Explore The Five Dimensions Of Impact

Who

Who do enterprises affect? How underserved are they in relation to the outcomes delivered by enterprises? To address these questions, we analyse the impact data categories under the ‘Who’ dimension.

The ‘Who’ impact dimension refers to the stakeholders who experience social and environmental outcomes. This allows enterprises and investors to maximise their impact by directing resources to those who are most underserved. This page provides guidance on the ‘Who’ data categories that enterprises and investors can use to collect, assess and report stakeholder data. If you are unfamiliar with the impact dimensions (and their respective data categories), we recommend visiting the short section What is Impact.

Introduction: The ‘Who’ dimension of impact

The data categories under the ‘Who’ dimension help enterprises and investors identify the stakeholders they affect — and understand how underserved they are in relation to the social or environmental outcomes delivered by enterprises.

Which stakeholders do enterprises affect — and how underserved are they in relation to the outcomes generated by enterprises’ activities?

To gain a comprehensive view of those they are affecting, enterprises and investors need to consider the following data categories:

These four categories under the ‘Who’ dimension of impact allow enterprises and investors to capture practical stakeholder data. Enterprises and investors can use this data to (re)allocate resources towards stakeholders who have the highest need or who are likely to experience the biggest degree of change.

The premise behind the ‘Who’ categories is simple: if you have X amount of limited resources to allocate towards creating impact, who would you direct these to? The more underserved stakeholder group? Or the well-served stakeholder group?

 

With a few exceptions, you would always want to direct these resources towards the most underserved stakeholder group to maximise your impact.

 

The impact data categories under the ‘Who’ dimension help enterprises segment their stakeholders based on who is likely to benefit the most.

The remaining sections in this page provide guidance on each of these four categories.

If you are unfamiliar with the dimensions of impact, we recommend first visiting What is Impact.

Which types of stakeholders do enterprises affect?

Enterprises can affect five types of stakeholders: customers, employees, local communities, suppliers & distributors, and the planet. 

The stakeholder data category allows enterprises and investors to identify who they are affecting, intentionally or unintentionally. These actors have traditionally placed an emphasis on just one or two stakeholder group(s), although in reality they affect more. If an enterprise or investor wants to understand their ‘total’ impact, it needs to consider all of its stakeholders.

Enterprises usually have an impact on the following stakeholder groups (see diagram below for an illustrative example):

  1. Customers who use the enterprise’s products/services
  2. Employees who work for the enterprise
  3. Local communities who are directly or indirectly affected by an enterprise’s activities (e.g. unhealthy factory emissions that negatively affect surrounding local communities; or affordable housing units for underserved communities as part of a CSR initiative)
  4. Suppliers and distributors who are affected by the enterprise’s volume of procurement, regulations and quality control (e.g. a zero-tolerance policy on child labour that affects suppliers)
  5. The planet which an enterprise affects through extracting, using and creating environmental resources; and through pollution that is emitted by these processes

Categorising stakeholders into these groups is the first step towards understanding who is affected by an enterprise’s activities. This classification can be further broken down for a more granular representation of the stakeholder (suppliers could be categorised into upstream or downstream activities; the planet could be classified according to ecosystems).

The next step is to capture the geographical boundary — the location in which the stakeholder experiences the social and/or environmental outcomes. This information is particularly useful for multinational enterprises as well as investors who make investment decisions based on specific geographical areas.  

Where do stakeholders experience the outcomes?

Capturing the geographical boundary of the outcome can help enterprises and investors put their impact into context.

Locating the place where stakeholders experience the outcome help enterprises and investors contextualise their impact. By identifying the geographical boundary, they can:  

  • Define an investment thesis with a geographical focus
  • Scope their area of influence (also known as zone of control)
  • Target stakeholders more effectively based on characteristics of the geographical area

Depending on its intended purpose, the geographical boundary defining where the stakeholders experience the outcome can be broad (such as West Bengal, India) or narrow (such as a 5km catchment area or a region classified as ‘very deprived’). The table below presents illustrative indicators and data values.

Next, we introduce the baseline category — the extent of the outcome experienced by the stakeholder prior to engaging with the enterprise. This data category provides a reference point for (re)directing resources towards the most underserved stakeholders. 

What is the baseline?

The baseline refers to the level of outcome experienced by stakeholders prior to engaging with the enterprise.

Capturing stakeholders’ conditions before an initiative begins, the baseline category is essential for:  

  • (1) Understanding how underserved or well-served stakeholders are
  • (2) Setting sensible goals based on findings from (1)
  • (3) Estimating outcome changes once the product (or policy) has been rolled out

Equipped with this knowledge, enterprises and investors can (re)allocate resources towards stakeholders who have the highest need or who are likely to experience the biggest degree of change.

