20 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You usually split the day between data and creative. Mornings often mean checking campaign dashboards and market-monitoring tools, looking at impressions, click-through rates, and conversion trends from the prior 24 hours.
Afternoons are for research and planning: analysing consumer buying trends, studying competitor activity, drafting targeting recommendations (who to reach and where), and preparing a short presentation or report for stakeholders using Canva or Asana to assign follow-ups.
Expect a mix: campaign and creative briefs in Asana, quick ad mockups in Canva, and data analysis in platforms like Amazon Redshift, Apache Hive or Hadoop for large datasets. Use Redshift or Hive to pull aggregated customer behavior, then export slices for campaign use.
You might also run statistical tests locally (Excel, R, or Python) to measure effects, then save visual reports and share them in Asana or slide decks when presenting findings.
You need practical statistics: A/B test calculation, lift measurement, confidence intervals and simple regression to link ads to conversions. These let you translate research into recommendations for product positioning or ad targeting.
Run tests inside Facebook Ads, extract results into Redshift or Hive if volumes are large, and use basic scripts or spreadsheets to compute significance. If a result is unclear, write clear next-step tests instead of overinterpreting noisy data.
AI can speed up pattern finding: cluster users, suggest audience segments, or summarise survey responses. Use it to generate hypotheses about consumer demand and targeting, then verify with A/B tests and real campaign data.
Never let AI decide final targeting or spend. Check AI outputs against privacy rules and company policies, and validate recommendations with actual performance metrics in Apache Hive/Redshift or Facebook Ads before scaling.
Prepare short reports that connect research to money: conversion rate lift, cost per acquisition (CPA), and incremental revenue from tested targeting. Use concrete time ranges (last 7 or 30 days) and baseline comparisons.
Pull raw metrics from Redshift/Hive, visualise trends in Canva or a slide deck, and include the statistical test used. Add a one-line action: what you recommend and the expected percent improvement.
Study basic statistics, Excel, and one data language (SQL, Python). Practice by setting small ad tests in Facebook Ads Manager and measuring results for a real or dummy product.
Learn Redshift or Hive basics (how to query event tables), use Asana for task workflows, and make simple visuals in Canva. Intern or help a small e-commerce shop run ads — that gives experience with consumer demand, survey design, and competitor study.
Both study consumers and competitors, but the Facebook Ads Specialist focuses on running and optimising paid campaigns and defining how to reach audiences on Facebook. Tasks include building audiences, testing ads, and measuring ad-driven conversions.
A Market Research Analyst spends more time designing surveys, running broad market studies, and developing new research methods. In practice you’ll overlap: use research skills (surveys, segmentation) to inform ad targeting and use ad performance data (from Redshift/Hive) to validate market hypotheses.