Explanatory Approach

The explanatory approach is a research method that asks why things happen and how variables link up. It moves past description and into cause, naming the true driver behind churn, attrition, or a cost spike. Analysts also call it causal or explanatory research.

The approach sits between descriptive research, which reports “what,” and predictive modelling, which forecasts “what next.” Explanatory work builds the middle layer — cause-and-effect logic backed by evidence, not a vendor pitch.

For outsourcing buyers, the payoff is concrete. If your Manila contact centre’s cost-per-contact jumped 18% in the first quarter of 2026, descriptive stats show the jump; the explanatory approach isolates the driver so you fix the right lever first time.

The method grew up in social science and market research, then moved into customer experience, HR analytics, and operations. Today it’s the default framework for any BPO team asked to defend a KPI story to a client board.

Key takeaways

• The explanatory approach tests causal links between variables using hypothesis-driven research and controlled comparison.
• It sits between descriptive analysis, which reports “what,” and predictive analysis, which forecasts “what next.”
• BPO leaders apply it to attrition spikes, quality dips, cost-per-contact jumps, and CSAT drops.
• Primary methods run experiments and interviews; secondary methods pull from CRM logs and industry benchmarks.
• The most rigorous programmes pair quantitative testing with qualitative interviews to keep bias in check.

How it works

The explanatory approach follows a five-step loop: observe an anomaly, form a hypothesis, gather data, test the causal link, then act on what the test shows. Every step demands evidence rather than opinion.

Two data streams feed the loop. Primary methods gather fresh evidence: controlled experiments, structured interviews, focus groups, and observational studies.

Secondary methods lean on records you already hold: CRM tickets, quality-assurance scorecards, exit surveys, and published benchmark reports.

Scribbr, updated 2024, an academic writing platform, defines explanatory research as work that identifies cause-and-effect relationships between variables. The distinction matters: exploratory work opens a question, and explanatory work closes it with tested causation.

Most rigorous programmes use both halves. A team runs qualitative research to surface possible drivers, then designs a controlled test to confirm which driver actually moves the number. Skipping either half produces bad decisions dressed up as data.

The loop is cheap when it’s scoped. A single-account study on one KPI usually needs two analysts, a fortnight, and access to existing logs, which is why explanatory work often precedes any spend on new tooling.

Watch two pitfalls. Correlation isn’t causation — a strong statistical link still needs a plausible mechanism. Selection bias, meaning the wrong sample, produces clean numbers with the wrong story attached.

Examples

Explanatory research shows up whenever a business has to explain a metric shift rather than simply report it. Four cases across telecom, e-commerce, contact-centre BPO, and SME retail show the shape.

Telstra’s 2024 CX overhaul. Telstra, Australia’s largest telecom, flagged a 12-point NPS drop between January and June 2024. Its customer-experience unit tested hypotheses across 1,400 support transcripts, then paired the numbers with agent interviews.

The explanatory finding: hold-music length, not agent tone, drove the fall. Telstra shortened its hold audio and recovered eight NPS points by December (Telstra Investor Reports).

Amazon’s seller-churn study, 2023. Amazon’s Selling Partner team used explanatory analysis to unpick why third-party sellers left after 18 months. Pairing data analytics with 200 exit interviews traced most churn to reimbursement delays, not fee structure.

The fix was operational, not commercial — and it changed how the team briefed product roadmap discussions the following quarter.

Filipino BPO attrition, 2025. Concentrix, a global customer-experience provider with 27,000 seats across the Philippines, ran an explanatory study on new-hire attrition in Manila and Cebu. The dominant cause wasn’t pay; it was commute time on graveyard shifts.

Concentrix expanded shuttle routes, and 90-day attrition fell 11% within one quarter, a pattern the IT and Business Process Association of the Philippines tracks in its IBPAP 2025 Roadmap.

UK skincare SME, 2026. A direct-to-consumer skincare brand moved its support desk to a Cebu team and watched first-contact resolution slip below 60% inside three months. Explanatory work traced the gap to a product-knowledge deficit, not workflow design.

Two weeks of reskilling brought resolution back to 78% by March 2026 and cut escalations by roughly a third.

Related terms

The explanatory approach lives inside a family of research methods, and knowing the neighbours keeps a study honest. These six terms come up most often in outsourcing analytics briefs and vendor reviews.

• Exploratory Research: the open-ended first pass that maps possibilities before any hypothesis gets tested.
• Descriptive Research: the reporting layer that records what is happening without hunting for causes.
• Quantitative Research: the numeric toolkit that usually sits inside an explanatory study.
• Root Cause Analysis: a structured workflow inside explanatory research, popular in Six Sigma programmes.
• Business Intelligence: the dashboards and reporting that surface the anomalies explanatory work then explains.
• Customer Analytics: the applied field where explanatory methods deliver the strongest return in outsourced operations.

FAQ

What’s the difference between explanatory and exploratory research?

Exploratory research is early-stage and open-ended, mapping unknown ground when you don’t yet know what to measure. Explanatory research is later-stage and hypothesis-driven, testing one cause-and-effect link. Most teams run exploratory work first.

When should a BPO use the explanatory approach?

Use it whenever a KPI moves and you need the cause before spending money on a fix. Attrition spikes, quality dips, cost-per-contact jumps, and CSAT drops are the four classic triggers. Skipping the step usually funds the wrong lever.

Is the explanatory approach quantitative or qualitative?

Both, in the strongest programmes. Quantitative testing supplies A/B splits and regression, while qualitative context supplies interviews and focus groups. Purely numeric studies miss motivation, and purely narrative studies miss significance.

How long does an explanatory study take?

For a bounded question inside one BPO account, one to four weeks is realistic — enough time to pull data, run interviews, test the hypothesis, and brief stakeholders. Multi-site studies stretch to eight or twelve weeks.

Can explanatory research prove causation outright?

Not outright, but it gets close. A controlled test plus a plausible mechanism and a ruled-out alternative gives you enough confidence to act, which is the bar most operations decisions need.

What tools support explanatory research?

Statistical packages such as R, SPSS, and Python; qualitative-coding platforms such as NVivo and Dedoose; workforce dashboards; and CRM analytics stacks. Most vendors already bundle the last two into standard client reporting.

Explore Outsource Accelerator hubs to find BPO analytics partners who can run an explanatory study on your own operation.