Businesses Require Deeper Insights to Stay Competitive in Today’s Dynamic Marketplace
Location intelligence and location analytics can be the differentiators that lead to success. For years, businesses have analyzed information and made decisions by parsing data based on region, state, ZIP code, or other geographic criteria. Today, much deeper insights can be gained using more sophisticated location intelligence and analytics, which help us answer questions such as:
- What locations are poised for growth and what locations are expected to stagnate?
- How viable is a business in relation to those around it?
Location analytics lets businesses link different datasets to uncover relationships that have remained hidden in the past. For example, a retailer might combine location and spend information about customers in a CRM system with a database of location-specific economic information so it can evaluate which store locations have the greatest potential for growth.
New Types of Location Analytics Available Now
Location analytics is possible due to a couple of factors. First, today there are many more databases available that supply useful location-based business intelligence. Such databases might include demographic and economic details that can be married to existing information that a business already uses. Second, new location analytics solutions are making use of cutting-edge technologies to generate deeper insights in less time using new sources of data. Some of the technologies being used include big data analytics, machine learning, and social media analytics.
With such capabilities available, businesses across all domains are realizing benefits with location analytics. Examples include:
- A telecom operator improves risk models with location-level attributes to predict the rates of fraud and delinquencies.
- An insurance company uses location analytics to improve the prediction of claim frequency and set pricing for different coverage areas.
- A large utility applies location analytics across multiple platforms – web, desktop, and mobile – to prioritize prospects, capture feedback in the field, adapt quickly to market changes, and improve lead value and closure.
- A global bank incorporates location analytics in its response model to improve the effectiveness of its direct mail campaigns.
- A large hospitality brand uses location analytics to identify the most promising locations for construction of new hotel franchise properties.
Data Required for Successful Location Analytics
An effective location analytics effort requires partnering with a specialized vendor for advanced spatial analytics and additional data sets. According to Jayesh Srivastava, a leading expert in D&B location analytics, “Vendors must provide more precise geo-spatial data for businesses than pre-defined geographic areas such as states, counties, ZIP codes, and metropolitan statistical areas (MSAs). Additionally, they must define neighborhoods using business attributes (population, density, industry, size, etc.).” Mr. Srivastava recommends using the following data attributes, which can be key in most location-based analyses:
- Location-level attributes that summarize the payment behavior and scores of the neighborhood around a business. This provides intelligence on the characteristics of the area and how it is performing. Examples of such attributes are the presence of certain industries, viability of the business environment, average business size and capacity, and percentage of business starts, failures, and bankruptcies.
- Location risk information that helps predict delinquency, out-of-business, inactivity, or bankruptcy at the business level.
- Location growth information that identifies locations which have an enabling environment not only to ensure viability now but also to assure future expansion.
- Macro-economic insights that include leading indices on the direction of the economy, GDP, and employment projections – at the country, state, MSA, and industry levels.
Actionable Location Analysis Requires Multiple Datasets
Given the broad potential and varied use cases for location analytics, any solution chosen must accommodate third-party datasets. An analysis was done using third-party foot-traffic data combined with geographic data, census data, and D&B business data to create innovative location insights. Given a store location, a model created from that analysis is able to predict where customers will come from in a location network.
Essentially, it’s a foot-traffic network model that enables location-based marketing. For example, if people are expected to come from a nearby subway station, marketers can buy billboards at that station and send digital advertisements to mobile phones of users around that location.
To learn more about innovative applications that can be derived using location intelligence and analytics, follow the D&B Perspectives on Analytics blog and keep abreast of new developments in this area.