November 9, 2021 • 5 min read
Do you remember the first time you looked up into the sky on a clear, dark night and watched the stars pop into view, one by one? Maybe you recognize a constellation or two, or could pick out the faint red glow of Mars. But if you looked long enough, you probably started to recognize the enormity of it all. Millions of stars, on and on. It can be overwhelming.
Customer experience (CX) data can have that same effect sometimes. Vast. Infinite (or so it seems). Always expanding. Just like you probably had trouble naming more than a handful of heavenly bodies, you might also have trouble making sense of your CX data. At least, not without some help.
When it comes to interpreting huge volumes of unstructured CX data, large enterprises need artificial intelligence (AI). From reducing manual tasks to helping you see and act on insights quickly, AI models can provide potentially limitless value across your enterprise.
Common use cases for AI models that process CX data include:
Categorizing huge volumes of unstructured, public data
Identifying actionable insights, such as negative or positive sentiment about your brand
Recognizing patterns in your data, like best times to publish content
Protecting your brand by identifying anomalies in text or images
But, simplistic, out-of-the-box AI solutions have limitations. After all, every brand is unique; you don’t just need AI, you need custom AI models that are specifically configurable to your business.
Fine-tuning out-of-the-box AI models to meet the specific needs of your brand can be complex and expensive, if not impossible.
First, classifying and managing messaging tags from across all of your digital and social channels is all but impossible. It takes too much time and resources to maintain them, and you’re faced with an always-increasing volume of data you need to manage.
Second, many out-of-the-box AI models require data scientists or machine learning experts to update and maintain them. This dramatically increases costs and slows down your ability to react to the needs of your business.
But, a platform equipped with AI Studio from Sprinklr can greatly reduce the time and effort it takes to make this happen. Sprinklr AI Studio is a capability that helps you quickly create AI text classification models that give you better control over your brand’s mentions and messages. Once you deploy them, you can validate the AI predictions of existing AI models, check their accuracy, and retrain them accordingly. Importantly, you don’t need to be a data scientist or a machine-learning expert to implement AI Studio, giving you direct control and greatly increasing the value of custom AI models across your entire business.
The real differentiator between standard and customizable AI models is the ability to unlock truly meaningful insights that drive customer engagement and grow your business. Here are four ways Sprinklr AI Studio makes it easy to customize message results and maximize the value of your CX data.
If you’ve been working with CX data for any length of time, you know it gets complicated under the hood. Huge volumes of dynamic, unstructured messages coming from a wide variety of digital channels need to be meticulously filtered through various rules, themes, and keywords. Using Sprinklr AI Studio, you can automatically classify these messages so that you can identify messages that matter to your brand, quickly and at scale, without manual upkeep.
If you’re Whole Foods, you may care about what your customers have to say about apples, the fruit. If you’re Apple, you care about what customers say about Apple, the brand. One-size-fits-all AI models struggle with these kinds of complex categorizations, and they certainly aren’t fine-tuned to your specific brand. Sprinklr AI Studio lets you better control mentions and messages by creating custom AI text classification models quickly.
If your AI model isn’t accurate, it can undermine confidence in your data. Whether you’re recognizing an error in the way your AI model is classifying data or trying to move quickly to implement a new category, time is of the essence. Sprinklr AI Studio gives brand teams the power to update these models in house, without the need for data scientists or machine-learning experts, in a matter of hours. Built in tools also give you real-time visibility into model performance against recognized industry benchmarks.
When you combine Sprinklr AI Studio with a unified customer experience management (Unified-CXM) platform, you make your CX data more actionable across your entire enterprise. Imagine how it might work for a customer care team: Instead of manually triaging a huge inbound volume of messages and risking SLA’s for response time, an AI Studio-trained model can quickly classify listening mentions specific to your brand. Care agents can now engage immediately with the most impactful or urgent messages, and make customers happier.
Much like technology has empowered amateur astronomers everywhere to understand more about the universe than ever before, Sprinklr AI Studio can empower your brand to understand more about your customers at an unprecedented speed and scale.
Learn more about Sprinklr AI Studio.
Hilal is a Product Manager within the AI team at Sprinklr. During his five years at Sprinklr, he has engaged with some of the top brands and helped them fulfill their digital transformation efforts across AI, customer care, and research-driven use cases. Outside of work, Hilal enjoys writing novels, completing English into Arabic translations, kickboxing, and taking care of his cat.
Custom AI models unlock insights that drive customer engagement and grow business. Here are 4 ways Sprinklr AI Studio maximizes the value of CX data.
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