Retail Market Basket Analytics
Retail Market Basket Analytics are used to analyze the relationships among items that a given consumer purchases (what they put in their “shopping basket”).
This information is then combined with comprehensive consumer information databases and qualified predictions are made about what other items this particular type of consumer is most likely to purchase.
Retail Market Basket Analytics uses market, consumer expenditure and transactional data to determine what products are most frequently bought together.
It is a multi-channel tool, so it does not require that the consumer be physically shopping in your retail location; whether in person, through telephone, mail order or the internet; the transaction is the critical piece of information.
Retail Market Basket Analytics uses categorical and numerical variables. Did the consumer buy the product (yes/no)? And if so, how many did they buy? And how much money did they spend?
Retail Market Basket Analysis is not only trying to answer questions about which products sell together, but also which products are bought by the same types of people (segments).
This kind of intelligence supports a variety of strategic and tactical initiatives, such as:
Product / inventory mix
New product development
Shelf space allocation
Store placement / layout
From a retail point of view (regardless of channel) Market Basket Analytics are powerful algorithms for determining the particular mix and placement of products; whether your focus is bricks and mortar retail location, the printed page of an annual catalogue or a page of the company web site.
Retail Market Basket Analytics are highly predictive of consumer spending patterns.
When this information is appended to current year Zip+4 level demographic and behavioral information, retail marketers and product managers get a clear picture of current customers and prospects needs and wants, allowing them to anticipate opportunities and plan strategically for acquisition.
Price Elasticity Analysis
To make effective decisions, businesses have to accurately predict market demand. Because demand is intrinsically connected to price, price elasticity is an essential computation for today’s successful retail marketers.
Although price elasticity analysis is fairly common to marketing professionals, determining the optimal price point for maximum sales and profit is a complex calculation requiring economic, consumer (expenditure and historical) and market data, sophisticated algorithms and serious processing power.
DMSRetail’s automatic analytics and proprietary predictive algorithms calculate accurate figures for an endless run of pricing scenarios, on both the supply and demand side.
Analytics of Supply calculates the volume increase necessary to offset any change in price. Retailers use this to measure and protect profitability.
Analytics of Demand calculates how much more product the market will need to satisfy the increased demand resulting from a percentage reduction in price.
Sophisticated pricing analytics help retailers make accurate predictions on how consumers will respond to different scenarios.
This information is used to develop competitive pricing strategies, fine tune sales forecasts and develop insights for purchasing, manufacturing, branding and communications.
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