What is CloseFactor?

CloseFactor is the world’s first go-to-market operating system for revenue teams. CloseFactor offers significant value to revenue (Sales and Marketing) teams by automating some of the most time-consuming and grueling parts of the outbound sales process - identifying your Target Accounts, forming the right message based on the right Sales Play for each of those Accounts, and then identifying who at each of those Accounts would be the very best people to help guide you to a closed-won deal.

Who are CloseFactor's Users?

CloseFactor's two core User personas are Account Executives (AEs) and Business Development Representatives (BDRs) working inside sales organizations (typically for a medium to large-sized tech company). These user personas are both incredibly motivated to act efficiently with the best resources available to them, with the goal of driving new opportunities and/or closed-won deals.

What are CloseFactor's core workflows?

CloseFactor significantly improves some of the most critical workflows someone like an AE or BDR has to go through to increase pipeline and/or close deals. For these users, getting as much pipeline and/or as much revenue generated, as quickly as possible, in the most efficient way, is what drives them. Depending on the company and role, though, a user may have hundreds of Accounts to comb through. The question for them at this stage is - where, in this big territory, do I look to come up with the revenue? Which Accounts, which people should I talk to... where do I begin? There's a few workflows to help with that, and CloseFactor handles them as outlined below.

Workflow #1

Account Prioritization

Whether we're looking at an AE with a dozen Accounts or a BDR with hundreds, which Accounts they choose to spend time on can quite literally make or break their quarter (as in - make or break whether they get a big bonus, make president's club, even keep their job in some cases). When looking at their territory, they want to make sure that whatever activity they do is spent focused on the Accounts most likely to actually yield real opportunities and real revenue.

Workflow #2

Account Planning & Messaging

Once the AE or BDR has identified their list of "Target Accounts", they need to come up with a great plan for how they're going to approach each Account. The user wants to come up with a solid understanding of how an Account is spending money, how an Account is using technology, and who at an Account is the right person or people to talk to about these things. Great sales people dedicate significant time to researching this.

Workflow #3

Contact Sourcing

Once the AE or BDR knows what they want to "say" to an Account - they have to find the people to talk to. This step, also, can be quite challenging. Some Accounts have dozens, even hundreds of people that *could* be a fit for their sales pitch - but the user doesn't want to talk to hundreds of people. They want to find the handful of people that are most likely to yield an opportunity and invest their time connecting with those people. But how does the user identify that handful of “best” contacts?

Introduction to Buying Indicators

What are Buying Indicators?

Buying Indicators are CloseFactor’s solution to one of the most difficult problems in the entire revenue gen space - specificity at scale. Technically speaking, Buying Indicators are complex, hyper-specific queries that look for specific “Indicators” of potential buying intent at an Account or with a specific person. These indicators are defined via strategic conversations with a VP of Sales (and other Sales execs) wherein what an “indicator of buying intent” looks like is determined. These definitions are often based on attributes of previous closed-won deals and tribal knowledge of their specific org.

How were Buying Indicators being used in CloseFactor?

Buying Indicators are used to assist the users, our AEs and BDRs, as they navigate the critical workflows outlined in the above section - Account Prioritization, Account Planning & Messaging, and Contact Sourcing. The Buying Indicators are like rocket fuel for these workflows - reducing the time it takes to get real insight by orders of magnitude - greatly increasing each users’ efficiency and creating tangible top-of-funnel pipeline growth for our customers.

Account Prioritization

Tactical Value

Buying Indicators let the user get a “birds eye view” of their territory and understand where the signals of buying intent are at a glance - something that would normally take hours (if not days) of Googling and LinkedIn searching.

Strategic Value

This allows the user to operate much more efficiently - not only saving them significant time from the “busywork”, but consistently helping the user target better Accounts. This leads to, on average, more efficient sales effort from the user, helping them hit & exceed their quota.

Account Planning & Messaging

Tactical Value

Buying Indicators provide the user with “x-ray vision” into each Account, allowing them to understand how an Account is spending money, how an Account’s tech stack looks, and how an Account’s Decision Makers and Contacts look. 

Strategic Value

This allows the user to have deep Account context almost immediately, meaning that they can hit each Account with a hyper-specific and relevant message. This leads to, on average, more opportunities, which in turn helps them hit & exceed their quota.

