Generative analysis vs. traditional data analysis: What’s the difference?

When it comes to making data-driven decisions, there's no shortage of options for business professionals. But with all the different methods and tools out there, it can be hard to know which one is right for you. In this post, we're going to take a look at two popular approaches - AI-powered generative analysis and traditional data analysis - and explore the key differences between them.

First, let's define our terms. Traditional data analysis is, well, pretty traditional. It's the kind of thing you might have learned in a statistics class or read about in a business textbook. It typically involves humans using a set of pre-determined metrics to analyze data and make predictions. Traditional data analysis is largely focused on quantitative datasets, and leverages tools like Tableau, Excel and Looker to run the analysis. And while it can be hugely insightful, this method alone does not capture the full picture. It's like trying to predict the weather by only looking at the temperature and barometer.

When traditional analysis is expanded to include qualitative data, it's often referred to as sentiment analysis and resembles the pretty charts and graphs below. Sentiment analysis can be quite cumbersome, time-consuming, and resource-intensive, especially with large datasets, due to the work involved with manual tagging, filtering out noise, and more. And while these tasks can be automated through certain tools, this type of sentiment analysis still comes with major pitfalls as it lacks the ability to detect sarcasm, negation (e.g. double negatives), word ambiguity, and multipolarity. Ultimately, you're left with a pretty picture but very little context.

Generative analysis, on the other hand, is a newer and more advanced approach. It uses AI and machine learning algorithms to analyze data - both quantitative and qualitative - to help humans make better decisions based on all of the available data - more than any human could reasonably be expected to compute in a reasonable time frame.

With the advent of OpenAI's GPT-3, this type of analysis has become a reality. Tools like Viable can now analyze qualitative data on par with human researchers, but faster, and while tackling the quantitative data simultaneously. These tools can highlight new trends week-over-week, surfacing the most important themes that are critical to your team or product's success based on the language within the data, rather than focusing on just volume. And the advanced NLP models using in generative analysis have no trouble tackling sarcasm, negation, word ambiguity, and multipolarity issues that normally plague traditional sentiment analysis.

The best part? This AI-powered data analysis is entirely automated and can parse through hundreds of thousands of datapoints without you having to lift a finger - no more spreadsheets or SQL queries. In short, generative analysis is more powerful, accurate, and fast. Generative analysis can be leveraged to help just about any department in your organization, but we’ve outlined a few common use cases below:

• Product: Product teams are inundated with customer feedback like NPS surveys, but the majority of responses sit unread and unanalyzed due to the qualitative nature of the freeform text answers. A generative analysis platform can take that work off the product team’s plate and provide new insights into customers. PMs can spot an issue dragging down their NPS score before it becomes problematic, or look for new feature requests to build into the product roadmap – and have the certainty that they're getting it right, not hearing it second hand.
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• Customer Experience: CX teams, like Product, receive unstructured feedback all the time, primarily in the form of customer support tickets. But customer-facing team members are rightfully focused on helping their users, with little time to dedicate to dig through the wealth of feedback within their help desk, even if that data could help make significant future improvements. With generative analysis, there's no need to divert resources to uncover customer insights. The help desk analysis is built and emailed automatically by the AI every week, and can help CX teams reduce support ticket volumes by identifying emerging themes or building better customer resources like FAQs.
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• Sales: Sales teams have a unique line of sight since they interact with not just customers, but also prospects who ultimately choose not to buy. What if a tool could analyze all their call transcripts, and find key themes found within those meetings? You could gain a better understanding of your target market's pain points or sales objections to your product. You could improve your pitches based on what resonated with your best customers, identifying conversion drivers with ease. You may even wind up with a better understanding of your competitors from the analysis — why customers chose them and what their differentiators are — that you could apply to future calls.  

These examples are just the tip of the iceberg. Generative analysis stands to unlock a treasure trove of insights for companies big and small, while saving resources and ultimately the bottom line. Curious to see what generative analysis can do with your data?

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