What You Need To Know About The Slack IPO

Slack, which is a fast-growing collaboration app, unveiled its IPO filing last week. The buzz is that the deal could be on par with Zoom’s, which soared over 70% on its debut (here’s a recent post I wrote on the company for Forbes.com).

In terms of the timing, the Slack offering is likely to hit the markets in a few weeks. The shares will be listed on the NYSE under the ticker of SK.

OK, what are some of the notable details in the IPO filing? Well, let’s take a look:

The App: The founders created Slack because they were frustrated with email. So they reimagined the experience, with a focus on team-based channels — not individual inboxes. Here’s how the S-1 describes it: “Channels offer a persistent record of the conversations, data, documents, and application workflows relevant to a project or a topic. Membership of a channel can change over time as people join or leave a project or organization, and users benefit from the accumulated historical information in a way an employee never could when starting with an empty email inbox. Depending on the size of the organization, this might provide tens, hundreds or even thousands of times more access to information than is available to individuals working in environments where email is the primary means of communication.”

Growth: It’s certainly robust. From fiscal 2017 to 2019, revenues jumped from $105.2 million to $400.6 million. Although, the company continues to post losses (they were $138.9 million last year).

A significant driver for the growth has been the expansion within organizations. For example, the net dollar retention rate is 143% and 575 customers pay at least $100,000 per year, compared to 298 in the year before.

Engagement: To get a sense of this, look at the metrics for the week ended January 31st. There were more than one billion messages sent and on a typical workday, a paid customer averaged nine hours connected to the app and more than 90 minutes of active usage.

Powerful Ecosystem: Slack has built a strong community of more than 500,000 developers. They have created over 450,000 third-party applications and custom integrations, which have provided for a much richer platform. Slack is even creating a low-code system that should mean even more growth of the ecosystem.

Market Opportunity: It’s massive. Based on Slack’s own analysis, the spending on communication and collaboration tools is at about $28 billion worldwide.

Competition: It’s intense. Slack must fight against some of the largest tech companies in the world, such as Google, Cisco and even Facebook. Yet the company considers its primary rival to be Microsoft.

In fact, the Slack S-1 notes: “Moreover, we expect competition to increase in the future from established competitors and new market entrants, including established technology companies who have not previously entered the market.”

Investors: They include tier-1 players like Accel Partners (24%), Andreessen Horowitz (13.3%) and Softbank (7.3%).

As for the co-founders, Stewart Butterfield holds 8.6% of the equity and Cal Henderson has 3.4%. They both have supervoting shares. However, there is an expiration provision for this.

Unusual IPO: Most companies use Wall Street firms to facilitate a public offering. But Slack is taking a much different approach: a direct listing. This not only means not having to shell out large fees but also allows anybody to purchase shares on the debut.

Keep in mind that the direct listing will not involve a capital raise.  But then again, Slack has about $841 million in the bank.

Artificial Intelligence (AI): What About The User Experience?

One of the key drivers of the AI (Artificial Intelligence) revolution is open source software. With languages like Python and platforms such as TensorFlow, anybody can create sophisticated models.

Yet this does not mean the applications will be useful. They may wind up doing more harm than good, as we’ve seen with cases involving bias.

But there is something else that often gets overlooked: The user experience. After all, despite the availability of powerful tools and access to cloud-based systems, the fact remains that it is usually data scientists that create the applications, who may not be adept at developing intuitive interfaces. But more and more, it’s non-technical people that are using the technology to achieve tangible business objectives.

In light of this, there has been the emergence of a new category of AI tools called Automated Machine Learning (AutoML). This uses simple workflows and drag-and-drop to create sophisticated models – allowing for the democratization of AI.

But even these systems require a background in data science and this can pose tricky issues with the development of the UI.

“Our mission when we designed Dataiku was to democratize data and AI across all people and to unite all of the various technology pieces out there,” said Florian Douetteau, who is the CEO of Dataiku. “We kept this mission in mind when we embarked on our UI. Enterprise AI is the future, and that means hundreds and thousands of people are using Dataiku every day as the core of their job, spending hours a day in the tool. So we keep the UI of Dataiku simple, clean, modern, and beautiful; no one wants to work in a space — virtual or otherwise — that is cluttered or that looks and feels old, especially when data science and machine learning are such cutting-edge fields. Another important consideration is ease of use, but not at the expense of robustness. That means making sure that Dataiku’s UI is simple for those on the business side — many of whom are used to working in spreadsheets — who don’t have extensive training in advanced data science as well as the most code-driven data scientist – but none of this as a tradeoff for deep functionality.”

