I'm Ashwini! Founder and CEO of Vue.ai. AMA!

Thanks so much for doing the session, Ashwini! :slight_smile:

Now that Vue.ai is expanding into multiple segments (grocery, electronics, and others), how does the notion of repeatability play out? As in, is there something that’s common between the routes to market for all these segments and that helped with the decision-making?

Or does the move come from having tested out multiple GTM (direct/channel/inbound) strategies over the years and now being ready for varied segments? Would be helpful to learn about the high-level thinking process here.

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Hi Aditi!

I was just looking up Locale. It’s an exciting space. Good luck!

In the category we’re working in, the need for AI enabled use cases means delivering in a way that the AI lands in some part of an existing workflow, which is either manned by a human or intersects some fairly old legacy system!

We realized fairly early on that most of our products would never work in a self serve model. Our challenges revolved around convincing people to trust the AI, trust value and trust that it won’t break the systems they already had in place. And that automatically meant we were going to pick the enterprise route for sales.

Then we had to figure out how to price the up front integration vs. the recurring API parts. We iterated a lot on the pricing. It was directly a function of the RoI and value we were able to generate for our customers. And then of course, the upsell and cross sell cycles continued once that trust was established.

We don’t do services. You can’t have armies of humans in the loop when you’re delivering real time personalization, data extraction, classification related use cases. But in the first few years, we had the luxury of customers who allowed us to experiment with them on use cases, to iterate and learn. This is very important for enterprise. Those early supporters, adopters can often be the difference between take-off and crash.

I speak to a lot of early stage founders and this is the singular question that keeps coming up - customization vs. productization! There is no right answer. Every business is different. If your goal is to build a SaaS company, don’t let the customization eat you whole, is all I tell them. Building in the ability to customize, into your product workflow can also be a great way to deal with this. Hope that helps!

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Hi Deepika

I’ll share a few rules/guardrails/guidelines I’ve had for myself over the years.

  • Don’t take No for an answer, keep at it, iterate and come back
  • Punch 10x harder than any of your peers. The playing field is not level. There’s no other choice
  • You can differentiate and build a massive organization that can fire up bellies full of ambition while being thoughtful, empathetic and caring. These are not mutually exclusive.
  • There is discrimination, there is bias. It just is that way. Fight it by actually building the company you’ve always wanted. Change starts at home.
  • Ensure diversity bottoms up and top down. Lead the way and show everyone we can change the status quo. You can’t achieve this by having a diversity officer, you need a culture of diversity.
  • Surround yourself with mentors and people who will help you see possibilities beyond a world filled with bias.

We stand on the shoulders of so many who have fought this over decades. Pay it forward if you can. Nothing changes the status quo like enabling a 1 more founder like you to succeed in whatever little way we can help :slight_smile:

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Hi Logesh!

It is a hard time for many industries. But people are buying ‘need’ based SaaS products more than they ever have before. In many ways, this is a great period for SaaS products that tackle must-haves, mission critical use cases. So more than sales cycles and CAC, this is actually a great time to think about product marketing and messaging.

We spent a lot of time thinking about how COVID impacted retail and iterated on our content to suit everything that’s changing in the market. Now more than ever before, its important people relate to you and your product. So invest there.

We’ve also been asking ourselves about who the winners and losers of this pandemic are. It’s an important one if you’re tuning your lead list. Chances are - you’re going to get a better chance of winning the guys who are survivors of this situation, than those who are not equipped to survive this market.

Good luck as you navigate this!

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That’s a great question, Akhilesh!

We’ve been wanting to expand for over a year now and been patiently waiting for the right time. Our AI has seen enough data over the years that it’s started to work really well in non-fashion categories too. We did some early tests in each of those other adjacent verticals and realized it was working very well. So we implemented it with a few existing customers who had multiple categories, measured the value and then launched.

Product and delivery wise, the teams are the same. The repeatability is very high there. On the GTM side though, it’s a whole other story. We have the same teams covering both today. And while it’s certainly getting a bit much, we’re just getting started here. And I’m a big believer in lean, highly ambitious teams that want to learn and grow. This new expansion has my team excited about the opportunity to learn more and they’re getting really creative. We’ll reach a point in 3 months when the demand outweighs the capacity of our GTM resources :slight_smile: I’m banking on it and planning for it.

We’re planning to have separate inbound/outbound/sales teams for fashion and non-fashion. Some verticals behave very similarly - like beauty and fashion. So we’re using this quarter as a way to capture all these similarities and differences before we implement 2-3 separate teams.

