there. The slow-moving items represent capital that is now locked in gold sitting unsold for months.
The problem is not the owner's judgement. It is the data available to inform that judgement. When the only reference is a general memory of "last year went well," there is no way to know which specific categories, weight ranges and price points drove the majority of the revenue — and therefore no way to replicate the right mix more precisely.
The most useful input for festival stock planning is a breakdown of what sold during the equivalent period last year — not in total revenue, but by category (necklaces, rings, earrings, bangles), by purity (22KT, 18KT), and by weight range (lightweight pieces under 5 grams, medium 5-15 grams, heavy above 15 grams). This breakdown tells you where the demand was concentrated — and that concentration is likely to be similar this year.
Once you know what sold, you need to know what you currently hold in each of those categories. The gap between last season's demand and the current stock position is your restocking requirement. Without a live stock report broken down by category and weight range, this calculation cannot be done accurately — and the stock decision reverts to guesswork.
Before buying new stock for the festival season, identify every item that has been sitting unsold for more than 90 days. Festival season is the best time to run promotions on slow-moving pieces — the increased footfall makes it easier to move them at a reasonable price. Clearing old stock before the season frees up capital and display space for the pieces most likely to sell.
During festival seasons, customers' purchasing behaviour shifts. Average invoice values tend to be higher — customers are buying gifts, making significant purchases for weddings and celebrations. Understanding the average transaction value from last festival season, and the distribution of transactions across price bands, tells you whether to invest more in higher-value pieces or whether your market gravitates toward accessible price points even during peak season.
Once the season begins, the data job does not end. Tracking which categories are selling fastest allows you to reorder or redeploy stock from slower-moving areas before you run out of the fast sellers. A live stock dashboard that shows sales velocity by category during the season — which categories are down to their last few pieces, which have barely moved — lets you make restock decisions in time to get new items in before the season peaks.
For multi-branch businesses, the same logic applies across locations. If Branch A is selling 22KT necklaces faster than expected and Branch B has excess, a timely stock transfer means you capture sales you would otherwise have lost.
Festival season stock planning is one of the highest-leverage decisions in a jewellery business year. The difference between getting it right and getting it wrong — in terms of revenue captured, capital efficiency, and customer satisfaction — is significant. The businesses that consistently get it right are the ones that make the decision with last season's data rather than last season's memory.
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