Personalization is resurfacing with big data
In the 20th century, retailers were able to offer personalized services to their customers as they were smaller in number, and products were placed in a storage room; however, as mass production became the norm and the number of shoppers increased, retailers have provided customers with direct access to products in larger stores, introducing cashiers as the main medium between the retailer and the customer.
With machine learning, retailers have been collecting data and shopping history to identify user demand, including preferences and shopping habits, going so far as predicting their future purchases. However, this is not all; retailers are also utilizing big data to categorize customers into groups such as: family-oriented, health-conscious, etc. in order to increase sales and basket size per purchase.Personalization for upselling and cross-selling
Personalization in retail does not stop at recommending preferred products. It is a crucial feature in increasing sales through upselling and cross-selling.
Retailers can upsell through personalization by providing a "users also bought this…" or a "you may also like this…" feature, which recommends users to add more products based on their shopping list. As for cross-selling, retailers can suggest users to purchase butter when they add bread to their shopping cart.
Yet, unlike coffee shops near offices, large retail stores may face a challenge in offering this personalized shopping experience for their customers due to the sheer volume of products that they offer, the number of customers they accommodate to on a daily basis, as well as the number of cashiers at the checkout.|Why retailers need personalization
According to research by Swan
on the UAE market, 62% of shoppers want to receive customized offers and reminders, while 59% said that personalized promotions would greatly improve their retail shopping experience. 
Also, a survey conducted by The Harris Poll this year revealed that 63% of consumers in the USA, UK, and Canada expect retailers to offer a personalized experience, and that it is the "standard". The findings also revealed that shoppers feel "recognized as an individual" when they are sent personalized offers. Yet, the survey revealed that retailers are not meeting consumers' expectations for seamless personalization, as 34% of consumers are frustrated when a brand sends them an offer for an item they have just recently purchased, while 33% receive offers that are irrelevant to their preferences; 31% of consumers are also frustrated when brands do not recognize them as existing customers. How to apply personalization in-store
Personalization has made a strong comeback since the previous century, as retailers are now expected to deliver a personalized shopping experience for consumers to retain them as loyal customers, and ultimately, increase sales.
To achieve this goal, retailers can develop a platform that retains customer data through machine learning, and recommend offers accordingly. Yet, though 94% of marketers recognize the significance of personalization, 95% remain "stuck" in the analysis stage – due to the enormous volume of data collected. 
To tackle this, Swan
, a newly launched shopping platform, aims to provide a seamless retail shopping experience for its users. Retailers can use the platform to display their offers and products for customers; Swan goes on to recommend products based on customers' shopping history and habits – achieving larger sales for retailers through upselling and cross-selling.
This solution allows retailers to have an online presence and offer a more personalized approach for customers, without the cost and lengthy time of analyzing data, scanning every item, and keeping track of the process.
All in all, with personalization, retailers can bring their offerings closer to their customers, keeping shopping a joyful experience for all!
 "RedPoint Global and Harris Poll Survey Exposes Gap Between Consumer and Marketer Expectations for Customer Experience." BusinessWire, 27 Mar. 2019
 "From Big Data to Big Personalization", Monetate