Data gathering in retail: Which technology is best for you?

The 3 main data collection and marketing solutions for retailers are beacon technology, AI video analytics, and wi-fi. Let’s look at how they work and the pros and cons for retailers trying to measure engagement, understand customer behaviour, and promote effectively.

Beacon technology:

Beacon transmitters are small devices that send signals to nearby smart devices using low-energy Bluetooth technology. When a user walks past a beacon it sends a code with a message to the users Bluetooth device (smartphone, smart watch, tablets etc), which pops up as a notification.

Apple introduced the first of beacon technology, the iBeacon, in 2013, followed by Google’s Eddystone and Radius Network’s AtlBeacon. All of these location technologies aim to connect and transmit information, making location-based interactions easier for both the user and business.

Pros:

  • Low cost: Beacon technology is relatively inexpensive in comparison to other technologies, making it accessible to smaller business too. A couple of hundred pounds could get you a dozen beacons.
  • Easy to adopt by businesses: Bluetooth beacons are easily integrated with existing applications because of the software development kit and back-end management tools. They are easy to deploy; in fact, some USB beacons can be set up as quickly as a computer mouse. Having the ability to connect with any Bluetooth devices and signal is relatively strong.
  • Personalised proximity marketing: Beacons outside of the shops can lure customers in by sending promotional notifications directly to their smartphones. Retailers can send more targeted ads and personalised offers to in-store shoppers. For example, a beacon placed in the shoes department could send notifications to shoppers who spend more than 5 minutes within the range of the beacons. The business assumes some interest from these shoppers and could encourage them to purchase shoes by giving a discount or bundle offer through the beacons.
  • Google Ads: Beacons, connecting omnichannel through Google ads. When a user searches for a product online- let’s say “little black dress”- and clicks on the Google ad but closes the page without buying the product, Google ad records that. Now, if the same individual walks into the store, their phone will pick up the identifier from the shop’s beacon and recognise that it’s the same phone. This data is then linked to the Google ads account as a store visit. The business might assume the Google ad was the reason the consumer discovered the product and/or brand and visited the store.

Cons:

  • Privacy anxiety: Consumers are getting increasingly worried about privacy issues. Although beacons may not store any personal data or track individuals, it can sometimes be difficult for stores to make consumers feel comfortable with them. This technology requires permission from the user in order to send notifications through the retailer’s app; and privacy anxiety is one of the reasons consumers may not give permission to beacon-based notifications, even at the cost of missing out on promotions.
  • Bluetooth connection: Another limitation to beacon technology is the need for strong Bluetooth connection. Not everybody has their Bluetooth on at all times; and others decide to turn it off to avoid receiving too many notifications. As a result, beacons cannot reach all of the in-store shoppers.
  • App requirement: An app is required a connection between shoppers and the beacons. However, there’s an app for everything now and our phones are cluttered, making consumers reluctant to add yet another retail app on their phones.
  • Android issue: IOS can scan for iBeacon at all times with minimal battery usage when the Bluetooth feature is on but Android applications can only scan for beacons through apps in foreground and drain battery while scanning. Furthermore, native Android support is lacking for beacon technology, which is also true for AltBeacon (Google cut Eddystone support in 2018). So, beacon technology is not fully effective on Android users and causes friction for shoppers.

AI video analytics:

AI based video analytics gathers data by attaching a small hardware to existing CCTV systems. The software recognises shoppers as objects and tracks their journey throughout the store. The video footage is then gathered and processed using artificial intelligence and machine learning, before being presenting as aggregated data.

The technology first came about as people counter but modern technology can provide much deeper insights into consumer behaviour and help make strategic decisions.

