Cue is a new way for startups to work with cross-system data

Today we’re excited to launch Cue, a new approach to how your startup can get real business value from your data without the usual chaos, confusion, and cost. Our goal is to be a competitive advantage for you, and we’re really hyped about it.

Having worked in growth, operations, and engineering at startups that took two very different approaches to data (manual Excel pulls and a lot of Zap automation versus a full warehouse setup with SWEs handling reporting for external customers) and talking to 80+ founders, GTM, and data executives, we saw firsthand the quiet choice most early-stage companies feel like they face: invest in data or invest in growth.

Of course, being able to understand your business and knowing which levers to pull by leveraging your data is arguably critical in efficient growth. But the reality and unsaid truth are, that there are usually more urgent fires and lower-hanging fruit that can often make the decision to allocate internal engineering to unify product and sales data or make a data hire instead of an AE that can close a million in sales pipeline a year a very painful pill to swallow, especially in today’s selling environment.

We think a big reason is that for these teams and many others, working with data is … really hard. It’s easy to snowball into building another tech stack with technical debt and bugs you have to manage, but one that of course doesn’t generate the revenue numbers your actual product does. You buy a bunch of data tools along the way and then still end up wanting to buy more data tools for each function (i.e. dashboards for customer success so they know what’s going on in the product, answers about marketing ROI, etc.) because no one on the business team has their data in a format purpose-built for their use cases so they can take action.

We have a big vision around how many software tools would be 10x better products (much faster to implement, sold at lower costs, easier to build) if they were re-built on top of a standardized data layer but all of that hinges on one key, possibly unpopular opinion: startups should not have to custom-build their data infrastructure and analytics from complete scratch.

Your team’s creativity and engineering bandwidth should be reserved solely for building and selling your core product, not for deciding metrics or getting graphs made, even if they’re really helpful graphs. Most SaaS businesses have more in common (metrics-wise) with their category than many might think, and we can use a somewhat standardized set of metrics and models to handle almost all of the work to get the results they want.

Today we hope we can take three things off of your plate with the following offerings:

No real company scoreboard or source of truth? Meet Warehouse AI.

Get your data ready for analytics in an afternoon — we connect to any of your existing apps and databases so your engineers don’t have to write scripts or build data pipelines, set up a managed warehouse for you if you don’t have one already, and enable you to export data from your new single source of truth to wherever you already work: marketing tools, other BI tools, or Excel sheets.

We provide standard model definitions for the most popular sets of tooling so you don’t have to sink a ton of time into adding or editing metrics, dealing with messy SQL queries, and debugging disparate definitions. Consolidate your data stack and cut set-up and maintenance time down with Cue.

Putting off manual data wrangling & dashboard building? Meet AI analytics.

Self-serve analytics, already customized to your business. For example - a startup that sells event management software can let’s say, plug in their billing, CRM, and product data and use one of our templates to get the closest thing to out-of-box analytics for the very first time. They can do things like drill down from top-level ARR to specific buyers, investigate marketing ROI by their customer segments (persona, industry, channel, etc.), and understand how many events each of their CSMs are managing.

We make this possible in two ways — bringing all your siloed data under one roof, and setting up standard metrics definitions based on your industry and business model if you’re not already tracking a bunch of metrics.

How are you proving ROI to reduce churn? Meet customer-facing analytics.

Easily embed dashboards into your product — we connect to your data sources to pull relevant data so you can implement and customize customer-facing graphs and dashboards that fit your branding, with or without code.

Easy access to compelling ROI numbers is a must-have for most sophisticated buyers (particularly in mid-market and enterprise) and can speed up your sales and upsell processes without a ton of manual lift.

If you’re running into one of the problems above (or think you might soon!), we’d love to learn more and see if we are the right solution to take it off your plate.

We have a generous free tier to help teams unify siloed data and are offering free fractional data engineering services and white-glove onboarding for every customer we onboard for a limited time. You can book a time to chat with us here — we’re excited to talk soon!