Market Research and Business Intelligence Training Course

Duration 10 days 20 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Market Research Managers, Marketing Managers and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-M-031

Before you join: Participants should have practical experience in a marketing, sales, product, customer experience, strategy or analytics role.

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 intelligence turns uncertain market signals into decisions on customers, products, pricing, channels and competitive response. Many teams collect survey results, sales reports and digital data but struggle to frame the right question, judge evidence quality or convert findings into a decision that commercial leaders can act on. Poorly designed research wastes budget, overstates confidence and can send product, marketing or service teams towards the wrong priority.

This course develops the practical discipline behind market research and business intelligence. Participants define decision-focused research briefs, select qualitative and quantitative methods, build sampling plans, write usable questionnaires and discussion guides, and assess data quality. They analyse market size, segments, customer needs, brand performance, competitor activity and commercial data. The course also covers dashboards, data visualisation, insight storytelling, ethical research conduct and the governance needed to protect respondents and business users.

Teaching combines short instructor briefings with realistic cases from banking, consumer goods, public services, technology and B2B markets. Participants work with research briefs, sample datasets, interview evidence, survey results and competitive intelligence sources. From the first week, each participant develops a market intelligence project relevant to their role. They leave with a completed research and intelligence plan, analysis outputs and an executive-ready recommendation pack.

The course suits professionals who already work with customers, markets, products, sales or performance data and need a more disciplined way to commission, conduct or interpret research. It is particularly relevant where decisions need defensible evidence rather than opinion, isolated metrics or anecdotal customer feedback.

What you will be able to do

  • Frame decision-led research questions using a structured research brief
  • Select qualitative, quantitative and secondary research methods for defined decisions
  • Design sampling plans, questionnaires and discussion guides that produce usable evidence
  • Analyse survey, interview and commercial data using segmentation and cross-tabulation
  • Estimate market size using top-down, bottom-up and value-chain approaches
  • Evaluate competitors through structured intelligence collection and comparative analysis
  • Build executive dashboards and insight narratives from validated market evidence
  • Present a complete market research and business intelligence recommendation pack

Benefits of attending

For you

  • Produce research briefs that secure clearer stakeholder decisions and supplier proposals
  • Challenge weak sampling, biased questions and unsupported claims with confidence
  • Turn customer and market data into credible commercial recommendations
  • Present competitor, segment and market evidence to senior decision-makers
  • Build a reusable intelligence project for an active business priority

For your organisation

  • Reduce research spend on poorly framed studies and unusable outputs
  • Improve product, pricing and campaign decisions with defensible market evidence
  • Identify customer segments and unmet needs with clearer commercial value
  • Create consistent standards for research design, data quality and reporting
  • Strengthen competitive monitoring and earlier response to market threats

Course modules

20 modules over 10 days

Participants can define the business decisions that market intelligence must support.

  • Decision hierarchy linking strategic choices, tactical actions and information needs
  • Market intelligence cycle from decision framing through monitoring and review
  • Distinguishing market research, business intelligence and competitive intelligence outputs
  • Decision rights mapped using RACI for research sponsorship and approval
  • Leading and lagging indicators identified for commercial decision-making
  • Evidence standards separating facts, assumptions, hypotheses and recommendations

Participants can convert a broad business issue into a decision-focused research brief.

  • SMART decision statements replacing vague requests for customer understanding
  • Research objectives aligned to decisions, audiences, timing and budget constraints
  • Hypothesis trees used to structure causes, questions and evidence requirements
  • Stakeholder interviews uncovering hidden assumptions and conflicting success criteria
  • Scope boundaries defining markets, customers, geographies and product categories
  • Research brief template specifying deliverables, milestones, governance and acceptance criteria

Participants can find, assess and document credible secondary market evidence.

  • Desk research plan organised by question, source, date and reliability
  • Government statistics assessed for definitions, coverage, timeliness and collection methods
  • Industry reports compared for methodology, sponsorship, assumptions and stated limitations
  • Company filings extracted for revenue, segments, investments and strategic priorities
  • Source triangulation resolving contradictory market estimates and competitor claims
  • Citation log recording provenance, access dates, caveats and reuse restrictions

Participants can assess market forces and external conditions affecting demand.

  • PESTLE analysis identifying regulatory, economic, social and technological demand drivers
  • Porter's Five Forces assessing rivalry, substitutes and buyer bargaining power
  • Value-chain mapping locating profit pools, gatekeepers and channel dependencies
  • Market definition using customer need, product scope and geographic boundaries
  • Demand drivers separated from market constraints and temporary demand shocks
  • Scenario assumptions documenting plausible changes in regulation, costs and behaviour

Participants can estimate market opportunity and explain the assumptions behind forecasts.

