Retail store expansion strategies are one of the most fundamental issues for growing retailers. Opening a new store can be a game changer if you get the location right, or your new store could be doomed to failure if the location doesn’t attract enough customers.
In addition to geographical factors, such as transportation accessibility and real estate prices, demographic factors and mobility patterns in the areas of interest play a key role in decision making. These data on population, purchasing power and consumption habits are what generate an optimal expansion strategy.
Predictive location, and foot traffic analytics reveal valuable information that helps retailers to choose the right location when setting up geographic expansion strategies.
How are predictive analytics models used to determine the optimal location for a new facility?
Predictive analytics is a type of statistical analysis that uses data mining, statistical modeling and machine learning to extrapolate trends from historical facts and current events and is often used for risk assessment and decision making.
The secret to site selection in such a competitive market is based on the ability to analyze the right data and be able to understand and interpret the site selection strategies of nearby locations to stay ahead of their expansion plans and gain a competitive advantage.
Where? This is the fundamental question that guides any site selection decision. Tools such as location intelligence and footfall analytics enable the aggregation, analysis and visualization of spatial data and bring significant advantages to the site selection process.
With advertising analytics based on big data techniques, marketers can quickly determine the effectiveness of each channel and thus adjust their strategies, enabling them to run hyper-targeted campaigns, choosing the right ad content for the appropriate ad networks.
What is it?
This type of analytics refers to the use of data and technology tools that help companies and marketers effectively monitor their marketing efforts to ensure that campaigns are targeted to the right audience and use the right channels for effective communication.
With Big Data management techniques, companies can optimize their strategic business planning, by taking advantage of market and companies' data.
Big data has emerged as a powerful tool that organizations can use to leverage data-driven decision making for better strategic planning, determine which market niches of their products, are growing or shrinking, obtain traffic data of their stores or website, determining where they come from, what kind of devices they use, dwell time, and foot traffic patterns to help analyze which promotions and efforts are successfully driving their business.
Big Data technological tools and spatial data play a very important role in business by measuring footfall and helping to understand consumer behavior patterns in any given area of interest or point of sale.
Consumers are shifting their spending from physical stores to e-commerce, physical stores will only survive in this new environment if they reinvent their business, taking advantage of new technologies and modern analytical capabilities.
Today, there is access to significant amounts of data on consumer behavior, information on the economy of different areas, competitors' sales, and market trends. However, only a handful of forward-thinking retailers are leading the way in advanced analytics as they use location intelligence, foot traffic analytics, and predictive modeling to make smarter business decisions.
The COVID-19 pandemic has exponentially accelerated the growth of e-commerce, but physical stores are still far from disappearing.
Today, retail businesses have accelerated their digitalization strategies by evolving omnichannel processes, trying to maintain their efficiency and generate added value without affecting their operations, as consumers' needs to interact virtually with the different points of sale have increased.
In order for retailers to stay ahead of digital competition, they must overcome cost and flexibility disadvantages; it's necessary to have real-time insight into what is happening inside and outside the point of sale.
Retailers must begin to take immediate action on unforeseen events at their physical points of sale, such as lack of inventory, shelf problems, environmental impacts, local events, loss of merchandise and customers, among many others, as they generate a significant loss for their business.
Unlike historical analytics, predictive supply chain analytics allows you to anticipate and prepare for the future, taking out the conjectures planning processes and improving decision making.
Predictive supply chain analytics use advanced technological tools such as machine learning, geomarketing, data mining that enables organizations to identify hidden patterns, understand market trends, identify demand, establish pricing strategies, achieve a high return on investment, optimize and reduce inventory costs.
A competitive analysis begins by defining the target and its scope, exploiting different sets of structured and unstructured data available thanks to innovative tools that help to identify and measure competing brands and estimate their sales.
With Big Data tools that help analyze large volumes of information like predictive models,location intelligence and mobility data, it's possible to generate a competitive analysis, which consists of identifying the main competing companies and estimating their turnover, quantifying their potential sales, identifying gaps in the market, predicting the cost of developing new products, discovering trends, among others.
New data management methodologies now allow retailers to take advantage of even the smallest piece of information to generate valuable insights that help optimize their marketing and customer loyalty strategies.
What promotions do we do to get more customers to the point of sale?
How do we make them stay longer in the store?
How do we improve the customer experience so that they buy more at each visit?