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

Thank you, Ashwini. I have a follow-on question and please feel free to answer whenever you get time. We see the evolution of OpenAI systems and so much chatter about GPT-3. If you were an early stage founder who is building a SaaS product in applied AI space, how would you think about this? What do you think would continue to be a value added service that customers will pay for to build a moat for your business vs. probably of getting disrupted quickly?

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Hello Ashwini!

Thank you so much for doing this AMA!
Really cool company. I’m wondering what sorts of patterns you noticed across your customer base that has helped you automate different parts of your on-boarding process.

Thanks again!
Patricia

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Hi Ashwini,

Thank you for finding time to do this!

I have a few questions:

  1. Coming from a non-retail background, what helped you in defining the use case you’ll solve for in your early stages clear & crisp?
  2. In context of 1, how did you figure out who within your ICP will be your product’s early adopters? Any tools or strategies that helped you?
  3. Apart from the tech leverage, how much does AI help in creating explicit value for customers? Or in other words, what role does AI play in your selling strategy?

-Anushree

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Hi Patricia!

I was just looking up Private AI - you guys are doing some much needed work yourself! Very cool. I might want to connect offline at some point about your work here.

We don’t actually use automation in onboarding. Most of our offerings are high end enterprise solutions and this often means white glove kind of onboarding - very human centric :slight_smile: It is a bit ironical that a complex set of AI requires a lot of human interactions with decision makers on the other side to get them onboarded. While we have productivity tools to help customers tell the AI if they want something changed or edited or to override decisions, the onboarding happens entirely offline.

We deliver all our products through API integration with systems/platforms retailers run on, so this up front part of it is very hands on integration. We’ve invested heavily in customer success and delivery teams to enable a smooth up front week or two’s integration and onboarding. I’m not entirely sure if this is what you were referring to. If it’s something else, happy to discuss.

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Hi Ashwini,

Thanks for doing this AMA!

AppDynamics’s founder, Jyoti Bansal talks about how there are two phases of product-market fit. One, of course, is to identify a target group that finds the solution most resonant. Second, is to arrive at the ideal sales model. He even calls it the product-market-sales fit.

What helped you the most in arriving at the latter? And given that its enterprise-focused, how do you think about the balance between customization and productization as well?

Thanks!

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Hi Anushree

Thanks for the questions. These are questions that I’ve spent a lot of time reflecting and thinking about over the years :slight_smile: I’ll answer a couple and come back to the rest.

  1. Not only did we jump into retail as complete retail noobs, we also jumped into enterprise with no background there either. Feels like a double whammy in retrospect. To be honest though, we were blind to both and it might sound weird - but it was a good thing in a way because we definitely lacked the fear we now feel, as people who know so much more about this industry than when we started.

We did 3 things really well:
One, we knocked on a lot of doors and spoke to a lot of retailers who were willing to talk to us. We had a hunch about a set of use cases in personalization that our Computer Vision stack lent itself really well to. So we built a bunch of demos and took it around and did a lot of show & tell but more importantly, used it as a simple starting point for conversations around problems retailers had. We were way ahead of the market (I don’t say that in a good way) and that meant people often got excited and gave a lot of feedback and ideas but never really followed through. We were able to get people to open up their systems, stack and show us what they had going. Lesser the pressure to buy, the more the intent to share, sometimes, especially in cases where your category is non-existent but exciting.

Two, we invested heavily in product marketing & customer marketing and had some really good demos to help explain what AI can do for their business. We led our story with here’s what Computer Vision is, here’s what it can do for your business and here’s why you should care. Half our problem was to help the retailer see why they needed to move status quo. And you really need those first 2-3 customers in enterprise to help launch you.

Which brings me to three, without even knowing Customer Success is what we were doing, we helped our first few customers so much - white glove like I said - that they really stuck with us, are still with us, speak at our events and have become our evangelists. We invested a lot in building those evangelists.

  1. On the ICP question - it was actually simple. There was no way the big box retailers or the brands were investing in AI in 2016 :slight_smile: It was the e-commerce, online, tech first folks who were going to invest in any of these features. This also meant competing with internal teams but we eventually won. So we replicated our success in that segment and went after the biggest online retailers as our starting point. Exec teams mostly.
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Hi Ashwini,

Thank you!

As a successful woman entrepreneur, what are your inputs to young women entrepreneurs to succeed around challenges and obstacles? please share your views.

Regards,
Deepika N

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Hi Ashwini,

Thanks for doing this AMA,

I would request your inputs in the COVID context

Value generation & Marketing with AI - With limited spend allocated, organisations are bit hesitant to experiment new ventures in the field of AI, where most part of the sales cycle goes into educating or creating awarness. How do you see this can be best tackled to keep the CAC and sales cycles realistic?

Regards,
Logesh G

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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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