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Writer's pictureDokimi GeoEngineering

The Power of Geospatial Data in Retail

Businesses manage a myriad of information, about sales, inventory, demographic profiles, customer profiles, mailing lists and much more. No matter which industry you’re in, success means making wiser decisions, faster than your competition. The intuitive potential of geospatial data analysis in retail can reveal gaps, trends, patterns and opportunities that cannot be detected by tabular data alone. It allows you to view, understand, questions, infer, analyze and visualize your data in ways that aren’t possible in spread sheets and graphs. Layering your data with geographical data highlights where you have your customers, around store locations or on websites, what they are looking for, and when they make their purchases. Geospatial data has the power to create applications for the retail industry that perform these customized tasks.

Success in the future of retail, manufacturing, and HORECA industries will be determined by the competitive advantages of investing in and implementing smart technology. The customized solutions provided by geospatial data in retail, provides spatial intelligence which can bring value to the organization and improve the bottom line.

Challenges in India

A big challenge in the Indian retail industry is the lack of a real supply chain and moving the industry from an unorganized to organized format. For example, the absences of fully functional cold chains hamper the FMCG and agribusiness industry the most. Geospatial data analytics can help provide optimum supply chain solutions with all its inherent obstacles. Customized mapping solutions for a distinctive market like ours can help smooth out the transition of the retail industry from unorganized to organized formats.

Map your way to success

Demographic analysis and site selection can help in store development and determining store location. Say you have a chain of stores around the city, and you want to expand to cater to the rural markets. How do you identify which locations will be the most profitable? Demography analysis layered with location data could help you identify a site that will help you extract the maximum out of your investment. In retail, speed is everything. Market situations need to be assessed quickly and ascertain the impact it will have on existing and potential stores. Location data analysis can upgrade these stores by ranking them by merchandise mix, in-store customer traffic and customer demographic to evaluate options to cross sell and up sell.



An example of time stamp analysis

Source – www.geoquadrant.io


Geospatial data in the supply chain integrates mapping analysis into decision making support by helping calculate arrival times, scheduling by time stamp, delivery routes and requests. Behemoths like Amazon credit their success to their well-oiled and perfectly optimized supply chain and operations systems. The COVID-19 pandemic has prompted most business to shift base to online market places. Optimizing a supply chain can help cut costs without cutting corners, all the while providing seamless service to customers. This can help reduce product cycle time, enable just-in-time scheduling, and provide real time dispatch reports. Further it can help your employees locate the shortest and safest routes, with rest stops, emergency services, and dictate goods production and flow from distribution centers.

Marketing Matters

Geospatial data technology has already transformed how brands market to their target audiences. For marketers, locations matter, especially in this hyper – connected world. You can now advertise to consumers in your vicinity, with individually customized communication, on multiple platforms, having the additional feature of tag-along third party promotions. If you’ve ever conducted a ‘near me’ search, then you’ve probably already been a part of an innovation called Hyper Local Marketing. Imagine you’re at a restaurant waiting for your food, scrolling through your phone, and an ad pops up showing you exactly the kind of laptop you require customized to your needs, where you could buy it, when it can be delivered, and who can help you adjust your payments for it. That’s a hyper local marketing campaign targeted at the customer profile you fit into.

GIS data analytics can help define targets and design promotion campaigns, customer spending and it can also help is tracking advertising effectiveness. Smart retail mapping in GIS applications can help visualize hidden patterns in sales data, allowing us to create accurate prediction models for sales forecast.


An example of topographic data layered with consumer profile data


Sui generis solutions

Most developed cities are opting for planned parking spaces and implementing ticketing systems. Simultaneously most developing cities are creating parking spaces and ticketing systems. Most systems involve physical tickets and manual entries into databases for chalan documentation. These systems can accommodate errors in entry, is dependent on manual dexterity and prone to frequent breakdowns. Geospoc designed an application that tracks cars in parking spaces by analysing satellite imagery and security cameras, records and books ticket times and categorises and stores the information by capturing number plate images. The application was both web based and mobile. This solution was applied to cities on the outskirts of metropolitan areas with heavy residential populations. In a country where car ownership and traffic congestion is increasing, public services can benefit from customized solutions like these.

Path to the Future

It is clear that location solutions can help businesses make informed decisions. With digitization and the on-going surge in e-commerce, the demand for better customer experiences will challenge the retail industry for years to come. And location data analysis will continue to serve as an important tool in that journey. Find out how GIS solutions can help you gain leverage in your industry, Find out how GIS solutions can help you gain leverage in your industry,

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