Retail buying decisions have been made for years based on gut feeling or anecdotal data.
Though this may work or might have worked before, the success won’t be reflected in all your retail locations. A report reveals that 40% of the inventory is never sold at full price.
So, understanding your local customer base becomes crucial when it comes to making retail buying decisions & the tricky part is consumer preferences shift rapidly.
This guide will help you tackle this and make better retail buying decisions by understanding your local customers better with data that guarantees sell-through.
What is retail buying?
Retail buying is the strategic process of selecting, purchasing, and managing inventory for retail businesses.
It’s the behind-the-scenes operation that ensures the right products are available to customers at the right time, in the right quantities, and at the right price points.
Here’s what the typical retail and merchandise buying process would look like:
- Researching and identifying products that will appeal to the target market
- Negotiating with suppliers and manufacturers to secure favourable terms
- Analysing past sales data to determine optimal inventory levels
- Planning seasonal merchandise assortments
- Monitoring SKU-level product performance and making necessary adjustments
- Ensuring merchandise aligns with your store’s brand image and customer expectations
Effective retail buying should combine trend awareness and market intuition with rigorous data analysis.
The end goal of this process is to maximise sales and profitability while minimising excess inventory and markdowns.
Traditional retail buying methods and their limitations
For decades, retail buying relied heavily on historical sales data, buyer experience, and intuition.
Traditional buying methods typically follow a cyclical pattern:
- Analysing the previous season’s sales
- Attending events and vendor meetings to form a basis for decision-making
- Making buying decisions based on historical performance
While this approach served retailers adequately in more stable market conditions, it has significant limitations in today’s retail environment and this becomes more true in the case of Tier II cities and beyond.
Limitations
i) Limited data and no real-time interventions
Incomplete or outdated data limits accuracy and the lack of real-time insights prevents quick adjustments to inventory and buying decisions. This can lead to missed opportunities and inefficiencies in stocking the right products.
ii) Slow reaction times
Annual or seasonal buying cycles make it difficult to respond quickly to emerging trends or shifting consumer preferences.
iii) One-size-fits-all approach
Mirroring the same strategies including the retail buying decisions that worked in one retail store to other retail locations, ignoring local market variances in customer preferences and demands.
iv) Forecasting inaccuracies
Relying solely on historical data for future forecasting fails to account for growing market conditions, resulting in inventory imbalances, either stockouts of popular items or excess of underperforming merchandise.
v) Supply chain rigidity
Fixed order quantities and delivery schedules lack the flexibility needed to adapt to rapidly changing market demands.
These are the common limitations that are attached to the traditional way of approaching retail buying decisions.
These will become increasingly problematic as consumer behaviour grows more complex and market conditions more volatile.
The right way: Forecasting demand & optimising retail buying with AI & external datasets
Utilising external datasets and AI has proven to be a game changer for many retail businesses. It provides granular insights that transform how retailers understand their markets, forecast demand potential and make inventory decisions.
By analysing the local market better at the store and product SKU level, retailers can uncover multiple factors at play in consumer purchasing behaviour:
i) Real-time interventions based on local events and trends
External allows you to tap into customer’s preferences, highly localised to a neighbourhood level. The custom ML model we build will take into external datasets with existing sales to understand real-time demand shifts.
This enables you to adjust inventory, pricing, and promotions dynamically based on local events, seasonal trends, and emerging customer preferences.
For instance, our model predicted that the oversized clothing category would see significantly lesser demand as opposed to the previous season. These insights, tailored at the store and SKU level let you understand the local market better and stock the right products at the right quantity.
You can book a 15-minute discovery call with us to develop tailored merch strategies across all your stores!
i) Local demographics and customer base
Understanding local market conditions enables you to develop detailed profiles of the populations surrounding each store location:
- Age & gender – Influence product preferences and shopping behaviour
- Average household income – Determines purchasing power and spending capacity
- Education level – Affects brand perception and product sophistication
- Total households – Indicates market size and potential demand
- Lifestyle – The presence of premium/value brands and income levels indicate lifestyle preferences and spending habits.
Demographic data of a location
Rather than making broad assumptions about the local customer base, you can use location-specific demographic information like the above to tailor inventory to the actual people living and shopping in each area.
For example, a sporting goods retailer might stock more sports sneakers in areas with higher percentages of young professionals & presence of gyms.
ii) Catchment area analysis
Understanding precisely where customers come from helps optimise inventory based on realistic market reach:
- Mapping customer travel patterns reveals the true trading area for each location
- Travel time analysis identifies convenience thresholds for different types of shopping trips
- Overlapping catchment areas between competing stores impact inventory differentiation needs
Catchment potential of a location
Catchment analysis shows where customers come from, helping stores stock the right products in the right quantities.
A store with a wide catchment may need diverse inventory, while one with a small, dense catchment can focus on high-demand items.
iii) Customer behaviour patterns
Utilising location data reveals how consumers interact with physical spaces and make purchase decisions:
- Foot traffic analysis shows peak shopping hours and days by location
- Mapping complimentary brand presence to identify complementary merchandise opportunities
Complimentary brands at a location
This helps optimise store layouts, timing, product procurement and product placement for better sales.
iv) Competitive landscape
Understanding the geographic distribution of competition should be taken into account before developing retail buying strategies.
Opening your store next to a competitor can actually benefit your retail as it attracts similar foot traffic but you should also take into account the overall customer density & preferences.
Competitor presence in a location at 10 min driving distance
Apart from this, analysing competitor pricing strategies and identifying competitor strengths and weaknesses reveals merchandising opportunities you could capitalise on.
v) Price preference localisation
Consumer price sensitivity varies dramatically by location:
- Affluent areas may prioritise quality and uniqueness over price
- Budget-conscious neighbourhoods respond strongly to promotional pricing
- Some locations show higher conversion rates for premium brands
Utilising location data allows you to fine-tune your price-point distribution across different stores, ensuring the merchandise mix matches local price preferences rather than forcing a standardised approach across all locations.
Conclusion
Integration of location data into retail buying will lead to a fundamental shift from standardised, historical approaches to dynamic, predictive strategies.
By understanding the unique characteristics of each store’s location you can forecast demand accurately at store and SKU levels.
Book a 15-minute demo with us to understand how our advanced ML models can help predict what merch mix works for your stores!
