Systematic and rational personal account development for B2B users. EasyStaff shares its best practice

Over my 3-year experience as a product manager, I have worked on dozens of B2B products. Most commonly, major restrictions were:

• a modest amount of users;
• limited team resources;
• lack of data analysis tools.

I don’t mean to propose a new framework or create a new methodology. Instead, I want to highlight where to pay most attention when developing B2B service and platforms

l will try to explain how to choose the right focus point for a B2B project and what development stages matter most. I will also dive into data collection and analysis to get the most out of available data pools.

This article will be helpful to B2B platform and service developers that operate within the constraints mentioned above.

Our team is a total of 4 people. Each teammate is working on separate projects of EasyStaff, which means that their time dedicated to account management is limited. Therefore, it was crucial that we set up a system of non-stop data collection that takes into consideration the team’s limited resources.

Role-wise, the team is built as follows:

1. Product manager controls goal achievement.
2. Head of Customer Success is responsible for client communication.
3. Frontend developer updates the interface.
4. UX/UI Designer manages a consistent design.

To begin with, key metrics must be defined. The key metric we focused on is conversion to a paying user. To influence the metric, we are actively researching funnel constraints from onboarding to first payment. In simple terms, we strive to help users get through the funnel as swiftly as possible.

We take conversion to a paying user (i.e. our metric in focus) and divide it into the funnel stages. Basically, the metric is divided by the number of stages. After that, we are able to see which stage exactly is underperforming.

Anywhere down the funnel, users may encounter difficulties. To figure out what exactly is not clear, we track the metric known as Customer Effort Score (CES). We track CES in two specific stages - where a first task is set to a freelancer and where money is converted. These two steps are prerequisites for a successful payment.

Customer Effort Score measures how difficult an experience is for users.

This is what it looks like.

After the task is set, a pop-up appears and asks the user to evaluate how difficult it was to set their first task.

Following closely, a prompt is shown that asks to describe the difficulties a user may have had.

If a user evaluates their experience as low and doesn’t provide any comment, they will be reached out to via email or text messages.

Evaluations and comments are automatically collected in a Google Sheet.
We check if changes we administered impacted the metrics. We do it regularly in statically significant time periods, as well as when testing hypotheses.

Alternatively, CES can be calculated with an online calculator. The lower the total is, the better. In our case, CES is 12, and this is a promising result.

UX tests are one of the simplest, yet effective tools to search for inconsistencies and problems that users may experience when interacting with the product.

Our clients are large business owners, C-level managers or accountants. Understandably, they are too busy to give us lengthy descriptions of their experience with EasyStaff. That’s why we are using this simple lifehack to gather data from them:

There are many website behavior analysis technologies today (CleverTap for one). We use this tool to actually track how new users interact with the platform after they register. For example, where the cursor is at, what elements are most clicked on, how much time is spent on different website pages, etc.

This approach outlines behavioral peculiarities of different segments for further analysis. A popular comparison is between those who onboard successfully and those who get stuck at some stages.

So with the help of CES, customer interaction and website behavior analytics, we make up a backlog of persisting problems that users of a B2B service are experiencing.

After data has been collected, the team gets together to prioritize backlog tasks. When grading urgency, we consider how frequent a problem is and the size of a segment affected by it.

Following that, we formulate a hypothesis in unambiguous terms. As a rule of thumb, we guide ourselves with these 5 questions:

• What is the purpose of changes in the product (to add new features, to improve design, to optimize registration, etc.)?
• What metric are we going to impact?
• What is the target cohort in this hypothesis?
• How much, in our opinion, will the metric change?
• What is the timeline for receiving a reliable result?

Once an exact hypothesis is formed, a task is created and we begin to work on it.

To test the hypothesis, we turn to cohort analysis. This is a great option for EasyStaff, since there are not enough platform users to operate in segments. Cohort analysis is faster and easier development-wise than A/B testing.

When working on a test report, we answer the following questions to evaluate test results:

1. How did the change impact CES of the segment?
2. How did the change impact conversion to a paid customer?
3. How did the change impact the overall conversion to a paid customer across all the segments?

B2B service development frameworks differ from product to products due to actual product goals, purposes and specifics. In this article I wanted to share our strategy in detail to let our approach be known. Like I said previously, the team we built and the operations we carry out allow us to continuously work on platform improvement and grow the key metrics despite rather limited resources.

We’re EasyStaff, a global freelance management platform. Our core features are task management for remote teams, tax invoicing and cross-border payments. If you want to grow your business internationally, stay compliant and save on tax, join EasyStaff now and book your free demo today.

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