Market Research Data Analysis Training Course

Duration 10 days 17 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Market Research Analyst, Customer Insights Manager and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-M-062

Before you join: Participants should have experience reading market research reports, survey tables, customer feedback or commercial performance data.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Market research teams often collect large volumes of survey, interview, tracking and customer data but struggle to turn it into decisions that product, marketing, sales and service leaders can act on. Common problems include poorly defined research questions, inconsistent coding, weak sample interpretation, misleading charts, over-reliance on significance testing and reports that describe findings without explaining commercial implications. Analysts need a disciplined route from business question to evidence, recommendation and follow-up measure.

This course develops the analytical skills required to prepare, analyse, interpret and communicate market research data. Participants work with quantitative and qualitative data, using descriptive statistics, crosstabs, hypothesis tests, regression, factor analysis, segmentation, conjoint analysis and thematic coding. They learn how to assess data quality, select appropriate methods, distinguish statistical significance from business importance, and state limitations clearly. The course also covers brand tracking, dashboard design, research ethics and data protection requirements.

Teaching combines short technical briefings with realistic cases drawn from consumer, financial services, public-sector, B2B and service research. Participants apply methods to a continuing research project from the first week, building a documented dataset, analysis plan, tables, charts and recommendations. The final capstone requires participants to produce and present a complete market research analysis report. Delivery is instructor-led over 10 days, available in classrooms in 50+ cities or live online, with approximately 6.5 contact hours each day. Participants receive a certificate of completion.

The course suits professionals who already work with market research, customer insight or commercial data and need stronger analytical judgement, clearer reporting and a repeatable approach to evidence-based recommendations.

What you will be able to do

  • Frame business decisions as answerable market research questions and hypotheses
  • Design questionnaires, measures and sampling approaches suited to analytical requirements
  • Prepare documented analysis datasets using codebooks, validation checks and audit trails
  • Produce descriptive statistics, crosstabs and visualisations that accurately represent research findings
  • Test group differences and associations using chi-square, t-tests, ANOVA and effect sizes
  • Build regression, factor and segmentation analyses for customer and market decisions
  • Translate quantitative and qualitative findings into prioritised commercial recommendations
  • Present a complete market research analysis report with methods, limitations and actions

Benefits of attending

For you

  • Produce defensible research findings rather than descriptive survey summaries
  • Select statistical tests suited to the research question and data type
  • Build clearer stakeholder reports with evidence, caveats and recommended actions
  • Assess agency outputs, sample quality and claims of statistical significance
  • Create a portfolio-quality market research analysis report

For your organisation

  • Improves the reliability of decisions based on customer and market evidence
  • Reduces reporting errors caused by weak data checks and inappropriate tests
  • Produces more usable segmentation, tracking and driver-analysis outputs
  • Creates clearer links between research findings and commercial actions
  • Strengthens governance around respondent data, methodology disclosure and research ethics

Course modules

17 modules over 10 days

Participants can convert a management issue into a focused research brief and analytical plan.

  • Decision statements translating management issues into answerable research questions
  • SMART research objectives tied to pricing, retention, and service decisions
  • Stakeholder maps identifying decision owners, users, constraints, and approval gates
  • Logic models connecting customer drivers, evidence needs, and business actions
  • Research briefs specifying population, decisions, timing, budget, and success criteria
  • Assumption logs separating known facts, hypotheses, risks, and unanswered questions

Participants can specify research designs and measures that support valid analysis.

  • Exploratory, descriptive, and causal designs matched to decision uncertainty
  • Construct operationalisation using observable indicators, scales, and behavioural measures
  • Likert, semantic differential, and ranking scales selected for analytic requirements
  • Question wording checks for ambiguity, double-barrels, recall error, and bias
  • Questionnaire flow using screening, routing, randomisation, and sensitive-question placement
  • Pilot testing protocols documenting comprehension, timing, technical, and coding issues

Participants can assess sample quality, sampling error and weighting decisions.

  • Target populations, sampling frames, and units of analysis defined precisely
  • Probability and non-probability sampling methods matched to research objectives
  • Sample-size calculations balancing precision, subgroup analysis, cost, and timing
  • Response-rate calculations identifying coverage, nonresponse, and recruitment risks
  • Quota controls monitoring representation during survey fieldwork and panel recruitment
  • Rim weighting adjusting sample distributions against population control variables

Participants can prepare an analysis-ready dataset with documented quality controls.

  • Codebooks defining variable names, labels, values, formats, and missing-data codes
  • Data imports checked against source files, field types, and record counts
  • Duplicate detection using identifiers, fuzzy matching, and documented resolution rules
  • Range, logic, and skip-pattern checks locating invalid survey records
  • Missing-data profiles distinguishing nonresponse, not-applicable, and system missing values
  • Audit trails recording transformations, exclusions, recodes, and dataset versions

Participants can summarise research data accurately and interpret key descriptive measures.

