Many companies and organizations currently utilize data analytics to assess their performance and understand customer motivation in an effort to pivot where needed to drive engagement and sales. Though this relentless pursuit of “why” has dominated the analytics landscape since inception, many advancements have conversely led to a deluge of data, making the quest to understand the motivations behind customer behavior an ongoing, significant challenge. Companies continue to grapple with deciphering why customers act in certain ways, often falling short of influencing desired actions.
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The bitter trut...
Many companies and organizations currently utilize data analytics to assess their performance and understand customer motivation in an effort to pivot where needed to drive engagement and sales. Though this relentless pursuit of “why” has dominated the analytics landscape since inception, many advancements have conversely led to a deluge of data, making the quest to understand the motivations behind customer behavior an ongoing, significant challenge. Companies continue to grapple with deciphering why customers act in certain ways, often falling short of influencing desired actions.
Also Read – Retail Media Networks are booming – here is how to choose the right network partners in 2024.
The bitter truth of analytics is that by the time the answer to “why” arrives, the bus has already left the station. Mountains of data yield insights too late to change course, leaving businesses with stale insights or the sad conclusion that “we can’t change that” followed by a return to square one. Wash, rinse, repeat. But nothing improves in this process.
And then, ChatGPT happened. Can ChatGPT manage the data overload in businesses? Not yet. Time series data is beyond current LLM capabilities. But ChatGPT can elucidate the “why” so that professionals can take action before the data becomes dated.
While LLMs have to be prompted with the necessary information, they excel at synthesizing information into clear, accessible explanations for any audience. This equips all individuals at all levels of a company or organization with concrete, step-by-step plans to address customer needs. LLMs have the potential to help us move beyond passive understanding to proactive action, equipping every employee with plain language (rather than a complex set of data to decipher) to achieve certain goals.
Analytics and LLMs aren’t just about understanding “why,” they’re about translating that into “how.” They help professionals identify both immediate and long-term actionable steps, separating controllable factors from uncontrollable ones. This allows even newcomers to break down complex challenges into manageable pieces, paving the way for real progress. In conclusion, the new 2024 holy grail in analytics isn’t just understanding “why” customers behave a certain way; it’s transforming that understanding into “how.”
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