Using Location Intelligence to Make Business Predictions across Industries

While we have heard about the importance of location for real estate, it also stands true for other businesses such as retail, logistics or finance. All of the aforementioned sectors can benefit greatly from the advantages that location intelligence (LI) provides, and when working in conjunction with business intelligence tools – it can go on to identify relationships between various entities and provide actionable insights.

Gaining access to real-life data helps stakeholders in the creation as well as optimization of their strategy, based on the information received regarding the behavior of the customers, the environment and other such parameters. The conjunction of AI and LI can help in the detection of patterns while also offering solutions – Learning and improving with every successive data entry.

How Location Intelligence Can Be Applicable For Making Business Predictions?

While the immediate applications of location intelligence are generally in areas where proximity plays an important role, such as retail, production, transport, etc. the next stage of the direct purposefulness of this technology can be for financial structures such as fraud detection, public safety, recruitment, etc.

• Looking At Market Analysis And Growth Planning:

Utilizing machine learning programs, can help in identification of clusters in hotspots in complex datasets. Using AI and LI  results in unlocking of patterns as well as trends that can aid businesses in gaining an understanding of their markets.

For example, looking at where to open the store, would involve in assessing how reachable it is from various parts of the area – to looking at demographic information that can help you in ascertaining what the hotspots of certain consumer behavior are. When you look at both these data sets together, you can assess the potential site reachability and the nearby demographic which can help retailers in predicting and understanding which customers end up favoring a certain kind of location.

GIS can help people by quickly providing answers to questions related to demographics, reachability on maps and dashboards, quantifying it with the help of location intelligence and the insights it provides with the help of AI, you can get additional information regarding consumer mobility and the purchase patterns.

• Tracking And Monitoring Assets:

When you are looking at recognizing objects and sorting them accordingly with the help of machine learning, the location component can pay enormous dividends in terms of time as well as money. Almost all businesses have assets that need to be tracked and accounted for. From small to large geographic areas, tracking and monitoring of assets with the help of location intelligence can provide the kind of visualization that is needed by businesses to understand their market better.

For example, if you have a program that can identify the number of vehicles that are present in the parking lot as well as their manufacturing details, it can help you as a retailer in gathering demographic intelligence regarding the customer base of your competitor. You can also take the example of an insurance company that could use LI to understand the liabilities in the neighborhood.

• Operational Efficiency:

Exercising caution, regarding future outcomes can a lot of time hinder businesses and in undertaking activities that involve high risk-taking. With the help of LI providing huge data sets, credible predictions can be made regarding businesses and the choices that they make – thus enabling businesses to take smarter risks. This inevitably ends up lowering the cost of risk-taking, that eventually helps businesses in reaching their optimum potential.

For example, the COVID-19 pandemic has ended up shining focus on the necessity of digitization of supply chains to help companies in dealing with disruptions. Of course, if you do away with the unprecedented and unpredictable nature of the pandemic, looking at disruption in any form, would mean that business prediction factoring in the existence of disruptions, when optimizing their operational strategy is important.

When working in conjunction with machine learning, LI can help businesses understand the risks associated with growth and expansion much better. Whether it is helping retailers in determining the optimal locations for brick and mortar stores or for insurance companies to price their policies, there are new vistas of forecasting available now with the help of LI.

By using other modern technology with location intelligence, businesses can get the benefits of pattern recognition, classification and prediction. Gaining access to contextual data, regarding where the customers work, play, shop, etc. enables businesses in making much more accurate decisions that extend beyond just site selection. By having access to accurate these predictions, it is much easier for organisations to consider their product and service offerings, beyond digital as well as physical channels.

(The author, Ashish Raj is Chief Operating Officer (COO) at Transerve and the views expressed in this article are his own)