My product is partially complete. Before I invest further time and discover harsh truth, how can I find customers to try it and ultimately pay me in a month?
Reddit blocks any promotional posts and it’s hard to get traction here on HN without karma points.
Please suggest that has worked for you.
Since you say you are offering ML models through a web-service, who else is doing that now? How do you think they find their customers? Can you leverage their customer base to find people that may be wiling to switch?
Step 1 is to identify who needs your product. Then find out where those people are. It's a bit more difficult with Covid due to lack of meetups, etc, but that doesn't mean it's impossible.
However, if I were you, I'd take a serious look at how you got to where you are? How did you build a product without knowing how you were going to get customers? How you are going to know if you've built the right product.
Rather than looking for "customers", at this point, I'd suggest looking for "advice", find somebody who has the problem, and show them your solution. Then ask them if it's something they need. If not, why not? Find out what they do need, etc etc.
Love the problem, not the solution.
Despite your progress on code, I would say you still only have an idea. A working idea, but you don't know if it's marketable.
Read Steve Blank - get out of the building and talk to people. Right now, you don't seem to know exactly what the problem is you're solving.
"How to Kill a Startup Idea with Google Keyword Planner and AdWords: A Case Study"
https://www.psl.com/feed-posts/psl-studio-kill-xylo
Earlier HN discussion: https://news.ycombinator.com/item?id=22110004
I also recommend Rob Walling's book Start Small Stay Small. https://startupbook.net/
The book is mostly a practical marketing guide written for developers who are trying to bootstrap a business.
Walling argues for a market-first approach: identify a good niche market of potential customers first (enough potential customers, you have a practical way to reach those customers through search/advertising/niche community newsletters or forums or so on, not too much competition). Then, before building product, run experiment to validate or reject your product idea based on market demand & estimated conversion.
My product is offering ML models (classification, recommendation, ranking) through web services. We are offering it for $149/month. You own your data. You can make REST api calls to get output of model. Let me know if you are interested. My email is aimlmodelfarm@gmail.com
Your product may be valuable to an organisation when:
(i) the org has a standard kind of decision they need to make regularly - for instance, sales forecasting. (ii) the org makes that decision sub optimally, in a way that could be improved by ML. (iii) managers/executives in the org are actually aware there's a problem (i.e. their sales forecasting is poor), and that sub-optimal decision making has serious consequences for them (increased costs, reduced profitability, reduced revenue, increased churn of clients/staff, increased risk) (iv) there are skilled personnel available who can analyse the situation and join the dots to realise that an appropriate ML model could form part of an effective solution. (v) the org has an existing process to generate and record the relevant input data necessary to make better decisions. (vi) there's enough executive support to do a proof of concept to evaluate if applying ML will produce a good return on investment. (vii) the proof of concept suggests there will be a sufficently profitable return on investment if the project succeeds. (viii) there are skilled personnel available who can productionise the proof of concept - this includes writing the REST API calls to your ML hosting product, as well as doing the remaining 95% of development work to integrate into the org's processes, such as data pre-processing, model output post-processing, data ingestion, report generation, preparing training documentation & training the orgs staff (ix) the org can retrain existing staff or hire new staff to perform the new job of care and feeding of data collection & monitor that the model isn't producing garbage decisions
Here's my guesstimate:
Suppose there's a business making repetitive decisions so sub-optimally that if your ML product were to be successfully integrated, it could lift that business' top-line revenue by 2% . Suppose it costs the business $150k to hire consultants to do an initial feasibility study that demonstrates that an ML-based solution for sales forecasting can lift revenue by a few percent, build a rough prototype, and productionise it. Excluding your product, the ongoing costs to maintain the overall IT system that incorporates this bit of ML might be $70k / year -- mainly staff costs to collect data & execute decisions based on the model output, say. There might also be $20k / year in maintenance costs of related IT systems & maybe a $10k / year retainer to consultants who give periodic tune-ups & support.
What's the smallest amount of revenue this business needs to have so it might possibly get a decent return on investment on this project? The business probably needs to have revenue of at least $7 million dollars.
Based on this kind of argument, your potential customers are:
* managers or executives of businesses with more than $7 million dollars in revenue that have opportunities where ML can be profitably applied. these people must be aware that they have a business problem and also aware that it might be possible to profitably apply ML to that business problem. they will only be able to consume your product if they have existing relationships with staff or consultants who can analyse, prototype, evaluate, integrate & deploy a ML-based solution for the business problem
* managers or executives of businesses with more than $7 million dollars in revenue that have opportunities where ML can be profitably applied but are not aware that they have a business problem where ML could help. it would be challenging to sell to these people as they would need to be shown that they have a problem/opportunity first, in language that they understand relating to their business's operations and goals. this would need to happen well before anyone talks about ML.
* consultancies who engage with businesses to identify and solve these kinds of business problems, that have in-house consulting expertise & ML modelling / feature engineering / domain modelling expertise, but lack the in-house software engineering & operations expertise to reliably run a web service that hosts a model
Here's some good news: the total cost to a business of deploying and operating an ML-based solution using your product is a lot larger than $150 / month. That means it is possible that you could increase your price by 5x to $750/month without it really changing the return on investment calculation from the business' perspective.
Less good news: the most profitable opportunities to apply these techniques are in the orgs with the largest scale -- large-huge enterprises, not small-medium ones. The complexity and cost of setting up an ML solution is often independent of the amount of volume going through the ML, but the benefits are proportional to the volume. These very large orgs may already have large internal IT teams with the in-house capability to do what your product does, as well as lots of enterprisey requirements around risks of their suppliers/vendors going bankrupt, compliance with data protection/privacy laws, auditability of decision making, encryption of data, network security policies, high availability, volume & latency of decision execution.
This is irrelevant without knowing the type of product.
It this is a $3.50 product that looks good relative to limited competitors: ~$50 and a day.
If this is enterprise software: a couple million and a year+
> it’s hard to get traction here on HN without karma points
Please dont try to turn HN into an advertising front.
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Overall, take the time to write a decent post and you might get a decent answer. You need to write without namedropping for obvious spam reason, the type of product, what it does, why it has a place in the market, what you think customers are, are you targeting countries, price vs competitors, is there a significant onboarding if software etc etc
I'm >10 years of marketing products all over the world and happy to offer advice as I enjoy my work, but the quality of post defines the quality of answer.
They're the ones buying some other app that solve the problem, or hiring someone to deal with it, or finding the problem a distraction to their real core business.
I've never used a network for this and I think relying on your network is handicap. Look for strangers. If a stranger is happy to talk to you about a product you're trying to sell them, you're on to something.
Out of those sign-ups, you may be able to convert ~20% to actually register for your product.
And out of those, you may be able to convert some to paying customers.
That's exactly what I did with my product Newsy - https://www.newsy.co
Can you share your product? Who knows, maybe you will get your first paying customer ;)
(I generally prefer a product to comment on than a plea for customers).
> https://www.amazon.com/Traction-Startup-Achieve-Explosive-Cu...