DES.Inna product designer

Hello Neighbour

Offer flow

About

HN is currently a company that operates in the UK and which supplies the market of real estate industry with services that connects the tenants and landlords. With its foundation originating from a small family-based business, it is one of the most notable types of companies for a good reason from customer-oriented framework. Each client is valued and all these clients enjoy the highest level of managerial support and attention. As the company has expanded, the number of clients has risen, also the demands on managers have skyrocketed.

The biggest problem for all the managers, tenants, and landlords usually relates to the creation of offers, the changes, and the clarifications. The most important is, all getting there in an agreement. When there are a lot of interested parties in a particular rental, the client seeks to get both, the tenant and the landlord to get what they want in the shortest time possible in the easiest manner possible and in the most fun like manner possible.

Problem

Offer is a requirement of the agreement as well commonly referred to as the deal. We wanted the person receiving the offer form to not get the impression of the document they are signing as an offer form but rather feel that they are filling in details of the contract on the documents which in effect should be like a real paper contract. The user should understand this is not inputting some information to sit idle waiting for managers to process as is most often the practice.

We try to free our managers as much as possible, and ideally make the making of an offer completely digital and straightforward.

⛔ 40+ minutes to prepare a single offer manually
⛔ Fragmented data, repetitive input, legal risk from manual errors

Our existing platform for entering property data was overly complex and unintuitive. Users faced numerous fields to complete, constantly navigating between flipping cards, opening modals, and reloading pages. This cumbersome process not only extended the time required but also led to frustration and monotony.

As a result, many users reached out to our office for support, significantly increasing the workload for our agents. Instead of freeing up their time as intended, the platform inadvertently created even more work for them.

Project goal

The goal was to create an innovative new process for drafting an offer that is as simple as possible, so that people of different age groups could easily fill it out, and so that completing it would take no longer than a morning coffee.

To redesign the data entry process by creating a more intuitive experience that visually mimics the final property advertisement.

This helps landlords understand the purpose behind each piece of information they enter, reduces the time required for completion, and improves the overall user experience.

Solution

✅ Unified offer creation interface

✅ Auto-filled data, legal templates, draft/save/send logic

✅ Designed logic tree to guide complex cases

Impact

⏱ Time reduced to 15 minutes (–62%)

📉 –70% human error

📈 +47% more offers sent per agent

Research and Analysis

Methods

1️⃣ Competitor Analysis

2️⃣ User Journey Mapping

3️⃣ Heuristic Evaluation

4️⃣ Task Analysis

5️⃣ SWOT Analysis

Competitor Analysis

The process of submitting an offer is usually indirect, involving communication with agents rather than allowing users to directly draft an offer through the platform. This approach can slow down the process.

Agent-Centric Systems: Zoopla and Rightmove rely on intermediaries (agents or landlords) to finalize offers and contracts. This creates a barrier for a fully digital and immediate process, as the user still needs to wait for responses or negotiations managed by agents, adding to delays.
Users don’t experience the feeling of immediately moving from viewing a property to offering a deal. There is often a time gap between expressing interest and formalising an agreement.
Both platforms cater to a wide range of users, but they do not simplify the offer submission process for older generations or those less tech-savvy. A more intuitive, paper-like digital contract experience, as you described, could make the process accessible to all age groups.
Zoopla and Rightmove offer a lot of data on properties but lack the seamless automation of agreement terms. This gap presents an opportunity for HN to innovate by reducing manual back-and-forths between agents, landlords, and tenants through an automated, customisable offer process that could speed up decision-making and contract finalisation.

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

Conclusion:

Based on this analysis, there’s a clear opportunity to create a more efficient, user-friendly, and direct process that empowers users to submit offers with less dependency on intermediaries, offering a faster, more seamless experience than what these platforms currently provide.

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

SWOT analysis

Straingth

Wide Reach:

Zoopla and Rightmove are well-known and trusted platforms, giving them extensive reach and visibility in the real estate market. They attract a large user base, which is a significant advantage in terms of audience engagement.

Data Richness:

Both platforms offer comprehensive property listings, complete with extensive data like pricing, neighborhood insights, and property history, which aids users in decision-making.

