Week 1 — Building in public

On what is this: building in public article and Twitter.

Winclap helps other companies to scale with AI and a team of growth experts. We started in Córdoba, Argentina, and are now operating in all Latam and the US. We are focused on solving how marketers currently make decisions without key data. We do this by applying AI to predict the missing data, and use that predicted data (and historical data) to recommend a course of action.

More specifically, in our budget allocation product, we help marketers in how they decide how much budget to invest in each campaign and media channel. We accurately predict the events or revenue that will be attributed to a campaign on multiple spend levels and apply an optimization algorithm that finds the spend level that maximizes the CPA or ROAS.

Our theory is that marketers usually fit into these categories regarding this decision:

1. marketers who don’t really make budget allocation decisions: budget does not meaningfully change in stable channels that are achieving the target KPI. Investment decisions are made purely on historical CPA or ROAS. I wrote an article that talks a bit about this here.
2. marketers who try to predict ROAS or CPA without the right tools: using historical data, some basic calculations and a lot of intuition, these marketers try to predict the future outcome a campaign will deliver.
3. marketers who have a marketing science team and have built a media mix model in-house: these are very few companies that have large budgets and the ability to recruit highly competitive talent. Their solution can only use 1st party data, and can’t leverage network effects and benchmarks from other companies.

All 3 categories would get value from our product: an upside in performance (better ROAS), less data ingestion and analysis costs, fewer maintenance costs, and others.

We are trying to figure out which is the best way to communicate the problem we solve. We know that we solve a basic operative problem: it is too hard to make a high-quality budget allocation decision with the current tools. Marketers need to collect data, build calculations and graphs, get benchmarks and with all that data, intuitively find the best allocation possible. All that is solved with our tool.

This approach is straight to the point and relatable for marketers in category number 2, but it excludes marketers in categories 1 and 3. Also, our approach to the issue also presents a solution to a greater problem: marketers make lots of decisions without all the key data. We can solve this with AI, by predicting the missing data using historical data, for many other decisions besides budget allocation.

By communicating the underlying problem we would include marketers in all 3 categories, but with a more abstract definition of the problem, which makes it difficult to relate to.

This is a bit of what’s in our minds these past few days. We’ll try to solve this by A/B testing our comms. Probably I’ll write a post in a few days/weeks about how we made this decision.

Thanks!

Originally published at https://www.linkedin.com.