  • Frequency tables and percentages profiling categorical responses and sample composition
  • Mean, median, mode, variance, and standard deviation interpreted correctly
  • Distribution plots identifying skewness, outliers, floor effects, and ceiling effects
  • Weighted estimates calculated from survey weights and population benchmarks
  • Confidence intervals communicating estimate precision rather than false certainty
  • Excel and SPSS procedures producing reproducible descriptive analysis tables

Participants can design charts and dashboards that support accurate business interpretation.

  • Chart selection matched to comparison, composition, distribution, relationship, and change
  • Axis scales, baselines, labels, and sorting designed for accurate interpretation
  • Small multiples revealing segment differences without cluttered multi-series charts
  • Data-ink principles removing decoration, redundant legends, and misleading visual emphasis
  • Tableau dashboards structured around decisions, exceptions, and drill-down questions
  • Accessibility checks covering colour contrast, labels, annotations, and readable formats

Participants can compare customer groups using crosstabs and appropriate significance tests.

  • Crosstabulations comparing segments using row, column, and total percentages
  • Chi-square tests assessing association between categorical variables and segments
  • Standardised residuals locating cells responsible for statistically significant differences
  • Banner tables combining demographics, behaviours, and attitudes for stakeholder review
  • Significance markers applied with stated bases, tests, and comparison rules
  • Base-size rules preventing unstable percentages and misleading subgroup claims

Participants can test research hypotheses and explain results without statistical overclaiming.

  • Null and alternative hypotheses stated in business-relevant analytical language
  • p-values, alpha levels, and confidence intervals interpreted without overclaiming
  • t-tests comparing two group means with variance assumptions checked
  • ANOVA testing differences across multiple groups and planned comparisons
  • Effect sizes reporting practical importance alongside statistical significance
  • Multiple-comparison risks managed through planned tests and transparent caveats

Participants can model relationships between customer outcomes and potential drivers.

  • Correlation coefficients assessed for direction, magnitude, outliers, and nonlinearity
  • Simple regression estimating relationships between customer outcomes and predictors
  • Multiple regression controlling confounders and comparing standardised predictor effects
  • Model diagnostics checking residuals, multicollinearity, influence, and heteroscedasticity
  • Regression outputs translated into prioritised service and marketing actions
  • Correlation-versus-causation claims bounded by design, evidence, and plausible alternatives

Participants can reduce attitude measures into reliable, interpretable dimensions.

  • Exploratory factor analysis reducing correlated attitude items into dimensions
  • KMO and Bartlett tests assessing factorability before extraction decisions
  • Eigenvalues, scree plots, and parallel analysis guiding factor retention
  • Varimax and oblique rotations selected according to factor correlation
  • Factor loadings, cross-loadings, and communalities reviewed for item retention
  • Reliability assessed using Cronbach's alpha and corrected item-total correlations

Participants can build and assess evidence-based customer segments for targeting decisions.

  • Segmentation bases combining needs, value, behaviour, and demographic variables
  • Cluster analysis preparing standardised inputs and selecting candidate solutions
  • Cluster profiles validated for size, separation, stability, and commercial usefulness
  • Personas grounded in evidence rather than unsupported demographic stereotypes
  • STP framework translating segments into targeting and positioning choices
  • Segment scorecards comparing attractiveness, accessibility, responsiveness, and strategic fit

Participants can interpret preference research to inform product and pricing choices.

  • Choice-based conjoint designs estimating attribute utility and price trade-offs
  • Attribute levels selected to reflect realistic offers and decision constraints
  • Part-worth utilities interpreted alongside relative attribute importance measures
  • Market simulators comparing concept shares under alternative product configurations
  • MaxDiff scaling prioritising features, benefits, and message claims
  • Conjoint limitations documented when translating stated preferences into forecasts

Participants can code qualitative evidence and integrate it with quantitative findings.

  • Qualitative codebooks defining codes, inclusion rules, exclusions, and examples
  • Open coding identifying recurring needs, language, barriers, and decision criteria
  • Thematic analysis grouping codes into patterns, tensions, and explanatory themes
  • Inter-coder agreement checks improving consistency across multi-analyst coding
  • Verbatim selection preserving context while evidencing findings and recommendations
  • Mixed-methods triangulation comparing interview themes, survey patterns, and administrative data

Participants can interpret changes in brand and customer experience measures over time.

