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Perfect Match: Harnessing Big Data to Find the Right Volunteers


Thursday, 18th June 2015 at 11:48 am
Xavier Smerdon, Journalist
Australia’s growing army of volunteers is presenting Not for Profits with a unique problem – finding the right kind of volunteer and IT expert Nathan Sri writes that data-mining might be the solution.

Thursday, 18th June 2015
at 11:48 am
Xavier Smerdon, Journalist


1 Comments


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Perfect Match: Harnessing Big Data to Find the Right Volunteers
Thursday, 18th June 2015 at 11:48 am

Australia’s growing army of volunteers is presenting Not for Profits with a unique problem – finding the right kind of volunteer and IT expert Nathan Sri writes that data-mining might be the solution.

Australians have a rightly earned reputation as a nation of helpers and the number of people willing to donate their time has never been greater. Research we commissioned earlier this year found more than 51 per cent of Australians have completed some form of volunteer work in the past, and a third are keen to complete volunteer work in the future.

Yet paradoxically, Not for Profits are struggling to match the right volunteer to the right role. How is this the case?

While more people are putting their hand up to volunteer, anecdotally the number of hours they wish to donate appears to be shrinking. They also have unrealistic expectations as many volunteers are surprised they can’t start the day after signing up. Add this up with the costs of on-boarding new volunteers and a clearer picture emerges of the staffing challenges facing the NFP sector.

The launch of new National Standards by Volunteering Australia earlier this month is partly an attempt to address these issues. For example, Criteria 5.1 requires NFPs to use strategies to attract suitably interested, knowledgeable and skilled volunteers and that “targeted methods are used to advertise and communicate volunteer opportunities to relevant community groups”.

Similarly, Criteria 5.3 states that volunteers be “selected based on their interest, knowledge and skills appropriate to the role, and consistent with anti-discrimination legislation”.

While an important step, the new standards provide the framework, not the solution, to addressing staffing issues in the Not for Profit sector.

The question remains: just how can  NFPs better find volunteers? They need to be working smarter not harder to find the right volunteers. Data mining may provide the answer.

For-profit companies have been using data analysis to research markets and products for years and US company 1Page – the first Silicon Valley start-up to list on the ASX – has even built an entire system to help HR departments find the right staff.

While such programs are beyond most NFPs financially, it is important they start thinking in terms of data and how it can help them target the right candidates (rather than the wrong ones).

All Not for Profits will list the skills required for a volunteer position and match those against a candidate, but this manual process is flawed for several reasons:

  • Lack of Scalability: Manually writing job descriptions for each role and measuring them against a resume is a time consuming and relatively inefficient process that must be repeated every time a position becomes available.
  • Reactionary: While there are many people seeking volunteer work, they often don’t know where to start or who to contact. New systems provide NFPs with the opportunity to be proactive in finding volunteers with the required skills and experience but haven’t approached your organisation directly.

The organisation I recently set up, lots:op, is a free global marketplace connecting NFPs with volunteers using key metrics taken from members (potential volunteers) with companies offering volunteer placements and other opportunities.

Think of it like a dating algorithm; a volunteer’s interests, age, location and qualifications are over-layed against the specific requirements provided by a member to create a “perfect match”.

A number of NFPs, including Red Cross and Save the Children, are using the power of data by:

  • Tagging their opportunities with any relevant interests a potential candidates might have that fits the role;
  • Defining specific criteria for the role (age, location, qualifications); and
  • Defining excluding-criteria for the role (any metrics that would exclude a person from applying).

When a volunteer searches for relevant opportunities, the algorithm works much like a Google search; the most relevant opportunities to that particular person will be displayed.

With the advancement of technology and increasing availability of smart, cost-effective tools, it’s important that charities learn to harness data to help streamline their most expensive or inefficient processes.

About the Author: Nathan Sri is Managing Director of lots:op He founded lots:op in March 2014 to help connect  people with organisations.. He has more than 20 years’ experience owning and operating successful information technology and e-commerce businesses, and is a staunch advocate for digital strategy and innovation. Sri also founded digital marketing solution provider Metrixa in 2004 and is Managing Director.  


Xavier Smerdon  |  Journalist |  @XavierSmerdon

Xavier Smerdon is a journalist specialising in the Not for Profit sector. He writes breaking and investigative news articles.

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One Comment

  • Ashul Ashul says:

    Interesting read, ironically VIKTOR online built by Volunteering WA in 1999 actually first started smart matching within the database then. This got moved to the cloud in 2008 and 3 years ago the Volunteer Profile was born that provides the power that volunteering resource centres had for matching opportunities to volunteers in the hand of volunteers themselves. Go Volunteer and State Peaks have been instrumental in educating grass roots organisations to use smart tagging, service focus, time and causes in attracting the right match using the system. I recently described how the national volunteering initiative works here http://www.linkedin.com/pulse/describing-complex-system-ashul-shah Ashul

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