Pros:

  • In-Depth Analysis: Video analytics can provide in depth analysis which human eyes can miss or struggle to keep up with. It can include every single shopper’s every move in the statistics, providing information such as footfall, dwell and path analysis, flow and heat maps etc. The retailer can also narrow the data down to specific date, time, and zones, giving a very in-depth analysis on how consumers use the store.
  • Accuracy: It was previously the job of a staff member to follow the consumers journey and report on consumer behaviour: how long did they spend in store, did they interact positively with a product, which departments are most popular etc. Modern software solutions can answer all of these questions with high accuracy – the industry average is above 85% – and eliminate any human errors. Unlike humans and manual recording, video analytics is fully automated and can gather large amounts of data while maintain high accuracy, zero lag, and minimum errors.
  • ROI: Video analytics, unlike other technology, is not limited to marketing. It can be used to optimise sales, marketing, merchandising, and strategic decisions. Data backed decisions have proven to yield higher results in the most cost-effective way. Moreover, since the data is available in real time, quick decisions can be made to reduce costs. For example, if it’s clear through data that shoppers are not engaging with a new product, the retailer can make it more visible in the store, promote it differently, or reduce the price. Video analytics also helps accurately predict things like footfall and engagement with new products, helping management teams create staffing schedules and R&D teams create products in demand. Ultimately, the ROI comes from optimising all areas of the business by making informed data backed decisions.
  • GDPR and permissions: Since video analytics doesn’t collect personal data it is fully GDPR compliant and shoppers are not required to give permission. Although, it is good practice to let shoppers know they’re being tracked in a similar way it’s done with CCTV monitoring.

Cons:

  • Large bandwidth network: Strong internet connection with large bandwidth is necessary in order to achieve real time data processing and data transfer. Some stores may need to invest in higher bandwidth network before integrating video analytics software.
  • Hardware costs: There are some costs associated with the hardware for video analytics. Also, retailers may need to invest in more cameras depending on what type of data they wish to gather.
  • Alerts: Retailers will need to be cautious about the alerts they set as there has been issues with staff drowning in alerts and not having the time to act. For example, if a busy retail store Oxford Street sets alerts for unattended bags, they may find that the technology picks up on many dormant objects.
  • Good CCTV camera resolution needed for better results: AI algorithms do a great job by analysis things that otherwise would have to be analysed by humans. However, as it is still a machine the one processing and analysing information, the better resolution the cameras have, the more accurate the software will provide results.

Wi-Fi:

Wi-fi data collection and marketing is done through retailer’s wi-fi network. Shoppers connect to the wi-fi network, giving permission to send notifications and track their phones.

Pros:

  • Collect Data: Shoppers connecting to wi-fi gives retailers the opportunity to collect data. A branded landing page can be created where the shopper could fill in information such as name, email address, DOB, gender etc. This allows the retailer to create a customer profile or add to an existing customer profile.
  • Data collection: Once customers are connected to the wi-fi network, the retailer can track more than just foot traffic as they can follow the full journey of individual shoppers. Retailers can understand how shoppers navigate the store, where they spend most of their time, and conversion patterns.
  • Personalised marketing: With the information provided by shoppers, retailers can deduce if they are new or existing customer. Thus, the promotions sent to their phones can be personalised; for instance, a new customer may get a first purchase discount. Similarly, a customer who only buys children’s clothes from the store may only see promotions for children’s clothes.

Cons:

  • Wi-fi connection: The biggest disadvantage to using wi-fi to gather data and market is the need for wi-fi connection. Retailers spend a lot of time and money to make wi-fi available and create the perfect landing page. However, nowadays most people have good data on their phones and don’t need wi-fi connection when shopping. Furthermore, it is inconvenient and time consuming for shoppers to fill in the information page, deterring some shoppers from connecting to the wi-fi networks, especially when they don’t plan to spend a long time in the store. This leaves a gap in data collection, making it unusable when making strategic decisions.
  • Data ownership: The data collected by the wi-fi network is owned by the wi-fi provider. Retailers have to purchase the data from the wi-fi provider in order to use it.
  • Active action from shopper: in order to collect information through a Wi-fi router, shoppers will have to actively connect to the store’s Wi-fi network and login with an email address or a social media profile in order to collect data, leaving a gap of missed data from shoppers that do not connect to the network.

In summary, AI based video analytics is currently the market leader as the most accurate and reliable solution. It is able to collect large amounts of data with little to no errors and provides businesses an all-in-one solution for their sales, marketing, merchandising, and strategic decision-making problems.  

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