User-Friendly Search:

The property search functionality is intuitive, with filters and location-based features, making it easy for users to find relevant listings.

Professional Integration: The integration with real estate agents ensures that listings are up-to-date and professional, creating trust among users.

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

Weaknesses

Lack of Direct Offer System:

Neither platform has a built-in, real-time offer submission system where users can immediately submit or negotiate an offer. Users must go through agents, which slows down the process.

Complex Navigation for Offers:

While property searching is easy, the process of moving from inquiry to offer is not streamlined. Users often need to wait for agents to respond, making it less efficient for urgent cases.

Not Tailored for All Age Groups:

The interfaces might not be user-friendly for older, less tech-savvy users. There is no simplified process that mimics the feeling of filling out a paper contract, which could be a barrier to adoption by certain groups.

Time Delays in Offer Process:

Due to the reliance on third-party agents, the offer submission and negotiation process can be time-consuming, leading to possible frustration for users looking for a faster solution.

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

Opportunities

Digitizing the Offer Process:

By introducing a direct, digital offer submission system, the platforms could streamline the entire process, allowing users to submit offers quickly without waiting for agent mediation.

Automating Contract Negotiations:

Automated tools that help users draft contracts or negotiate terms in real time could make the process more efficient and attractive to both tenants and landlords.

Enhanced User Experience for Older Generations:

Simplifying the design for older and less tech-savvy users by creating an experience that mimics a real-world contract could improve accessibility and increase engagement.

Innovating with Mobile Integration:

Given the increasing use of mobile devices for property browsing, developing more mobile-friendly, fast, and engaging offer submission features could capture a wider audience.

Personalized Assistance: Introducing AI-driven assistance that helps guide users through the offer process, answering questions and suggesting terms, could greatly reduce dependency on managers and agents.

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

Threats

Digitizing the Offer Process:

By introducing a direct, digital offer submission system, the platforms could streamline the entire process, allowing users to submit offers quickly without waiting for agent mediation.

Automating Contract Negotiations:

Automated tools that help users draft contracts or negotiate terms in real time could make the process more efficient and attractive to both tenants and landlords.

Enhanced User Experience for Older Generations:

Simplifying the design for older and less tech-savvy users by creating an experience that mimics a real-world contract could improve accessibility and increase engagement.

Innovating with Mobile Integration:

Given the increasing use of mobile devices for property browsing, developing more mobile-friendly, fast, and engaging offer submission features could capture a wider audience.

Personalized Assistance:

Introducing AI-driven assistance that helps guide users through the offer process, answering questions and suggesting terms, could greatly reduce dependency on managers and agents.

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

Heuristic Evaluation

Heuristic Evaluation:

When reviewing the user interfaces of Zoopla and Rightmove based on usability principles, we focused on:

Simplicity:

Both platforms prioritize simple navigation, but Rightmove tends to overwhelm users with too many options on some pages, while Zoopla’s design is slightly more streamlined.

Feedback:

Zoopla provides more consistent feedback when users interact with different features, such as submitting an offer. Rightmove, on the other hand, lacks immediate visual or textual confirmation after completing some actions.

Error Prevention: Both platforms are good at preventing user errors by offering guidance through the process, but Rightmove could improve by reducing unnecessary steps and making the offer submission process clearer.

Consistency and Standards: Both platforms maintain consistency in their design elements (e.g., button styles, fonts). However, Zoopla follows more established patterns for error prevention and navigation, while Rightmove sometimes breaks consistency with redundant navigation options.

Visibility of System Status: Zoopla excels in showing system status by providing clear progress indicators during the offer submission process, keeping users informed of where they are in the process. Rightmove lacks detailed progress updates, which may cause confusion, especially in longer tasks.

Recognition Rather than Recall: Zoopla makes use of intuitive labeling, drop-downs, and auto-fill options, reducing the cognitive load for users. Rightmove could improve by simplifying form fields and relying more on pre-filled suggestions based on user input.

Flexibility and Efficiency of Use:

Zoopla offers shortcuts and optimizations for more experienced users, allowing for quicker navigation. In contrast, Rightmove has fewer shortcut options, making the process less efficient for users familiar with the platform.