Yes, it’s a tough balance to strike – but it is critical.

Actually, to get a sense at how this can work, consider Intuit’s TurboTax. The software deals with an incredibly important but complex topic for consumers. The technology also involves advanced AI systems and algorithms, such as by leveraging data to surface industry-specific personalized topics.

“When we went out and asked thousands of consumers about their tax preparation, most responded with emotions of fear, uncertainty and doubt,” said Eunie Kwon, who is the Director of Design at Intuit. “Once we started to unpack their reasons for these feelings, we found opportunities to influence their experience by applying some basic psychological principles and laws of UX heuristics to simplify through mindful design. To reduce cognitive load, we balanced the fundamental elements of design through content, visual expression, animation, and recreated the informational experience to reduce fatigue, friction and confusion. To improve workflow, we dissected the complicated tax forms into adaptable and consumable interview-like experiences. We added ‘breather’ screens where we acknowledge to the customer how much they’ve completed and the accuracy of their input. We also added ‘celebration’ screens to drive confidence that informs them of their progress while educating them on the changes in tax laws along the way.”

Such approaches are simple and make a lot of sense. But when developing software, they may not get much priority.

“The main lesson learned when designing for TurboTax is balancing simplicity while ensuring 100% confidence for a customer’s tax outcome,” said Kwon. “Every year, we are faced with new mindsets that evolve the behavior of how consumers interact with products and apps. The expectations for simplicity and delight change so often that we need to look at our experience and find improvements that meet those expectations, while driving complete confidence through their tax experience.”

What You Need To Know About Chatbots

According to BI Intelligence, about 80% of businesses will use chatbots — which are applications that engage in interactive conversation using natural voice or text – by 2020. Juniper Research also forecasts that this technology will save businesses about $8 billion annually by 2022.

“Due to advancements in AI technology, natural language processing and speech recognition, the cost of developing chatbots has come down drastically, which is fueling the explosive growth of this market,” said Jeri John Deva George, who is the Head of Zoho’s SalesIQ and Cliq.

Yet it’s customer service that chatbots will likely see much of the traction, at least in the next few years. “With chatbots able to engage customers seamlessly around the clock, this is poised to completely change the online customer experience game, while saving time and money,” said Patrick Welch, who is the president and CMO of Bigtincan. “The main goal of these chatbots is to assist customers with getting to their end goal as quickly as possible, whether it is finding out more information, or making a sale. In the end, it’s ideally to replicate the success of top performing customer agents.”

So then, what are the ways to implement chatbots in your organization? What are the best practices and, well, the gotchas? Here are some things to keep in mind:

Set Expectations: Chatbots are not silver bullets. In fact, there are many ways they can go wrong!

“For a great customer experience it is crucial to not try and ‘trick’ customers into thinking the AI chatbot is a real person – and make it clear when they have switched from a bot to a person,” said Chris Bauserman, who is the VP of Product and Segment Marketing at NICE inContact. “Start with a focused pilot covering topics that you already successfully provide self-service options for, test and learn quickly, then iterate and expand from there.”

Understand The Customer Process: Look at ways to better personalize the experience. Otherwise, a chatbot may ultimately be worse than using a traditional approach.

“Customers may get annoyed if they have to repeat all their details to a human agent after having painstakingly typed it into a chatbot interface,” said Michael D. Mills, who is the Senior Vice President of Global Sales at the Contact Center division of CGS. “Failure to centralize customer service information can lead to negative experiences.”

Focus On Data: In other words, there should be ongoing data analytics to understand trends. “This will help a brand build profiles on its customers which will then personalize the experience even more,” said Jonathan Taylor, who is the CTO of Zoovu. “Collecting this insight will also help brands understand how the navigation of their site works.”

Think Different With Design: Your experience with designing websites or mobile apps may lead you down the wrong path. Consider that chatbots have their own unique requirements.

“How should your company sound?” said Gillian McCann, who is the Head of Cloud Engineering & Artificial Intelligence at Workgrid Software. “Think carefully about brand and personality and what it says about your company. Also be prepared for users to say the most unexpected things and build in conversation flows that can handle going off topic. There should also be an awareness of local or cultural differences in language.”

The Future of Chatbots

While there has been lots of progress with chatbots, the technology is still in the nascent stage.

“I must emphasize that chatbots augment humans, not replace them entirely,” said Antonio Cangiano, who is the AI Evangelist at IBM. “As a result, it would be a mistake to expect so-called strong AI a la Sci-Fi movies, at this stage.”

