How it works


With the help of advanced algorithms, TrackIn processes surveillance camera footage into insightful and actionable data in real time. Data on footfall, real time occupancy, flow and heat maps, path and dwell analysis, and product specific insights can be extracted from the dashboard. Retailers can hone in on specific date, time, and area of the store; and even compare the data with other stores. Artificial Intelligence (AI) used to process the data is highly accurate even with lower resolution than HD.


Benefits for retailers

GDPR compliance

Article 9 of the GDPR states, GDPR is only applicable when biometric (also referred to as personal data) is processed; and according to the Independent Commissioner’s Office  (ICO), data is considered biometric when specific technical processing of images reveal physical characteristics of an individual’s face by measuring the distance between eyes, nose, and mouth which are specific to each person.

TrackIn provides retailers aggregated data and does not collect or process any biometric data. Shoppers are detected by the algorithm as a conjunction of a body shape and a rounded top (head) when they enter the store; thus, no biometric data is collected or stored by TrackIn.

Although active consent is not required by the data subject when biometric data is not being collected, for full transparency, TrackIn encourages clients to display signs to make shoppers aware of TrackIn’s software use in the store, the same way they display signs informing shoppers about the use of CCTV systems.

GDPR compliance is necessary when collecting any sort of data that can be used to identify an individual; for example, facial recognition features on video analytics software are subject to GDPR because the individual is easily identifiable.

Similarly, in the online world, cookies and online terminals are an example where customers’ email addresses, payment card numbers, and delivery addresses are collected and stored – these are considered personal data.


      1. Strategic decision making.

According to the McKinsey survey, the overall business performance gets a boost when c-suite management make informed decisions backed by data. Data gathered from video analytics can be used to make decisions across sales, marketing, development, operations, and finance.

For example, using data, management teams can assess the role of a specific store and decide whether it adds value to the business’ overall success. If data reveals the store- despite having low conversion- has a significant impact on online sales by helping shoppers discover new products, the management teams would decide to keep the store. Conversely, if a stores conversion is lacking in comparison to another store and footfall is low, the decision might be to close or rebrand it.

Furthermore, when opening a new store, data provides insights into what the demographics of that location demands from your brand. Starbucks increased its revenue by 26% between 2016 and 2019 having used data to work out profitable locations and engage customers with meaningful marketing promotions.


     2. Consumer behaviour

Real time data will help retailers understand the changes in consumer behaviour and act accordingly. Data on footfall, heat maps of hot and cold areas, popular routes taken will reveal how consumers use the store; and retailers can use this information to maximise the store layout, product placement, and promotions to achieve the desired conversion.

Moreover, mapping out customer paths and dwell times at different displays will reveal how consumers interact with products. Retailers can even compare interaction with conversion and make necessary changes to product placement, price, or promotions.

Data can also help with improving bounce rate. Is the issue the lack of sales staff or queuing time? What is the average time customers spend in store, at different times of the day? How long are they willing to queue before abandoning the shopping cart? How can you command more attention from your customer? By answering these questions, data can help identify the problem.


     3. Marketing.

Customer analytics is one of the key drivers of effective marketing. To attract new customers and reengage existing ones, retailers will need to understand their customers’ behaviour and target them with appropriate marketing tools at the right moment.

Measuring, for example, the effect of your window displays on shoppers. How long did they observe the display before entering? How many didn’t enter the store after seeing the display? Is the data different for different demographics and at different times of the day or week? Having answers to these questions will help retailers improve footfall through the use of effective window displays.

Once the customer is inside the store, further marketing efforts are made to help guide them through the shopping journey. Knowing your customers’ likes and dislikes will help you promote the right products and place promotions in the optimal way.

To stay competitive and relevant, many retailers have changed marketing tactics. So, in order to figure out which marketing is effective on specific buyer personas, retailers need to rely on data to give you an accurate answer. By precisely targeting your marketing towards customers, retailers can improve conversion and cut costs at the same time.


     4. Predictions.

Data gives retailers the power to accurately predict future trends and demand, as well as have the competitive edge when reacting to them. Having the knowledge on which products and marketing tools have the highest impact, retailers can predict the success of future marketing campaigns and develop products that are in demand.

Inventory forecasting is also easier when retailers understand consumer behaviour. They can accurately predict the number of products required in certain stores and even plan for busy periods or seasonal changes to demand.


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