In today's fast-paced commercial environment, Customer Relation Management (CRM) systems like Salesforce and HubSpot have become the backbone of successful customer success and sales strategies. Yet, keeping CRMs up to date and in sync with the vast volumes of customer information generated daily has been a challenge to solve.
Audio transcription, also known as speech-to-text AI or speech recognition technology, plays a key role in CRM enrichment by capturing all customer interactions word-for-word and supplying the relevant data to the CRM database automatically in real time.
In addition to core transcription, advanced ASR features, like speaker diarization and word-level timestamps, are increasingly being used to provide more granular insight into customer interactions in CRM enrichment through speech-to-text technology.
In this article, we examine the key features and parameters to consider when shopping for the right ASR API solution for all common use cases where CRM enrichment is key.
Audio transcription, also known as speech-to-text AI, utilizes natural language processing (NLP) techniques to capture audio data and convert it into a comprehensible text format, which can be used for various enterprise applications.
If we think of audio data as a valuable enterprise asset – speech-to-text is the tool to leverage it. For CRM enrichment specifically, AI transcription unlocks the possibility of having audio-based insights fed continuously into the system for subsequent retrieval and use by human agents and call bot assistants. All this while minimizing the time spent on manual inputting and database adjustments.
By transcribing audio and video recordings or even live conversations, businesses can keep track of essential details from every call, including customer preferences, pain points, and negotiation history, which are vital for informed decision-making.
In the world of sales, the ability to keep track of customer interactions is extremely valuable to stay on top and ahead of the game, especially as the company pursues active scale and growth.
Consider a scenario where a customer service call is recorded and transcribed using advanced speech-to-text technology.
In this transformed textual format, your sales team can not only automatically record and retrieve all the essential information about the customer in question (e.g., name, job title, type of company) but also identify keywords and phrases that reveal common customer requests and sentiments, enabling you to build custom outreach strategies more effectively.
Call-based information can also serve to establish a more effective and organized ticketing system, leading to better customer support and favoring product-led growth.
The depth and accuracy of customer insights within a CRM system depend directly on the quality of the initial transcription. When transcriptions are precise and tailored to the specific use case, the resulting CRM entries will be more reliable and directly actionable – yet accuracy is not always a given in ASR.
Traditional speech recognition systems struggle with accents, background noise, and overlapping conversations. The more recent ASR models like Whisper ASR, based in part on language models, are also prone to hallucinations — that is when the transcript is filled with words that were either not spoken at all or got repeated several times by mistake.
Beyond accuracy, there’s the issue of speed. While not all customer interactions need to be transcribed and analyzed in real-time, a growing number of use cases – partially where call bots play a key customer support function – require low-latency live transcription solutions, which rely on a more intricate mechanism than batch transcription and need more computational resources to yield precise results.
In addition to the general challenge of speed and accuracy, CRM enrichment is largely dependent on the ability to identify who spoke in the conversation and when. In multilingual environments, there’s an additional requirement for automatic language recognition, translation, and even code-switching. Let's explore these in more detail.
Audio transcription APIs are the technological backbone for integrating speech recognition capabilities into various applications, including CRM systems. But as explained in one of our previous blog posts on the best speech-to-text providers in 2023, not all APIs are designed and optimized for the same purpose and use case.
When looking for a provider for CRM enrichment specifically, it’s important to assess not only the core transcription capabilities — accuracy and speed – but also the additional features that can significantly enhance the value of the transcribed customer data.
Speaker diarization refers to the ability of the ASR system to recognise speakers and accurately assign what’s being said to each person in a transcript.
One can easily see the value of this feature in scenarios where several stakeholders are present, and the ability to attribute what’s being said to the right person is key to nurturing successful business relations. Even in calls with only two interlocutors involved, diarization helps to avoid potentially costly confusion between the sales representative and your customer.
Diarizaton is among the most sensitive and complicated features in ASR to get right. Few diarization algorithms are natively made to handle short speaker turns in call centers vs. long turns in meeting recordings with equal precision. The feature’s output can be adversely affected by speaker overlap and must be specifically optimized to represent brief utterances like a 200ms “yes” or a barely perceptible “um” in a multi-speaker conference.
At Gladia, we developed a propriety diarization solution to address these challenges in a manner that enables effective CRM enrichment – let us know here if you’d like to learn more.
Transcription hints are contextual clues or instructions, used to guide the speech recognition system in transcribing certain phrases, terms, or words accurately. For instance, by telling the system that the recording is about food and beverages, it will be less likely to confuse phonetically similar words like ‘cider’ and ‘fiber’ and pick the former each time. More on how it works in our article on the inner workings of prompt injection.
In turn, custom vocabulary requires fine-tuning the ASR model on industry-specific terms, sets of words, phrases, or specialized terminology that would be commonly used or understood in general language models.
Both techniques can be used to improve accuracy within specific business domains for terms like company names, medical drugs, legal and financial vocabulary, and so on.
Thanks to the underlying attention mechanism, our Whisper-based API allows you to add contextual hints to help the system capture, identify, and extract specific information, such as names, dates, or technical terms. To make it further sensitive to context, we have developed a context reinjection technique, whereby the last sentence transcribed is used to anticipate the following ones. More on that in our docs.
If your business operates across multiple geographies, having multilingual support will be key. Language support includes the ability to automatically detect the language, transcribe with equal or similar levels of accuracy across languages, translate to and from several languages, and perform code-switching. The latter refers to the system’s ability to transcribe conversations in which multiple languages are spoken.
Catering our product to international companies, Gladia API supports 99 languages for transcription, speaker diarization, translation, and code-switching for multilingual CRM enrichment.
Transcribing is but the first step towards hands-free CRM enrichment. For all the rest, we recommend Lettria.
Lettria is a no-code text analysis platform that provides AI-driven CRM-enrichment tools for sales and CS teams. The platform is able to analyze sales and support calls, extract key items, and add relevant information in the relevant fields in customers’ CRM software.
Thanks to our partnership, Lettria users can easily import Gladia JSON files into their projects, allowing them to access high-quality audio transcriptions natively with full support.
You can learn more about our partnership on Lettria’s website and developer documentation, and try our API directly below.
Speech-To-Text
Over the last few years, Large Language Models (LLMs) have become accessible and transformative tools, powering everything from customer support and content generation to complex, industry-specific applications in healthcare, education, and finance.
Case Studies
In recent years, fuelled by advancements in LLMs, the numbers of AI note-takers has skyrocketed. These apps are increasingly tailored to meet the unique needs of specific user groups, such as doctors, sales teams and project managers.
Speech-To-Text
It happens all the time. A user submits a query to a large language model (LLM) and swiftly gets a response that is clear, comprehensive, and obviously incorrect.