Yes, this is critical to keep in mind.  But the future does look very promising as chatbots are likely to be impactful for your business. “There will be a shift away from chatbots being simply reactive,” said Stefan Ritter, who is the co-founder and Head of Product at Ruum by SAP. “As AI becomes more advanced and chatbots collect more data, bots will start to develop the ability to predict what a user’s next move might be, or what problem they may be experiencing, and act on it in real time.”

Zoom IPO: What Can Entrepreneurs Learn From The Mega Success?

While much of the attention for IPOs this year has been on high-profile operators like Slack, Lyft, Uber and Pinterest, the standout deal has so far been Zoom. Last week the company launched its offering and the stock rocketed by 72% on its first day of trading, putting the market cap at nearly $16 billion. Now Zoom is among one of the most valuable cloud companies in the world.

All this is the vision of Eric Yuan, the company’s founder and CEO.

Here’s a backgrounder:  While attending college in China during the 1980s, he majored in Computer Science because he admired tech entrepreneurs like Bill Gates. By 1997, Eric came to America – after much difficulties with the immigration system – and joined the engineering team at WebEx. It proved fortuitous as the company would revolutionize the conferencing market.

Then by 2007, WebEx sold out to Cisco and unfortunately, the innovation started to lag. Eric tried to push for change but he was mostly rebuffed. In 2011 he started Zoom, raising a seed round from a variety of angels.

As should be no surprise, Eric was fairly unconventional in his strategy — that is, by the standards of Silicon Valley.  For example, he did not spend lavishly (the original offices were quite modest) and there was little emphasis on sales and marketing.

All in all, the formula has been spot on.  As of today, Zoom is growing at 100%+ and is profitable. There are also 50,000 corporate customers and 344 of them pay over $100,000 a year.

OK then, what are the takeaways for entrepreneurs? What are the lessons here that can help with your own venture? Well, let’s take a look:

Customer First: Eric is obsessed with making the best product possible. Keep in mind that he worked on the Zoom platform for two years before it was launched. He would also personally answer questions from customers and reached out to every customer that cancelled.

The bottom line: Zoom has a Net Promoter Score (NPS) over 70.

“Zoom succeeded when so many others didn’t,” said Roy Raanani, who is the co-founder and CEO of Chorus.ai. “When Eric started the business, he knew the video conferencing industry intimately and understood that existing products didn’t meet user needs. He had a vision that everyone should ‘Meet Happy’ and focused on creating a simple product experience that ‘Just Works,’ hiring a great team that focused on executing the fundamentals right, and putting customer and employee happiness at the center of Zoom’s culture.”

Viral: Conferencing is inherently viral and allows for the creation of network effects that can make it tough for competitors to attack. Granted, this is not easy to pull off but Eric’s focus on creating a strong product has been critical.

The Zoom S-1 notes: “Our rapid adoption is driven by a virtuous cycle of positive user experiences. Individuals typically begin using our platform when a colleague or associate invites them to a Zoom meeting. When attendees experience our platform and realize the benefits, they often become paying customers to unlock additional functionality.”

Big Market, Big Problem: When Eric founded Zoom, there was lots of skepticism. Wasn’t the market already won? How could he battle against rivals like Microsoft, Google, WebEx and GoToMeeting?

But like any entrepreneur, Eric was convinced of his vision. He understood that there was much that could be done in the industry, which was not investing enough in new technologies.

“The Cardinal rule for any startup: Solve a really big problem and solve it well,” said Jamie Sutherland, who is the founder and CEO of Sonix. “Zoom did this. While there was lots of rhetoric around the idea that video conferencing had been solved, the reality was that it wasn’t really solved well. Everyone…literally every human I knew had pains with video conferencing. And when it doesn’t work, it is a huge pain. So, couple that with an enormous global market, and you’ve got a huge opportunity.”

How To Get The Max From RPA (Robotic Process Automation)

Robotic Process Automation (RPA) is not sexy (the name alone is evidence of this). Yet it is one of the hottest sectors in the tech market.

Why all the interest? RPA allows companies to automate routine processes, which can quickly lower costs and allow employees to focus on more important tasks. The technology also reduces errors and helps to improve compliance.

Oh, and there is something else: RPA can be a gateway to AI. The reason is that the automation may help with finding patterns and insights from the data as well as to streamline the input with NLP (Natural Language Processing) and OCR.

Yet despite all this, there should definitely be care with an implementation. Keep in mind that there are still plenty of failures.

