Demonstrating the Impact of Showrooming on Tenants'​ Online Performance with Footprints AI

Showrooming, the phenomenon where customers visit physical stores to examine products before purchasing them online, has become a significant challenge for retail property operators.

Footprints AI, one of the most advanced AI-powered Retail Media platforms worldwide, offers a data-driven approach to demonstrate the impact of showrooming on a tenant's website traffic and sales performance. This presentation below explores how Footprints AI tackles this issue by leveraging customer behavior data, predictive modeling, and connected infrastructure inside malls & stores.


The platform leverages in-store customer intention data to predict and influence physical retail sales more effectively. Using a combination of indoor positioning, predictive models, and omnichannel targeting, Footprints AI captures the complete path to purchase for customers in the physical retail environment. This enables brands to target media audiences based on predicted shopping behavior and different stages of the purchasing journey.

Footprints AI's cutting-edge software employs a sophisticated AI model to understand and predict shopping behavior, acquiring data from Wi-Fi, smart sensors, and other connected infrastructure. This proprietary technology combines offline and online customer data to create a comprehensive view of customer behavior, enabling retailers to generate new revenue streams, improve efficiency, and gain a competitive advantage in the Retail Media Network market.


The showrooming effect is a phenomenon where customers visit physical stores to check out products firsthand, only to purchase them online, often from websites with better prices. This behavior can hurt sales for physical retailers, especially if they lack a strong online presence. On the other hand, this behavior can undermine the pricing model of a retail property operator in relation to their tenants, as tenants can have an impact on their sales indirectly that the retail property won’t be aware of and they wouldn’t be able to count it into their leasing model.

It's essential for retail property owners to understand the showrooming effect for the following reasons:

  • Ensuring tenant success: Property owners want successful tenants because it impacts the overall performance of their shopping mall. By understanding the showrooming effect, they can offer support and guidance to help retailers minimize its negative impact.

  • Assessing value provided: Property owners should recognize the showrooming effect to better evaluate how their retail spaces contribute to tenants' online traffic and sales. This understanding can lead to more informed lease negotiations and help showcase the mall's value to potential tenants.

  • Embracing change and innovation: Being aware of the showrooming effect enables property owners to adapt their business models and shopping centers accordingly. They might add experiential elements or incorporate technology that bridges the gap between online and offline shopping, encouraging customers to buy in-store.

  • Collaborating with retailers: Property owners can partner with their tenants to develop strategies to counteract the showrooming effect. This may include offering in-store-only deals or promotions, enhancing customer service, or creating a seamless omnichannel experience.

  • Curating a diverse tenant mix: To mitigate the showrooming effect, retail property owners should consider hosting businesses that provide services or products not easily replicated online, such as restaurants, salons, or fitness centers.

In summary, retail property owners need to comprehend the showrooming effect to ensure the long-term success and viability of their shopping centers. This understanding allows them to better support their tenants, measure their properties' impact on online traffic and sales, and adjust their business models in response to the evolving retail landscape.


Footprints AI employs a combination of data acquisition, AI-driven analysis, and predictive modeling to establish the correlation between in-store customer behavior and the uplift in a tenant's website traffic and sales performance:

  • Data acquisition: The platform gathers data from Wi-Fi, smart sensors, and other connected infrastructure already in place in retail properties. This data includes information about customers' in-store behavior, such as dwell time, product interactions, and movement patterns.

  • AI-driven customer behavior analysis: Footprints AI uses proprietary AI technology to combine offline and online customer data, creating a comprehensive view of customer behavior. This allows the platform to analyze customer shopping habits, searches, visits, and purchases across both physical stores and online platforms.

  • Predictive modeling: By employing predictive models, Footprints AIidentifies media audiences based on their predicted physical shopping behavior and different stages of the purchasing journey. This enables retail brands to target media campaigns more effectively, driving traffic to their websites and ultimately increasing sales performance.

  • Establishing correlation: Footprints AI uses advanced data analytics to establish a correlation between in-store traffic and the uplift in a tenant's website traffic and sales performance. By understanding the showrooming effect on online performance, retail property operators and tenants can adapt their strategies, accordingly, maximizing the benefits of both physical and digital channels.

  • Continuous optimization: As Footprints AI continually updates its AI models with new data, the platform can refine its predictions and correlations, ensuring that retail property operators and tenants can stay ahead of changing customer behavior patterns.


While there isn't a specific mathematical model or machine learning technique dedicated solely to tracking and attributing the showrooming effect, there are various technologies available within Footprints AI’s unique set of pre-trained data models to match the physical retail environments that can be applied to understand the correlation between in-store traffic and online sales uplift. These methodologies can be adapted to develop models or algorithms that suit your specific use case.

  • Regression Analysis: Regression analysis is a statistical technique used to model the relationship between a dependent variable (e.g., online sales) and one or more independent variables (e.g., in-store traffic). By analyzing this relationship, you can identify the correlation between in-store traffic and online sales and adjust for other factors like marketing campaigns or seasonal trends.

  • Time Series Analysis: Time series analysis involves studying data points collected over time to identify patterns or trends. By analyzing in-store traffic and online sales data as time series, you can uncover the relationship between them and identify any causality or correlation.

  • Machine Learning Techniques: Various machine learning techniques can be employed to model the relationship between in-store traffic and online sales uplift. Supervised learning algorithms such as linear regression, support vector machines, or random forests can be trained to predict online sales based on in-store traffic and other relevant features.

