Enhancing a flagship online scientific database to best-in-class

Highlights

“Transforming a simple scientific information repository into a dynamic data-driven tool”

Context

The product is an online materials science database provided by a scientific publishing company. Created with data from a series of journals which have been compiled since 1882, it is viewed as a highly-respected source, providing details of experiments throughout its long history, including Albert Einstein, which scientists today could use to inform their own experiments and papers.

However the online database, which had been running for seven years before I came on to the project, had seen a decline in subscriber numbers. The product owners wanted us to understand the reasons for this decline, and explore ways in which we could get those subscribers back, as well as encourage new ones.

The Challenge

“Review the database and understand from its users why they were using it less and less, and develop ways to improve subscriber numbers again.”

Research

Recruiting users

• The product’s user base covered a wide range of different institutions and companies which depended upon materials science data for their work.
• However, I found that the Product Owners did not have direct connections to the end users of the products, as the business relationship was conducted through Sales Teams and Buyers, and the Product Owners had only been interested in analytics metrics.

• Therefore, I had to go out and recruit users myself, by researching companies and institutions, writing to Heads of Departments, approaching individuals and using on-page surveys to collect initial impressions and request participation.
• While this was time-consuming, I was able to create a group of users who I could conduct research with to champion design decisions, and test with to assure assumptions.

Research methods

• As I am not a Materials Scientist, I needed to ensure that I was asking the right questions in my research sessions. Thankfully, the company had a few subject matter experts on hand, who were able to give me an initial walkthrough of the platform, and how it would be used by scientists to find data for their work.
• In order to observe user experiences, I focussed on examining end-to-end interaction processes with research subjects (see below), asking them to show me how they would find information of interest to them, so that I could assess how successful they were, as well as identify pain points and opportunities for improvement.
• Users would often have their own suggestions from their own previous experience of the platform, which also provided useful starting points for further ways we could improve it.
• During this process, I encouraged other members of my production team to listen in to the call, posting questions on Slack for me to ask the users, which helped to encourage collaboration and ownership of the research process, and foster empathy with the users.

Examining the primary user flow

• What motivations bring them to the platform
  • Reasons for using the site, experiments and tests being run, information needed to locate, location and device accessed on, and how they accessed it.
• How they looked for information on the platform
  • How they looked for information on the platform
• How they worked with information discovered
  • Reading info on the page, accessibility issues, page location, ease of retrieval
• What they did with the information afterwards
  • Using information (data, text, etc.), taking it away (download, print, formats, etc.)

Discoveries

Major discoveries from the research

1. Primarily, the database was a reference for users to look up data from previous experiments, that would help with their own experiments and papers.
   • The main process that a user would follow would be to search for a material (such as carbon, steel or benzene), and a property (such as melting point, boiling point, or surface tension)
   • They would then follow the search results to find their chosen piece of data.
   • Having located the required data, they would then take that information by writing it down, or printing it out, and put it into their own experiments and papers, to support their own hypotheses.
2. However, there were some significant issues which they found with the current state of the database, which impaired their work:
   • They often found the process of trying to locate information difficult, with confusing numbers of pages and steps along the way, often causing them to give up in frustration
   • Searches would often return results which were confusing, and didn’t seem relevant
   • Once they had located the data, they found the process of then searching through the scanned image PDFs cumbersome, as they had no way to search for a result within the PDF, and could only find their results by hand
3. After defining these frustrations, the users told us that they were enough of an impairment that they felt the product wasn’t worth the subscriptions fee, and that had caused them to cancel their subscriptions.

Personas

To summarise research findings, and to advocate key points to inform product decisions, I created personas that included Researchers and Scientists in both the Corporate and Academic settings, each with different practices, such as theoretical or practical work, or motives such as financial benefit or academic discovery. As they were a fundamental part of our customer relationship, I felt it was important to include a persona of the Buyers as well, so that we could understand the motives of those who purchased the software without a strong scientific background.

Analysis: co-location

Our product team gathered at our Pune office for a week together to analyse the findings:

• Product Owners and Business Analysts shared business aims and requirements for the product
• I shared my research findings and presented the personas, conducting empathy sessions to help the team understand and explore user requirements
• Explore problems by eploring current user journeys, and thinking about how we could improve them
• Ideate on solutions by sketching screens and interactions, identifying knowns and unknowns for further research ad testing, and providing a starting point for prototyping
• Create an Agile roadmap for the coming months, estimating workload and timelines

By involving stakeholders and team members, we helped to reinforce the business and user motives behind decisions, as well as encouraging them to make use of their own skills, rather than just have work handed to them without context.

Solutions

Improving user journeys

Following our analysis session, we identified that reviewing and improving the journeys that users took through the site would be the most effective thing we could fix, with the least effort.

