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Using Generative AI with BlueConic: How tools like ChatGPT can save you time

What are the top CDP use cases for generative AI using ChatGPT, Google Bard, Bing Chat, etc. with the BlueConic CDP?BlueConic supports your CDP use cases with rich first-party data capabilities. As you aim to deliver more personalized and tailored experiences to your customers, the integration of artificial intelligence will become increasingly vital. Generative AI, with its ability to understand and generate human-like language, presents exciting possibilities for enhancing BlueConic capabilities.

How to use generative AI tools with a CDP

In this article, we will explore how leveraging generative AI within your CDP implementation in the near future can begin to revolutionize customer segmentation, improve personalization efforts, and unlock valuable predictive insights.

Generative AI Terms and Definitions

In order to better understand how generative AI works and what its potential benefits are, it is important to be familiar with the following terms:
Large language model - This is a type of artificial intelligence model designed to understand and generate human language. These models are based on deep learning techniques and are trained on vast amounts of textual data, allowing them to learn complex language patterns, grammar, context, and semantic relationships. The larger the model, the more capable it becomes in understanding and generating human-like text.
Training data - Training data refers to the large dataset used to train a generative AI model. The model learns from this data to understand patterns, structures, and relationships within the information it processes.
Neural networks - Generative AI models are typically based on neural networks. These networks consist of interconnected nodes (neurons) arranged in layers, and they process data in a way that simulates the functioning of the human brain, allowing the model to learn and make predictions.
Transfer learning - Transfer learning is a technique where a pre-trained model is fine-tuned or adapted to a specific task or domain. This approach saves time and computational resources while enabling the model to be more effective in specialized tasks.
Prompt - The initial input or instruction provided to the AI model to initiate the process of generating output. The prompt is typically a piece of text or a specific format that serves as a starting point for the AI model to understand the desired task or context. For instance, in the case of a language model, the prompt could be a sentence or a few keywords that instruct the model on what kind of text to generate. Depending on the complexity of the AI model, prompts can vary in length and specificity.

AI Tools

While a list of generative AI tools will never be exhaustive due to the popularity and growth of the category, here are a few that might be helpful to explore:

Tool Category
ChatGPT Generative AI Chatbot
Bing Chat Generative AI Chatbot
Google Bard Generative AI Chatbot
Watson Assistant Generative AI Chatbot
Intercom Generative AI Chatbot
Jasper Content Creation & SEO
GetGenie Content Creation
Writesonic Content Creation
Writer Content Creation


Dos and Don’ts

Do Don't
Start out small - If you don’t know where to begin with generative AI, start with the simplest tasks. For example, paraphrasing sentences, shortening content, or coming up with synonyms for overused terms. Use it to replace the human element - To keep content relevant, relatable, and branded, don’t use AI to replace humans or writers in every scenario. AI is most powerful when combined with human skills.
Be transparent - If you are using AI-generated content in a customer-facing setting, let the audience know. Transparency helps build trust and prevents misleading information. Share confidential data - Ensure that any confidential data is not used to train or fine-tune AI models. Work with your company to create specific guidelines or restrictions.
Be clear and specific - When interacting with generative AI models, a well-defined prompt helps the model understand the desired context and generate relevant outputs. Violate copyrights or plagiarize - Refer to copyright laws and avoid using AI to generate content that infringes on others' intellectual property rights.
Use your best human judgment - Always verify the outputs generated by an AI model for accuracy and appropriateness before using them in any public context. AI models may sometimes produce incorrect or biased information. Overlook biases - Be aware of biases that may exist in the training data and outputs of AI models. Take steps to mitigate and reduce biases in the generated content.


Generative AI + BlueConic Use Cases

Scaling content creation for A/B testing

  • A challenge for marketers can be to come up with high-quality, engaging copy for each of their target audiences. The quality (e.g. tone of voice, clarity, conciseness, persuasiveness, language) of content can make a big difference in conversion rates. Therefore, a huge use of generative AI is to scale content creation, from inspiration to research to writing the copy itself, aimed at targeting new or specific audiences.
    • After the content is created, BlueConic Dialogues can be built to target segments of customers with that content; or show variants. Based on a visitor’s profile plus the current page they are visiting, BlueConic can serve the right content to the right person at the right time. Additionally, BlueConic Dialogues can be used to A/B test AI-generated copy ideas or to automatically optimize towards showing the copy that converts best.

Building better chatbots

  • In the past, chatbots have been fairly rudimentary and require a lot of manual configuration. However, the integration of generative AI with chatbots allows for a new breed of chatbots that is able to converse more naturally with customers.
    • BlueConic can feed the chatbot with customer information from the profile (e.g. previous purchases, subscription details) so that the chatbot does not need to ask any unnecessary questions. For example, if BlueConic tells the chatbot a customer’s contact information, the chatbot will not need to prompt the customer for it. For this use case, it is important to have a paid, licensed, or premium version of an AI chatbot so you don't expose your customer data to training sets.

Streamlining marketing campaigns

  • Marketing campaigns require lots of behind-the-scenes planning to get up and running, and generative AI can help with several of these steps. The most obvious use is to write copy for different types of marketing campaigns, from website banners to blog posts to emails. However, generative AI can also be used to conduct research for campaigns (i.e., What is trending right now in my industry?) or to repurpose already created campaigns for new audiences (i.e., How can I use this back-to-school marketing campaign for Black Friday? How can I restructure this Mother’s Day campaign for a Father’s Day audience?).
    • Once you have the nuts and bolts of your marketing campaign figured out, you can go ahead and build them within BlueConic.


Can generative AI be used to write code for use in BlueConic?

AI can be used to assist in writing code, however, whether AI should be used to write code entirely depends on the context and the specific use case. You never want to copy and paste proprietary code into a generative AI tool. If you're wondering whether or not certain code snippets can be input into generative AI tools, consult your security team first. 

When AI can be used for writing code:

  • To create code templates for developers.
  • To automate simple, repetitive tasks.
  • To generate non-proprietary code, such as CSS for BlueConic Dialogues.

When AI probably should not be used for writing code:

  • When the code involves proprietary or customer information. 
  • When the code is overly complex, it would take an extremely detailed prompt to get the AI to successfully execute the task, so this may not save time or effort at all.

AI can be used to augment the coding process, however, developers should exercise judgment and discretion in deciding when to use AI-generated code and whether or not it creates security vulnerabilities.


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