Contact Sourcing

Tactical Value

Beyond just helping the user understand their Accounts, Buying Indicators can surface one-of-a-kind, hyper-specific insights on individual Contacts at an Account as well. For any number of people at an Account, Buying Indicators can signal which are the best to talk to.

Strategic Value

This allows the user quickly narrow down the massive list of possible Contacts to a smaller, more focused list of the very best Contacts, backed up by data. This lets the user perform Outbound workflows far more efficiently - which in turn helps them hit & exceed their quota. 

Before-State Problems & Redesign Goals

Why redesign the Buying Indicators?

Despite the Buying Indicators being the CloseFactor platform’s “bread and butter” - and having proven its immense value to our users and critical role in the platform - it wasn’t all perfect. In this role, I spend a significant about of time as our User Researcher, so I’d meet 1:1 with users and work to understand their pains. And when it came to the use of Buying Indicators in the platform, there were some specific patterns in the pains that came up time and time again.

Problem #1

Unclear Utility

A common pain I’d here coming from the users, especially ones that were new to the platform or had just been onboarded, was that they didn’t actually know what the Buying Indicators were (conceptually speaking) or how to use them. I found myself often having to explain how to use the Buying Indicators to new users.

Problem #2

Inconsistent Structure

Another common pain I’d here coming from the users, often time from users that had more experience was the platform, was that the inconsistency in the way Buying Indicators were constructed and presented added a significant amount of confusion to the platform, and really affected predictability of use.

Problem #3

Limiting Product Capability

This pain was identified through users sharing their inability to be able to take more advanced logic-based action with Buying Indicators. With the current unstructured state of the Buying Indicators, there was no way for the platform to really be able to make (structured) logical actions - like Boolean logic, swapping in different data types inside of Buying Indicators, and much more.

What tactical goals did we want to achieve by redesigning Buying Indicators?

Tactical Goal #1

Clear Utility

We want the users to be able to easily understand the meaning of and the utility of the Buying Indicators, even if it’s their very first time using the platform.

Tactical Goal #2

Consistent Structure

We want the Buying Indicators to be presented consistently and predictably in the UI, to reduce confusion and help the users understand how each Buying Indicator actually works.

Tactical Goal #3

Increased Product Capability

We want to remove the limits to the platform brought about by unstructured Buying Indicators and define a consistent architecture that will unlock new logical capabilities.

What strategic goal did we want to achieve by redesigning Buying Indicators?

Strategic Goal

Increase User Engagement

There’s real strategic value to be had if we successfully accomplish the tactical goals of the project here. Since Buying Indicators are so core to the CloseFactor platform, we’d really hope to see a noticeable bump in User Engagement after releasing the changes to production. The strategic (business) value to increasing engagement this way, from my perspective, is how higher engagement can really help with long-term user retention, long-term value realization (from a customer perspective, more users using = more value for the spend), and more easily justifiable renewal conversations (critical in the world of B2B SaaS). Here - we're looking at two types of engagement. Feature-level and Platform-level engagement. Feature-level can help us understand if the changes made had a significant impact on the use of the affected features & workflows, and Platform-level can help us understand if those changes raised the tide of engagement trends across the board.

Feature-Level Engagement

Feature Pageviews

This tracks the sum pageviews for the two affected features. With a successful launch, we’d expect to see these numbers increase.

Pageview Ratio

This tracks the proportion of the affected feature’s pageviews relative to the sum of all in-platform pageviews. With a successful launch, we’d expect to see these ratios increase

Repeat Feature Usage

This tracks the number of unique users that used the affected features at least twice in a month. With a successful launch, we’d expect to see the number of users meeting this criteria to increase.

Platform-Level Engagement

All Pageviews

This tracks the sum pageviews for the entire platform. With a successful launch, we’d expect to see these numbers increase.

Average # of Pageviews

This tracks the avg. # of in-platform pageviews by our Super Users on a monthly basis. With a successful launch, we’d expect to see this average increase.

Average # of Sessions

This tracks the avg. # of Sessions by our Super Users on a monthly basis. With a successful launch, we’d expect to see this average increase.

