Entain CRM

Data-rich efficiency for care agents in the gaming industry


UX Design consultant

14 weeks

Entain Customer Care



UX consultant advising project team for Entain's customer management software, researching with agents to create efficient methods for agents to operate with high volumes of data at speed

Providing customer service agents with intuitive software to improve workflow and case resolution metrics

Audited Entains existing software to make recommendations and designs for an improved user experience for customer care service agents. This is a shortened version, for the full case study please get in touch on hello@rca.work

The task

Combining the data of thousands of customers of a large conglomerate, into a single, in-house system. As an independent UX consultant, the stakeholders invited me to audit and improve an existing two-year project to better suit its intended role. Running retro workshops revealed concerns and successes of the current project and I devised a plan to bring user-centred design to a tech-lead project.

Jarvis current system

Observing agents

It was eye-opening to see just how stressful the agents' job was. They were required to deal with a vast amount of data whilst chatting to multiple gamers simultaneously. I wanted to reduce stress with efficient functionality. Observing the agents in real-life as well as interviewing them, allowed me to gain deep insight into their behaviour and task flow.

Agents operate in a pressured, intense environment, handling multiple cases simultaneously, often with agitated customers

Auditing the current system

Auditing the existing Jarvis system exposed areas for improvement. Agents were required to navigate between complicated, unformatted tables across multiple pages. Storing this information separately conceals potential causal relationships, and the lack of hierarchy and formatting makes it difficult to discern what's meaningful amongst the noise.

Heuristic analysis
Conducting a hueristic analysis allowed me to quantify performance issues related to layout, structure, widgets, menus and information clarity. It was clear that automating calculations and reducing the agents' cognitive workload would allow for improved customer service.

Metrics analysis
Analysing customer contact metrics highlighted the top 10 queries the agents dealt with.

Focus areas

My research exposed the clunky information architecture and illogical naming causing agents to struggle to navigate through the correct information.

The design system and colour differentiation made it problematic to find important information whilst mass amounts of customer data meant agents were unable to find what they needed when they needed it. I decided to make this more seamless as well as including branding.

Poor differentiation between active and inactive players made it difficult to differentiate which player was which during multiple chats. I needed to enable agents to view all of the gamers' information and know exactly which data applied to which user.

70% of queries followed the same user path for the agent but agents were having to repeat the process instead of the software doing it for them.

An inefficient note-making process came at the end of the case, instead of throughout, which created a backlog causing errors and poor quality.

Can we improve the accessibility of important information and increase efficiency for agents with some relatively minor adaptations to the layout?

Principals for a better experience

For the redesign, I created seven guiding principals to enable agents to act efficiently:

A new vision - A visiontype to move the needle

Menu navigation
From the results of card sorting, I prototyped a navigation to test the new information architecture. Prominently anchored at the top of the page, the 60 menu items are accessible through 5 tier 1 categories: Account, Controls, Financial, Products, Loyalty.

Each category revealed its own page with an overview of the information held in easily navigable categories.

User navigation
The player navigation bar at the top shows the active player in purple, meaning the agent can effectively toggle multiple accounts simultaneously.

Tables & combining data sources
The next part of the prototype aims to display data in a more meaningful way. Removing constraints on the visibility of a dataset, agents will have greater freedom to explore. They can now move around within large datasets to interact with, select and differentiate between information.

I began testing the utility of merging tables with an improved transaction history table. The agent can now better understand a players query by seeing a comprehensive overview of activity, with their withdrawals, deposits and bonus use. Colours are used to distinguish between items.

Bringing events together in a natural format

Merging different tables by their common timestamp field, seperate datasets can now sit together in a chronological timeline, presenting the players activity in a less abstract way

Player timeline
By merging tables, agents are able to make connections and see causal relationships. These were previously separate and demanding to extract key information from.

Customer queries can now be solved more efficiently and seamlessly by connecting data and presenting information in a clear and hierarchical format.

Data point query investigation
I investigated the exact data points that agents would refer to during their investigations. The results gave us the most frequently used data points for each query. Aligning this with the top 10, means we can select the data points which are most useful.

The outcome - Customer History

The Customer History allows the agent to understand the series of events that preceed a customer contact, exposing the connections which help resolve customers queries

With the current system, agents attempt to look into the cause of a customers issue by navigating between multiple pages to decipher information held in unformatted and often complicated tables. Storing this information separately conceals potential connections relevant to the issue, and the lack of hierarchy and formatting makes it difficult to discern whats meaningful amongst the noise. Displaying a player's activity in a richer and more natural way, helps create awareness and instil confidence in agents to correctly diagnose the cause of an issue.

The Customer History combines sub tables into one easily explorable master chronology. It acts as a flexible way to allow multiple tables to appear together, using colour to easily distinguish the chain of events that precede a customer contacting.

The timeline gives an immediate overview of the player's account. The flexible timeline allows agents to create combinations of tables as per their investigation. When a player's account opens, the default General timeline presents a collection of the most useful data, including deposits, withdrawals, alerts, changelog, notes etc.

Each row becoming an event snapshop containing 4 significant datapoints following a similar pattern: the ID, the amount and type or comment, and the status.

Raising the event from an abstract row in a dataset to a contextually significant moment, helps the agent understand a players activity

Event cards
Clicking each event snapshot will open a full event card. The formatting of the information makes it easy to digest, elevating important details whilst all others are listed vertically below.

Grouping numerous events, such as game or bet transactions, helps with the efficiency of the timeline.

Table specific dashboards
Agents can conduct more specific investigations by selecting a table item in the menu bar. This will open controls to move through, filter and search within that table. A corresponding dashboard will then show information related to that table and offer specific controls to filter for things like Status or Type.

When a table is activated, the column headers come into view.

Timeline templates
The flexible timeline allows agents to create combinations of tables as per their investigation. Other available templates can then be selected to bring up a different set of tables depending on the specific need of an investigation or perhaps the team/role within Jarvis.

By allowing agents to customise and save templates, we will be able to collect data showing different variations in which data was used during different contacts. Correlating this with the resolution rate of customer queries, this feedback could help us improve the system and agent training.