IMS Sensory Media is a sensory marketing company. They serve advertising clients in over 290 shopping malls, acting as a regular provider of technologies such as digital signage screens, audio marketing, scent marketing, and in-store advertising, all designed to stimulate the senses of visitors.
In the online world, marketing campaigns are not necessarily simpler to execute in terms of creative process or configuration, as this also requires specialized knowledge; however, measuring their results is much easier. Thanks to tools like Google Ads, Google Analytics, or heat maps, it is easy to determine how many people visited a website, where they came from, and how they engaged with the presented ad content. For e-commerce businesses, observing customer behavior and analyzing reactions to marketing campaigns is therefore possible instantly. Our client, however, operates in the physical world, where data on traffic or interactions with advertising media is not easy to define.
In the physical world, verifying the conversion and reach of marketing activities is a difficult task. IMS was looking for a solution that would allow them to monitor unique viewers of their media inside shopping centers. The problem was that no one was regularly collecting such data, except for the malls themselves, which are reluctant to share it as it is considered their know-how. Gaining access to information on the number of visitors could be possible, provided the team allocated a substantial budget to hire people to conduct traffic measurements at each location. However, this is inefficient in terms of both time and money. It would involve delegating staff to each of the 250 malls to count daily events (an individual seeing a display while in the mall) for several hours a day.
"Thanks to traffic data from dataplace.ai, we know the reach of advertising campaigns in over 250 shopping centers managed by IMS. This information complements our service and ensures our competitiveness" - Michał Mozyrko, Head of Media Sales
Data on the number of people who have had contact with an advertisement is undoubtedly very important to IMS clients. For the company itself, this can be a key argument in negotiating service prices and choosing this specific communication channel, while also providing significant added value for their clients. Therefore, the goal of the partnership was for dataplace.ai to provide information on the number of people visiting the malls where IMS has its media.
IMS wanted access to weekly summaries of foot traffic levels for each mall with the smallest possible margin of error. The task for dataplace.ai was to provide such reports. Using our experience in collecting, analyzing, and estimating data, we knew
that we could support IMS in this matter.
We decided to prepare models based on location data from millions of mobile phones and implement machine learning to calculate visits, ensuring the process was automated.
To this end, we created a dedicated workspace in our application for the IMS team, where the company receives information on the number of customers in individual shopping centers throughout the week, allowing them to observe, compare, and analyze data regularly. The user-friendly interface allowed non-technical staff to view information and quickly check the insights they needed. We also developed appropriate models and data delivery formats, which, in addition to the application, are provided as XLSX reports.
Together with IMS, we developed precise outlines of the mall areas, which allowed us to define the coverage zone from which the client wanted to obtain data. Through our technology, we observe how many visitors appear within these areas every day. The study was and is possible to implement thanks to the analysis of location data from nearly 4 million mobile devices. These devices send anonymous location data to our server, and by analyzing it in real-time, we are able to understand how people move on a daily basis and how they interact with stores, services, and buildings.
We used this type of analysis to solve a specific client problem by automating the process of collecting and processing data on people entering shopping malls across Poland. This approach not only saves time but also reduces the costs of gathering information. Our machine learning model was trained on data incorporating seven additional factors to calculate and interpret information about the number of people seen within or around a building. Thanks to these extra variables, the model is self-correcting and provides the most reliable information possible.
IMS gained high-quality data at a great price, which would otherwise have required hiring people to manually count interactions with media displays or mall traffic. It is now an automated process, and more importantly, IMS has gained access to a tool that allows their staff to retrieve data on any chosen location in Poland at any time, as well as add new locations to the list of analyzed sites. Consequently, when starting a partnership with a new mall, the client can immediately see the level of foot traffic it generates.
IMS can now demonstrate the effectiveness of their services with real numbers, which indirectly contributes to increased sales. IMS clients also benefit from this solution, as they know exactly what to expect and what they are paying for. By knowing the audience size, they are aware of how many people they can reach with their message and can evaluate the results of a campaign after it has been executed.
The data we present also perfectly illustrates trends. Any deviations from the norm are immediately visible in the changing percentage of visitors. As a result, IMS knows which periods are peak times in the mall and when they can adjust prices based on supply and demand.
Dataplace.ai regularly provides IMS with traffic data for all supported shopping centers, delivering valuable insights regarding:
Based on our algorithms, we also provide other data about shopping malls that can be useful for companies. This includes information regarding:
