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Report: 62% of orgs use voice technology to increase revenue

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According to a new report by Deepgram and Opus Research, 77% of companies are using voice technology to identify new business opportunities, and 62% are using it to increase revenues.

Last year’s report examined how companies of all sizes leveraged voice technologies built on Automatic Speech Recognition (ASR) to drive efficiencies and productivity. As businesses increasingly recognize the inherent value of voice and the data it holds, the companies set out to expand the report’s focus beyond ASR to the entire speech technology industry to unearth the motivations for using voice technology within businesses.  

A big change from last year’s report is that voice technology has moved from a cost-saving technology focused primarily on uses like compliance to a revenue generating tool that can open new business opportunities. The report found that 77% of companies are using voice technology to identify new business opportunities, and 62% are using it to increase revenues. To fuel this innovation, 75% of respondents plan to increase their speech technology budget in 2022, and 92% believe this will drive widespread use of voice technology within five years.

Interestingly, 24% of respondents from companies with fewer than 500 employees believe adoption could take longer, between five and ten years. This surprised us and may be attributed to the fact larger organizations recognize the immediate impact that voice technology can have on their business through automating processes such as agent enablement or customer experience with conversational AI, whereas smaller companies that are automating other use cases have less confidence in near term market adoption. 

The report also confirmed that customer loyalty continues to be top of mind for companies in 2022. But this is more challenging to secure than ever, especially with unstructured data like audio, images and video making up 90% of all available intel. As such, 73% of respondents noted customer experience analysis as the most impactful use of speech technology. 

With opportunities increasing and a widespread voice-enabled future on the horizon, now is the time for enterprises to invest in and prioritize voice technology.

Deepgram and Opus surveyed 400 decision-makers from managers to the C-suite to uncover the key motivators for speech technology use among enterprise companies.

Read the full report by Deepgram.

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3 ways emotion AI elevates the customer experience

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Technology serves as a way to bridge the gap between the physical and digital worlds. It connects us and opens up channels of communication in our personal and professional lives. Being able to infuse these conversations — no matter where or when they occur — with emotional intelligence and empathy has become a top priority for leaders eager to help employees become more effective and genuine communicators.

However, the human emotion that goes into communication is often a hidden variable, changing at any moment. In customer-facing roles, for example, a representative might become sad after hearing why a customer is seeking an insurance claim, or become stressed when a caller raises their voice. The emotional volatility surrounding customer experiences requires additional layers of support to meet evolving demands and increasing expectations.

The rise of emotion AI

Given how quickly emotion can change, it has become more important for technology innovations to understand universal human behaviors. Humans have evolved to share overt and sometimes subconscious non-lexical signals to indicate how conversations fare. By analyzing these behaviors, such as conversational pauses or speaking pace, voice-based emotion AI can reliably extract insights to support better interactions.

This form of emotion AI takes a radically different approach than facial recognition technologies, more accurately and ethically navigate AI usage. Customer-facing organizations and their leaders must raise their standards for emotion AI to focus on outcomes that boost the emotional intelligence of their workforce and provide support to create better customer experiences.

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Emotion AI is not a new concept or practice of technology. It has been around for years, but recently has gained momentum and attention as more companies explore how it can be applied to specific use cases. Here are three ways that customer-facing organizations can use voice-based emotion AI in the enterprise to elevate customer experience initiatives:

Increase self-awareness

Think of emotion AI as a social signal-processing machine that helps users perform better, especially when they’re not at their best. In the world of customer experience, representatives undergo many highs and lows. These interactions can be abrasive and draining, so offering real-time support makes all the difference.

These situations are similar to driving a car. Most individuals consistently perform driving fundamentals, but do not drive as well when tired from a night shift or long road trip. Tools like lane detectors can provide additional support, and emotion AI is the workplace equivalent. Not only can it offer real-time suggestions for better interactions with others, but the increase in self-awareness helps foster deeper emotional intelligence. Ultimately, when better emotional intelligence is established, more successful customer service interactions can occur.

Improve employee confidence and well-being

Customer experience is intrinsically tied to employee experience. In fact, 74% of consumers believe that unhappy or unsatisfied employees harm customer experiences. The problem is that showing up to work engaged and at our optimal efficiency every single day and in every instance is not a realistic expectation for employees.

Emotion AI can remove anxiety and self-doubt around performance by helping individuals through difficult experiences and encouraging them during positive ones. This added support and confidence promotes employee engagement and creates a space for employee wellbeing to shine. Any investment in improving work experiences or making workflows more frictionless is a reliable way to boost employee experiences and see ROI across multiple enterprise divisions.

Understand the customers’ state

Consider the driving metaphor again. While it’s vital to ensure a tired driver receives the aid they need to get home safely, the context makes the difference.

Call center representatives consistently multitask — conversing with customers while updating or identifying records, seeking to find a solution and managing inquiries promptly. Utilizing voice-based emotion AI to analyze the sentiment on both ends of the line can provide detailed insights needed to perform and connect. When emotion AI can identify customers who are “highly activated” with excitement or anger, agents are more equipped to take stock of the situation and find the best approach forward. Expanding situational awareness around customers’ mental states and analyzing the data can help enterprises consistently improve call outcomes.

Investing in emotion AI technology could not be more pertinent as we look to the future. Forrester’s 2022 U.S. Consumer Experience Index found that the country’s average CX score fell for the first time after years of consistent, positive growth. While a myriad of influences are at play, from supply chain shortages to the Great Resignation, the reality is that customers have grown to have higher expectations of the businesses they interact with, and it is no longer an option to underperform.

Finding opportunities to ignite emotion across the enterprise and use technology to improve service interaction is critical to customer satisfaction. It’s up to organizations to invest in technology that celebrates and improves emotional intelligence for continued success — and it starts with introducing technology like emotion AI.

Josh Feast is CEO and cofounder of Cogito.

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Copycats can drown   • TechCrunch

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Welcome to Startups Weekly, a nuanced take on this week’s startup news and trends by Senior Reporter and Equity co-host Natasha Mascarenhas. To get this in your inbox, subscribe here.

To end the year, let’s continue to return to columns that I wrote that have aged, well, interestingly. In July, I wrote about how Y Combinator is building a Product Hunt, Product Hunt is building an Andreessen Horowitz and Andreessen Horowitz is building a Y Combinator. It was a not-so-subtle nod to how top institutions are trying to be accelerators, discovery engines, content marketers and check-writers all in one.

Enter the latest. Future, Andreessen Horowitz’s formal foray into tech media, is shutting down less than two years after first launching, according to Business Insider. To me, the shutdown is less about a venture firm failing to jump into the editorial space — the firm is still very much creating content and even building a new podcast on tech and culture as we speak — and more about how the medium is truly the message.

The whole allure of going direct as a founder and venture capitalist is built around assumptions. First, that you have something important to say. Second, you have to believe that you can package that content in a compelling way, consistently. And third, perhaps most importantly of them all, your important, well-packaged content needs to find an audience that trusts it.

It’s one of the many reasons that media is a hard business, and one of the reasons I’m not surprised to see Future shut down (despite the fact that the venture firm could, presumably, keep funding a version of it). Some think that there was an obvious advantage to the firm having a home to house smart content on its portfolio companies, but just because something makes sense doesn’t mean that it has the impact that an institution would hope for.

A16z has built a reputation around being a services-oriented firm. To me, the story is less that a venture firm with billions in assets under management failed at a plucky experiment. It’s more that, in the pursuit to be an accelerator, discovery engine, content marketer and check-writer, organizations are teaching us in real time what translates and what doesn’t.

We often think about the webs of venture capital in a conflict of interest type aperture — and there’s more to come on that angle in the weeks to come. But this week has me thinking about how the intertwinement of different trends, themes and products shifts as priorities do, too.

You can find me on TwitterSubstack and Instagram, where I publish more of my words and work. In the rest of this newsletter, we’ll talk about executive turnover, red flags and good news.

Executive turnover and the art of conflict

Tech’s labor market has certainly raised many questions around the stability of certain industries and roles — and if growth can protect a company from having layoffs. The big news of this week was that Bret Taylor stepped down from his co-chair and CEO position at Salesforce, a month after losing his job as Twitter’s board chair after Elon Musk bought the social media platform.

But that’s not the only kerfuffle in town this week.

This week, DoorDash and Kraken cut portions of their workforce. BloomTech, formerly known as Lambda School, cut half of staff in its third layoff since the beginning of the pandemic. And on Friday, Opendoor CEO Eric Wu stepped down, to be succeeded by CFO Carrie Wheeler. Turnover is everywhere, both voluntary and involuntary, which makes me think a lot about the second-order consequences.

