News
AI-Based Product Recommendations Boosting E-Commerce Sales

Cross-selling is a technique that comes from traditional offline stores, where the seller encourages the customer to buy additional products that complement their main purchase or meet their other needs. It’s a win-win strategy that not only increases the order value but also enhances customer satisfaction.
With the emergence of AI-based algorithms that can gather information and analyze consumer behavior, cross-selling has become an even bigger sensation. These algorithms can provide personalized suggestions and adapt in a matter of seconds. The businesses implementing this approach have already seen a large increase in their sales and customer satisfaction.
How AI Assists in Cross-Selling
There are several ways in which AI can assist an e-commerce platform with sales and cross-selling. They are all based on constant consumer behavior analytics.
Enhances Personalization
The biggest use of AI is definitely for personalization. By collecting and analyzing massive amounts of consumer data including purchase history, browsing patterns, demographic information, likes, and dislikes, AI algorithms can provide hyper-personalized recommendations for every online buyer. Such suggestions will be tailored to individual customer preferences and can even anticipate the needs the customers didn’t know they had.
Moreover, algorithms help us to address the “choice overload” — an abundance of available products online that often leads to a decrease in customer satisfaction. When you receive personalized picks tailored to your preferences, you don’t have to waste time and energy scrolling through endless items that you don’t need. You can quickly scan through things that you already liked/looked for before/need without even realizing it.
Offers Predictive Analytics
Another feature that will be very beneficial for businesses utilizing AI for cross-selling is predictive analytics. If personalization is more about the customers and their needs, predictive analysis helps e-commerce platforms decide what to sell, anticipate upcoming trends, and predict potential cross-selling opportunities.
Algorithms use sophisticated predictive analytics models to analyze historical data and learn about customer behavior and purchase patterns through the years to obtain insights about future trends. By putting these skills to use, platforms can enhance their relevance among competitors, increase conversion rates, and allocate their resources more effectively without spending the funds on something that won’t be popular among consumers.
Adapts in Real Time
Besides analyzing historical data and identifying trends based on it, AI algorithms can also monitor customer interactions and feedback. You can target not only one specific platform like yours, but also other e-commerce shops, or even the specific brand or product or the type of customer, etc.
By doing so, AI algorithms can help you adjust cross-selling strategies to optimize relevance and effectiveness and suggest only the most relevant products to your customers. Moreover, the monitoring process is constant, so you can adapt and modify your offer almost in real time.
Provides Seamless Integration Across Channels
As marketing becomes omnichannel, we get used to utilizing different devices simultaneously. For example, you saw a clothing ad on Instagram, clicked on it, added the top you liked to the cart, and then decided to check it out from your laptop. Of course, you expect to see this same top waiting in your cart on the desktop version of the website. Moreover, you feel frustrated if it’s not there and start contemplating whether you need it at all.
Here, AI comes in handy as well. It facilitates integration by aligning cross-selling efforts across multiple channels and platforms, delivering a consistent and unified experience for customers regardless of their preferred mode of interaction whether it’s online, offline, or on mobile devices. We, as customers, expect a seamless and cohesive experience across all touchpoints, and AI is a powerful tool to achieve such cohesion.
AI-Powered Cross-Selling Strategies in Practice
Artificial intelligence technology has been around for some time. So, it’s not just theory anymore — we can easily find AI-powered strategies in practical use. Here are some examples.
Jiffsy — First Store Display App With AI Recommendations
AI can be used for different purposes. For example, in Jiffsy — an app working directly with fashion brands — artificial intelligence is utilized to create modern and unique storefronts without the need to use code and know all the intricacies of backend development.
With the help of Jiffsy, each e-commerce platform can create and maintain a mobile-first storefront with AI recommendations. The app uses Jamstack and PWA technologies that can’t be matched by any Shopify theme. Jiffsy features a unique product feed powered by artificial intelligence.
It can provide personalized recommendations for visitors, which are based on their browsing and purchase history. Through this feature, customers can discover new products based on their fashion preferences. Such feeds increase brand engagement, resulting in higher sales.
Alibaba Mobile Apps With Personalized Product Recommendations
One of the first and largest e-commerce players that started using AI was Alibaba Group, which was established in 1999 and includes different businesses including Taobao, AliExpress, Banggood, etc. These apps are famous around the world for the precise work of algorithms, allowing them to offer customers hyper-personalized recommendations.
Alibaba has its own generative AI called Tongyi Qianwen, which is one of the largest and most powerful AIs known. It has been integrated into the Group’s apps to produce marketing materials, create images, and generate action plans, but it can also analyze large quantities of data like consumer demographics and behavior to provide personalized product recommendations. It does it so successfully that it is now the best-known e-commerce titan in the East and is often compared to Amazon.
In a Nutshell
Artificial intelligence is one of the fastest-growing technologies that are shaping our world today. It has many applications, but it is the most known to users thanks to the personalization capabilities it provides. AI algorithms can collect and analyze massive amounts of data and use it to learn about consumer behavior, predict trends, offer personalized recommendations, provide seamless integration between devices, and adapt in real time. It works for businesses by enabling cross-selling and up-selling options, increasing revenue, and boosting customer satisfaction by meeting their needs and offering tailored experiences.
Insight
Common cancer marker may play active role in preventing the disease, study finds

