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Importance of data annotation to Retails'​ AI adaptation

Experts estimate that spending on artificial intelligence in the retail sector will be around $12 billion by end of 2023. The projected growth of the retail industry will be from $23.6 trillion to around $27 trillion by 2023. The enormous growth gave way to major competition in the retail industry & to sustain retailers are gearing up for better customer engagement & satisfaction. Customers in today’s day & age expect personalization. Millennials being a majority of the consumers spend around $600 billion on retail alone, every year, and the industry is now, innovating.

Retailers are using AI worldwide with customized & seamless in-store experiences. AI helps empower businesses with information that can be leveraged to improve retail operations. AI helps in providing high-level customer service & improving business opportunities. AI adoption & implementation in different functions of retail business has significant benefits. Although, it requires a lot of data annotation work to train the ML algorithms to function properly. In this blog, we talk about the role of data annotation along with the benefits of adopting AI.

Importance of Data annotation

AI/ML projects require enormous data to train the models for significant results. The raw data needs transformation into structured data for the machine to understand the input data & come up with the correct output. This is where data annotation & labeling comes in. Accurate data annotation is imperative for the success of AI/ML projects. Data annotation helps retail businesses in several ways as listed below:

  1. In-store traffic analysis

  • Consumers moving around the store can be mapped for optimal placement of products & promotions.

  • Promotions capturing engagement can be measured along with the capture rate of pass-by traffic.

2. Real-time performance

  • Empty spaces & missing products can send alerts.

  • Segmentation annotation can help distinguish between individual items.

  • Inventory management

3. Customer sentiment analysis

  • By analyzing facial expressions, customer sentiment toward a product on the shelf can be understood.

4. Checkout monitoring

  • By monitoring checkouts in real-time, smart checkouts can minimize theft.

  • With the help of pixel-perfect image annotation, retailers can monitor each item passing through checkout.

AI application areas in retail where data annotation becomes inevitable

AI in retail is offering customers a high level of convenience and helps streamline processes. Product traceability is faster, checkout processes are smooth and expedited.

Some of the AI application areas in retail that require data annotation include the following:

  • Self-service checkouts

  • Automated warehouses

  • Virtual fitting rooms

  • Shopping assistants

  • Customer journey mapping

  • Trend analysis


Artificial intelligence (AI) is rediscovering the retail business. Retailers are using AI to connect to customers, offering customized promotions in real-time, automating warehouses, mapping customer journeys, trend analysis, and much more. All these help in waste reduction and smooth operations. It is all a game of data and collecting data is also not a challenge. But businesses struggle to draw insights from that data which requires serious intelligence; AI-enabled solutions are the key to these intelligent insights.

However, all ML solutions require data annotation. Therefore, data annotation becomes an important adaptation for all AI projects. To stay in competition retail industry needs to focus on AI/ML-enabled solutions and for the success of these projects, there is a huge requirement for data annotation so that data can be trained effectively. This challenge can be worked on in-house or outsourced to annotation experts.



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