Benchmark 7

Overview

Benchmark 7: “Utilise robust data for impact measurement” is not just imporant for measuring whether your volunteering initiative was a success after the fact. Many companies are using it as a critical component for programme design and energizing the team on an ongoing basis.

Benchmark 7 Components:

Varied methods for end user / external data collection
Varied methods for internal stakeholder data collection

1. Varied methods for end user / external data collection

To best assess the needs of your beneficiaries, partners and local communities, the recurring theme was to draw upon a variety of data collection methods.

Approaches ranged from the CSR team conducting extensive stakeholder analysis and industry heatmaps every 6 months at AMD, to having the luxury of an internal data team for regular research (such as at McKinsey’s Generation.org or at Benevity who conduct regular research based on the 2 million volunteers using their platform across industries).

Others recruit external third-parties to help with KPI measurement robustness, as AT&T and Gap Inc. do, or utilise benchmarking datasets (CECP are starting to work on this).

In addition, local social enterprise / non-profit partners are often better placed to track a wealth of information on beneficiaries. Gap, for example, use their non-profit partners to help track cohorts (such as demographics) to help improve their future programmes.

Technology also presents an important opportunity for employers: not only for conducting automated analytics, but for obtaining previously unobtainable data on end-users which can enhance volunteering programmes for maximum uptake and social impact.

For example, DonorsChoose utilise a tool called Looker to measure user engagement to help optimize their products for greater impact. Here at Prospela.com we collect data on volunteering habits, preferences, impact and engagement and can tune our product accordingly in real-time.

2. Varied methods for internal stakeholder data collection

Most companies conducted internal analysis of some kind, citing it as critical to measuring both business impact of their volunteer initiatives (often in the form of employee engagement) as well as to understand how to appeal to employee’s intrinsic and extrinsic motivations.

Warner Bros utilise employee surveys as the basis for their CSR strategy, as do smaller start-ups such as Lever to get a pulse on culture & CSR success twice a year.

Although be careful not to think that building an employee survey is a simple task. Whilst there are (literally) hundreds of surveying products on the market, it seems there is a science to it: Josh Bersin believe’s the problem is really more of an analytics & AI problem than a survey problem – Glint, newly acquired by LinkedIn, is one such technology-centric example.

Larger, more established corporates often tie in automated analytics (for example the number of employee volunteer hours) to a central HR system for a birds-eye view of employee engagement.

Pearson, gets useful feedback from employees (incl. on CSR initiatives) through regular calls and webinars hosted by senior leaders, as well as through their online collaboration tool, Neo, where employees exchange ideas, information and expertise.

What each tier does well

Established Company

Established

This tier performed strongly overall for both internal and external data collection (scoring 3.81/5 on average). Most companies had either the internal resource (in the form of a data anlytics team) or the capacity to utilise third-party evaluators for data collection and measurement.

Did tech help?
Use of tech was strongly correlated to high-performance on this benchmark for this tier (0.77 at a 99% CI). For internal data collection specifically, most (if not all) companies utilised online surveying tools, often with built-in AI capabilities as well as the ability to link with their their internal HR systems.

High-Growth

High-growth

Similarly to other tiers, High-Growth companies performed well against this benchmark in general, although none mentioned that they utilised external third-party evaluators.

Did tech help?
In addition to the internal online employee survey tools favoured by Established companies, High-Growth companies that had built their own tools (our outsourced) mentioned keeping a regular eye on engagement analytics such as Daily Active Users or # of volunteer hours. As such use of tech was extremely strongly correlated to high-performance (1.0 at a 99% CI).

SME

SMEs

Despite not having the resources to bring in third-party evaluators for data collection & analysis, SMEs performed almost as well as their larger peers for data collection.

Did tech help?
Use of tech was strongly correlated to strong data collection practices (0.97 at 99% CI), much more so than for Established companies. Interestingly SMEs seemed more comfortable in adopting tech and data analytics at an individual team level, than seemed the case within larger companies.

Filling the gaps

Toggle through each tier below to explore the weakest areas of performance against this benchmark:

Established

High-Growth

SMEs

How tech might help

Most companies felt they could do more with benchmarking vs. peers. Initiatives like CECP have laid the ground-work for CSR benchmarking in general through their online surveying and data dashboards (however, are not volunteering-specific).

In their Giving in Numbers 2017 report, CECP show the most popular themes for CSR donations and the type of philanthropy given (e.g. direct cash, cash from corporate foundation arm, non-cash). For volunteering specifically, companies are able to see median hours volunteered per employee per year across a range of industries in (ranging from 0.34 hours in Healthcare to 4.05 hours in Energy sector).

Note, however that data is based only on large US-based companies, and is not necessarily representative of what top performers are doing (rather, what the average company is doing).

Indeed, it seems the UK’s BITC is working on a similar benchmarking exercise & company dashboard in conjunction with the Government’s Careers & Enterprise Company.

Case Studies:

Tech as an enabler

Blackrock are a brilliant example of both CSR & HR team collaboration and drawing upon tech to collect, analyse and communicate volunteering data. The HR team shares data regarding employee engagement, productivity and learning & development to the CSR team via their HRIS. Such employee analytics can help measure business impact and ROI and to help in further volunteer programme design.

Education-specific best practice

McKinsey Logo

McKinsey is a joint founding funder of Generation.org, an initiative which trains underserved young people in employability skills through boot camps, employer mentoring and alumni community building. Generation.org monitor students daily, obtain feedback on a weekly basis, track graduates of their programs into the workplace, perform employer surveys and even measure ROI for employers (such as recruitment cost). All of such data from their end “users” feeds directly into their design & delivery of future education programmes.

Start-ups

Remind, a popular tech start-up providing real-time messaging for 70% of US public schools, partners with organisations and nonprofits in its local neighbourhood to volunteer, such as running coding hackathons within schools.

They’ve started experimenting with measuring longer-term outcomes of their efforts, leaning on their school partners for data on their impact on students e.g. through increased attendance and attainment, and increased turnover of homework.