Raw location data is widely available everywhere. It emanates from each and every smartphone, wearable, and linked device there is. Location data, however, has little commercial value in its raw form. It is not privacy friendly and requires complex improvement before use in the market.
Filtering for erroneous or duplicate data, stringent anonymization and security standards, supply correction, and the aggregation of information for usage in diverse use cases, such as real estate investing, and analyzing retail shop performance are all parts of the refining process.
These procedures frequently demand for highly skilled technical teams. The accelerated development of location intelligence and analytics products, however, creates space for inefficiencies because of the inherent noise in location data and its complex refining procedures.
In the age of AI, applying machine learning algorithms can get around all the problems listed above and create business insights that are future-proof. Machine learning gathers different data sources, like the location, demographic, and weather information, and creates a relationship between all of these sources and a goal source, such revenue data.
When there is sufficient training data, the computer may automatically learn these relationships and calculate the effects of different data sources on the target source. Businesses are able to make better informed decisions faster and with more capital efficiency thanks to these machine learning models.
Foot traffic data generated by machine learning can be trusted to be more accurate and timely, which is something we are frequently informed is crucial for our clients so they can invest, sell, create, launch, or pivot more quickly.
Indeed, using machine learning to guide organizations is now considered normal practice among top global corporations. Here are a few snapshots that show how location data powered by machine learning is upending three different businesses to serve as an example.
Machine learning algorithms can be used to examine a specific neighborhood or zip code in the context of real estate investing to understand who lives there and how that is changing. By anticipating or even forecasting certain insights based on the collection and combination of data, machine learning models can bring significant value in this situation.
The investor’s output value comprises the capacity to tell a better story about an investment opportunity as well as comprehensive insights that are easy to understand and based on all pertinent data. A faster transition from property research to deal drafting is possible for investors thanks to machine learning.
There are several applications of machine learning and foot traffic in the insurance industry. Making underwriting plans for business interruption coverage is arguably one of the more critical ones. Utilizing mobility data is a way for determining how a catastrophic event will actually affect foot traffic and, consequently, income at a business.
A novel approach to doing this is to use location intelligence in parametric applications, which are insurance policies that are instantaneously activated by an actual incident rather than through a drawn-out claim process. As a result, insurers’ underwriting procedures are more effective and secure.
In order to infer mobility patterns and gauge visitor trends to their physical stores, retailers are frequently most interested with leveraging machine learning-powered foot traffic data sets. With the aid of these information, retailers are better able to analyze trade areas, comprehend the success of their stores, and choose the ideal locations for their upcoming physical locations.
The same models may be used to compare performance across stores and to analyze how each one is doing in comparison to its rivals, the market as a whole, and other brands. As a result, retailers can track their regional dominance and market share over time, stay one step ahead of the competition by spotting untapped areas and opportunities.
Here are a few “must-do” best practices for utilizing machine learning-powered location data in your organization to assist you in getting started.
- Ensure Data Quality: When it comes to applications for machine learning, data quality is crucial. For location data, this is especially true. Before implementing machine learning, businesses must confirm that the location data they are utilizing is precise, current, and relevant to their particular use case.
- Pick the Right Tools: The effectiveness of location data intelligence projects can be significantly impacted by the algorithms and tools used for machine learning. For instance, while certain machine learning algorithms are better suited to handle high-dimensional data, others are more suited to handling enormous volumes of data.
- Addressing Privacy Issues: Location data can contain sensitive information, and its use must adhere to privacy laws. It’s essential to have a strong data governance strategy that protects privacy while allowing for insightful research.
- Build vs. Buy: Building a solution internally requires a lot of knowledge and takes time, but it also gives you more control. Although purchasing a solution can be speedier and need less technical knowledge, it could not be as adaptable to particular needs.
- Getting Ready For Implementation Challenges: Like the implementation of any technology, a machine learning solution has its share of difficulties. These could include technical challenges, such as connecting the solution with current systems, as well as managing change and workforce upskilling.
- Securing Buy-In: Focus on presenting the benefits of location data insight to stakeholders to gain their support. Make your case through case studies, success stories, and data-driven arguments. Show how investment in this technology may result in better decision making, increased operational efficiency, and new insights that can drive corporate success.
Machine learning-powered location data intelligence is a potent instrument with transformative potential. However, as with any tool, it must be used with caution and responsibility. The recommended practices provided here serve as a starting point, but each organization’s journey will be unique.
