00:00:04 Introduction

Hello everyone, I hope you are all doing well and thank you for attending. This is our first webinar of 2025. Today, we are going to start from the ground up by looking at the Post-Distribution Monitoring (PDM) data lifecycle—not just as a survey, but as a continuous loop of accountability. Then, we will tackle a major pain point: the siloed approach to data. We will look at why it is critical to move towards a relational model if you want to save time and ensure accuracy.

From there, we are going to get practical, discussing how to design indicators that actually close that feedback loop. Finally, I will open up ActivityInfo and show you exactly how to set up linked forms and validation rules so that you see real-time analysis and action. By the end of this, hopefully, you will see how a simple change in data structure can completely transform your monitoring quality.

00:01:13 What is post-distribution monitoring?

To make sure we are all on the same page, let's start with the basics. PDM consists of three words: post, distribution, and monitoring. "Distribution" is the event itself, the actual transfer of a commodity, cash, or a service from our organization to the beneficiary. "Post" is about timing; it happens strictly after the intervention has taken place. It is that period of reflection where the promise of the project meets the reality of the household experience. Finally, "monitoring" is a systematic, repeatable, and consistent process designed to provide the right information to manage a program effectively.

Formally, PDM is a structured, repeatable process of collecting information after assistance is delivered to understand four key things: use, satisfaction, outcomes, and risks. Beyond a simple definition, PDM is the ultimate tool for MEAL (Monitoring, Evaluation, Accountability, and Learning). It drives monitoring by tracking outputs, ensures evaluation by looking at short-term outcomes, enforces accountability to affected populations, and facilitates learning by identifying what worked and what didn't.

00:03:13 Modalities and core DNA

While the monitoring aspect stays the same, the distribution aspect can look very different depending on your sector. PDM generally covers three main modalities: in-kind food rations or hygiene kits, cash assistance, and vouchers. We also see it used in other modalities, including infrastructure or community assets, like a newly installed water pump. Regardless of the aid type, the goal remains ensuring aid is used effectively and recipients are heard.

When we monitor physical goods, our focus is primarily on logistics, quality, and use. With cash, monitoring shifts from the item itself to the outcome and choice, asking questions about spending patterns and sufficiency. Vouchers sit in the middle, focusing heavily on the redemption process and the vendor's behavior. Despite these differences, every PDM survey shares a core DNA visualized in three pillars: verification (the "what"), satisfaction (the "how"), and protection and safety. Our goal is to connect these three circles to get a full picture of program success.

00:07:14 The PDM data lifecycle

The PDM lifecycle consists of four distinct stages. Stage one is planning and sampling. This is where we lay the groundwork, defining the methodology, terms of reference, budget, and timing. We typically schedule the survey two to four weeks post-distribution to catch beneficiaries while their memory is fresh but after they have had time to use the aid. We use our original distribution list to select a representative sample, typically 10 to 30 percent, and perform inclusion checks.

Stage two is collecting. This is the implementation phase in the field. We look at quantitative scope using structured questionnaires to capture standardized indicators. We complement that with qualitative depth using focus group discussions to dig into the "why" behind the feedback. We prioritize digital efficiency by utilizing mobile data collection platforms like ActivityInfo with offline capabilities. Throughout this, we follow strict ethical protocols for informed consent and data confidentiality.

Stage three is analyzing. We move from raw data to actionable intelligence through comparative analysis, comparing reported delivery against the original distribution list. We analyze core themes like information awareness, fairness of targeting, and distribution timeliness. We also look at usage and outcomes to see how the aid was utilized. Finally, stage four is acting. This is where we close the loop. We use our findings for mid-course corrections, fixing supply chain issues, refining targeting, and addressing safety concerns. We close the loop by sharing findings with donors and, most importantly, back to the community.

00:16:08 The challenge of data silos

Between the collection and analysis steps, most M&E practitioners experience a "black hole of time." In a traditional system, you get stuck in an endless loop of data cleaning, trying to match results back to the planned list using VLOOKUPs. By the time you finish, the chance to act has often passed. This happens because of data silos. A silo means your distribution list and your survey are two strangers who don't speak to each other. They exist in different files or software accounts.