Designing a scalable architecture for Buying Indicators

Building an Architecture

Before designing and future-state UIs, the first task at hand was to define a standard, consistent architecture for the Buying Indicators from which new platform capabilities could be unlocked, and upon which the future-state designs of the affected features could pull new capability. This architecture was the very first attempt in the history of the company to actually standardize how we construct Buying Indicators, standardize how we (and our users) understand Buying Indicators, and envision how we might create a better and more scalable structure for the future of the platform.

Understanding Buying Indicators: Customer Example

To begin to build this new architecture, the first thing I did was breakdown and assess how Buying Indicators are being used currently. Recall that the greatest pain mentioned by our users was that it was unclear how the Buying Indicators should be used - but through many conversations, patterns emerged, and converged around our three core workflows from above - Account Prioritization, Account Planning & Messaging Planning, and Contact Sourcing. To the right, I mapped out one of our largest customer’s Buying Indicators to this novel structure, to see if there was some alignment there.

Mapping the Use Cases of a Buying Indicator

After confirming that there was definitely “something there” with the new use case structure, I mapped out the second level underneath primary Use Case - a new means through which to group Buying Indicators together, visually - creating the categories of Active Investment Signals, Tech Stack, and Buying Centers.

Defining the Anatomy of a Buying Indicator

After running the above concept by my team and getting it in front of our users (and getting positive responses from both parties), I took the time to more formally define an architecture for the Buying Indicators. The big insight here was breaking down Buying Indicators into three “parts”, which could be mixed and match to create custom (but predictable) constructions - Use Case, Output, and Data Source.

Validating the Anatomy with other Buying Indicator Collections

One final check before confirming the new structure and baking the new capabilities into the future state designs was to make sure that the new architecture would work with other customers’ Buying Indicator collections. I took the new architecture and checked it against other collections - and the new structure held up strong, and every Buying Indicator had a corresponding section. This confirmed that the new structure was sound.

New Architecture -> New Capabilities

Now that this new architecture was created, I could begin the redesign of the most key Buying Indicator workflows with the new technical capabilities this architecture unlocked. Two substantial feature redesigns are covered next, both of which are based on the work done here with the new Buying Indicator Architecture.

Workflow Redesign #1: Account Prioritization

CONTEXT

Account Prioritization Workflow

Whether we're looking at an AE with a dozen Accounts or a BDR with hundreds, which Accounts they choose to spend time on can quite literally make or break their quarter (as in - make or break whether they get a big bonus, make president's club, even keep their job in some cases). When looking at their territory, they want to make sure that whatever activity they do is spent focused on the Accounts most likely to actually yield real opportunities and real revenue.

Before State: Heatmap

In the before state, this workflow was handled by the users on the Heatmap. The Heatmap is CloseFactor’s most functionality-dense (and old) feature in the platform. It did “get the job done” when it comes to Account Prioritization, but it was limited with how much customization the user could do to it, it was confusing to users, espeically new ones, and wasn’t tailor-made to address the use case.

What wasn't working?

In more specific terms, the Heatmap didn’t support customized ranking and prioritization of Accounts - users were very limited to working within the confines of what the Heatmap’s column-sorting capabilities could do. This, plus an inability to filter down to a more targeted group of Accounts using custom threshold setting, really hurt the Heatmap’s viability for this workflow. And, it was simply cluttered, and put too much information in front of the user when they didn’t need it.

What to change?

Change #1

Support Multi-Indicator Stacking

We want the users to be able to stack multiple Buying Indicators together to prioritize their Account exactly how *they* would like to.

Change #2

Add Custom Thresholds

We want the users to be able to set custom thresholds on Buying Indicators to let them get more specific with their Account filtering, enabling more customization than was previously possible.

Change #3

Improve Ease-of-Use

We want the solution to be easier to use and easier to onboard to than the Heatmap - by creating a use-case specific solution, this was very doable.

Redesigned State:
Account Prioritization UI

The redesigned state was a complete departure from the Heatmap - the prioritization workflow had been given its very own dedicated UI, which we hadn’t done in the platform anywhere before. It was cleaner, it was tailored to fit into the workflow of the user, and really customizable, to the extent that users could set up really advanced searches and prioritizations of their territory quickly, and much easier than had been possible previously.

What changed?

