Retailers have long known the value of developing data warehouses and similar repositories to amass point of sale (POS) data, and information from systems including customer relationship management (CRM), supply chain management (SCM), enterprise resource planning (ERP), and a wealth of 3rd-party datasets. Using analytical tools retailers can run queries against a data repository to generate business intelligence (BI) and operational as well as ad hoc reports to better manage their operations.
The missing or under used component has been leveraging location analytics, or the where factor to further improve decision outcomes. While typical BI systems handle the who, what, when and how factors, the combination of BI and geographical information systems allow for new types of analyses by adding the where factor to analyses, and doing so in the context of all the others. Adding the location context when analyzing business data allows for revealing spatial relationships, trends, dependencies, and patterns that may have been undetectable in a traditional enterprise applications or BI.
Location is a central factor in business. Almost all business data have a location component. The location can be an address, a sales territory, a delivery route, an administrative boundary, a customer’s address, a store location, the competitor’s store locations, or the less fuel-consuming route between the warehouse and the delivery point. Location awareness is beneficial for business performance management, and location analytics can make all the difference in gaining better insights about customers.
Without the geospatial context that enables location analytics, key data points simply remain invisible amongst the other data points. While the whole idea of KPIs and dashboards is to provide insights at a glance, it is the addition of location analytics that brings the data to life in a real-world context. Here’s a quick example of the pragmatic value of location analytics:
– Without Location Analytics. Your reports show that sales for one location has dropped 15%. You look at the numbers and wonder why.
– With Location Analytics. Curious as to why sales at one location have dropped by 15%, you display your locations & sales over a twelve month period by month, on a map, and then add an overlay showing locations of competitor stores, by month over the same twelve month period. Using advanced visualization widgets such as a Time Slider, you watch the opening and proximity of competitive locations over time and the corresponding effects on your same story sales results. You immediately spot the problem – a competitor’s new location near the store that dropped 15% last month and worse the sales losses started months before and is getting worse! Time to respond with a targeted marketing campaign.
Our Vision of Location Analytics
Galigeo pioneered the field and leads the industry of integrating geospatial data with all types of relevant business information including transactional, operational, analytical, demographic, and other aggregations—from a spectrum of your own internal and 3rd-party sources—to create location analytics solutions.
By combining geographical dimensions with enterprise data, Location Intelligence enables organizations to gain critical insights from enhanced Business Analytics. Consequently the need is to connect directly to enterprise applications, to enable mapping of large-scale datasets while applying spatial processing to enhance data visualization, reporting and predictive analytics and to allow retail companies to solve complex location-specific business problems, such as improving store site selection or enhanced trade area analysis.
Location intelligence helps retailers derive better insights and analysis a number of ways, including:
Trade Area Analysis
Location analytics enables rich multivariate analysis. For example, you can use Galigeo solution to drive trade area analysis, which helps reveal where customers are located in relation to a precise point-of-sale. By analyzing the demographics of an area to profile customers, it becomes easier to align sales and spend marketing budget where it is easy to reach the most customers in the most cost effective manner as possible. Accurate trade area analysis can make all the difference when it comes to improving marketing decisions that enhance operational performance and increase return on marketing operations investments.
By analyzing multiple sets of location-based data, trade areas analysis efficiently monitors store performance and measures key statistics that impact a store’s performance. Incorporating a trade area analysis approach inside the location analytics environment visualizes and layers the variables into one map. This approach can help organizations in a wealth of ways, including:
– Map existing customers in relation to store locations
– Develop demographic profiles
– Calculate time and distance customers must travel to reach a store
– Perform competitive analysis and market gap analysis
– Evaluate market penetration
Prediction of future probabilities and trends are not an easy thing to do. At a minimum, companies must have access to relevant data stored in a data warehouse, analytics cubes and/or data mining tools, and a team capable of analyzing the information to build predictive models. Companies have come to learn that by adding a powerful location analytics solution to their toolset, they are able to present the probabilistic outcomes visually with maps. Mapping large scale data, include third party data, and applying spatial processing improves the value of the predictive effort, and broadens the sharing of such outcomes to more teams across the company. The delivery of predictive analytics outcomes via map-based visualization which might further reveal spatial dependencies or trends difficult to discover otherwise. These improved insights about customer behaviors can lead to: – Cross-selling opportunities – Identification of fraud and risks – Retention of high-value customers – Anticipation of customers’ needs
Data Visualization & Reporting
Well-designed location analytics solutions enable retailers to more quickly and easily transform data into visual information that provides actionable insights. By visually examining existing data on a map, through multiple layers, or widgets (time slider, heat mapping, etc.) one can better identify hidden trends, enhance customer relations, monitor customer behavior, and gain an understanding of their overall marketing strategy. When companies adopt a location analytics solution they are positioning themselves to quickly acquire insights that help them take action effectively. By integrating the spatial dimension to data analysis, data can be analyzed and displayed through a map allowing enterprises to exploit the data proactively through dynamic and interactive dashboards and detailed reports.