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How can AI improve your e-commerce service recommendations?

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E-commerce is a highly competitive and dynamic industry, where customers expect personalized, relevant, and timely service recommendations. To meet these expectations, e-commerce businesses need to leverage the power of artificial intelligence (AI) to analyze customer behavior, preferences, and feedback, and to generate optimal service suggestions. In this article, we will explore how AI can improve your e-commerce service recommendations in four key areas: product discovery, customer segmentation, cross-selling and upselling, and customer loyalty.

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Olabode Taylor

Machine Learning || Artificial Intelligence||Electrical Engineering||Data Centre Management

1 Product discovery

AI can help you improve your product discovery process by providing customers with more accurate and diverse recommendations based on their search queries, browsing history, and purchase patterns. AI can use natural language processing (NLP) to understand the intent and context of customer queries, and to match them with the most relevant products in your catalog. AI can also use computer vision to enable visual search, where customers can upload images of products they like and find similar or complementary items in your store. Additionally, AI can use machine learning (ML) to learn from customer feedback and ratings, and to optimize your recommendation algorithms over time.

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Denis Kornev

MSPC, MS ISEM, PhD, LSSBB
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Powerful AI algorithms based on self - learning structures and probabilistic models can help customers accelerate product search, make it more accurate and precise. Thanks to developed techniques such as Recurrent Neural Network and Convolutional Neural Network text processing, NLP, image recognition, and computer vision functions can be integrated into product discovery engines increasing customer satisfaction and improving their buying process.

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2 Customer segmentation

AI can help you improve your customer segmentation process by creating more granular and dynamic segments based on customer data and behavior. AI can use ML to identify patterns and clusters in your customer data, such as demographics, psychographics, preferences, needs, and values. AI can also use ML to track and predict customer behavior, such as purchase frequency, recency, and value, as well as churn risk and lifetime value. By using AI to segment your customers, you can tailor your service recommendations to each segment, and deliver more personalized and relevant offers, discounts, and promotions.

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Olabode Taylor

Machine Learning || Artificial Intelligence||Electrical Engineering||Data Centre Management
(已编辑)
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The use of content based recommender algorithm built with ML models that will make use of clients attributes to recommend products and services that appeals to them will go a long way in improving sales

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Denis Kornev

MSPC, MS ISEM, PhD, LSSBB
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ML algorithms as a branch of AI are suitable to identify patterns in customer purchasing habits and behaviour, making their segmentation more precise and structured. Such an approach provides significant opportunities for recommendation system development and integration using discovered segments’ patterns as a system core and benchmarks.

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3 Cross-selling and upselling

AI can help you improve your cross-selling and upselling process by suggesting additional or higher-value products or services that complement or enhance the customer's current or intended purchase. AI can use ML to analyze customer purchase history, cart contents, and product attributes, and to generate recommendations that increase the average order value and the customer satisfaction. AI can also use NLP to generate natural and persuasive product descriptions, reviews, and testimonials that highlight the benefits and features of your service recommendations. Moreover, AI can use ML to test and optimize your service recommendations based on customer response and conversion rates.

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Denis Kornev

MSPC, MS ISEM, PhD, LSSBB
举报内容
Cross-selling and upselling are essential parts of corporate marketing strategy. It is not surprising that self-learning AI-driven technologies are used to support these add-ons, predicting sales trends, and forecasting marketing niche possibilities.

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4 Customer loyalty

AI can help you improve your customer loyalty process by providing customers with more value, convenience, and engagement through your service recommendations. AI can use ML to personalize your service recommendations based on customer preferences, needs, and feedback, and to offer customers rewards, incentives, and loyalty programs that increase their retention and advocacy. AI can also use NLP to communicate with customers through chatbots, voice assistants, and email marketing, and to provide them with timely and helpful service recommendations, such as reminders, tips, and suggestions. Furthermore, AI can use ML to measure and improve your service quality and customer satisfaction, and to identify and resolve customer issues and complaints.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can AI improve your e-commerce service recommendations?

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2

3

4

5

1 Product discovery

2 Customer segmentation

3 Cross-selling and upselling

4 Customer loyalty

5 Here’s what else to consider

Service Operations

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How can AI improve your e-commerce service recommendations?

1

2

3

4

5

1 Product discovery

2 Customer segmentation

3 Cross-selling and upselling

4 Customer loyalty

5 Here’s what else to consider

Service Operations

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