In an effort to directly address the changes to be made that I’d identified above, the first (and most substantial) change was the ability for the user to select *multiple* Buying Indicators to user for their analysis and prioritization activity. The new UI also includes threshold-setting for any and every filterable dimension, and writ large, the UI is much cleaner and simpler. And - based on the feedback we received - much easier to onboard to than the Heatmap.

User Response

User response was really positive with this change, which was great to see. Here we were basically introducing an entirely new feature into the top-level nav of the platform, so frankly, the stakes were high. But it became clear and obvious pretty quickly that we had solved some big problems and unlocked new, previously-unrealized value with this redesign.

Workflow Redesign #2: Account Planning & Messaging

CONTEXT

Account Planning & Messaging Workflow

Once the AE or BDR has identified their list of "Target Accounts", they need to come up with a great plan for how they're going to approach each Account. The user wants to come up with a solid understanding of how an Account is spending money, how an Account is using technology, and who at an Account is the right person or people to talk to about these things. Great sales people dedicate significant time to researching this.

Current State: Overview

In the before state, this workflow was handled by the users on the Overview page. The Overview is the users’ first view into an Account, and one that should give the users an idea of what’s going on inside of an Account, even if it’s brand new to them. It did “get the job done” to a degree, but it didn’t tell much of a story about an Account - the UI was really just putting up some data, but it was unstructured. Now, though, with our new architecture, we could feasibly structure the UI in an entirely different way.

What wasn't working?

The Overview didn’t do enough to really tell a user what they need to know about an Account - the insights on the Overview were okay, but not great. We were bunching up all of the (very different) Buying Indicators together, not giving enough oxygen to the Contacts on this page, and not doing enough to make the use cases of the different Buying Indicators clear.

What to change?

Change #1

Clearer Account Summary

We want the users to be able to more easily understand, as soon as they arrive at the Overview page for an Account, what that Account “looks like” - whether it’s worth selling into, how they should sell into it, etc.

Change #2

Group Buying Indicators by Use Case

We want to group the Buying Indicators by Use Case so that when the users do arrive on the Overview UI, they can easily and intuitively understand how an account is spending money, what tech an Account has in use, and what kinds of decision makers they have.

Change #3

More Space for Contacts

We want Contacts to have more real estate and presence in the redesigned UI - Contacts are the vessels through which the users take action on an Account, and not having them so prominent in the current state hinders actionability.

Redesigned State: New Overview

The redesigned state was a complete overhaul relative to the before state - we introduced an entirely new way of displaying the Buying Indicators on this page, and introduced brand new categorizations and interactions into this page - all for the sake of making it easier to use and helping the user unlock more value from the Buying Indicators. It was cleaner, it was tailored to fit into the workflow of the user, and much more intuitive than the previous state.

What changed?

In an effort to directly address the changes to be made that I’d identified above, the first (and most substantial) change was the new display style of the Buying Indicators themselves - broken down into this new chip-style, color-coded format, with the new added structure introduced by the sectioning of the Buying Indicators by Use Case. The new UI also includes advanced, insight-packed hover state for each Buying Indicator, and writ large, the UI is much cleaner. And - based on the feedback we received - a far more effective and insight-packed “Overview” than the previous state.

User Response

User response was really positive with this change, which was great to see. Here we were completely redesigning a popular and heavily-used feature in the platform, so the stakes were high. But it became clear and obvious pretty quickly that we had solved some big problems and unlocked new, previously-unrealized value with this redesign.

Launching & Measuring Impact

Launch

The prior stages took place over a series of months - with ideation starting in Fall of 2022 and the final product launching in early 2023. Now that the redesign has been in production for some time, we’re able to look back and reflect on the progress made, and understand what kind of impact the redesign project had on the affected features, on the platform, and of course, on the users.

Reminder: What did we want to accomplish by redesigning the Buying Indicators?

Tactical Goal #1

Clear Utility

We wanted the users to be able to easily understand the meaning of and the utility of the Buying Indicators, even if it was their very first time using the platform.
 

Tactical Goal #2

Consistent Structure

We wanted the Buying Indicators to be presented consistently and predictably in the UI, to reduce confusion and help the users understand how each Buying Indicator actually works.

