Five dimensions of impact | WHO Whom do enterprises affect? How underserved are they in relation to the outcomes delivered by enterprises? To address these questions, we analyze the impact data categories under the 'Who' dimension.

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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. Understanding the ‘Who’ allows enterprises and investors to maximize their impact by directing resources to those who are most underserved.

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 limited resources to allocate towards creating impact, to whom would you direct those resources? The underserved stakeholder group? Or the well-served stakeholder group? All else being equal, you would want to direct these resources towards the most underserved stakeholder group to maximize 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 of this page provide guidance on the four impact data categories associated with the ‘Who’ dimension.

Which types of stakeholders do enterprises affect?

The stakeholder data category allows enterprises and investors to identify whom 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 its ‘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 and 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 corporate social responsibility 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 labor that affects suppliers)
  5. The planet, which an enterprise affects by extracting, using, and creating environmental resources; and by the pollution that is emitted by these processes

Categorizing 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 categorized into upstream or downstream activities; the planet could be classified according to ecosystems).

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 is essential for:

  • Understanding how underserved or well-served stakeholders are
  • Setting informed impact targets
  • 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:

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

Selecting a baseline indicator

An effective baseline indicator should mirror the outcome indicator as closely 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.

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.

Where do stakeholders experience the outcomes?

Locating the place where stakeholders experience the outcome help enterprises and investors contextualize 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’).

How can enterprises segment stakeholders?

Assessing stakeholders across socio-demographic and behavioral characteristics can provide useful insights for segmenting stakeholders into discrete and actionable groups.  Below we present two case studies illustrating the value of collecting and assessing stakeholder characteristics.

Using behavioral 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 organization to identify two distinct consumer segments who saw kerosene as a more attractive solution. The first group, the inconvenience-averse, associated rechargeable bulbs with time away from productive activity, as visiting bulb recharging centers required traveling large distances. The second group, the blackout-averse, did not want to be caught without light, maybe because their children needed to study or their cattle to be fed after dark. For them, kerosene was a quick-fix solution.

These behavioral 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 behavioral 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 purpose. This prompted Safaricom to investigate the reasons behind this consumer behavior, 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 behavioral 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 behavioral data will help my enterprise segment stakeholders into meaningful, actionable groups?

For enterprises that set goals (or expectations) against stakeholder characteristics (e.g., level of income or gender), the following question may guide 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 behavioral characteristics?

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

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