Here’s why this is important, via Brava Leaders CEO Karla Monterroso:

We are at the beginning of creating what multicultural institutions look like and how they will operate. I do think a lot of the turnovers that we’re seeing, whether it is the layoffs or the new management, means that people are coming in to create homogeneity in their companies yet again.

So, they do a layoff, and they take all the complexity out. They slice off the parts of the organization that created friction. And that friction is essentially what makes multicultural institutions more effective because they’re asking different kinds of questions. But a lot of the leaders that are coming in do not have the range to manage a multicultural organization or company. And because they don’t have the range for it, they just cut it out. Then that creates homogeneity because that is what makes a band of leaders comfortable right now. And we’re going to need leadership that is actually much more comfortable with complexity.

Co-CEO of Salesforce, Bret Taylor, speaks at the Vivatech show in Paris, France, June 15, 2022. (AP Photo/Thibault Camus)

Image Credits: Thibault Camus / AP Photo

Are red flags really that hard to spot?

Equity also unpacked the latest blog post written by famed venture capitalist Bill Gurley — in which he lists out the red flags that investors should look out for when investing in startups. As you may be able to tell by our title of the episode, we certainly had thoughts.

Here’s why this is important: While I’m all for highlighting explicit mistakes that budding investors should avoid, Gurley’s post missed a key point — which is that many investors do know how to identify red flags, they just choose to ignore them in pursuit of “the outlier.” What will actually stop investors from backing the next FTX is to create an environment where conflict is prioritized over groupthink.

"Subject: Tropical storm in the beach paradise ResortLocation: Playa del Carmen, Riviera Maya, Mexico."

Image Credits: YinYang (opens in a new window) / Getty Images

[Insert good news here]

We’re officially at the time of year, and part of the news cycle, when I’m desperately searching for good news to highlight.

Here’s what made me smile this week:

Famous Golden Gate Bridge with buildings in the background in San Francisco, California, USA

Image Credits: Wirestock (opens in a new window) / Getty Images

A few notes

Seen on TechCrunch

San Francisco police can now use robots to kill

Elon Musk suspends Kanye West’s account for breaking Twitter rules

LastPass says it was breached — again

Instafest app lets you create your own festival lineup from Spotify

Here’s everything AWS announced in its re:Invent data keynote

Seen on TechCrunch+

Box reaches $1B run rate in spite of a quarter dogged by currency challenges

ChatGPT isn’t putting me out of a job yet, but it’s very good fun

Startup valuations are declining — but not consistently

Proptech in Review: 3 investors explain why they’re bullish on tech that makes buildings greener

As BlockFi files for bankruptcy, how contagious will FTX’s downfall become?

If you like this newsletter, do me a quick favor? Forward it to a friend, tell me what you think on Twitter, and follow my personal blog for more content. We only have a few more issues of Startups Weekly until next year, some come back next week — OK?

Stay warm,

N



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Better together: Offsetting cybersecurity’s labor challenges with API integrations

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The labor challenges afflicting cybersecurity teams far and wide are no secret. A razor-tight hiring market coupled with surging demand and an accelerating threat landscape has created a perfect storm of complexity, resulting in a widening skills gap that is driving higher levels of burnout and human error across the sector. In fact, Verizon’s independently commissioned 2022 Data Breach Investigations Report found that 82% of breaches today involve some degree of human error. Whether it’s an unsuspecting end user or a bleary-eyed analyst, the vulnerabilities caused by cognitive overload shouldn’t be overlooked.  

Take the recent high-profile Uber data breach. A malicious actor, posing as an internal IT administrator, used digital collaboration channels to trick an Uber employee into giving up their VPN credentials, leading to a total compromise of the rideshare giant’s network infrastructure. The breach exemplified the consequences of a social engineering attack targeting the always-on hybrid workforce. And with the rate of such attacks accelerating in volume and velocity, it’s clear that more visibility of these threats is needed for security teams to effectively remediate them.

Many organizations are investing in a plethora of new, best-in-class security products in response to staffing shortages. However, reactive patchwork spending on the industry’s latest niche products shouldn’t be viewed as the answer, as the tool sprawl often creates additional complexity that hurts organizations more than it helps. Enterprises, on average, have 60 to 80 different security monitoring tools in their portfolio, many of which go unused, underutilized or forgotten. Forcing security teams to master a myriad of tools, consoles and workflows shifts priorities from managing risk to managing technology.

