The Next Enterprise Messaging Startup After Slack

Email as an identity was a 90s gaffe. It could have been fixed just like Y2K. Anyone who would fix it soon could be a billion dollar tech unicorn.

Billion Dollar Startup idea that will bring down email

In messaging, especially for enterprises, email still rules. But it lives under the name enterprise messaging. Credits: MS Team, HipChat, and Slack.

But war for enterprise based messaging apps is far from over.

Mattermost just entered this bifold battle, a year after Slack consolidated the market by acquiring HipChat from Atlassean. Mattermost flaunts clients such as Samsung, Uber and NASA. Its USP is ultra-sensitive, open source customization over how client data is stored.

Crucial for enterprises who would pay billions for privacy and security.

Messaging is a growing market where it’s not yet winner takes it all. Security obviously comes crucial for end users, but for corporations, it’s the productivity gained via messaging that they would pay many more billions for.

Here goes a fictional pitch for founders with VC contacts, scratching their heads over how to secure funds for the next great idea in B2B space.

Email Analytics is in the cradle:

Email was the first Internet communication tool that was most widely accepted. Yet, users of email haven’t progressed beyond Read Receipts.

The concern here is privacy. But obviously, privacy can be taken for a ride in numerous ways where the law hasn’t caught up. Before GDPR, companies made hey while the sun shone. Past GDPR, they are watching us more, unabashedly telling us they are doing it for our own good.

But inventors of Read Receipts weren’t creative enough. Maybe they wanted to save bandwidth. Or simply the time was not ripe for more mature email analytics.

Enter Mailchimp. This consumer oriented, newsletter served email platform offered far richer data points surrounding:

• Subscribers’ demography
• CTRs
• How many times emails were opened / forwarded
• Subscribers with maximum open rates

Their killer USP, however, was their allowance of 2000 free subscribers- ideal for indie bloggers with home based businesses. That’s great for mass market adoption for Mailchimp.

As a startup, however, what you could get from (analytics + improved bandwidth + data aggregated) could be much more than that stat.

So when will Email Analytics grow up?

Richer email analytics would make more data available for senders. Data that they would kill for. Data that will not face privacy taboos.

Imagine seeing that screen soon after you sent that email to your potential investor # 1. It represents your investor’s (email receiver’s) mailbox, with only your message(s) visible.

(I couldn’t find a nice blur filter, but hey, I don’t write on VC money yet ??)

Now, for the sake of extending the horizons of your imagination. What if you could see live:

• Some mouse pointer hovering over that message from me, finally opening it (investor trying to recollect who is the sender)
• Your own email opened, for the next 2 hours (investor giving serious thought to your next big idea)
• Closed soon in 1–2 seconds (this startup is crap / founder sucks / I will take a look later)
• Opened again and again for 5 seconds, 50 seconds, 4 minutes respectively, with 20 seconds gap each time (investor comparing your proposal to someone close)
• Hit Reply. You scream with joy! Screen goes blank. (everything that follows is Investor’s data)

The situation is made up for a founder seeking funding, but it could be any organization’s sales manager fidgeting over his / her client’s response.

But what about privacy?

• The data exposed to you isn’t anything beyond what you have sent.
• What you get is not your receivers’ data, but their behavioral insights centered around your proposal. This is very much like Read Receipts, just deeper at several levels.
• They get the same Email client that you got, and you both agree in advance, that you are privy to each others’ email opening / reading behavior. So when they send you “No” for that $5 Million funding, they will observe your reading that “No” for 3 hours too ( + possibly hitting your screen with something ??).

What are the benefits?

• Instead of having to peg your hopes on low-probability partner / supplier, you could decide to move on early.
• Instead of sitting idle on a potentially convertible buyer, you could make smarter followups and close the deal. Forget automated campaigns. Put your efforts and money where the mouth is.
• Between those two extremes, every possible buyer-seller interaction advantage that a local shop-owner enjoys watching his customers’ facial expressions.

What are the downsides?

The biggest downside is initial adoption.

As always, big players will play privacy freaks when they are the ones with pockets. But they will welcome it for sending their own bids.

Those were the oversimplified extreme use cases.

But these reservations will be short-term. AI has already entered our messaging with Gmail message auto-suggestions and Apple auto-corrects. Do not forget, they both serve enterprise email customers.

If a third party can be a snoop dog to our entire email account (filled with invoices), our potential stakeholders’ can serve us better if they have some insights.

WhatsApp blue ticks read receipts, despite being bilateral, are known to induce social anxiety and break so many relationships.

The onus to play it fair remains with the startup- by not having different tier plans that serves more data for high-paying customers, taking advantage of its stakeholder who has low-price tier.

Trust can be injected by making the client open source, with each stakeholder sharing a close-knit community supervising the amount / type of data being exposed via messaging. This assumes that the enterprise has tech team that can supervise such code. If not, it’s an opportunity for extra players too.

Conclusion:

With relative ease to set up and scale a tech startup, scale of business friction will tilt towards the big problem: Sales. Present analytics solutions in the market are not suited for every market.

Messaging analytics, if implemented fairly, can be a great conversion booster. The next enterprise messaging app after Slack doesn’t have to be AI based, but something that relies on & leverages relevant data, with fairer means.

Originally published on Medium.com