Axial – Search

Search on Axial allows customers to find firms and individuals and contact them based on their company’s focus, their stated transactional intent, or their prior transaction history. It also allows customers to search for specific transactions based on type, industry, location, or financial metrics.

As the lead designer for customer’s search experience and discovery on each of the three major iterations in Axial’s history, I directly shaped the direction and evolution of the entire search product. The most current iteration searches across multiple data sets to provide better results and context, expanded functionality with advanced search features, and the ability to save and export searches.

For the latest iteration shown here, I gathered qualitative and quantitative data of customer’s search behaviors and expectations in order to define use cases. After developing these matrixes, I wireframed each of the various components and results. I then developed a clickable prototype and demoed the new features and functionality with customers both in person and remotely over screen share. Finally, I assisted in QA efforts and testing to ensure the quality of the release.

Examples of popular searches on Axial revealed by analyzing search queries

  • “Private Equity Firms” in “New York City” with an intent to “Buy or invest controlling equity in companies in the Aerospace and Defense industry, with an EBITDA of over $10M”
  • “Investment Banks” in “Texas” with prior transactions in the “Oil & Gas Drilling” industry
  • “Aerospace & Defense companies with an EBITDA over $10M” looking to “raise $100M – $200M through an equity raise”
  • “People” who specifically are responsible for “sourcing deals” in the “Manufacturing Sector” and are looking to “provide mezzanine debt”

Typeahead & Suggested Search

Accounting for the fundamental differing business logic and requirements between searching for other firms and people on the network, vs searching for specific deals or companies raising money on Axial was the first challenge. Secondly, assisting and revealing the kinds of searches possible on Axial was critical. In the primary navigation, we surfaced a search type option, which also provided quick access to saved searches and advanced search tools. As a user begins to type a query, we provide both suggested keywords, company names, or try to match against a popular filter such as industry or location.

Ranking & Context

In order to provide as much detail for each search result from across multiple entities and data points, we needed to provide context to the searcher about what matched their search. Along with the strength of that context we also incorporate other measures of relevance such as individual responsiveness and amount of information provided to apply a “page rank” score to each result for each search, and then rank results accordingly to the user.

Filtering & Advanced Search

Supporting both a funneled approach (ie: typing in keywords against a type of search, then applying filters one at a time to narrow down results) and an additive approach (ie: starting from the widest possible set of results and applying multiple filters at once to show all matching results) allows general users and power users respectively to perform searches in ways that are most effective to their process. Some challenges we encountered that we solved through smart auto-complete fields were complex taxonomical hierarchies such as geography or industry, Depending on the search, a geography filter could be a narrow to wide (City or metro area, to state, region or country).

Measuring Success

After nearly 6 months of design and development, the last iteration of search set new benchmarks for success and engagement with customers. Calculated over a rolling 30 day period immediately following launch, mean daily new unique searchers increased 80% – 110%, mean daily new searches increased by over 50 – 75% and mean daily user actions taken up by 60 – 70% over prior feature usage highs.

Video of Axial Search