The concept of baseline data is akin to market entry research: before entering a new market, enterprises take the ‘pulse’ of the target industry by collecting data on a variety of factors including opportunities, challenges, growth dynamics, leading players, and consumer drivers. Based on this analysis, enterprises can:

  • (1) Determine whether consumers are currently well- or under-served by products available in the market
  • (2) Set an entry strategy if they believe that consumers are underserved by the competition
  • (3) Once the product is launched, continuously assess performance against the baseline and other metrics (such as peer benchmarks) to drive results

A hypothetical example illustrates the value of the baseline. Imagine a global food manufacturer seeking to work with smallholder cocoa farmers in Côte d’Ivoire who are not connected to global markets. Before establishing a partnership with the cocoa cooperatives, the company collects data on the income levels of the farmers to understand how underserved they are in relation to the outcome (i.e. decent income). Through this assessment:

  • The company finds that 50% of farmers in the region live under the World Bank’s International Poverty Line (1).
  • The company then sets impact goals around improving the farmers’ income by at least 20% (2).
  • Having rolled out a pilot initiative with 1,000 farmers, the company collects data after a year to assess whether income levels have improved from their initial reference point (baseline) (3).

 

Selecting a baseline indicator

An effective baseline indicator should mirror the outcome indicator as much as possible. For example, if the outcome indicator is the % of people with an active savings account, then the baseline indicator should capture the same information. In cases where enterprises may not have access to a matching pair of quantitative indicators – due to a lack of data or resources –, interviews and secondary research should be used to set a reference point. This data is crucial for assessing whether progress has been made since the product (or policy) has been launched. The table below illustrates the relationship between outcome and baseline indicators.

Further disaggregating stakeholders, based on socio-demographic and behavioral characteristics, can provide enterprises and investors with valuable insights for producing more targeted interventions. The next ‘Who’ category covers segmentation based on stakeholder characteristics. 

How can enterprises segment stakeholders?

Socio-demographic and behavioural data helps enterprises and investors segment stakeholders into clear, discrete and actionable groups. 

Assessing stakeholders across socio-demographic and behavioural characteristics can provide useful insights for segmenting stakeholders into discrete and actionable groups. Enterprises can use this segmentation to (re)allocate resources towards cultivating tailored solutions — from adjusting product pricing to creating marketing campaigns —, driving both social and financial results. Below we present two case studies illustrating the value of collecting and assessing stakeholder characteristics. 

Using behavioural data to create solutions that fit the needs of consumers

 

Rwandan-based Nuru Energy faced a challenge: despite selling a significantly healthier and cheaper product (smoke-free rechargeable bulbs), many of its target customers still preferred using kerosene as a source of light. This was difficult to reconcile for the company, as kerosene fumes cause more deaths than malaria and equal the effects of smoking two packs of cigarettes a day.

 

To understand the low adoption of its product, Nuru Energy researched the habits of its target consumer — those living on less than US$5 a day in rural areas. The process led the organisation to identify two distinct consumer segments who saw kerosene as a more attractive solution. The first group, the inconvenience-averse, perceived rechargeable bulbs as time away from productive activity, as visiting bulb recharging centres required traveling large distances. The second group, the blackout-averse, did not want to be caught without light, maybe because their kids needed to study or their cattle to be fed after dark. For them, kerosene was a quick-fix solution.

 

These behavioural findings have prompted Nuru to test alternative solutions (including larger-capacity bulbs and door-to-door bulb recharge services) to improve adoption rates among these two segments.

 

Source: INSEAD (2017)

 

Leveraging socio-demographic and behavioural data to create tailored marketing campaigns

 

A few months into the M-Pesa pilot, Safaricom and Vodafone discovered something surprising: Kenyans were using the mobile money app for peer-to-peer transfers rather than for loan repayments to microfinance institutions (MFIs), which was M-Pesa’s initial core purpose. This prompted Safaricom to understand the reasons behind this consumer behaviour, as the pilot results were intended to guide M-Pesa’s introduction into the market. 

 

To understand the drivers behind this usage pattern, Safaricom deployed a threefold approach:

 

  1. A competitive analysis revealed that people typically used inconvenient, expensive and unreliable mechanisms — such as asking a taxi-driver to hand-carry cash.
  2. A large-scale survey showed that only 3% of Kenyans had an MFI loan, while 17% had transferred mobile money at least once in the former 12-month period. These findings confirmed that targeting remitters rather than MFI borrowers would enable Safaricom to address the needs of a larger segment.
  3. Building on the results from the previous research, Safaricom developed a socio-demographic profile of its target customers: these were likely to be male, young, migrant, wage-earners living in Kenya’s largest cities.

 

Based on these market structure findings, Safaricom launched the nation-wide “Send money home” advertisement campaign, reflecting the targeted young male migrant workers, as well as M-Pesa’s ease of use and affordability. Safaricom’s investment in the campaign paid off: by August 2008, 17 months after launch, only 18% of non-users of M-Pesa were unaware of the product.

 

Source: GSMA (2012)

 

Collecting socio-demographic and behavioural data

To gather relevant stakeholder characteristics, enterprises need to be as precise as possible, while bearing in mind the question the data seeks to inform:

  • Which socio-demographic and behavioural data will help my enterprise segment stakeholders into meaningful, actionable groups?

For enterprises who set goals (or expectations) against stakeholder characteristics (e.g. level of income or gender), the following question may serve as guidance in deciding what type of data to collect:

  • Which data may indicate to my enterprise whether we are meeting the goals (or expectations) set against socio-demographic and behavioural characteristics?

The indicators and data values for these categories can be qualitative or quantitative, sourced from company administrative data, government data, surveys, among various other sources. The table below shows illustrative indicators and data values.