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10 Chatbot Strategies eCommerce Brands Use to Boost Sales In 2023

By :

Online shopping isn’t just about silent category browsing. It is about customer communication first. Hearing and in-time guiding customers at each step of their journey is key to sales growth. 

Sounds like a task for a 24/7 customer service team, heh? It’s a good thing that a chatbot tool for business can automate part of these processes. 

Moreover, 40% of shoppers are ready to use it. Tommy Hilfiger is one of the many brands that use that knowledge. Its chatbot brings the brand an 87% rate of returning customers. Another case is the Just Eat chatbot, with a 266% conversion rate.

Intrigued? There are more examples in the article! Find out ten chatbot strategies eCommerce brands use to convert customers on websites, messengers, and social media 👇

24/7 assistance on FAQs 

Imagine a never-sleeping support manager answering repeating customer queries around the clock, with no vacation or coffee break. 

It is an automated chatbot. Think about such an employee when building your customer service 😉

Launch it and:

  • Provide visitors with instant self-service at any time. 
  • Save budget by focusing managers’ time on solving high-priority issues.

Example from the Hitee chat👇

In addition to simple questions, this FAQ chatbot can provide customers with information about insurance options.

Notify consumers about new products

This case is popular in fashion and luxury retail. Instead of mainstream emails, they talk about new collections in messengers. And for a reason! For instance, compared to the 25% Open Rate of email, Facebook has an impressive 80%.  

Thus, when the new collection is live, its subscribers see +1 in DMs. It is a company chatbot telling customers about new items in stock. Casually and cheerfully, it engages them to browse for more pieces directly in a messenger without switching to a website. 

Example from Burberry👇

This luxury retail brand implemented a Facebook Messenger chatbot to introduce customers to their latest collection of bags.

A chatbot by Burberry on MessengerImage source.

Recommend products

The ability to generate endless chatbot ideas makes it an ideal tool for businesses. And this scenario is a good confirmation of that. Launch a chatbot that will define customers’ preferences in an up to five-question dialog. 

Examples of product recommendations from Lego👇

The company launched Ralph the Gift Bot to help its customers choose the perfect gift: 

Process orders 

Allowing customers to order in a chatbot is a great idea to save your managers time and follow the introverts’ desire to avoid direct communication. 

Here is how it works. Customers choose an item and place an order without leaving a chat. For this, people share personal details like name, telephone number, and billing address, and a chatbot will route them to the checkout page on a company website. 

An example from the 1-800-Flowers store

In addition to the gift choice, its users can also submit their order information. A chatbot is like your inbound lead conversion administrator who collects recipients’ addresses, names, and phone numbers, billing addresses and only then routes them to a website checkout page.

Finally, the best thing here – to make the customer experience better, the chatbot offers to save this data.

Tell about sales and promotions

Enhance your sales campaign with a proactive chatbot message. Choose a segment you want to send it to and launch a personalized offer, for instance, 20% off on a new dress collection for customers who visit relevant store categories. 

As for the conversation scenarios, there are two options:

  • Showing products on sale and routing to a checkout or shopping cart.
  • Offer personalized recommendations of items on sale.
  • Capturing customers’ emails in exchange for a coupon.

Here is an example of how it can work 👇

This chatbot engages customers with a bright image, and then shares coupon codes.

Recover shopping carts

70% of online buyers leave items in their carts instead of buying. The fix?

  • Launch a website chatbot to engage visitors when they are trying to leave.
  • Launch a messenger or social media chatbot to re-engage those who left. 

E-commerce marketers switch to this strategy because of the low Open Rate of the classic follow-up emails and the high price of the SMS channel. 

An example of a cart-recovering chatbot 👇

Perfuel Pet Suppliers sends follow-ups in a Facebook Messenger chatbot for registered customers who left the store without a purchase. 


Image source

Upsell and cross-sell

Depending on the product page customers visit, or their shopping cart, a chatbot can suggest additional products or upgrades.

Here is an example of how it works on Shopify👇

When a customer is on a particular product page like jeans, in some time a chatbot message appears “I see you eyeing our new black Levis jeans..” and offers to discover matching items.

Gobot eCommerce Chatbot
Gobot eCommerce Chatbot

It is a great example of how businesses transform customer experience and personalize it. 

Help customers track orders

In a short conversation, a chatbot will define the issue, capture the order number, and share its status instantly. All you have to do is to integrate it with the logistics system. 

Order tracking chatbot example👇

MR.DIY, a Malaysia-based home improvement retailer, launched such a chatbot for its website visitors. In real-time, the chatbot delivers information on where is a customer’s order: 

It brought MR D.I.Y an 80% growth in its containment rate. 

Collect customers’ feedback

There are several challenges that e-commerce businesses face when trying to gather customer feedback:

  • A low response rate of the marketers’ attempts to get customers’ feedback via email.
  • Customers post negative feedback on socials or review websites.
  • A lot of time is spent collecting, managing, and analyzing customer feedback. 

The fix? Automate the process with a chatbot.

For example, contact them on checkout after the payment or after a conversation with a customer manager. 

For example 👇

You can send a short survey with stars and a comment field or turn the process into a conversation by reacting to the rating the customer gave you.

Image source.Image source.

Engage customers in the loyalty program

Use a chatbot to automate the way you:

  • Engage customers to join your loyalty program.
  • Register them.
  • Provide loyalty points updates.
  • Suggest rewards they can redeem.
  • Answer FAQs.

Loyalty program chatbot examples 👇

The first case is about loyalty program registration. The chatbot collects customers’ contacts and promises to notify them about discounts.

Image source.

The second is about points updates and announcements. It actually does what the first promised – send loyalty program updates and engage to continue shopping.

To sum up

Inspiring examples, right? When correctly set up, chatbots provide personalized interactions, resolve queries swiftly, and bring you an army of loyal customers. But to make any examples work in your business, mind the following rule – segment and personalize its workflow with the info about customers’ behavior and preferences. 

About the Author: Evelina Carillo is a friendly and skilled writer and blogger with more than a decade of experience in crafting all sorts of content for the marketing and business world. She’s also spent five years diving into the exciting world of EdTech, where she’s continued to learn and grow in her field.

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

Image Source

  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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