  • Brand tracking KPIs separating awareness, consideration, preference, and usage
  • Net Promoter Score analysed with respondent mix and driver context
  • Time-series charts distinguishing seasonality, trend, campaign effects, and random variation
  • Wave-to-wave comparability checked across samples, questionnaires, fieldwork, and weighting
  • Control charts flagging material shifts beyond expected sampling variation
  • Tracking reports linking movement to actions, owners, and follow-up measures

Participants can communicate findings credibly while meeting research ethics and disclosure requirements.

  • Executive summaries stating decision, evidence, implication, recommendation, and limitation
  • Story structures moving from finding to business consequence and action
  • Evidence hierarchies separating observed results, inference, and recommendation
  • GDPR principles applied to lawful processing, minimisation, retention, and access
  • ESOMAR ICC Code principles governing consent, transparency, and respondent protection
  • Disclosure statements covering methodology, sample, field dates, weighting, and limitations

Participants can create a traceable analysis plan that links evidence to decisions.

  • Analysis plans mapping research questions to variables, methods, outputs, and decisions
  • Tabulation specifications defining cuts, bases, metrics, tests, and annotations
  • Driver trees linking customer outcomes to controllable operational and marketing factors
  • Recommendation matrices ranking actions by impact, confidence, effort, and ownership
  • Sensitivity checks testing whether conclusions change under alternative specifications
  • Implementation measures defining owners, milestones, leading indicators, and review dates

Participants can build, review and present a complete market research analysis report with defensible recommendations.

  • Research brief refined into scoped questions, hypotheses, measures, and decisions
  • Analysis dataset cleaned, documented, and checked using an audit trail
  • Descriptive, comparative, and driver analyses selected against stated objectives
  • Findings synthesised into segment, product, service, or brand recommendations
  • Executive report built with tables, charts, methods, limitations, and actions
  • Stakeholder presentation defended through evidence, caveats, and implementation priorities

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Market research analysis plan template
  2. Survey data quality checklist
  3. Crosstab and significance testing guide
  4. Customer driver analysis model
  5. Insight reporting storyboard template
  6. Capstone market research analysis report

Who should attend

  • Market Research Analyst — strengthens analytical rigour from data preparation through reporting
  • Customer Insights Manager — converts customer evidence into prioritised action recommendations
  • Marketing Manager — interprets research results for brand, campaign and product decisions
  • Product Manager — tests customer needs, feature priorities and concept choices
  • Business Intelligence Analyst — adds research-specific sampling, weighting and survey analysis methods
  • Research Agency Account Manager — improves the quality of client-ready analysis and interpretation

Skills and tools covered

Survey data analysisStatistical testingCustomer segmentationRegression modellingQualitative codingInsight reporting

Tools and standards

Microsoft Excel, IBM SPSS Statistics, R, Tableau

How the course runs

Training methodology

The instructor introduces each method through short briefings, worked examples and guided interpretation of research outputs. Participants analyse realistic survey, interview, brand-tracking and customer experience cases in small groups, selecting methods and defending their choices. Simulations focus on stakeholder requests, ambiguous findings and competing interpretations. Individual application planning connects each technique to participants' own research responsibilities. The course uses spreadsheet-style datasets and demonstrates relevant procedures in Microsoft Excel, IBM SPSS Statistics, R and Tableau where appropriate.

Requirements and prerequisites

Participants should have experience reading market research reports, survey tables, customer feedback or commercial performance data. They should be comfortable with percentages, averages and basic spreadsheet calculations, and able to discuss a current business, customer or market question from their role. Prior use of Excel is helpful because examples use spreadsheet-style data structures. Statistical expertise, programming experience, prior use of SPSS or R, and a mathematics qualification are not required. The course explains statistical concepts before applying them to market research decisions.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should be familiar with market research reports, survey outputs or customer data, and comfortable with basic percentages and averages. You do not need prior statistical training, programming experience or SPSS expertise.

Bring a current or recent research question, report, dataset description or business issue where possible. You do not need to bring confidential raw data; course datasets and templates are provided.

It is designed for market research analysts, insight professionals, marketers, product managers and business intelligence staff who interpret customer or market evidence. It is most useful for people who need to explain findings and recommend action.

This course concentrates on analysing and communicating market research evidence, while still covering the design choices that affect data quality. It differs from general data analytics training by addressing sampling, questionnaire measures, weighting, survey significance, qualitative coding and research disclosure.

Participants can use the analysis-plan template, data-quality checklist, crosstab guide and reporting storyboard on current studies. The methods apply to customer satisfaction, brand tracking, concept testing, segmentation, employee research and B2B studies.

Each training day runs from 08:30 to 16:30 and provides approximately 6.5 contact hours. The first part establishes research design, data preparation and core analysis; the later modules cover advanced methods, reporting, governance and a capstone analysis presentation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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