Currently, digital transformation is reimagining online experiences, shopping, and even shopper expectations within brick-and-mortar stores. The COVID-19 pandemic altered not only our daily routine but even the means of carrying out business. Despite the many unwanted changes, it has also triggered many technological innovations.
The necessary quarantine needed to reduce the spread of COVID-19 has swiftly amplified the desire for virtual fitting room technology. Augmented shopping involves the try-before-you-buy option, drawing in customers by giving them the chance to try out products online. Clothing brands are employing virtual fitting rooms and showrooms to enable shoppers to try products in virtual spaces. Other brands are using apps to allow buyers to get a visual look at how their potential furniture will appear in their premises, by moving their smart device cameras around the space in real-time.
As several Retail IT Solutions are attempting to leverage big data, demand forecasting driven by machine learning is one of the cutting edge methods to streamline logistics and manufacturing operations, customer and supplier relationship management as well as executing smart marketing campaigns.
When compared with the traditional forecasting processes, machine learning approaches are proving highly adjustable to changes and swifter to execute. This is one of the main reasons why ML-powered demand forecasting is relevant to the current reality.
In the midst of the pandemic, several proprietors utilize e-commerce platforms in an effort to enhance online sales processes. Post-pandemic, those proprietors holding both e-commerce and brick-and-mortar, would need to ensure both offline and online inventory, promotions, and transactions are synchronized. As a result, integrating eCommerce Industry Solutions will be the most efficient solution both during and post this pandemic.
Machine learning and Data Science- driven technology has the capacity to put forward recommendations to consumers even before they themselves are aware of what they want. Recommendations serve to incentivize consumers on the basis of needs and desires, instead of hassling users to download an app. Once machine learning methods are implemented, the recommendation system captures details about comparable consumers to create shopping profiles. After that, the system develops customized calls to action to specific customers, thus accelerating the buying journey.
NLP (Natural language processing) and AI have experienced high- level growth over recent years. This technology does not even require that you open your browser or app to communicate with voice assistants – these intelligent applications are equipped to recognize the customer’s speech and act in response to the sound of your voice. As oppose to typing in a search query, buyers use voice searches to look for products or any information that they need. Voice commerce including the use of smart speakers is increasingly being infused with the latest technology; as a consequence, it is blazing a trail for the future of retail.
Given that social distancing is a critical means to remain virus-free during the pandemic, staff-free and cashier-less stores are poised to transform the retail scene. Currently, a high percentage of customers would prefer stores that offer self-checkout or contactless options. This digital transformation will be facilitated by tools like machine learning, facial recognition, computer vision systems, IoT devices, and RFID tags.