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Hey Krish
So I have some strong and unfiltered opinions about this :sweat_smile:

Big news in AI is always hyped. Real progress in AI happens in the real world, in the context of your day to day business. And it can take months or years to see RoI or real value. Don’t get me wrong, I’m not saying its not exciting. The reason everything is so hyped about AI, is precisely because of how exciting it is. But as someone who plays a big role in advising our customers about noise vs. signal, I can say learning to filter through the hype and taking away some actionable items for your business is where the emphasis should be. Along those lines, here are a few:

  • Focus on the use cases and problems you can solve: Ask yourself if you can use GPT-3 to build a new feature into your workflow? Can you improve the search you use? Can you improve customer support? Can you use this to generate automated emails and messages to customers? Copy on the website, perhaps? To filter out negative comments and reviews, so you can plan course of action?

  • Does it scale? A large % of AI that is out there, whether it’s tools or solutions being put out - does not scale in production. This is actually one of the main reasons we started Mad Street Den. Separate the hype and demo from the ability to scale what you see in the context of the use cases you see.

  • Experiment, iterate, deploy, learn. Like any other product development activity, if not even more - AI needs the care and time and cycle of experimentation and deployment and feedback - to see if it’s even relevant. We can fool ourselves into believing something is true, if the flash quotient is high :slight_smile: I’ve noticed this doesn’t last. Customers move onto the next thing and stop using this. We have actively advised many of our customers to not use some of our initial features, despite their being a very high demand for it in a particular period. We stopped offering those features and let our customers to go shop for them elsewhere. 3.5 years in, I can tell you it was one of the best decisions I’ve ever made. Every little thing that doesn’t move you forward, sets you backward in some way.

Hope that helps!

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Hi Ashwini,
When it comes to AI - I believe there are 3 types of customers:

  1. Sceptical types - they have been bitten by past failures or failure stories (e.g. chatbot nightmares). It is hard to convince them that all AI is not the same.
  2. Excited/Curious types - who believe this is brand new technology and want to know what it does, how its different etc.
  3. I want magic types - those that believe in a Sci-Fi version of the world

Do you encounter the same? What’s your strategy for dealing with the different types :)?

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Dear Ashwini

Glad to have yo with us, super impressed by your podcast episode & moreover candid answers with FactorDaily.

My questions is around building the ML/AI product as a “system of intelligence” e.g. classification based ML engine with growing intelligence over an already data stack of “system of records” e.g. CRM data within the product roadmap & taking this into wider customer audience. I have 2 questions on early customers choice & pricing.

  1. As any new ML/AI offering is looking to nail the business problem waiting to be baked by user feedback should we dog food with existing customers and give it enough gestation period to delivery outcomes ? Before we move to new customers. Is this how we shd plan the product rollout as initially biased outcomes is highly probable. So tolerance level of existing customers will be more compared to new ones. Happy to hear your thoughts.

  2. How do we price an ML / AI offering as an add-ons. Take e.g. if our SaaS ACV is say $5K, should be price it as a premium add-ons initially & then move to a value-based pricing e.g. X no. of predicted conversions * unit cost. Any good playbooks on pricing such products will be super helpful

Thanks & super excited to read your answers.

Best
Abhi
Co-Founder | ExtraaEdge

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Hahaaa! That right there is your lead list generation strategy, no? :slight_smile: It’s not been easy, we’ve taken time to figure out the high intent vs. low intent folks in this space. But it directly has a bearing on which of those categories they fit.

That said - You do have a 4th. The folks who are ready, see the need for change, and can be educated or already know how this works. There is a growing market of these folks. More and more people know exactly what to ask for, when and how. So much so that many of them are building their own teams to work with AI.

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Hi Ashwini,

Glad to connect here.

When one’s product/brand narrative is based on a tech(AI in your case), and given that tech changes at a fast pace and tends to get commoditised, how do you manage the risk of having to change the narrative every so often?

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Hi Abhi!

Thanks for the note.

WRT 1. YES. If you don’t understand how your ML system works, its RoI, you can’t put it out there with new customers. In a way, this is like any big new product feature you launch. The downside, though, is that ML features can be disruptive (in a negative) if rolled out wrongly. It can create incorrect data, can bias your systems and really hurt your business. So test extensively with a smaller base before rolling it out. Unless, it’s simple automation of workflows.