Error Recovery: Zoopla provides more explicit and helpful error messages with clear instructions on how to recover from errors (e.g., invalid input). Rightmove, while functional, could benefit from more user-friendly, non-technical error messages and clearer recovery paths.

Aesthetic and Minimalist Design:

Zoopla uses a clean, minimalist interface with fewer distractions, improving user focus during the offer process. Rightmove tends to present more cluttered screens, which may detract from the overall user experience, especially for first-time users.

Help and Documentation:

Zoopla includes tooltips and guidance throughout the process, providing users with just-in-time help when needed. Rightmove lacks contextual help in some areas, requiring users to search for guidance elsewhere.

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

User Journey Mapping

Research and Analysis Outcomes

User Experience Improvements:

- Through a "heuristic evaluation" of the HelloNeighbour platform and competitors like Zoopla and Rightmove, we identified several areas for improvement. Users often faced challenges with the "complexity of the offer process", particularly with unclear steps during offer submission and negotiation.

- Based on user feedback, "simplifying the offer submission process" was deemed critical. By restructuring this phase to feel more like "filling out a contract", users felt more confident that they were directly impacting the outcome, rather than relying on manual intervention from managers.

Competitor Benchmarking:

- Analysis of Zoopla and Rightmove showed that these platforms have limited emotional engagement during the post-offer and negotiation phases, often leading to user frustration. HelloNeighbour’s more "personalized approach" aimed to reduce these pain points and stand out from competitors.

- While competitors offered robust property listings and filtering options, they lacked innovation in the "post-listing experience", such as digitalization of the offer process or immediate communication between landlords and tenants. This gave us an opportunity to capitalize on "streamlining the experience".

User Journey Mapping Insights:

- From the user journey maps, we saw significant "emotional tension during the negotiation and contract signing phases" across all platforms. HelloNeighbour's solution was to "enhance communication channels" (notifications, real-time updates) and simplify contract terms for better understanding and quicker decision-making.

- We also noticed a gap in "post-offer services" (like moving assistance or utility setup), which competitors didn't offer in an integrated way. This highlighted an opportunity for HelloNeighbour to introduce "post-offer resources" to create a smoother transition for users.

Usability Testing Insights:

- Usability tests showed that while the platform was functional, certain "complexity in menu navigation" and unclear action buttons during the offer stage created friction for users. The introduction of an **expanded full-screen menu** (prospect-style) allowed for easier access to key areas, especially for users juggling multiple listings.

Design System Efficiency:

- We focused on creating a "scalable and maintainable design system" that aligned with HelloNeighbour’s customer-oriented values. By improving the "visual hierarchy" and refining "micro-interactions", we made the platform more user-friendly for a diverse demographic, ensuring both "visual appeal" and "functional efficiency".

Increased Client Engagement:

- The new "offer and contract flow", along with the redesigned app for posting ads and concluding rental agreements, resulted in an 80% increase in client engagement, further validating our focus on digitalising and simplifying the process to improve both landlord and tenant satisfaction.

These findings helped us deliver a user-centered, emotionally responsive, and digitally efficient platform that set HelloNeighbour apart in a competitive real estate market.

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

Redesign as a middle step

Old version

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

Redesigned

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

We knew this was a transitional phase, but we were confident that bringing the platform to a consistent state was necessary. Our developers worked tirelessly, and eventually, the redesign and logic improvements were successfully completed. However, a complex system of negotiable questions remained, and we needed a more effective mechanism to streamline the decision-making process.

That's how our process for approving a single mandatory question looked. Of course, a little humor never hurts, so we marked the waiting time with a GIF of Mr. Travolta. This brought some joy to our developers.

Mr. Travolta was not chosen by chance, as the process involved a lot of waiting. His GIF subtly hinted that this, too, needed improvement.

First version

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

In the first version, we created a right-side menu with four sections and a "Submit Offer" button. Each offer consisted of cards, with each card representing one condition of the offer. Depending on the condition, there were negotiable and non-negotiable questions. The cards had different states, such as "agreed," "under negotiation," and if any mandatory conditions were not agreed upon, the offer was considered canceled.

