To stay ahead and provide the right seasonal styles via the most accessible sales channels, assortment planning becomes very much critical to fashion retail. Assortment planning is tactical and sensitive to demand fluctuations and local market conditions, even if it is connected to retail planning at a higher level. At a high level, assortment planning outlines what products a retailer will sell in what locations and channels. It includes defining financial targets for a specific time period at more detailed levels of the product hierarchy. These targets are then tied to the overall merchandising strategy.
Effective assortment planning
There are various methods by which brands can enhance retail performance. Retailers can establish an ideal product mix that supports their business objectives by examining client demand, sales statistics and inventory levels. Assortment planning is crucial for shops to maximise sales and profits by providing the ideal combination of goods and services.
By carefully controlling inventory levels, identifying slow-moving products and adjusting inventory levels accordingly, assortment planning in retail enables retailers to save inventory expenses, thereby helping to optimise margins and turnover and lowering stockouts, markdowns and operating expenses.
It can also assist brands in adjusting to shifting consumer preferences by identifying the shifts and indicating which type of product will be best suited for stores, industry dynamics by identifying which direction the industry is going towards in terms of trends and corporate objectives which include increasing profit margins, retention, loyalty and satisfaction of customers and performing better in a competitive environment.
Technology and tools such as artificial intelligence, machine learning and data visualisation, to automate and improve data gathering, analysis and integration are effective strategies to allocate assortment efficiently.
To further understand and categorise clients and their demands, customer-centric and data-driven techniques like persona development – the task of creating a representation of different types of people who will interact with your product or service; customer journey mapping – a visual storyline of every engagement a customer has with a service, brand or product; and clustering analysis – the process of dividing an organisation’s customers into groups or ‘clusters’ based on demographics, preferences etc., can be employed, helping to tailor the assortment to the target market.
Zara, a retailer which is very effectively using business intelligence (BI), manages its inventory levels and assigns the appropriate products to the appropriate outlets depending on availability and demand. In order to select the most efficient routes, modes and frequencies for every cargo, Zara also employs BI to optimise its logistics and delivery procedures. Zara has established an initiative with Jetlore, a consumer behaviour prediction platform that uses AI to map consumer behaviour into structured predictive attributes like size, colour, fit or style preferences to make better merchandising decisions.
Another fashion giant, H&M, employs AI algorithms and more than 200 data scientists to predict and analyse trends. Its AI algorithms obtain fashion trend data by capturing information on search engines and blogs. This information informs everything from how much H&M buys, when it buys and where it should be placed in its stores. The AI not only forecasts new trends the company’s buyers should be aware of but also informs them of whether H&M should restock currently popular merchandise and if it will only be suitable to one customer’s preferences or the whole segment.
For assortment tools for merchandising, Team Apparel Resources spoke to Saikat Mitra, Ex-Senior VP, Creative Director, Van Heusen, who said that predictive tools and cutting-edge technologies are being used in the industry. From a planning point of view, there is also looking back at old data and hitmaps for predictions and understanding trends which play into assortment planning for retail. Also, micromarket merchandising and cluster merchandising are the techniques that can be applied here but it is not a one-size-fits-all type of process. Once retailers start looking at it that way, then they can start tailoring processes as per the market, seasons and consumer preferences and target stores and merchandise in the same way.
Pricing and its importance
A lot of merchants have set prices for fashion items using straightforward techniques that depend on standard markups that are applied to all product lines. More advanced tactics are now driven by inflationary demands to maintain business margins. Retailers and suppliers employ technologies, such as AI-infused sophisticated data analytics platforms, to precisely establish particular item discounts and promotions in addition to wholesale and retail markups. This can be done by analysing the target market, pricing points, the store’s brand identity and seasonality of fashion trends.
Predictive AI analytical tools are being used by forward-thinking merchants to identify ideal discount and price points, taking into account inflation, cost of sales, break-even points, markups and market forces, before their products are released into the market. These technologies are not only faster, cheaper and more efficient than conventional techniques in figuring out pricing strategies, but they can also suggest value-added features that are supported by cost-benefit analysis such as easy returns, personalised recommendations, in-store offers and loyalty programmes.
Price optimisation software divides prospective clients into groups according to geography, affinities and other criteria. After that, analysis tools will calculate or forecast price elasticity and offer recommendations for pricing depending on the various groups or segments that the store has specified. Depending on the programme, suggestions for more comprehensive sell-through plans and markdown cadences might also be given.
Fashion companies use social media and online marketplaces as well as other methods to sell their goods. They can compare their prices across all channels with competitors’ prices with the aid of price intelligence tools.
Prices are subject to sudden changes during sales season. Businesses can identify products that aren’t selling well and reduce prices to clear inventory with the use of price intelligence. In order to improve pricing decisions, it is also essential to forecast product demand.
AI is making it simpler for companies to research and evaluate the prices of rivals. They can decide on prices more wisely, thanks to this fashion industry data. Businesses can determine prices by using customer-centric pricing, which bases prices on what customers are willing to pay. Focus groups, tiered or dynamic pricing – a pricing strategy where companies offer different discounts or benefits based on the quantity of goods or services purchased or the level of features included in the product or service and market research – are the methods used to achieve this. Big data analytics is used by firms to enhance their pricing tactics. Massive volumes of information on fashion business, rivals’ prices and market trends are gathered and analysed by big data. Price intelligence solutions benefit from Natural Language Processing (NLP) – a machine learning technology that gives computers the ability to interpret, manipulate and comprehend human language and the ability to gauge customer sentiment towards goods and services, using live chats as an example. Price intelligence tools can use machine learning to build models to predict future prices. This helps businesses set the best prices to stay competitive and profitable.
Solutions known as pricing intelligence tools can assist companies in determining the optimal rates to charge for their goods. Technologies, including data mining – the process of using computers and automation to search large sets of data for patterns and trends, turning those findings into business insights and predictions; online scraping – a process of importing data from websites into files or spreadsheets; and data warehousing – creating a central repository of information that can be analysed to make more informed decisions, are the foundation of intelligent tools. These systems gather data from the fashion industry, spot trends and project future ones. The market today is flooded with tools designed to assist businesses in enhancing their pricing strategies.