Tactical Goal #3

Increased Product Capability

We wanted to remove the limits to the platform brought about by unstructured Buying Indicators and define a consistent architecture that would unlock new logical capabilities.

Strategic Goal

Increase User Engagement

We wanted to see a noticeable bump in User Engagement after releasing the redesign, on both a feature-level and a platform-level.

Qualitative Impact: Happier, more delighted users.

From a qualitative perspective, response to the launch of the redesign was quite positive. The new and improved workflows added value to the platform, and users were particularly thankful that we’d made clear we listened to their pain and design a solution to remove it. In 1:1 UXR calls and onboarding sessions I was a part of after launch, it was clear to me that there was less friction for newer users when onboarding to these workflows now, where there had previously been noticeable confusion. From a big picture perspective, I would call this redesign a success.

Account Prioritization

Based on the feedback received, the new Account Prioritization UI and workflow seemed to have directly solved the biggest pains of the previous state involving the Heatmap. According to the users, the biggest value unlocked centered on the new ability to stack multiple Buying Indicators together and set custom thresholds and limits on individual Buying Indicators, with the net result of enabling them to create significantly more targeted searches of their territory than had been previously possible. At a higher level, we heard that this UI was simply easier to understand than the Heatmap, which is exactly what we’d hoped for.

Account Planning

Based on the feedback received, the new Account Planning workflow (and the redesigned Overview UI) seemed to have directly solved the biggest pains of the previous state involving the old Overview. According to the users, the biggest value unlocked centered on the new presentation style and ranking of the Buying Indicators, with the net result of enabling them to “understand” what an Account’s investment, business initiatives, tech stack, and key Contacts look like at just one glance, something that used to take minutes if not hours of manual searching across the web. At a higher level, we heard that this UI made understanding what the Buying Indicators are actually meant for easier, which is exactly what we’d hoped for.

Quantitative Impact: Increased Feature-level and Platform-level user engagement.

From a quantitative perspective, in-platform engagement following the launch of the redesigned workflows saw notable improvement. From the feature-level, pageviews on the redesigned Overview increased, as did the pageview ratio for Overview. Repeat usage on these features, which had been slumping in the months leading up to the launch, bounced back to the previous highs, even setting a new high for the Overview UI. From the platform-level, all pageviews increased post-launch, with the avg. number of pageviews per user per month roughly doubled following the launch. The avg. number of sessions per user per month also saw a steady increase post-launch. In summary, we can see healthy increases in engagement at both the feature-level and the platform-level following the launch of this redesign, so from that big picture perspective, I would call this redesign a success.

Feature-Level Engagement

Feature Pageviews

Pageviews on the newly-redesigned Overview UI increased by 36%, with Accounts UI pageviews holding steady through the timeframe.

Pageview Ratio

The proportion of monthly pageviews going toward the newly-redesigned Overview UI increased by 43%, with Accounts UI pageviews pulling back slightly through the timeframe.

Repeat Feature Usage

The number of users that used the newly-redesigned Overview UI increased by 127%, while the Accounts UI saw an increase of 130%.

Platform-Level Engagement

All Pageviews

Total Pageviews platform-wide increased by 128% following the launch of the newly-redesigned Overview UI and the new Accounts UI.

Average # of Pageviews

The average # of pageviews per month increased by 13% following the launch of the newly-redesigned Overview UI and the new Accounts UI.

Average # of Sessions

The average # of sessions per month increased by 25% following the launch of the newly-redesigned Overview UI and the new Accounts UI.

Concluding Thoughts

Outcomes & Looking Forward

The months-long project was a big bet for the product team and a big investment for our organization, and one that we’d hoped would make a really noticeable impact on the efficacy and ease-of-use of our core workflows. Looking at the data post-launch - happier users, strong gains in some important usage metrics, I feel confident that this redesign was indeed a successful one. As goes with product development - when one thing launches, another iteration begins. This redesign project has been in production for some time now, and we’ve already been fielding the next wave of feedback which will inform the next iterations of these features. That’s what I love about product, especially at this level - all the learning. Only through being deeply embedded with your users and strongly dedicated to tracking engagement and outcomes can a designer (or any product person, really) truly understand how their work influences the platform and, by extension, the business writ large. Thanks for taking the time to read this case study - on to the next iteration!