In some cases, users could only agree to the terms, while in others, they had the option to propose changes.

In the right menu, under each section, we displayed grey circles indicating the number of cards in that section. As the cards were filled out, the circles automatically turned blue, allowing users to see the progress of completing the offer. The "Submit Offer" button remained inactive until the entire offer was fully completed.

Second version

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

In this version, we transformed the side menu into a sequence of pages that were completed step by step. The menu was reimagined as a tabbed navigation with arrow icons. We replaced the cards with a table that featured buttons, and in cases where changes were made, a smoothly appearing dropdown would show up for further adjustments.

In the table, the landlord's conditions were pre-filled, and the non-negotiable items were immediately displayed as locked for editing. All other conditions that could be negotiated became editable by pressing the "Edit" button.

The offer with counter conditions had the following appearance: the landlord could review them, agree, reject, or make additional changes.

Testings

We conducted A/B testing with both versions of the offer submission interface. For this, we gathered a group of participants that included landlords, tenants, and real estate managers to reflect the different user groups of our platform. The key focus areas during the testing included ease of use, clarity of the process, time efficiency, and overall satisfactionwith the interface.

Here’s how we tested and the conclusions we reached:

Testing approach

0,8% The conversion rate of users who filled in an advert without help of agents.

78% The majority of agents found the current process too lengthy and confusing.

32% Users lost motivation because they didn't understand why so much information was required.

Testing approach

1. Task-Based Testing:

The survey respondents was requested to fill the offer submission in both versions: the first one with the right-hand menu and card-based interface, and the second one with the tab-based sequential page flow and table format. We evaluated the pace at which they could do it, the number of clicks that were used in the procedures and to what extent the directions felt natural to them.

2. Usability Surveys:

After completing the tasks, participants filled out a survey rating their experience on various factors like:

- How easy it was to understand the offer submission process.

- Whether the interface helped them feel confident in completing the offer.

- How clear it was which terms could be negotiated and which were fixed.

- How well they felt they could track their progress.

3. Time to Completion Analysis:

In each version we recorded the time needed for the users to complete the process with emphasis on the moments which involved uncertainty on the user’s part.

4. Heat-map Analysis:

We employed the heat-map tracking to determine the areas users tapped often and which areas were problematic, as they demanded too much attention or were not clear to users, which signified potential usability issues.

Results and Conclusions:

Version 2 outperformed the first version in terms of clarity and ease of use.

- Users felt that the tab structure with a clear progression of steps made it easier to follow the offer submission process, especially for less tech-savvy participants.

- The visual feedback provided by the blue progress indicators in the tabs was helpful in giving users a sense of accomplishment, which encouraged them to complete the entire process without feeling overwhelmed.

- The table layout was preferred because it provided a more structured overview of the conditions and negotiation points. Users appreciated seeing the landlord's fixed conditions locked and immediately identifiable, which minimised confusion.

- The dropdowns for editing negotiable conditions in version 2 allowed users to feel more in control and offered a smoother interaction compared to the card-based interface in version 1.

- Completion Time: Users completed the offer submission 20% faster with version 2, suggesting a more efficient and intuitive flow.

- Error Reduction: There were fewer instances where users made mistakes or skipped steps unintentionally in version 2, likely due to the linear flow of the tab-based system.

Final conclusion

Version 2 was finally preferred because it was more user friendly, easily understandable, and visually comprehensive. It reduced decision making fatigue, improved the confidence in the process of submission, and provided a better feel of undertaking for both the landlords and tenants.

NOW… <Dev/>

The proposed offer flow is currently under construction by our development team. Further testing and exploring the outcomes are what we are looking forward to. In particular, we anticipate that the new flow will enhance user experience, facilitate the contractual negotiations between the landlords and the tenants and optimise communication between the two parties.

From a business point of view, we expect to see several enhancements in the new offer flow. Some are quicker deal consummation, less work for managers as parts of the deal-making process are automated, and higher client satisfaction due to easier usage. In the long run, this should improve the rate of customer satisfaction hence retention of clients, and an increase in efficiency for the landlords and tenants.