Sales data contains valuable insights into customer purchasing behaviour.
By analysing sales numbers over time, retailers can identify patterns and trends that indicate which products customers are buying, when they are buying them, how price sensitive they are, and more. Sales data analytics enables retailers to make data-driven decisions about pricing rather than relying on assumptions.
Identifying Price Elasticity of Products
Looking at how sales volumes fluctuate relative to price changes helps retailers gauge the price elasticity for different products. Essentially, this measures how sensitive customer demand is to changes in price. Products with relatively inelastic demand see smaller changes in sales volume when prices go up or down. Products with elastic demand see bigger swings in demand with price fluctuations. Knowing the price elasticity for different items allows retailers to set optimal prices.
Setting Prices Relative to Customer Value Perceptions
Sales data shows the optimal price points where value perception and willingness to pay are maximised for customers. Granular data from receipts shows how small price differences impact purchase rates. This indicates value thresholds that shouldn’t be crossed. Big data analysis enables dynamic pricing that stays close to the sweet spot between too cheap and overpriced. Using price optimisation software from Retail Express makes retail price monitoring a lot easier.
Benchmarking Against Competitors’ Pricing
Retail price optimisation is critical in a competitive retail niche. Retailers can use AI pricing software to analyse competitors’ pricing trends through data collection and price monitoring. This gives key insights into how to benchmark pricing against other players in the market. Big data analytics enables continuous optimisation of pricing based on competitors’ strategies and customer response. Although AI has experienced a lot of bad press, using AI and machine learning in the retail sector has many benefits, as it can automate optimal pricing decisions based on all available data.
Testing and Optimising Through Experimentation
Sales data analytics allows retailers to experiment with different prices and promotions for a product and immediately see the impact on metrics like units sold, revenues generated and profit margins. Testing different price points and offers enables fact-based price optimisation, as opposed to following assumptions or gut instincts. Retailers can fine-tune pricing to maximise sales and profits.
Implementing Localised Pricing
National retailers can analyse granular sales data from different geographical locations to determine optimal pricing differences for each market. This is known as geographical pricing discrimination. Locations with more demand elasticity might have lower prices, while inelastic markets support higher prices. Customised pricing maximises overall sales and profits.
Providing Personalised Promotions
Sophisticated analytics of individual customer purchase histories enables customised promotions tailored to buying habits. For example, retailers can offer targeted coupons and discounts to lapsed customers or for complementary products often purchased together. Personalised promotions keep customers engaged and increase order values.
Comprehensive sales data analytics gives retailers deep insight into optimal pricing strategies based on actual customer behaviour, competitor activity and more. Instead of pricing through guesswork, data-driven analysis and experimentation enables pricing that maximises both revenues and profit margins.