So let’s take a look at some things to consider to improve the odds of success:

Deep Dive On Your Current Processes: Rushing to implement RPA will probably mean getting subpar results. There first must be a thorough analysis and understanding of your current processes. Otherwise you’ll likely be just automating inefficiencies.

“Best practices for automation projects always begin with process mapping and re-engineering of all business scenarios,” said Sudhir Singh, who is the CEO of NIIT Technologies. “This allows all automation design to be completed upfront and can avoid multiple re-iterations during delivery.”

But truly understanding your processes can be time-consuming and difficult.  This is why it could be a good idea to bring in an expert.


Although, there are also several software systems that can essentially do an MRI of your processes. An example is Celonis, which has partnerships with top RPA players like iPath, Automation Anywhere, and Blue Prism. “Our system creates a business process map,” said Alexander Rinke, who is the CEO of Celonis. “With it, you can see what needs improvement.”

Start With The Mundane: RPA is best for those processes that are routine and repetitive. These are basically the kinds of things that … bore your employees. And yes, this means that RPA can span many parts of a business, like finance, HR, legal, the supply chain and so on.

It also helps if the processes do not change much. After all, this means fewer upgrades to the bots, which lowers the complexity.

Determine Whether to Replace or Supplement People: This is important as it will guide you in the type of RPA to use.

“By supplementing people, a business can implement attended bots that are assistants and helpers to team members that serve the purpose of speeding up processes and eliminating human error,” said Richard French, who is the CRO of Kryon. “This setup will empower staff to focus on advanced and complex tasks, while bot assistants handle their administrative assignments. “

But if you want to find ways to reduce headcount, then you should look at those vendors that focus on unattended bots.

Create a Center of Excellence (CoE): There needs to be a well thought-out plan for funding, training, governance and maintenance of the RPA. And to carry this out, it’s recommended to setup a CoE that can manage the process. Often this includes a mix of business people, IT personnel and developers.

Scaling: It’s often easy to get early wins. But the major challenge is making RPA more pervasive.

“Many companies that do implement the technology never scale past the first 50 automated processes,” said French. “The reason is that it is difficult for executives to think beyond and understand what processes will further improve the ROI or efficiency once there is already something in place.”

This is why having a CoE is so critical. What’s more, the team will likely need to change over time, as the needs and requirements of the RPA implementation evolve.

Implementing AI The Right Way

For many companies, when it comes to implementing AI, the typical approach is to use certain features from existing software platforms (say from Salesforce.com’s Einstein).  But then there are those companies that are building their own models.

Yes, this can move the needle, leading to major benefits. At the same time, there are clear risks and expenses. Let’s face it, you need to form a team, prepare the data, develop and test models, and then deploy the system.

In light of this, it should be no surprise that AI projects can easily fail.

So what to do? How can you boost the odds for success?

Well, let’s take a look at some best practices;

IT Assessment: The fact is that most companies are weighed down with legacy systems, which can make it difficult to implement an AI project. So there must be a realistic look at what needs to be built to have the right technology foundation — which can be costly and take considerable time.

Funny enough, as you go through the process, you may realize there are already AI projects in progress!

“Confusion like this must be resolved across the leadership team before a coherent AI strategy can be formulated,” said Ben MacKenzie, who is the Director of AI Engineering at Teradata Consulting.

The Business Case: Vijay Raghavan, who is the executive vice president and CTO of Risk and Business Analytics at RELX, recommends asking questions like:

  • Do I want to use AI to build better products?
  • Do I want to use AI to get products to market faster?
  • Do I want to use AI to become more efficient or profitable in ways beyond product development?
  • Do I want to use AI to mitigate some form of risk (Information security risk, compliance risk…)?

“In a sense, this is not that different from a company that asked itself say 30 or more years ago, ‘Do I need a software development strategy, and what are the best practices for such?,'” said Vijay. “What that company needed was a software development discipline — more than a strategy — in order to execute the business strategy. Similarly, the answers to the above questions can help drive an AI discipline or AI implementation.”

Measure, Measure, Measure: While it’s important to experiment with AI, there should still be a strong discipline when it comes to tracking the project.

“This should be done at every step and must be done with a critical sense,” said Erik Schluntz, who is the cofounder & CTO at Cobalt Robotics. “Despite the fantastic hype around AI today, it is still in no way a panacea, just a tool to help accomplish existing tasks more efficiently, or create new solutions that address a gap in today’s market. Not only that, but you need to be open about auditing the strategy on an on-going basis.”

