AI can help ecommerce companies identify shoppers who are eligible for the next level of a brand’s loyalty program.
The last quarter is the most anticipated time of the calendar year for people and businesses alike. With an exciting lineup of festivals and events that continue for the foreseeable future, or say,through the New Year and much beyond, the quarter is defined by unstoppable shopping sprees, a positive buying attitude, and an overwhelming uptick in sales across all categories and channels. In 2020, however, things were a bit different due to obvious reasons.
Let us simplify the grand scheme of things to an extent. There are multiple market vectors that are at play simultaneously. On one hand, there’s a rush of retail consumers, especially towards the online channels. As a result, the Diwali season turned out to be a success with the overall sales closing north of 10.8% this year. Nevertheless, it has happened after almost half a year of near-inactivity in the market.
Despite majority of market transactions have been consolidated by larger players – who have better access to the market as well as advanced tools and technologies. On the other hand, there is also fierce competition amongst regular e-tailers in the eCommerce space. Who wins this war ultimately depends on who’s better equipped for it – the technological edge will surely lead to a telling difference.
Lighting up the road ahead with AI-based Hyper-personalization
The pandemic has changed people’s preferences. After months of lockdowns and remote working, they are looking for more engaging, personalized, innovative, and seamless shopping experiences, be it in-store or online purchases. A customized and personalized shopping experience helps brands meet and exceed customer expectations while also instilling a sense of confidence in the final purchase. This ideal situation leads to fewer returns and greater customer loyalty. Amazon, for instance, is a classic example of personalization.
The eCommerce major recommends personalized categories based on your previous purchases, onsite searching, and browsing history. Such a level of personalization – despite having scope for improvement – is estimated to increase sales by 10% according to a report.
Hence, it won’t be an exaggeration to say that the key to success for etailers may lie in personalization. And right tech implementation in the form of advanced data analytics solutions can deliver desired results for both customers and online shopping portals.
Hyper-personalization could promise big returns to etailers
Here are some reasons why leveraging AI and advanced data analysis to achieve personalization could be a good idea.
Targeted Customer Loyalty Programs
Studies show that returning customers account for 40% of eCommerce revenue while being only 8% of the total customers. Another data indicates that a 5% increase in customer retention increases profits by 25%. It proves that customer retention is well worth the investment of businesses’ time and resources. Besides, their long-term loyalty can become security for e-tailers during the holiday season. Here advanced analytics-backed customer loyalty programs can work wonders.
Artificial Intelligence (AI) and behavioral data analysis can help eCommerce companies to monitor their customer base and identify shoppers who are eligible for the next level of a brand’s loyalty program. Obviously, lucrative offers and privileges remain the key highlights in attracting consumers.The approachequips them with the ability to analyze customer behavior, demand, and cost-to-serve per account.
This information can be used to target those who are likely to spend more by assessing their average order size, spend, frequency, returns and more. Such variables give a lot of actionable insights into the companies, thereby recommending how to target specific customers with tailored offers that positively impact their loyalty programs. Amazon Prime is anothergreat example of a paid loyalty program by Amazon whose benefits include two-day delivery, free streaming of videos, and music, while Myntra Insider and Nike Plus are also noteworthy in this regard.
Customized product recommendations
None of the customers want to spend hours finding the right fit or ‘that perfect pretty black dress’. In fact, disappointment in finding the right product in time can turn the customer away from the platform, and no eCommerce player, irrespective of its size, can afford to do so. Machine Learning (ML) algorithms automate the entire process by learning the likes and dislikes of the customer over time, getting better and better with every result. It uses historical data to understand user preferences and patterns in purchasing to ultimately ‘suggest’ products that are similar to their taste. This is applicable for food, apparel, movies, songs, and so forth.
Spotify leverages ML to compile highly-personalized Discover Weekly Playlists for its customers. Depending on the customer’s reaction towards the recommended songs, it smartly figures out that they are not amongst your favorites. Flipkart too personalizes the shopping experience for its customers by suggesting ‘Products you may like.’
Allowing customers to pick up shopping where they left
Tech giants like Netflix, Amazon Prime Video, and so forth have taken personalization a notch higher.They have introduced features such as allowing people to resume watching videos from the exact point where they left. It helps the users to smoothly continue with the storyline without wasting time browsing through the video as well as loading and waiting. The same is being followed by e-tailers. They leverage their returning user’s previous sessions and preferences to help them continue shopping where they last dropped off.
Providing personalized pricing
Personalized pricing can be considered as the next-gen personalization. Today, customers expect their brands to respect their individuality vis-à-vis the product needs, features, and price sensitivity. eCommerce players can leverage technologies to extract previous buying details of customers to significantly boost sales volumes and purchases. Advanced analytics can predict a customer’s buying propensity. This knowledge is then fed to their CRM systems to create customized promotions for each customer to improve their chances of conversions. As brands go for deeper optimizations, AI is making it possible for etailers to extend more relevant products and prices to their customers.
Smarter recommendations in social retargeting
Retargeting solutions are one of the most successful conversion drivers in the eCommerce industry. Brands are leveraging past data on session history such as cart information to target more customer-centric ads than the generic ones. They act as a reminder for valid customer intent and inspire a purchase, while also helping deflect cart abandonment
Inclusion of offline data for eCommerce personalization
Brands having an omnichannel presence can integrate their online and offline customer data to offer a more personalized experience. Clothing brand Sports has become a pioneer in this segment by offering an app that uses augmented reality to scan clothing items from the store’s physical catalog or wardrobe via mobile phones, thereby paving the way for their purchase online later.
Hyper-personalization is driven by Digital Twin
Creating a digital twin in marketing takes hyper-personalization to the next level. It is essentially a virtualized model of a physical entity that renders way better results than the clichéd customer segmentation or cohorts. Most marketing strategies focus on customer segments considering their same age, location, and employment whereas digital twin enables processing deeper insights on behaviors, interests, motivations, relationships, attitudes, social connections, etc.Doing so makes them more realistic and dynamic.
The approach identifies where customers are in their purchase journey and optimize strategies based on what they know, what’s driving them, and what will appeal to them the most. Using this information, etailers can gauge their predictions across key areas viz. conversion, purchase intent, brand choice, spend levels, and so forth. Hence, it empowers etailers to better position themselves and pitch relevant messages to customers at scale.
COVID-19 &New Year: Setting the stage for Hyper-personalization
The Indian festive season followed by New Year gives immense opportunity for businesses to connect with their existing and prospective customers. However, the first step towards striking the right chord lies in being prepared with a complete understanding of customer preferences. With COVID-19 in the backdrop, hyper-personalization is incomplete until it also reflects the core values of compassion and sensitivity.
So, it is about establishing trust and comfort quotient with customers and making them feel valued. State-of-the-art technological approaches have become a true enabler in this regard.
(Suhale Kapoor, EVP and Co-Founder, Absolutdata and the views expressed in this article are his own)