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In the first part of this piece, we explored the challenges startups face with event mapping. Now, we're going to look at how Crunch can solve these issues by leveraging LLMs and modern transformer models more generally. In addition to utilizing existing ML techniques, these models show great potential to mitigate some of the most pressing issues in product analytics and development today.
We’d spoken about four specific buckets difficulties that early stage startups face because of their need for event mapping:
Tools and Infrastructure
Let’s discuss how the problems we discussed for each can be worked on, one by one:
When navigating the landscape of product analytics, traditional event tracking tools present limitations that can lead to significant costs. However, AI, particularly with the advent of transformer models, offers innovative solutions to these challenges.
Contemporary NLP can automate the process of event tracking. Models can be trained to understand the semantics of different events within the context of an application, thus reducing the need for manual event definition and tracking.
Transformers in particular excel here with their self-attention mechanisms, which allow them to recognize patterns in sequential data, like user interactions. This feature captures the context and order of events, making the insights richer and more accurate. This automation means developers no longer need to manually update event tracking tags with every UI/UX change, saving both time and reducing the potential for human error.
As data volumes increase exponentially, scalability becomes a major challenge. However, transformer models, with their inherent capability for parallel processing, allow for efficient handling of large data volumes. This is unlike traditional models, which often require sequential processing. This capability reduces the computational load, thereby reducing the need for server scaling. Furthermore, modern techniques like model quantization and pruning can be employed to reduce the memory footprint of these models without significant loss in performance, enabling deployment on lower-capacity servers or edge devices.
The usual approach for startups is to rely on expensive DBMS solutions for data storage and retrieval. However, AI can optimize these processes with unsupervised learning algorithms capable of clustering event data based on similarity, making storage and indexing more efficient. Transformer models, with their attention mechanisms, can quickly identify and retrieve relevant data subsets, optimizing the system efficiency and reducing the need for costly, resource-intensive DBMS solutions.
Machine learning models can automate the process of identifying relevant data from different sources and transforming it into a unified format, reducing the need for manual intervention. Transformer models, particularly suited for handling heterogeneous data, can learn unified representations that streamline data integration. Techniques such as AutoML and Automated Feature Engineering can further enhance this process by generating and selecting the most predictive features. This not only saves time and resources but also reduces the risk of errors that can result from manual data integration and transformation.
Perhaps obviously, reducing server costs would be a boon to any technical business, not just early stage ones. Clearly, transformers are an architecture that is relatively untapped in this regard.
However, a caveat here is that transformers themselves are computationally intensive as well, but there are some (increasingly adopted) practices that can mitigate these issues as well:
Quantization reduces the numerical precision of the model's parameters, effectively shrinking its size without causing a significant loss in performance.
Model pruning reduces the model's complexity by removing less important connections - those that contribute least to the final prediction. This simplifies the model, further decreasing its computational requirements and facilitating its deployment even on lower-capacity servers or edge devices.
This is often a labor-intensive task involving complex coding for each unique SDK. However, machine learning algorithms can drastically streamline this process. Models that supervised learning algorithms that learn from labeled data and make predictions based on that learning. For SDK integration, these algorithms can learn from the data structures of various SDKs and use this knowledge to predict and adapt to new SDKs, thereby reducing manual coding effort significantly.
AI models also revolutionize event logging by employing real-time data processing. They use online learning algorithms, which learn from a continuous stream of data. This allows the AI model to identify patterns and trends in user behavior as they happen, autonomously deciding what events to log based on these real-time insights. Such a mechanism can reduce the need for manual logging, making the process more efficient and cost-effective.
In quality assurance, AI models can leverage unsupervised learning to detect anomalies. Unlike supervised learning, unsupervised learning algorithms can learn from unlabelled data, identifying patterns and outliers.
So, within a product analytics context, these algorithms can analyze vast amounts of data to detect any aberrations that might signify potential issues, allowing for early identification and resolution before they impact the product.
Models available today can employ predictive analysis for proactive maintenance. Using time series analysis and regression algorithms, these models can predict possible future issues based on historical data. Such predictions enable the team to take proactive measures to prevent or minimize the impact of potential issues, reducing the overall maintenance workload.
NLP can parse and understand human language, making it possible to automate the generation of user-friendly documentation. Moreover, by processing real-time data, these algorithms can dynamically update the system documentation, keeping it in sync with the system's current state. This significantly reduces the need for constant manual documentation updates and extensive training sessions.
In due time, it is easy to see how such documentation could be automated and personalized as well. This would be a step above from customer service chatbots, as the model would proactively understand what issues a user may be facing, and in plaintext, guide them on how to solve it, and even understand their issues and automatedly log them for the product team to analyze.
With regards to managing large amounts of data, AI models can help by employing a combination of data compression algorithms and distributed systems. Data compression algorithms, such as Huffman Coding or the Burrows-Wheeler Transform, minimize the storage space required by reducing data redundancy. These algorithms work by identifying and eliminating repetitive patterns in data, making the data storage process more efficient and cost-effective.
Distributed systems can also help manage the processing of large data volumes:
Using tools like Apache’s MapReduce and Spark, data can be divided and processed in parallel across multiple nodes, significantly reducing processing time and enabling real-time data analysis.
Supervised and unsupervised machine learning algorithms are indispensable for extracting meaningful insights from vast datasets. For instance, clustering algorithms group similar data, illuminating patterns and trends in a way that raw data can't.
To further enhance our understanding, data visualization plays a crucial role. Currently, libraries like Matplotlib or Seaborn are used to generate plots and charts, which simplify complex data into easily digestible visuals.
NLP can boost the ease of use in this realm even more. With the integration of NLP into product analytics tools, one can imagine a scenario in which queries are posed in simple, everyday language, without the need for complex syntax or understanding of programming. It bridges the gap between data science and those who could benefit from its insights but are deterred by the technical barriers.
For example, imagine if a marketer could ask "What were the sales trends for Product X in the last quarter?" and receive a detailed, yet comprehensible, visual chart in response. This kind of intuitive, conversational interaction with analytics tools democratizes data science, empowering a wider array of team members to extract valuable insights from their data.
Such an improvement in UX brings plenty of direct benefits to executing event mapping more effectively:
-For example, a system could be trained to recognize user behaviors and events from raw data using the power of NLP. Furthermore, NLP can help in deciphering unstructured data, such as user feedback or reviews, and map them to specific product interactions.
-By making the data and insights more accessible to non-technical stakeholders, NLP can enhance cross-team collaboration. This would allow for quicker, more informed decision-making, thus reducing the time spent on event mapping related discussions and debates.
Event mapping is just one of the many ways in which modern LLMs can be a game-changer for your analytics workflow.
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