Education and Collaboration: Even though AI tools are getting much better, they still require data science skills. The problem, of course, is that it is difficult to recruit people with this kind of talent. As a result, there should be ongoing education. The good news is that there are many affordable courses from providers like Udemy and Udacity to help out.

Next, fostering a culture of collaboration is essential. “So, in addition to education, one of the key components to an AI strategy should be overall change management,” said Kurt Muehmel, who is the VP of Sales Engineering at Dataiku. “It is important to create both short- and long-term roadmaps of what will be accomplished with first maybe predictive analytics, then perhaps machine learning, and ultimately – as a longer-term goal – AI, and how each roadmap impacts various pieces of the business as well as people who are a part of those business lines and their day-to-day work.”

Recognition: When there is a win, celebrate it. And make sure senior leaders recognize the achievement.

“Ideally this first win should be completed within 8-12 weeks so that stakeholders stay engaged and supportive,” said Prasad Vuyyuru, who is a Partner of the Enterprise Insights Practice at Infosys Consulting. “Then next you can scale it gradually with limited additional functions for more business units and geographies.”

How AI Will Change B2B Marketing Forever

Back in 2006, Phil Fernandez, Jon Miller, and David Morandi founded Marketo. At the time, they only had a PowerPoint. But then again, they also had a compelling vision to create a new category known as marketing automation.

Within a few years, Marketo would become one of the fastest software companies in the world, as the market-product fit was near perfect.  By 2013, the company went public and then a few years later, it would go private. Then as of 2018, Marketo agreed to sell to Adobe for $4.75 billion.

The deal will certainly be critical to scale growth even more and there will certainly be major synergies. But I also think there will be a supercharging of the AI strategy, which should be transformative for the company.

Yet this is not to imply that Marketo is a laggard with this technology.  Keep in mind that the company — in 2016 — launched Predictive Content. The system leverages AI to help marketers offer better targeting based on a prospect’s activities, firmographics, and buying stage.

After this, Marketo created other offerings like:

  • Account Profiling, just announced at Adobe Summit, uses a customer’s current customer data to determine the best prospective accounts to target based on billions of data points in real-time.
  • Predictive Audiences for Events selects the best audience to invite to an event and then forecasts attendance and recommends adjustments to meet customers’ goals.

But all this is still in the early days. “AI will become pervasive throughout B2B marketing efforts, improving performance and increasing efficiency throughout the entire buyer’s journey,” said Casey Carey, who is the Senior Director of Product Marketing for Marketo Digital Experience at Adobe.

In fact, here are just some of the important capabilities he sees with B2B marketing:

  • Audience Selection: “AI can inform improved audience selection and segmentation. Armed with tools to identify a target audience based on past behaviors, marketers can offer tailored experiences that will resonate with potential customers.”
  • Offers and Content: “AI can help marketers deliver higher value to potential customers by applying machine learning to the content selection and delivery process. This includes creative, formats, and offers. By creating personalized messages based on previous choices and behavior, marketers are able to engage in ways that resonate every time.”
  • Channels: “AI can help marketers determine the best time and place to engage with potential customers based on past channel performance and what you know about the individual.”
  • Analysis: “Using AI, marketers can quickly understand what’s working and what’s not so they can make adjustments to improve performance and drive a better return on their investments.”
  • Forecasting and Anomaly Detection: “It is not enough to know what to do, but you also need to understand what the impact will most likely be – this is where AI can help. By analyzing past results, AI can predict outcomes like campaign performance, conversion rates, revenue, and customer value. This provides a baseline for planning and then making mid-course adjustments as anomalies occur or other changes are needed.”

Yes, this is quite a lot! But Casey has some spot-on recommendations for marketers on how to use AI. “Rather than trying to understand the technology behind AI solutions, savvy marketers should focus instead on finding opportunities to use them,” he said. “If you catch yourself saying, ‘If only I could figure how to put all this data to use,’ consider an AI application. On the other hand, despite everything that can be achieved by strategically implementing AI, there are still areas where AI solutions are not appropriate, such as situations where there is poor quality or insufficient data. AI is, after all, artificial intelligence. It’s only as good as the data you feed it.”

But AI is not something to ruminate about — rather, it is something that must be acted on. “Two things are happening that are making AI more prevalent in marketing,” said Casey. “First, prospects are expecting more relevant and compelling engagement along their buyer journey, and second, more data is becoming available to inform our marketing strategies. As a result, the historical way of manually analyzing data and using rule-based approaches to marketing are no longer enough.”