  1. Pricing entirely depends on value add. If your ML add-ons are providing incremental outcome of some kind, then customers will be willing to pay more. So test your hypothesis like you do with any other product feature. Talk to customers, build, iterate and evaluate value before pricing it. 2 things I’ve seen: customers pay for incremental growth in revenue or costs saved by AI.

Good luck!

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Hi Arun!

Glad to connect. I know my marketing team is a happy customer of Recotap :slight_smile:

I worry less about commoditization with AI and more about fighting against the status quo. AI is not getting commoditized anytime soon. There will always be verticals and horizontals that have a place and we’re barely getting started. I’d say as a market we’re about 4-5 years from seeing AI in the market on scale.

The status quo, however, is a big problem. Inertia is a big problem. That’s who we’re fighting :slight_smile:

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Another one on the quick team building. If we have to sensitise our engineers to learn & become well versed with all 3 - diagnosing the business problem, finding right AI/ML tools / algos to solve for them ( regressions, classification, anomaly etc. ) & then running fast iterative sprints. What kind of learning playbooks you will suggest.

Is there a resource for engineering managers like us to guide team to build ML sensitivity understanding within broader engineering team. Which can be used as a continuous learning framework, toolkit, courses & resources. For e.g. https://hackernoon.com/tagged/machine-learning.

Excited to hear your thoughts on skill building on ML/AI for teams.

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Abhi - I can tell you that we have something coming … to address just this. In 9 months :slight_smile: We’re building a platform focused resource center. But that’ll have to wait.

Meanwhile - Datadog, Datarobot - are all companies that have great reading materials for engineering managers and more. If it helps at all - I’d be happy to offer an hour or so of my team’s time to help you answer any questions you have - if you’re interested.

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Wow, excited to hear in 9 months.

I think it will be absolutely great to steal 30 mins from your team / experts based on your convenience. I’ll ask Astha to share your details to get in touch with you. I really appreciate this help :slight_smile:

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Hey @Ashwini,

Thank you so much for taking the time out to answer all the questions with such care and in such depth!

It’s been so great to hear about your journey and learn from your nuanced perspective on the AI space and the enigma it can be. :slight_smile:

There is, undoubtedly, a ton of great startup and SaaS advice in there, but I esp. loved how you phrased this: “You can differentiate and build a massive organization that can fire up bellies full of ambition while being thoughtful, empathetic and caring. These are not mutually exclusive.” What a great, much-needed thought! :bow:

So glad we could get a chance to host you and get to learn from you. Thank you, again!

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Also, a big thanks to @pthaine, @Anushree, @aditi1002, @Deepika, @Logesh, @Akhilesh, @wingman4sales, @aballabh, and @arun for joining in today and asking some amazing questions.

We’ll see you around for the next AMA very soon. Stay tuned for more details! :zap:

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Thanks, Ashwini!

That’s exactly what I was referring to :slight_smile:
Thanks so much for your reply, it’s really useful info!

Would love to connect offline!

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Thanks Ashwini!

This is such a useful frame to separate hype from real potential for not just AI-related developments but any kind of technological advance (leap :)) we hear about. Definitely forces one to pay conscious attention.

And I’m saving this, such an important reminder for both founders and product people:

We can fool ourselves into believing something is true, if the flash quotient is high :slight_smile: I’ve noticed this doesn’t last. Customers move onto the next thing and stop using this. We have actively advised many of our customers to not use some of our initial features, despite their being a very high demand for it in a particular period. We stopped offering those features and let our customers to go shop for them elsewhere. 3.5 years in, I can tell you it was one of the best decisions I’ve ever made. Every little thing that doesn’t move you forward, sets you backward in some way.

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Thanks for sharing that in-depth reflection, Ashwini!

You’ve put it so well. How wrestling with the regular challenges of building for a new category gets considerably more complex when everyone in the market also has not-so-helpful, deep-rooted preconceptions of what is/isn’t possible.

Love how you’ve thought about this all along. This, especially:

I can tell you when we crossed our 3 year mark, for me it was all about helping the team undo, unlearn, rethink, remake and rebuild themselves not as people building bleeding edge tech but about helping them think of themselves as people who have a responsibility to help the world around them become AI-Natives. I can tell you this is one of the best things that’s happened to the org as a whole, maybe we should have done it sooner. But the responsibility that comes with enabling people with AI is a very real one and that can help counter the fear and the high threshold for change.

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