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Identify the Factors Contributing to Low Conversion Rates 1. Ineffective Product Marketing Strategy ‍If your product positioning is off, if you choose the wrong promotional channels, or if your advertising materials lack appeal, your conversion rates will suffer. For instance, if you sell therapeutic pillows designed to relieve neck and cervical pressure, targeting men aged 30-45, but primarily advertise on Instagram—which skews towards female users—your conversion rate is likely to be low.‍ 2. Slow Website Loading Speed ‍When users click a link to your landing page, their first impression is heavily influenced by how quickly the page loads. If it takes more than three seconds, the likelihood of users bouncing increases significantly. Don’t let users abandon you at this crucial first step!‍ 3. Confusing Website Layout ‍If your website navigation is unclear and the page design is chaotic, users can easily get lost. Customers’ first impressions are based on professionalism, visual appeal, and clear guidance—all of which directly impact bounce rates. For a brand website, simplicity and clear sections are essential, with attractive banner images leading to specific product displays. The homepage should typically feature four to eight products in a carousel or collection. ‍‍ 4.Lack of Trust in the

Recently, renowned artificial intelligence expert, Andrej Karpathy, sparked considerable discussion with a tweet suggesting that future AI models, known as Large Language Models (LLMs), may become smaller while still demonstrating intelligent and reliable “thinking.” This notion seems counterintuitive, as we often associate larger models with greater intelligence. So, what’s behind his assertion? Why Do Models Need to Be Large Initially? Karpathy explains that the current large models are so extensive due to inefficiencies in the training process. These models are designed to memorize vast amounts of information from the internet, including numerous irrelevant details. For instance, they might retain obscure numerical hash values or trivia that few people recognize. While these memories are not particularly useful in practical applications, they occupy a significant portion of the model’s parameters—essentially, the model’s “brain cells.” ‍ Improving Data Quality is Key So, how can we create smaller models that remain intelligent? The answer lies in enhancing the quality of the training data. Today’s models often grapple with vast amounts of irrelevant information because our datasets contain many impurities. By training models with high-quality data, we can reduce the number of parameters required to store unnecessary information. In essence, if we can provide models

What’s Dropshipping Business ？ Dropshipping business is a e-commerce model that allows sellers to run an online business  without holding inventory. Instead of stocking products, product suppliers will ship product  to customers directly . This method is similar to affiliate marketing, but sellers can create their own brand. One of the main benefits of dropshipping is low startup costs. Dropshipping are adopted by more and more startup founders，especially who with little experience or limited startup budget. Global dropshipping sales are predicted to reach $467 billion, the total revenue by dropshipping business will be $301.11 billion in 2024. That indicates that the dropshipping will be a potential market. ‍ How To Start  Dropshipping Business With No Money Finding hot-selling products is a skill. Bestselling  products can significantly boost sales by quickly attracting a large consumer base，which enhances brand awareness. The visibility of a bestselling product draws in more potential customers. Additionally, bestsellers often benefit from word-of-mouth and social media promotion, and reduce marketing costs. Consumer satisfaction with these popular items fosters loyalty, encouraging repeat purchases and strengthening the brand’s relationship with  customers. However, for beginners who lack marketing knowledge and data analysis skills, developing new products can be a major hurdle.

The advancement of AI technology has led to the widespread use of AI customer service chatbots across sectors such as e-commerce, education, and banking. In e-commerce, businesses integrate these chatbots with human customer service to cut costs and boost efficiency. Despite these advantages, the robots sometimes fail to accurately understand queries or provide irrelevant responses, raising questions about these shortcomings. This overview explores the underlying principles of AI customer service chatbots and the causes of their occasional failures in understanding inquiries.‍ Working Principles of AI Customer Service Chatbots The operation of AI chatbots can be outlined as follows: Data Input, Data Processing, Data Output, and Feedback Learning. 1. Data Input: – Users interact with the robots via text, images, or voice to express their questions or needs. 2. Data Processing: – Natural Language Processing (NLP): Robots use NLP techniques, including tokenization and syntactic parsing, to interpret user input and discern intent. – Knowledge Base and Dialogue Management: Robots utilize a pre-existing knowledge base and a dialogue management system to generate responses based on user intent. 3. Data Output: – Robots create replies and communicate them back to users in text, speech, or image form, guided by the user’s intent. 4.