How AI Can Transform Your SaaS Embedded Analytics

Artificial Intelligence (AI) has transformed numerous industries, revolutionizing the way we approach complex tasks. Today, AI is more advanced than ever, capable of tackling a wide range of applications and providing valuable insights.

In our webinar hosted by Qrvey CTO David Abramson, he delves into the role of AI and its impact on embedded analytics including traditional AI and machine learning as well as the exciting emergence of generative AI. 

In today’s environment, SaaS companies are under constant pressure to deliver more, deliver faster, and deliver it cheaper. Let’s explore how AI is shaping the future of embedded analytics and uncover three key use cases where its potential truly shines.

What Does AI Look Like Today?

Traditional AI, often referred to as Machine Learning, has been the cornerstone of AI advancements for the last decade. It involves training models using vast amounts of data to perform specific tasks. Examples of traditional AI include:

• Data classification, where sentiment analysis helps understand customer opinions, and pattern detection, such as fraud detection, which safeguards businesses from malicious activities. 
• Decision-making is another area where AI excels, providing personalized recommendations and enhancing user experiences. 
• NLP (Natural Language Processing) and search algorithms that allow for more accurate and efficient retrieval of information.

The New Wave of AI: Generative AI 

One of the most exciting developments in AI is Generative AI, and I’m sure everyone by now has heard of ChatGPT. GPT (Generative Pre-trained Transformer) has the ability to generate human-like text and has garnered attention across various domains, including marketing, content creation, and conversational AI.

GPT can produce coherent and contextually relevant content by analyzing vast amounts of text data. This improves task efficiency and boosts productivity in numerous applications. We also see this new AI powering other use cases such as chatbots in customer support, generating media such as images and videos.

The use cases span marketing and supporting software development by generating code used to develop software products. The applications continue to grow as people become more and more familiar with the technology and its potential. 

Embedded Analytics: AI’s Impact

Embedded analytics is an area where AI can also make a significant impact. Traditional AI can enhance capabilities and provide users with powerful insights such as:

• Forecasting models are now more accurate, enabling businesses to make informed decisions based on real-time data. 
• Classification algorithms perform sentiment analysis on text and unstructured data, helping businesses gauge customer sentiment accurately. 
• Pattern recognition algorithms detect outliers, identifying unusual patterns and potential risks. 
• Light-weight recommendation engines offer personalized suggestions, enhancing user experiences and driving customer engagement. 
• Natural Language Processing algorithms facilitate intelligent search, enabling users to extract relevant information effortlessly.

Generative AI in Embedded Analytics: Unlocking New Possibilities

Generative AI, such as GPT, creates new opportunities for AI to enhance the end-user experience. Earlier in the year, we wrote about how Qrvey is incorporating ChatGPT into its embedded analytics product, but our recent webinar explores three key use cases where generative AI offers exciting potential in embedded analytics:

Developer Productivity:

Generative AI empowers developers by automating repetitive tasks. It can assist in writing and integrating code, saving time and effort. Developers can leverage AI to develop reports and dashboards more efficiently, streamlining the process and enhancing productivity. AI can also suggest data outcomes and options, enabling developers to make informed decisions and optimize their workflows.

Power User Content Generation:

For power users, generative AI provides a powerful tool for creating data formulas and customizing or creating additional analysis content. It offers more detailed data insights, enabling power users to derive valuable conclusions and extract hidden patterns. Generative AI enhances content generation capabilities, empowering users to create richer, more engaging content.

End User Conversational AI:

Generative AI enhances end-user experiences by offering AI-driven dashboard and chart insights. Users can interact with the analytics platform, asking additional questions of the data and receiving meaningful responses. AI provides suggestions for the next actions or outcomes, assisting users in making data-driven decisions and maximizing the value of their analytics.

Read more on our take on the future of AI analytics.

Additional Considerations of AI in Embedded Analytics

As AI continues to evolve, it holds the potential to reshape analytics, improve efficiency, and drive innovation across industries. Ethical and privacy concerns will continue to undergo evaluation and deliberation. This conversation will continue for some time.

Furthermore, not every use case will be a good fit for generative AI. Generative or traditional AI will depend on historical data to train AI models. For younger businesses, this may pose a challenge during the startup and early growth stages. 

The Future 

The emergence of generative AI, particularly GPT, has opened new horizons, enhancing productivity, enabling power user content generation, and providing conversational AI capabilities. We look forward to embracing the power of AI in embedded analytics to unlock the true potential of data.