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December 2018

Updating listing logic and response to castings.

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Finding talented performers.

Every product, regardless of its scale, has a key feature which is its foundation. Such a feature, or rather a unique functionality in the Linkmuse, was castings.

Castings were a sliver of cards with names, descriptions and fees. They also implied the possibility of responding to them. All castings were created and published by agents, casting directors, productions, and agencies in need of talent.

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Challenges of casting functionality.

The functionality provided for generating castings and responses from performers was far from perfect. This was evidenced not only by the findings arising from usability tests and in-depth interviews, but also by metrics.

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Monthly technical support tickets, interviews and drop in conversions pointed to problems related to complexity of scripts, lack of notifications and low readability of information.

Improvements, suggestions and monetisation.

First of all, we clustered the problems and their priority, conducted additional qualitative research and conducted a competitive analysis of sites with similar functionality.

It was important not to delay the implementation of the solution, as the activity figures were dropping from week to week, so the formulation of the scope of work and decomposition of the task was carried out in parallel with the data collection. Most of the discussions were on two topics: What can we improve? What can we offer?

The answers to these questions were not long in coming. Improvements were to be made in the casting and response phases, response processes, as well as in search and filtering of content in search engines.

Proposals, on the other hand, concerned monetization, or more specifically, paid promotion of castings from customers and feedback from performers. The hypothesis implied a significant increase in reach and views by keeping the first positions in the feeds.

Understanding the audience and investing in the future.

The conclusions from the research and the feedback from the clients about the performers' verification came down to one idea - the implementation of aggregation of customers, performers, castings, services, products and even sites.

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Previously, the company was already planning to move in this direction and was going to develop products independently under its own brand, which undoubtedly required serious expenses from the company as a marketplace.

In turn, the new data helped to understand the audience and their desire to make, promote and sell their own content. Thus, the noticeboard was born, which was to continue to expand to locations, jobs, courses, articles, and a database of performers.

Improving the process of creating castings.

Creating and publishing castings took customers a long time, from 10 to 30 minutes, which absolutely frustrated them. The endless forms and steps, lack of prompts and confusing scripts, sometimes leading to a personal account after casting creation, potentially impacted overall satisfaction with using the service.

So, we revised and prioritised all the fields and optimised the steps. In addition, tooltips based on frequently asked questions were added. The tooltips were supposed to greatly simplify the creation of the casting and to play an important role in gamification in the future.

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As a result, in the split test, the form with prompts outperformed the casting creation conversion by a factor of 3 to 73.2% and reduced the session by a factor of 4.5 to 2 minutes.

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Among other things, with the subsequent registration update and merging of profiles, casting creation became available to everyone without prior authorisation, not just customers.

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Iterative work and usability tests.

The first experience users encountered when opening the service was interacting with the casting feed. Problems included cluttered rankings, broken searches and crooked filtering.

We fixed these problem areas iteratively and in parallel with usability tests. The update of the feed also affected the casting cards, their informative character, by which a performer could estimate his chances of success at a glance.

When implementing the paid services, we used the services of other ad sites as a starting point. When testing several hypotheses, the most successful ones were: One-time rise to the top (every 3rd casting a day after publication); One-week rise in the top (every 7th casting on the day of publication).

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More availability for customers.

While the detailed casting page was useful for performers, more emphasis was placed on customers who had all the previously scattered functionality gathered in one place.

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This way, the feedback from performers became more accessible and user-friendly for customers by merging scenarios and simplifying profile cards.

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Favourable results in numbers.

A few months after the release, we already had overall analytics on the upgrade. For example, conversion rates for casting creation had increased by a factor of 3 to 61.38%, while the bounce rate had dropped by a factor of 8 to 3.1%.

At the same time, the average session in the feed increased 1.5x to 4.5 minutes and the average response rate per week increased 1.9x from ~3600 to ~6800.

Also of interest, we tracked the conversion rate itself in responses, which doubled from 17.6% to 35.2% and the conversion rate in opening the detailed casting page, it also doubled from 6.4% to 13.44%.

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Versatility of use of the service.

The final version for desktop devices was filled with all the necessary tools for the customers' work.

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The in-house publication editor extended the customisation options of the ads, which were not previously available.

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And mobile adaptation allowed users to use the service anytime, anywhere.

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Figma screens.

You can see all the screens and flows that our team has developed in the Figma preview or by clicking on this link. The screens were developed before the design team switched to Auto-Layouts, which were added to Figma in November 2020.

Article credits.

The list of persons reflects those who provided support and cooperation in the process of developing the feature and the article. Also the list indicates all those who are responsible for the content of the article.

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Aleksei Matveev

Lead Product Designer

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Olia Chupakhina

Product designer

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Dima Melentev

UX Researcher / UX Architect

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Artem Taranov

Head of Product

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Dmitry Kuznetsov

Project Manager