The power of big data analytics has been widely acknowledged by the decision makers and analysts worldwide. But still, big data has not been utilized to its potential by the analysts, especially the location data. Location data, also known as the geospatial data or geographical information, has been on the rise ever since the advancement of technology. The network of GPS-enabled devices, satellites and emerging internet of things (IoT) contribute greatly to geospatial data and this information will continue to grow exponentially with the advancement of these technologies. Geographic Information Systems (GIS) are often used for accessing, manipulating and analyzing the geospatial data. Many companies have already developed analytics ecosystem which helps them draw insights from geospatial data as it has become an important source of information in both big data and traditional data analytics.
Geographic information systems (GIS) have been used extensively with geospatial data to create analytics products that can help in manipulating and analyzing the location and geographic information. These tools use sophisticated visualization and predictive techniques to help decision-makers make informed decisions. Geospatial data today has moved out of the sole domain of the GIS professionals and has entered into the realm of everyday users. Therefore, it is important to understand how geospatial analytics can help marketers boost their product sales and thereby help in increasing the revenues of the companies.
Introduction to Geospatial analytics
Geospatial data is often confused with just address-related data (pin codes, location, landmarks etc.), actually, this is much more than that. Geospatial data is matched to specific longitude and latitude by a process called geocoding. These geocode are then used for visualizations and analytics with other data sources. Major sources of geospatial data include:
Satellite data: The data from the satellites contribute majorly to geospatial data generation and satellites also help GPS enabled devices to work efficiently and stay connected.
Global positioning system (GPS) devices: GPS enabled devices to help in finding out the location of the person and also help the person in navigation, get details of a place etc.
Data from other sources: Location from other sources like websites, social media, check-ins, tagging etc. also contribute to geospatial data and once geocoded, this data also helps in geospatial analytics.
Geospatial analytics is an approach in which statistical techniques are applied to geospatial or location data. In this, statistical techniques are combined with geomatics so that it becomes easy to capture, store, analyze and visualize different types of geographical data. This allows the data to be analyzed in a variety of contexts and also enables the analysts to use different applications for processing such complex and enormous data.
Need for Geospatial analytics
Geospatial analytics enables the companies to maintain a competitive advantage over other players who do not use it. It enables the companies to make better prediction models which can help them understand the future demands and risky decisions. Geographical data along with demographics and other factors can help companies better understand the market and make well-informed decisions.
Geospatial data is already used extensively in many domains to fuel analytics and get better outcomes. This data looks fairly complex and difficult to handle initially but rather than adding to the complexity in the already complex analytics world, geospatial data has the power to bring order. It helps in revealing the relationships between the already existent datasets. Geospatial analysis helps in establishing competitive advantage and is a rudimentary element in the analytics data these days. It has become important for every business to incorporate this while formulating the strategy for the long term company’s success. In this article, we’ll try to analyze how geospatial data can be used for boosting the sales of the company.
Leveraging Geospatial data for boosting sales
Companies have long been investing heavily in technologies that help them understand customer sentiments. Sentiment analysis helps in identifying what customers are talking about your brand, whether it’s good or bad. It helps in comprehending the reactions of the audience which can be a major driving force for curating a plan for future content and campaigns. Sentiment analysis also helps in taking proactive measures so as to prevent negative outcomes for a product.
Geospatial analytics takes sentiment analysis to a next level by adding geographical dimension to the data. Understanding the geographical dimension helps the companies figure out the sentiments of the customers in different geographies distributed across various demographics. Sentiment analysis by geography helps in better understanding the customers and also identify the influencers, brand loyalists, and dissatisfied customers. Identifying the customers and classifying them helps the companies to predict future growth of the brand and also the sales. The companies can take proactive steps to boost sales further and also target the potential customers.
Geospatial analytics is most widely used in marketing for market segmentation. Customer segmentation is used by marketers for dividing the customers with common characteristics into groups. These groups are then used for effectively running the marketing campaigns and evaluating the success of the marketing campaigns. The ultimate goal of the marketers while doing customer segmentation is to identify the customers which are easy to retain and also identify potential new customers.
Adding geographic dimension while segmenting the customers help the marketers to maximize promotional activities. Customer segmentation based on geospatial analytics helps marketers to design campaign based on store location or for customers pertaining to the specific geographic region. Geospatial analytics enable companies to go a step further to use the data from customer’s GPS-enabled devices to push specific offers, campaigns and content on a specific day, or time of the day to boost sales and also assess their time spent on the website, store and other locations pertaining to the brand.
Transportation and Logistics
Transportation and logistics play a very important part in driving the sales and revenues of a company. Logistics contribute a major chunk of the company’s expenses and optimizing it not only helps in reducing the costs but also play a very vital role in boosting sales. Many transportation companies have already built many linear programming models by which they optimize their existing resources and maximize the efficiency. Analytics in transportation and logistics has already helped these companies reduce their operational cost and delivery times.
Geospatial analytics is a step further which will help the companies to utilize the existing linear programming models and also harness the advantages that geospatial analytics offers. Location data from the vehicles help in real-time monitoring of the shipments and also this data can be used for finding optimal routes based on the traffic conditions.
Another important application of geospatial analytics in optimizing routes can be by computing the distances between the points and finding out the route with the least distance and time depending on the time of the day and other factors. Real-time street data and customer locations can also be added to the data for getting maximum accuracy. Optimizing transportation and logistics using geospatial analytics will not only help the companies reduce cost but also utilize their resources to get maximum efficiency. It will also enable the companies to reduce the estimated delivery times for the customer which ultimately will have a direct impact on the sales of the product.
Recent studies and research in the field of geospatial analytics have proved that it has its application across wide domains and if utilized properly, can prove to be extremely fruitful for the companies in the companies. Geospatial analytics not only has its application in retail but other domains like insurance, travel, banking, and utilities have been using it for quite a long time to optimize their operations and add new customers.
Studies have shown that customer behavior analytics when combined with geospatial analytics can boost up the probability of purchase by almost 70% and also help in increasing the shopping cart of a smartphone-enabled customer by up to 60%. Already accumulated information of the customers and the company data can be used in geospatial analytics for building models that have better accuracies. An increase in the use of handheld devices and smartphone users around the globe is going to contribute to the growth of geospatial analytics immensely and it is not just the companies, but also the customers who will be benefited by it.