• We started by identifying “red routes”, key journeys that users took through the site to find information, and identified that the most common was where they looked up a characteristic (say “boiling point”) of a material (say “Benzene”)
• We had previously identified in research that the user journey had “bloated”, due to “experience rot” (continual addition of extra pages and features outside the original remit that confuse the original objectives and impair user success).
• We therefore created improved user journeys as clickable prototypes, so that we could quickly test ideas with users, ensure that they aligned with their mental models and gain feedback before we revised the actual database.
• During this testing, we also discussed opportunities from extra functionality, including digitising the data, which I could then advocate to the business as opportunities for future product development

A responsive approach

During our research, we identified not only the different requirements for each persona, but also discussed with them the different scenarios in which they would access the database. For example:

• People accessing the site on a mobile device would often want to check a single, specific piece of data, and appreciated the ability to access the information quickly, without having functionality that they did not require at the time get in the way.
  • This also worked for people who would view the database on a smaller window on a desktop, ensuring that their view focussed upon the information that was key to their search
• People who accessed the site while sat down, with a larger device such as a laptop or desktop, would want to spend more time investigating, and would want more supporting information

By returning to our “red routes”, we could then examine them with an extra dimension, ensuring that the layout and display for different device sizes responded to those requirements

Improving search

The next effective solution that we chose to follow, which would take a bit more time and effort, was to improve the way in which the search worked.

• We had already identified in the research that users were getting search results that did not make sense to them, and so our first step was to identify why those incorrect results were appearing, which we did by testing with users how they used the search, what results they expected, and which they did not.
• We then reviewed with the developers, and discovered that it was the markLogic text-based search that was the problem, which only searched for text strings, and didn’t understand context.
• This was a problem due to the technical nature of our users’ search terms. For example:
  • “tin” refers to the metal; tin
  • “TiN” refers to the material Titanium Nitrate
  • As the search did not understand the context, typing either of those terms into the search would yield the same results.
• We therefore explored a number of solutions, including:
  • case-sensitive searching, allowing users to search using chemical symbols
  • contextual drop-downs which would allow users to select the context that they meant around their search term
  • bringing in search database experts to implement a graph search, which created a contextual model between inputs (such as recognising tin as a metal, which would therefore have a link to steel, which is also a metal, but not tincture, which is a chemical process, but has the letters “tin” in it)
• We implemented our solution by working on a separate version of the search, only reachable by the use of a specific URL, which stood alongside the original search. By directing people to this search, we were able to try out ideas quickly, ensuring that our ideas worked before increasing user interface fidelity, which reduced the required amount of development before a solution could be tested.
• As well as testing these ideas remotely with users over video call, we were able to take them to Chemistry Conferences in the USA, where we could conduct guerrilla testing with attendees at the company stall. This helped ensure a wider, less specialised understanding of how our search worked, as well as get a large number of results by which to measure our success.

“This is great. It really will save me a lot of time searching in the future.”

Corporate Researcher during testing at Materials Research Society Boston Conference 2016

Developing the homepage

As well as catering to the requirements of the scientists and researchers who used the platform, by examining the needs of the Buyer, we recognised that the homepage had three important roles:

1. To provide a starting point for users to explore the content
2. To update returning users with new developments
3. To demonstrate value for non-technical users such as Buyers

We therefore redesigned the homepage to include the following features:

• A summary of the different types of content for new users to explore,
• A timeline of latest additions and improvements to update returning users
• Details of the depth of information and sources to demonstrate value for Buyers

These changes were added over time, to accommodate the work around supporting these features, and other efforts on the database.

Making data digital

One of the most fundamental solutions that we identified with the product, with the highest level of impact but also the greatest level of effort, was the fact that the data needed to be digitised. At the start of the project, data existed solely within the database as scanned-in pages from scientific books and journals, which frustrated users, and led to users feign that the product was not worth the subscription price.

it was this impact on subscription revenues that allowed me to petition Product Owners to organise a way of tackling this problem. Using the company’s relationship with client institutions, they were able to find a group of post-doctorate scientists to work through the scanned pages, performing specialist data entry to extract and annotate data for the graph search database, converting the scanned data into a fully digitised format. This work took about a year to complete, but this had a profound effect on the how we could make the product more valuable to users.

Surfacing results early

The digitised data meant that we could include it within search results, meaning that simple questions could be answered sooner, and demonstrating value more quickly. These could be displayed as search snippets, filled with simple answers, that led on to pages with more complex insights.

Working with data

We recognised that the majority users searched with the same pattern, namely a material (such as iron, benzene or carbon) and a property (such as boiling or melting point, band gap, or similar). By understanding this, we could use our newly digitised data to improve our offering:

• When a user first searches for a material and property, then we can surface the simplest answer in the search snippet, as described above.
• If the user wants more in-depth information, then they can click through to a page providing a dynamic graph, which uses the findings from scientific papers to plot data in a visual away, demonstrating the behaviour of the material and the property against a scale – for example, demonstrating how the boiling point of steel changes when submitted to different atmospheric pressures.
• Alongside this graph, the results are also provided in a tabular format, which allows users to work with the data displayed in the graph. They can change criteria, expand or limit the dataset, all to ensure that they get the information they require for their purposes.
• As the last part of the model we discovered, this data can then be exported into a range of format, such as visual images of the graph, or spreadsheets of the results, facilitating use in the user’s work.

“This is awesome. I don’t think anyone else is doing this. Where do I sign up to get it?”

Student Researcher during testing at American Chemistry Conference, Philadelphia 2016

Retrospective

• Our work reversed the customer attrition that 
the product was experiencing, and brought a 32% subscription increase from academic, corporate 
and other clients
• Within two years, that extra revenue paid for all of our salaries and the money that the company had paid into the project.
• Developing the digital data pages won accolades 
for the product as a “best in class” tool from the 
American Chemistry Society conference in 2016
• In 2017, the Indian Government bought licences to provide the product in all libraries across their country
• A site survey, run at the end of my tenure as UX design lead, showed that 69.2% classified the product as “great”, a 22% improvement on when I started.