  • Deep Learning Techniques: Neural networks, especially Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks, can be used to model complex relationships and temporal patterns in data. These models can capture non-linear relationships between in-store traffic and online sales uplift, accounting for the showrooming effect and other factors.

  • Multi-Touch Attribution Models: Multi-touch attribution models can be used to allocate credit for online sales to various touchpoints in the customer journey, including in-store visits. By applying machine learning techniques to these models, you can better understand how in-store traffic impacts online sales.


In the context of showrooming, Footprints AI can use a mathematical approach to identify patterns and relationships between online users and offline visitors of shopping centers. This helps us understand how certain offline behaviors influence online behaviors.

For example, we might associate a large group of online shoppers browsing a specific website with a smaller group of visitor segments within a shopping mall. If we can establish a strong connection between the two groups, it would indicate that certain online behaviors are significant in determining how shoppers behave in the physical store. And viceversa.

By finding these connections, we can transfer the knowledge of behaviors and preferences identified in the offline space to the online domain. This helps shopping malls better leverage the showrooming effect.


Tracking and understanding the uplift on a tenant's website without direct access and SDK codes can be challenging. However, there are some alternative methods and proxy measures that can be employed to estimate the impact of in-store traffic on a tenant's website.

  • Aggregated data sources: Publicly available data sources, such as Google Trends or SimilarWeb, can provide insights into website traffic and user behavior. While this data may not be as granular as data obtained from direct access, it can still help Footprints AI to offer a high-level understanding of traffic trends and potential uplift.

  • Surveys and customer feedback: Collecting feedback from customers through surveys or questionnaires can help Footprints AI gain insights into their online shopping behavior after visiting a physical store. This data can be used to estimate the showrooming effect and its impact on a tenant's website.

  • Social media and online reviews: Monitoring social media activity and online reviews related to a tenant's physical store and website can provide insights into customer engagement and sentiment. Increases in online engagement and positive sentiment following a store visit may indicate an uplift in website traffic.

  • Wi-Fi and location data: Footprints AI can collect anonymized data about customers' in-mall behavior, including store visits. Correlating this data with online traffic trends for the tenant's website, as obtained from aggregated data sources, can help estimate the impact of in-store traffic on the website.

  • Collaborative partnerships: Establishing a partnership with the tenant can provide access to more detailed data on their website traffic and in-store visits. This data-sharing agreement can be beneficial for all parties, as it can lead to a better understanding of the showrooming effect and enable the development of strategies to optimize the customer experience.

Some of these methods may involve the collection and processing of personal data. We will ensure compliance with relevant data protection regulations, such as the General Data Protection Regulation (GDPR) and obtain necessary consents from users before collecting such data.


In order to improve the quality of the probabilistic method in attributing showrooming to a tenant’s uplift in online traffic and sales, there are some creative approaches that the mall can have in order to collect more quality data and also have the validation of the attribution model that the AI technologies generate.

  • In-Mall Reward Program for Online Purchases

Retail property operators can create a reward program to incentivize shoppers who purchase from their tenants' online shops. For example, customers could receive vouchers or discounts for in-store purchases if they present a receipt from a recent online purchase made on a tenant's website. This encourages customers to visit the physical store and make additional purchases, while allowing the retail property to track and confirm the uplift in online traffic and sales.

  • One-Stop-Shop Return Service

Another creative method is to offer a one-stop-shop return service for products purchased online from the e-shops of tenants within the mall. This service allows customers to return items in one place, making the process more convenient for them. By centralizing returns, retail property operators can gather data on the volume and value of online purchases made from their tenants' e-shops. This information can then be used to confirm the uplift in online traffic and sales.

  • Exclusive In-Mall Events and Promotions

Retail property operators can collaborate with their tenants to organize exclusive in-mall events and promotions for customers who have made online purchases. For instance, customers could be invited to a special event, such as a product launch or a workshop, and receive exclusive offers or free samples upon presenting their online purchase receipt. This approach not only encourages customers to visit the physical store but also allows retail property operators to track the correlation between online sales and in-store traffic.

  • Cross-Promotions with Other Retailers

Retail properties can partner with other retailers or service providers within the mall to offer cross-promotions for customers who have made online purchases from their tenants. For example, customers who present a receipt from an online purchase could receive a discount or a free service from a partnering retailer, such as a complimentary coffee, discounted meal, or free parking. This encourages customers to spend more time at the mall, and it enables retail property operators to gather data on online sales uplift and customer behavior.

  • Collaborative Loyalty Programs

Retail property operators can work with tenants to create collaborative loyalty programs that reward customers for both online and in-store purchases. Customers could earn points for every purchase made on a tenant's e-shop and redeem those points for discounts or rewards in-store. This strategy not only drives traffic to physical stores but also allows retail property operators to collect data on the connection between online sales and in-store visits.

By implementing these creative methods, retail property operators can actively engage with shoppers, incentivize them to visit physical stores, and gather valuable data to confirm the uplift in their tenants' online traffic and sales.


Footprints AI offers a data-driven solution to demonstrate the impact of showrooming on a tenant's website traffic and sales performance. By leveraging data acquisition, AI-driven customer behavior analysis, and predictive modeling, Footprints AI effectively establishes the correlation between in-store traffic and online performance. This valuable insight empowers retail property operators and tenants to adapt their strategies, maximizing the benefits of both physical and digital channels and turning the showrooming effect into an opportunity for growth.

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