An integrated cybersecurity framework

The companies best positioned to offset cybersecurity’s labor challenges are those adopting best-of-breed security tools and platforms that offer a deep library of API and third-party integrations. Above all, an integrated framework empowers organizations to effectively navigate their unique environments by consolidating tools and reducing human error through the following three processes:

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  • Improved protection via security intelligence and threat sharing: This enables rapid recognition and response to incoming threats via machine learning analytics tools, strengthening a human analyst’s ability to formulate swift and comprehensive cyberdefense measures.
  • Improved efficiency via automation: This enables offloading of repetitive and mundane manual tasks to AI-enabled tools, streamlining human workflows by accelerating and improving key facets of incident response and vulnerability management.
  • Improved prevention via sharing and consolidating tool data: This enables complete, real-time visibility into an organization’s entire security environment to promote the creation of targeted alerts that uncover unknown threats.

In collaborating with a wider range of security vendors, organizations leveraging API integrations benefit from the combined knowledge of all integrated platforms to greatly improve overall security posture. The extensive access to timely threat intelligence allows security teams to align prevention, investigation and response plans across multiple security controls, as well as increase the speed of their detection and remediation efforts.

Amid the widespread adoption of cloud-based hybrid work environments, it’s increasingly clear that organizational security architectures must consist of scalable, tightly integrated solutions that combine the right balance of automated prevention, detection and response capabilities to effectively protect data across its lifecycle.

Enhancing detection and increasing cybersecurity efficacy

An open API integration framework is the embodiment of unlocking strength in numbers. It stitches together the critical functions and processes performed by foundational security tools — email security, endpoint security, web security, NDR, data security — into a single meshed framework that operates in unison and shares centralized threat intelligence data across its ecosystem. By connecting all the pieces of the puzzle, organizations gain the resources to enhance their prevention and detection capabilities in complex environments.

In one scenario, an API framework could enable automated processes to continuously flow between an email gateway and security service edge (SSE) to corresponding SIEM/XDR systems. This would allow security teams to share rich logging, metadata, indicators of compromise, malicious URLs, user activity, data movement and machine learning analytics in real time. The AI-powered SIEM platform automates the analysis of that threat data, sifting through the noise to generate actionable alerts with contextual information for security teams. Meanwhile, the real-time contextual insights provide simplified guidance for analysts to alleviate potential threats and, if needed, formulate a swift response to an attack.

With access to a wider range of threat data touchpoints, cybersecurity teams can also create customized scripts within the overarching API library. This gives them “targeted capabilities” that more directly align with their specific needs and skillsets. For instance, the team could create a script that simultaneously analyzes email security logs from Vendor A, data protection logs from Vendor B, web security click logs from Vendor C, and spam filter logs from Vendor D, based on which intel is most relevant to their specific use case. Filtering the exceedingly high volumes of incoming alerts enhances the efficiency of the entire team, empowering analysts to identify needles in the haystack by prioritizing the right alerts at the right times for maximized protection.

Automating manual processes and workflows

Despite the growing number of innovative, best-in-class products available on the market today, it’s important to remember that a multi-vector social engineering attack is exceedingly difficult for hybrid security teams to combat regardless of the tools in their stack. Quick and agile responses are non-negotiable in these situations, but with resources stretched thin and employees working from multiple locations, executing swift corrective action free of human error is easier said than done. Even the most experienced and skilled security teams are susceptible to mistakes while trying to remediate an attack. Therefore, identifying how to automate well-defined processes wherever possible is imperative for tightening these response durations and ensuring security teams can remediate quickly and effectively.

With access to an open API library, organizations can integrate the capabilities of additional AI/ML security tools into their existing security architecture to automate the repetitive steps of protection, detection, response, mitigation and intelligence sharing. Whether it’s informing an endpoint security provider of an emerging alert, or securely moving data from one storage solution to another, API-driven automation can handle the routine, error-prone tasks cybersecurity teams perform every day. Streamlining these otherwise human-centric workflows allows overstretched analysts to instead focus on more critical threat assessments requiring extensive time and attention. That, on a macro level, strengthens the security posture of the greater organization.

There’s no magic bullet that will completely reverse cybersecurity’s labor challenges in the immediate future. But there are proactive steps organizations can take now to provide the critical support their security teams need today. For effectively navigating a complex threat landscape, there’s no better place to start than with the applied adoption of a deep API integration framework.

After all, cybersecurity is a team sport. Why defend alone when you can defend together? 

Joseph Tibbetts is senior director for tech alliances and API at Mimecast.

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