Boost Festive Sales With AI - #ARM Worldwide

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The ongoing festive period is in full swing with brands planning to make the most out of the increased purchasing capabilities of customers, who in turn, are targeting the lucrative promotions and discounts available. However, it’s a different ballgame this year. The transformation in the sphere of e-commerce we’ve seen since March 2020 has been herculean. With consumers limited to the digital world for most of their daily activities, online penetration and e-commerce sales have evolved.

It’s no secret that e-commerce platforms are leveraging Artificial Intelligence – amongst other innovations – to amplify online penetration and sales. With these AI tools, retailers on e-commerce platforms are now able to regulate supply chains seamlessly. Order management and fulfilment systems are refining processes which otherwise would have been hectic, and also offer data on insights and growth on a day-to-day basis. Along with all this, technology is also drastically changing customers’ shopping experiences. 

Here are some ways you can leverage innovation during festive sales

Visual Search & Recommendations: AI-Driven 

Visual search creates a new path along the customer journey, but it also serves to bypass other paths – such as traditional person-to-person brand interactions. Now, consumers are personalising their own journeys with the help of AI-driven technology. E-commerce matching algorithms leverage users’ behaviour to suggest products with similar designs/features/prices all at their fingertips. 

Pinterest recently launched ‘Lens’ – a feature that uses machine vision to detect items on the web or Pinterest library and suggests similar items – you could call this a ‘Shazam’ for products. Similarly, another AI-driven search tool app – ‘Snap’ allows users to take a photo of any product, upload it to the app, and then view other similar items. These capabilities replicate the role of a sales representative in a retail store whose primary duty is to understand users’ needs and preferences.

Visual Search & Recommendations: AR-Driven 

As per a study carried out by Gartner – up to 100 million users worldwide are leveraging AR developments to help smoothen their online shopping experience. AR and VR capabilities are the key innovations that can help bridge the gap between offline and online retail, and effectively merge the best capabilities of both. Based on the “try-before-you-buy” approach, augmented shopping attracts customers by allowing them to interact with products online. For example, ARkit-based apps help visualise what a piece of furniture would look like when placed in their living room, or how the new winter fashion range would look on them. 

This utilization of AR offers a realistic interaction between users and brands with added convenience and comfort.

Hyper-Personalised: Targeting

Access to more data and processing power is enabling e-commerce leaders to examine their customers and new trends in behaviour like never before. Access to structured and unstructured data sources like social media, loyalty cards, sales, and market research create deep psychographic profiles of known customers to spot emerging trends and predict unknown customers demographics.

These capabilities help drive activity from pre-defined target audiences, ensuring the best return on investment in the case of marketing spends related to paid media and other targeting tools. Platforms can even leverage targeted banners and other ads to assist users in identifying products/promotions/sales that they are likely to be interested in.

Hyper-Personalised: Product Recommendations

Brands can now collect various forms of data to add value to a consumer’s buying journey by offering helpful and relevant recommendations. Sources of this data include – 

  • Product reviews
  • Product specifications (price, style, colour etc)
  • Purchase history
  • Social media
  • Web analytics

Predictive product recommendation amalgamates all these different data sets to offer hyper-personalised selections. A key technology driving this function is collaborative filtering. Collaborative filtering (CF) is a technique commonly used to build personalized recommendations on websites/apps. With the help of this technology, algorithms are used to make automatic predictions about a user’s interests by compiling preferences from several users. For example, a site like Amazon may recommend that the customers who purchase product A and Bm purchase book C as well. This is done by comparing the historical preferences of those who have purchased the same products.

Demand Forecasting : Plan

If you fail to plan, you are planning to fail – Benjamin Franklin

AI helps brands forecast demand and sales. Based on multiple insights, e-commerce and retail vendors can keep a reserve stock of particular products and even run certain promotions and ads based on these insights. Accurately estimating these ebbs and flows in demand based on geographic and historical customer data helps ensure a positive ROI. Here are the key factors to pay attention to when forecasting –

  • Location of target audience
  • Seasonal preferences
  • Type of product (luxury/necessity)
  • Competition in the market

Demand Forecasting: Save

The next step is to make use of all the information accrued from your demand forecasting activity. An effective demand forecasting plan can help your e-commerce activities in the following ways – 

  • Improves financial decision making 
  • Offers customers the right products at the right time 
  • Lower warehousing expenses 
  • An effective pricing strategy that reflects demand 

With its disruption, the COVID-19 pandemic has brought along a sea of opportunities and revenue streams for e-commerce players. Combine this with the raging festive season in India – and there is a lot to take advantage of for brands. 

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