Ki-67, a protein used to measure tumour growth, may also help prevent chromosome errors that drive cancer, a study suggests.
The findings could change how scientists view Ki-67, a marker commonly used in breast cancer and other tumours to assess how quickly cancer cells are growing.
Researchers found the protein may help preserve genome stability by maintaining the structural integrity of centromeres, key parts of chromosomes that help ensure DNA is shared correctly during cell division.
The research was led by professor Paola Vagnarelli at Brunel University of London in collaboration with scientists at the University of Edinburgh and the Technical University of Berlin.
Professor Vagnarelli said: “Doctors already measure Ki-67 to see how aggressive a cancer might be. But our results suggest it is actually helping maintain genome stability.
“That means it may be more than a marker. It could potentially also be a therapeutic target.”
The study examined three proteins that attach to chromosomes during cell division and help rebuild the molecular system that tells each new cell what kind of cell it is.
Every human cell carries identical DNA. What makes a liver cell different from a brain cell is which genes are switched on and which are kept inactive.
When a cell divides, that entire system of switches must be rebuilt. The three proteins involved in this process were Ki-67, Repo-Man and PNUTS.
Vagnarelli’s team developed a method that individually removes each protein from a living cell at the precise point of division. Older techniques could not isolate that moment cleanly.
They found that cells rely on all three proteins to reset themselves after division, but each failed in a different way when removed.
Without PNUTS, gene activity spiralled out of control and thousands of genes switched on at once.
Without Repo-Man, cells escaped safety checkpoints that usually stop damaged or abnormal cells from continuing to divide.
“What we didn’t expect was how clean the separation was,” said Vagnarelli.
Each protein fails in its own specific way. There is no redundancy, no safety net. Which means there are three separate points at which this process can go wrong.
“When the system breaks down, cells can emerge with the wrong number of chromosomes. That condition, called aneuploidy, is seen in disorders such as Down syndrome and in many cancers.
“We also found that these chromosome errors can trigger inflammatory signals inside the cell.”
Aneuploidy means a cell has too many or too few chromosomes, which can disrupt normal growth and function.
Inflammatory signals are chemical messages that can make a cell behave as if it is responding to injury or infection.
“These cells behave almost as if they are under attack,” said Vagnarelli.
“The immune response switches on because the genome is unstable.
“That link between chromosome imbalance and inflammation could help explain patterns we see in several diseases.”
The researchers said the findings may help cancer scientists better understand how chromosome instability, loss of gene regulation and cells dividing before they are ready contribute to tumour growth.
They said understanding the normal machinery that prevents these errors may help researchers find ways to push cancer cells into making mistakes they cannot survive.
“We now have a clearer map of the machinery that resets the cell after division,” said Vagnarelli.
“That knowledge gives us a starting point for thinking about new therapeutic approaches.”
News
Abdominal obesity may lead to more severe menopause symptoms – study

Abdominal obesity may lead to worse menopause symptoms, including forgetfulness, irritability and night sweats, a new study suggests.
The findings point to a possible link between fat stored around the waist and more severe midlife symptoms.
Researchers said waist-to-height ratio could help identify women who may benefit from more targeted support.
Dr Monica Christmas is associate medical director for The Menopause Society.
Christmas said: “Unintended weight gain during the menopause transition, especially in the midsection, is one of the most commonly reported complaints, with the most significant gains experienced in the years leading up to the final menstrual period and a couple of years after.
“This not only affects self-image but also imposes negative health risks and, as the study highlights, is associated with higher prevalence and severity of menopause symptoms.”
The study used data from more than 1,100 women who took part in the Study of Women’s Health Across the Nation.
Abdominal obesity is a build-up of fat around the waist. It often includes visceral fat, which is deep, active fat surrounding internal organs.
This type of fat releases inflammatory proteins and toxic fatty acids that can contribute to insulin resistance, cardiovascular disease, high blood pressure and a higher risk of some cancers.
Insulin resistance means the body does not respond properly to insulin, the hormone that helps control blood sugar.
The Menopause Society said abdominal obesity is estimated to affect more than 60 per cent of menopausal women.
As oestrogen levels fall during menopause, women tend to store more fat around the waist rather than the hips, even if their overall weight does not change.
The researchers noted that obesity patterns and menopause symptom burden can vary by region, but research into the effect of abdominal obesity on these symptoms remains limited.
They also said earlier studies have mainly looked at single symptoms, rather than how symptoms connect with each other.
In this study, researchers used network analysis, a method that looks at how symptoms are linked, to compare symptom patterns in women with and without abdominal obesity.
They identified abdominal obesity using waist-to-height ratios, which compare waist size with height and can be used as a simple measure of health risk linked to body fat around the middle.
The researchers concluded that women with abdominal obesity had both a higher prevalence and greater severity of a range of symptoms, as well as a distinct symptom network structure.
In particular, women with abdominal obesity reported a higher prevalence and greater severity of dizziness, hot flashes and night sweats than women without abdominal obesity.
Sleep disturbances and palpitations were also reported more often in women with abdominal obesity. Palpitations are feelings of a fast, fluttering or pounding heartbeat.
The researchers said assessment of abdominal obesity using waist-to-height ratios may help stratify women who are likely to benefit from targeted, network-based interventions rather than isolated symptom management.
Christmas said: “Educating women early about healthy lifestyle interventions to prevent midlife weight gain is key to improving mental and physical well-being during a tumultuous time frame.”
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