Backing CSR with Enriched Analytics Allows Everyone to Succeed
Corporate Social Responsibility (CSR) is more important than ever before. There are so many causes on a local, national, and international level that needs funds and efforts to create real, measurable change. Global warming, global conflict, poverty and so many more are top of mind for businesses, as well as their employees and customers. It can be easy for a company to say, “let’s donate millions of dollars to fight poverty”, but the real work comes in when it’s time to act on that decision.
As a result, CSR solutions are becoming an increasingly integral part of any organization’s CSR efforts because of their ability to provide enriched data to base decisions on. Analytics derived from that data gives CSR managers and overall leadership the information they need to understand what causes are most important, who internally and externally can support a cause, where funds need to be allocated, and measuring the real-world impact.
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Decisions on CSR Start with Analytics
Utilizing data to make informed decisions is an integral part of any organization’s CSR efforts, and if a business does not have a CSR solution, it’s an absolute ‘must’ they need to consider. These types of solutions measure all aspects of giving including demographic data, socioeconomic data, census data, and more. Each area of data provides useful information to CSR managers to learn how funds are being distributed throughout their initiatives, what portion of funds are coming from employees or customers, when distribution can have the most impact, and more. Each individual dataset tells their own story, but combining all data together provides an even more holistic view of how a CSR campaign is succeeded, and how it can evolve.
Any form of analytics needs a foundation of rich data that allows senior decision-makers to make calculated choices based on fact.
Particularly in the world of corporate giving, this is especially true as there can easily be a disconnect between what a company board wants to support and what employees or customers are interested in. For example, a board may be on the same page about driving a majority of resources towards a specific cause like fighting poverty, but the data shows most of their employees are more interested in driving funds towards global warming. The organization can still support both causes, but it would be more worthwhile to bring more awareness to global warming efforts to increase employee engagement further.
Another important use case could be a C-suite of a business meeting with a state senator where they’ll likely be asked about how their business helped that senator’s state. The holistic view given from an analytics solution can easily answer that question, and the same goes for meeting with heads of individual charities.
Driving Engagement Forward with CSR Activities
Aside from analyzing data to see how funds are being distributed, an even more important benchmark is the ability to accurately measure impact. Using that holistic view from an analytics solution, that includes actual outcomes and changes that occur within specific causes, can provide this much-needed perspective.
A prime example of this is in retail banking.
A bank opening a new branch in a low-income community can make it their mission to support low-income housing as part of their Community Reinvestment Act (CRA) efforts. After two years, that bank can look through available public data in association with the data from their CSR solution to find that homelessness in that community fell since they began their efforts. Not only does this measurement help the organization know they’re making a positive difference, but also gives them a better case when looking to expand their businesses in other regions with similar issues.
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The incorporation of more advanced analytical techniques, like Artificial Intelligence (AI) and Machine Learning (ML), is also moving engagement forward. Although these technologies have been around for some time now, their use in the space of CSR is new and exciting. In companies and foundations with robust grantmaking and engagement programs, there can be what seems like endless amounts of data to analyze.
In CSR activities, AI can help streamline the process by quickly discovering trends and sharing useful insights to inform decision-makers and optimize programs.
The use of ML can enable recommendation engines. For example, over time a system can understand the causes most sought after by the organization, and by knowing the funds available, it can recommend how to use those funds to maximize impact. We also see compelling use cases for nonprofit and donor engagement and process efficiency.
Taking advantage of new technology is important, but it must be purposeful. CSR technology should be focused on the key goal that organizations have been trying to optimize for a long time; maximizing impact. Businesses and foundations need to respond to changes quickly, effectively, and decisively.
Whether it be from new company focus areas, social changes, natural disasters, or even a pandemic, CSR is always changing. By having a system in place that can quickly showcase analytics in an easy-to-understand way, leadership will have the agility to make well-informed decisions backed by rich data to maximize their impact. Over the last year and a half, it’s been clear that CSR is more important than ever, and with the right technology in place, all organizations big or small can create change that drives impact in a positive way.
[To share your insights with us, please write to sghosh@martechseries.com]