Some revenue decrease was seasonal through the January period as well as changes to payment schedules.
On the whole, reduced revenues were a result of the shift of focus to business development within larger pharma and medtech companies (which take longer to secure as clients at a higher price point) and the investment of time in laying a solid foundation of understanding with 'huumun' in preparing to pitch together.
However, the company anticipates that this will level out or improve as it becomes cash flow neutral through reduced expenditure, and income increases along with the company’s eligibility for R&D tax incentive and COVID19 business support payments.
A highlight during the quarter included the announcement of a revenue-share alliance with UK-based huumun, a digital solution provider for the global pharma sector.
The collaboration allows the two companies to provide an extended, sophisticated end-to-end sales and marketing offering across traditional and digital platforms.
This is anticipated to realise revenue for Opyl in the coming months and will create a valuable business development pipeline – improving the company’s chances of client acquisition and hitting revenue goals this financial year.
The COVID19 environment has had both a positive and negative influence on Opyl.
The company is currently seeing a marked increase in interest from prospective clients, (over and above the huumun business development effort), validating the role of digital, particularly social media to organisations when their sales teams are grounded.
The use of social media in a healthcare context has also increased due to physical distancing and the need to access and share information around the globe – which social media does so well.
The timing of client payments has been arriving later than normal in the past six months and while retainer-based client revenue remains constant, COVID-19 has had an influence on some accounts.
The company has continued to invest in its research platforms during the March quarter.
The focus is on developing a machine learning, artificial intelligence-based software interface which can predict the likelihood of a clinical trial completing each phase.
To date, the clinical trial predictor tool has been progressed to the end of stage two (proof of concept) in readiness to enter stage three and finally stage four before calendar year-end.