This disconnection is the biggest danger to monitoring quality. You might collect PDM surveys using a mobile app, but your beneficiary list is in a separate Excel file, and historical records are in a SQL database. Because these tools aren't talking to each other, you have to manually export and transform data, creating opportunities for errors. The top five challenges of the siloed system are data overload, biased responses from re-asking profile details, recall reliability gaps, inconsistent quality control, and broken accountability loops.

00:20:50 Moving to a relational model

The solution is a relational shift. This changes the whole architecture by linking reference data—including items, IDs, and distribution centers—directly to the beneficiary registration, which links to the distribution event, and finally to the PDM. Because the PDM form looks up the distribution record in real-time, we eliminate the cleaning bottleneck.

In a relational system, zero matching is required because data is linked at the point of entry. You have real-time verification in your survey form, allowing the enumerator to confirm feedback against the facts of delivery. This reduces respondent fatigue because profile and distribution data auto-populate. Finally, results feed directly into a dashboard, allowing for immediate course correction while the project is still active. This moves us from a system that records history to a system that changes it in real-time.

00:23:40 Designing actionable indicators

To close the loop, we need to design indicators that force us to act. Closing the loop is an institutional process where every data point is followed by a documented management action. We link data to the four pillars of delivery: people, processes, location, and feedback.

For example, regarding information and targeting, if indicators show people didn't understand why they were selected, we close the loop by fixing communication before the next round. regarding logistics, if data shows damaged goods, the logistics team uses that feedback to hold vendors accountable. regarding usage, if items are sold, we might adjust program design, perhaps switching to cash. Finally, regarding safety, any report of harassment triggers an immediate investigation.

00:27:28 ActivityInfo demonstration

Let's look at how this works in ActivityInfo. In a standard non-relational PDM form, you ask the beneficiary for demographics, distribution details, and specific feedback. This is a disconnected data island; if a beneficiary says they didn't receive cash, you have no way to verify it against the original record on the spot.

In a relational setup, we first define reference data like IDP centers and items. We have a household registration form acting as a single source of truth, capturing demographics once. Then, we have a distribution record form linking the household to the items delivered. When we open the relational PDM form, instead of typing a name, we select the distribution record. This auto-populates details like items distributed, quantity, and household demographics. We can then ask verification questions like, "Our records show you received one kitchen kit; did you receive exactly this amount?" This allows for immediate verification and higher data quality.

This relational approach feeds directly into analysis. We can create notebooks and pivot tables that update in real-time. For example, we can filter by IDP center to see distribution quantities, consent rates, satisfaction levels, and protection concerns. We can map these results to identify geographic issues. This system makes the organization more accountable and efficient.

00:47:25 Q&A session

How often should the PDM be conducted and who should collect the information? Ideally, PDM is conducted two to four weeks after distribution. It is usually done per distribution. Information should be collected by MEAL staff or independent enumerators, avoiding those who participated in the distribution to prevent bias and ensure neutral feedback.

Do you have complete data analysis software with a reporting system? Yes, ActivityInfo covers the whole data lifecycle. As demonstrated, data collection is directly connected to reports and dashboards. You can create maps, pivot tables, and other visualizations directly within the platform.

How do we handle anonymous PDMs? With anonymous PDMs, you cannot link the survey directly to a specific distribution record. This is often done for ethical concerns or harassment reporting. While you lose the ability to verify against specific records, you can still design comprehensive questions to capture necessary feedback.

Is the data secure? ActivityInfo is a cloud-based server, and the company is ISO certified, treating data with the highest level of security. You can control access through roles, ensuring enumerators only see or edit specific data. Information management systems are generally more secure than sharing Excel files or using hard copies.

Can PDM be used for agricultural inputs or WASH projects? Yes, definitely. For agricultural inputs, you can monitor quality (e.g., seed germination), usage, and timing. You can link feedback to specific seed batches. For WASH, such as water pumps, the "beneficiary" might be the community. You monitor if the pump works, if access is equitable, and if the location is safe.

Can ActivityInfo be used for pre-post KAP surveys? Yes. You can use it for baseline and endline surveys, calculating percentage increases, and sampling. The platform is highly customizable and scalable for various monitoring needs.