In the current competitive retail world, the apparel brands have to make strategic choices in order to increase their market share. Although traditional market research provides useful information, there is a new approach that is increasingly emerging, which is a more accurate and data-oriented market research, Point of Interest (POI) data. Using the geographic positioning of stores, competitors, and potential customer groups, POI data offers intelligence that is actionable and determines growth opportunities of leading brands in the apparel industry.
PoI Data and its significance
POI data can be defined as geospatial data on particular stores, malls and competitor stores. All points have unique features such as the store type, location, pedestrian traffic and in some cases demographic data of the localities. In the case of apparel firms, POI data is a treasure trove since they are able to estimate the best sites to open up new retail outlets, the competition intensity and upcoming market trends.
As a result of the development of digital mapping tools and location-based analytics, brands are not driven by intuition only anymore. POI data can be used to make accurate evidence-based decisions. It has especially gained importance in the case of some of the most elite apparel brands, which have operations across various cities and countries, where location dynamics can break or make expansion successful.
Mapping the Strategies of Expansion with the help of the location of stores
The evaluation of the available network of stores can be considered one of the most direct uses of POI data in retail development. The leading apparel brands, which include Nike, Zara, and H&M, have their stores located strategically depending on a number of factors that are based on location. Mapping their existing stores, brands will be able to determine clusters in the most successful locations and identify those regions that are underrepresented and suggest unexplored opportunities.
Such as an example where a brand might realise a concentration of outlets in urban retail areas but in new suburban areas the brand may not be represented. This POI data insight can be used to compute an expansion strategy balancing between high footfall areas and growth ready areas. Further, the store density in relation to cities will assist the brands in resource allocation which will maximize inventory, staffing and marketing campaigns, on geographic demand.
The POI Data was analyzed as a competitor
The POI data is also useful in competitive intelligence. Not only do the apparel brands keep an eye on their shops but also those of their rivals. Through a study of the position of competing brands, the firms are able to find the patterns of expansion strategies used by competitors, including premium shopping areas or busy malls.
Such analysis can help apparel brands to make strategic decisions: to compete directly in such a congested space or to find unexploited opportunities in promising locations. As one of the examples, a brand that observes a low level of competitors in a new city area can choose to open flagship stores in this location and attain first-mover benefits and establish a well-developed brand image.
Market Opportunity Identification by Demographic
A large number of POI datasets combine demographic and socio-economic data, such as the average income levels, population density, and age distribution. In the case of the apparel brands, it is essential to know the demographic in the areas surrounding the potential stores.
Luxury fashion retailers, like Gucci and Louis Vuitton, tend to use POI information to identify the places to establish a store in a neighborhood where consumers are wealthy. On the other hand, fast-fashion retailers, such as Uniqlo or Forever 21, could prioritize the regions where the proportion of price-sensitive population is larger among young people. Store location in combination with demographic analysis allows the brands to adjust the expansion strategy according to the preferences of the customers, maximizing the rate of sales and guaranteeing profitability in the long term.
Anticipating New Retail Centres
In addition to the study of existing markets, POI data can be used to determine the new retail centers by the apparel brands. Urban areas are dynamic and new business districts and neighbourhoods are established. Through monitoring the tendencies in store openings, the flow of people, and the movement of consumers, brands will be able to predict where they can find the future hot spots.
Such predictive ability comes in handy especially in rapidly expanding economies whereby urbanization and development of infrastructure are driving the retail opportunities. As an example, a brand can observe that there is an increasing number of international retailers in the suburban area, which means that the consumer interest increases. The brand is able to build brand loyalty in the market by penetrating the market at an early stage before the competitors flood the market.
Store Formats and Locations Optimization
The POI data is also used to make decisions regarding store format and size. The outlets of the apparel brands are often run as a combination of flagship stores, small boutique outlets, and outlets. Through foot traffic analysis, competition distance, and consumer behavior of a particular area, brands can be able to decide on the most appropriate store format in that area.
As an example, a big flagship store in high-footfall urban centers would be the perfect solution to represent the brand and draw as much attention as possible. By contrast, a smaller boutique could be a cost efficient method of testing the market in a suburban or secondary market before making an investment in a larger presence. POI information makes sure that every type of the store fits the market conditions to the maximum and meets operational efficiency and revenue potential.
Adding to the Omni-Channel Strategies
The convergence of online and offline shopping is essential to the apparel brands in the present day. The POI data facilitates the use of omni-channel measures in light of physical stores since the data can determine the locations that digital sales can be augmented by physical stores.
As an example, mapping e-commerce delivery areas and dense clusters of customers would allow the brands to determine the locations of the physical stores that should be used as pick-up stores or experience centers. It is not only making the customers more convenient, but it was also increasing brand loyalty and the total sales. Such brands as Zara and H&M are utilizing this data-based strategy to coordinate their online and offline processes to work hand in hand.
Examples of leading Apparel Brands that use POI Data
Nike, Adidas, and Zara are just some of the good examples of clothing companies that use POI data successfully. An example is Nike that uses location analysis of stores around the world with the competitor data and demographic data to create the best market penetration. Adidas targets those cities and high-end shopping areas, where POI data are used to predict demand and transform the format of the stores. Zara uses thorough location intelligence to get stores in cities where the youthful, fashion-trendy consumer is clumped together swiftly.
Through these brands one can see how POI data can be translated into real expansion policies to help them stay on the competitive edge in a highly competitive market environment.
Challenges in Using POI Data
Although POI data has some useful information, there are some challenges that brands should overcome. The accuracy and completeness of the data is essential because the information provided can be outdated or wrong, which would result in ineffective location decisions. Also, big data analysis needs sophisticated analytics and its skills. The POI data should be incorporated with other intelligence collected on the market including consumer behavior and economic indicators to have a complete picture.
These challenges notwithstanding the benefits greatly outweigh the risks. Those brands that spend on correct POI information and advanced analytics have a considerable advantage in strategizing expansions, reducing the risk, and achieving even higher profits.
The Future of the Apparel Expansion using POI Analytics
With the ever-changing technology, the contribution of POI data in the expansion of retail will become more advanced. Machine learning algorithms and artificial intelligence (AI) may be used to process very large POI datasets and reveal the latent patterns included in them and forecast market trends with extremely high accuracy.
The apparel brands will also not just map the location of stores in future, but simulate possibilities of events as competitive actions, future urban development and consumer behavior changes. The predictive method will allow brands to be proactive with their decisions and make them based on data, so that a new store will fit in the desired needs of the market accurately.
Besides, the combination of POI data with mobile location tracking and social media analytics, and real-time consumer insights will provide a comprehensive picture of the retailing environment. The brands are able to react dynamically to expansion strategies, marketing campaigns, and increase customer engagement, reaching new standards of operational efficiency and market responsiveness.
Conclusion
The POI data has become a revolutionary tool to be used by apparel brands in their effort to grow strategically. The brands can identify current retail hubs, competitors, demographics, and store locations to make an actionable insight through the analysis that can guide all decisions in the expansion process. POI analytics helps to ensure that growth initiatives are accurate, data-driven and in line with market demand, whether it is optimization of store format or improvement of omni-channel strategies.
The success of such top clothing brands as Nike, Zara, and Adidas prove that the use of POI data has ceased being a matter of choice, as it is the only way to remain competitive in the ever-changing retail landscape. With the development of technologies, the combination of AI and real-time analytics with POI data will allow the brands to grow even smarter to ensure that every new store will bring continued success and dominance in the market.
