Buy vs. Build in GTM Data Automation

Bar Graph showing higher cost of Building Workflow Automations v Buying Workflow Automation

Modern go-to-market teams are becoming more data-driven than ever before. The amount of data gathered from the product, marketing site, messaging channels, 3rd party SaaS apps, and other digital properties has created an arms race to see who can get their reps better equipped to engage customers.

These trends have made it incredibly challenging for RevOps and Analytics teams to keep up. At some point, ad-hoc BI dashboards, Excel files, and email chains are no longer scalable. The Cambrian explosion of data infrastructure has made it even harder for these two teams to collaborate.

Many companies turn to custom development work to create more sustainable, robust tools. There’s always the trade-off between spending valuable engineering resources or taking the risk on a 3rd party vendor. Understandably, the fear of unknown risks or issues runs high. So we always get the question: should I buy or build data infrastructure for key GTM initiatives?

Here are four considerations we’ve picked up through countless conversations and engagements.

1. Time and Cost Of Building

Building data automation in the GTM space is especially costly and time-consuming. Oftentimes, multiple technical experts are required:

• A data or analytics engineer to create tables
• An analyst to create metrics or queries
• A Salesforce engineer to create the right CRM fields (or worse, objects)
• A software engineer to build custom front-ends or triggers
• BizOps / RevOps to manage the project

Because the modern data stack has become fragmented, its technical complexity has increased too. This could include handling time-outs, data inconsistencies, or weird 3rd party APIs. For example, even an ‘internal’ solution with Zapier may not be able to handle thousands of rows of data.

If there’s a general shortage of resources, or if there’s a significant benefit to moving 3–5x faster, then buying a customizable tool would be beneficial.

2. Cost of Maintenance

A key failure risk lies in underestimating how much maintenance is required for GTM tooling, including internal build-outs. When an engineer moves onto a new project, a new business requirement, or an underlying change in the data schema, the business team often gets hung out to dry.

Or there’s a 2-quarter delay in getting a request on the roadmap.

We typically see orgs solving this in one of two ways:

1. ‘Start over’ by going back to ad-hoc dashboards or Excel files to bridge the gap
2. Only deploy tooling that they can control and operate

Oftentimes, leveraging an intuitive 3rd party tool makes it easier to avoid these roadblocks, especially if the ‘code’ is abstracted away. The ‘code’ could lie in the integrations, the data models, custom metric definitions, or user views.

Ultimately, this depends on how much the business wants to invest in a ‘data culture’, and whether Ops has the responsibility to support that.

3. Better UX and Adoption

There’s no question that reps are very particular about how they digest data. The average adoption rate of BI is 15%. Even getting adoption to 40–50% among reps could yield 3x the business impact.

Just as with software products, internal data products live and die on the user experience. There’s a reason why many reps love a tool like Scratchpad, but can’t be bothered to log into Looker. Features like alerting, in-app actions, automated routing, workflow tracking, reporting, all sound secondary, but are crucial in driving adoption.

It’s not practical to expect engineers to build these generic but impactful features in-house. If rep adoption is an issue, then it’s likely worth considering an appropriate vendor.

4. Pre-Built Templates and Knowledge Base

When buying a specialized data tool, the tool ideally comes with the knowledge and expertise that the vendor has accumulated across many engagements. This becomes useful given how fast the GTM function is evolving in 2021. The rise of product-led growth, customer-led growth, account-based selling, community adoption, etc. are examples of this.

These best practices not only help teams get started quicker but also help them stay at the forefront of key GTM tactics without needing to hire expensive specialists for each function. It helps drive experimentation while mitigating a huge portion of costs.

If you are interested in trying out WB or would like to learn more about what our automation software can do for you, let us know here.