The impact of diio, measured by Berkeley Haas

It all started with a simple question that is hard to answer well: how much impact does diio have on its customers?

To answer it, four MBA students from the UC Berkeley Haas International Business Development program worked with diio for more than three months from Berkeley, and then for two weeks at our offices in Santiago, Chile.

The goal was to turn perceived value into concrete measurement: a model capable of estimating diio’s ROI using data, interviews, and real customer context.

The project had three main deliverables: a whitepaper on the productivity problem facing commercial teams, an ROI calculator to estimate diio’s impact for each company, and an implementation playbook to integrate that measurement into diio’s commercial processes.

A bit of context about the study:

Measuring more than activity

Today, commercial teams face a paradox that is hard to ignore. They have never had more tools, data, and systems to help them sell better. At the same time, a large part of their week is still trapped in administrative tasks: updating the CRM, documenting meetings, coordinating next steps, organizing scattered information, and manually reconstructing what happened in every customer interaction.

The Berkeley Haas whitepaper identifies an increasingly common pattern: companies invest in technology to gain productivity, but often end up creating more fragmentation.

A conversation lives in one platform. The summary in another. The CRM in another. The follow-up in another. And between each system, there is a new manual task, a new chance for error, and a new loss of context.

In that scenario, the challenge is no longer to accumulate more data. What matters is for information to be connected, contextual, and useful for decision-making. That is where a new category emerges: revenue systems that understand the purpose of every interaction.

From conversations to decisions

The Berkeley Haas report proposes looking at commercial interactions through a different question: did this conversation achieve the objective it was meant to achieve?

That changes how we read a meeting, a follow-up, or a negotiation.

Were relevant pain points identified?
Were the next steps clear?
Did the opportunity move forward?
Does the team know where to act now?

That is precisely the category we are building at diio.

diio unifies conversations, context, signals, and workflows so teams do not have to manually reconstruct what already happened. From that information, it helps detect where to prioritize, which opportunity needs attention, which follow-up cannot be left pending, and which behaviors are moving the business forward.

The difference is not having more information. The difference is deciding earlier.

A project with customers, data, and real context

The work began in Berkeley in late January, when the team was assigned to diio through the IBD program. The first stage focused on understanding the company, the industry, and the scope of the project. Before building any deliverable, the team worked with diio to define what needed to be measured, for whom, and at what level of depth.

Then came a stage the team called “getting smart”: talking, researching, and comparing. They interviewed industry leaders, including Salesforce, IBM, and Anthropic, reviewed market reports, and spoke with people across the sales, technology, and artificial intelligence ecosystem. They also spoke with diio customers, including Buk, Toky, and Webdox, to understand how the platform impacts real teams.

Based on that combination of research, interviews, and aggregated customer data, the study modeled diio’s impact across four main dimensions:

8 to 10 hours recovered per week, per executive.
Time that was previously spent on administrative tasks, documentation, and manual system updates.

5% to 7% improvement in win rate.
An improvement associated with better follow-up, greater visibility, and better-executed commercial conversations.

6% to 8% revenue lift.
Estimated revenue impact based on improvements in productivity, commercial execution, and opportunity progression.

3x to 5x ROI in the first year.
An estimate that connects the use of diio to concrete business outcomes, beyond operational efficiency.

Why this matters

For many commercial tools, time savings are the easiest benefit to show. And yes, saving time matters. A lot.

But the most relevant point of the study appears when that recovered time is connected to better decisions, better conversations, and better results.

If an executive saves hours, but the quality of their execution stays the same, the impact is limited. If a leader has more reports, but still cannot see where opportunities are being lost, the information falls short. If a company invests in technology but cannot connect that investment to revenue, the ROI remains incomplete.

That is why this project was so important for diio. It helped us organize and quantify something we see every day with our customers: when conversations stop getting lost across disconnected systems, teams can act with greater clarity. And when they act with greater clarity, the impact shows.

The value lies in recovering context, reducing friction, and turning every customer interaction into a real opportunity to move forward.

A clearer way to talk about ROI

The ROI calculator developed with Berkeley Haas helps each company estimate diio’s impact based on its own context.

Every team has different sales cycles, meeting volumes, conversion rates, and operating costs. That is why measuring ROI seriously requires looking closely at those variables.

The calculator helps ground the impact in concrete questions:

How much administrative time can the team recover?
What happens if win rate improves?
How much value is created by moving opportunities forward more consistently?
How does commercial visibility translate into revenue?
How long does it take to recover the investment?

Rather than delivering a standard number, the tool opens a more precise conversation about the value diio can generate for each organization.

What comes next

Working with UC Berkeley Haas allowed us to look at diio from the outside, with more distance, more method, and more rigor.

It also reinforced a conviction that guides our product vision: teams need an assistant capable of understanding context, detecting signals, and helping them decide what to do now.

The full report goes deeper into the commercial productivity problem, the growth of technology stacks, the need to measure quality — not just quantity — and the role of platforms like diio in this new category of systems oriented around the purpose of every interaction.

Read the full whitepaper by clicking here!