  • TAM, SAM and SOM calculated from defined customer and product boundaries
  • Top-down market sizing using published totals and transparent allocation assumptions
  • Bottom-up sizing built from customer counts, purchase frequency and spend
  • Value-chain sizing reconciled through units, prices and channel margins
  • Forecast scenarios applying growth rates, adoption curves and capacity constraints
  • Sensitivity analysis testing assumptions with the greatest effect on estimates

Participants can select a defensible research design for a specified decision.

  • Exploratory, descriptive and causal designs matched to decision uncertainty
  • Qualitative and quantitative methods compared against evidence requirements
  • Primary and secondary data combined through an evidence plan
  • Cross-sectional and longitudinal studies selected for tracking and change detection
  • Experimental design distinguishing correlation, causation and confounding variables
  • Method selection matrix balancing confidence, speed, access, cost and risk

Participants can plan qualitative research that reveals needs, meanings and barriers.

  • In-depth interviews structured around experiences, motivations, language and decision journeys
  • Focus groups designed for idea exploration, interaction and concept reactions
  • Ethnographic observation documenting behaviours, contexts, workarounds and unmet needs
  • Jobs to Be Done interviews identifying triggers, desired progress and obstacles
  • Discussion guides sequencing rapport, exploration, probes and closing questions
  • Thematic analysis coding transcripts into patterns, exceptions and supporting evidence

Participants can create sampling plans and fieldwork controls suited to research objectives.

  • Target population defined through inclusion criteria, exclusions and unit of analysis
  • Probability sampling methods: simple random, stratified, cluster and systematic samples
  • Non-probability sampling: quota, purposive, convenience and snowball recruitment
  • Sample size estimated using confidence level, margin of error and variability
  • Screening questions preventing ineligible, duplicate or professionally biased respondents
  • Fieldwork quality controls covering quotas, callbacks, validation and interviewer supervision

Participants can write and review surveys that produce valid, analysable responses.

  • Questionnaire flow moving from screening through behaviour, attitudes and demographics
  • Closed questions written with mutually exclusive and collectively exhaustive response options
  • Likert scales designed with balanced anchors and consistent directionality
  • Avoiding leading, double-barrelled, loaded and ambiguous survey questions
  • Skip logic and routing rules reducing respondent burden and data errors
  • Pilot testing checking comprehension, timing, technical defects and response distributions

Participants can apply ethical and data governance controls to research activities.

  • Informed consent covering purpose, participation, recording, storage and withdrawal
  • Personal data minimisation applied to research collection and retention plans
  • Anonymisation and pseudonymisation distinguished for reporting and data sharing
  • ESOMAR and ICC principles applied to respondent treatment and transparency
  • Research risks assessed for vulnerable groups, sensitive topics and power imbalances
  • Data access controls assigning ownership, permissions, retention and disposal responsibilities

Participants can prepare research datasets and assess whether analysis is trustworthy.

  • Data dictionary defining variables, codes, formats, labels and permitted values
  • Cleaning rules identifying duplicates, missing values, outliers and inconsistent responses
  • Straight-lining and speeders flagged as indicators of low-quality survey completion
  • Missing-data treatment selecting deletion, imputation or explicit reporting approaches
  • Weighting adjustments correcting known sample imbalances against target population profiles
  • Quality assurance log recording transformations, exclusions, checks and unresolved issues

Participants can summarise survey data and identify meaningful subgroup differences.

  • Frequency tables reporting counts, percentages, bases and missing responses
  • Measures of central tendency compared: mean, median and mode
  • Cross-tabulations revealing differences by segment, channel, geography and customer type
  • Statistical significance interpreted without overstating practical business importance
  • Index scores benchmarking brand, service or product measures against reference groups
  • Open-text responses coded into categories, themes and quantified issue frequencies

Participants can build actionable customer segments from needs, behaviour and value.

  • Segmentation bases compared: demographic, behavioural, needs-based and value-based approaches
  • Segment criteria assessing measurability, accessibility, distinctiveness and commercial attractiveness
  • Cluster analysis outputs interpreted with business judgement and operational constraints
  • Segment profiles combining needs, behaviours, economics, triggers and channel preferences
  • Personas grounded in evidence rather than fictional detail or internal assumptions
  • Targeting priorities ranked using segment size, growth, fit and profit potential

Participants can identify experience priorities from customer journey evidence.

  • Journey maps documenting stages, tasks, channels, emotions and handoffs
  • Moments of truth identified through impact, frequency and failure consequences
  • Voice of Customer evidence integrated from surveys, complaints, calls and reviews
  • NPS, CSAT and CES selected for distinct experience measurement purposes
  • Root-cause analysis linking customer pain points to process and policy failures
  • Service priorities ranked using customer impact, feasibility and ownership

Participants can assess brand, product and pricing questions using appropriate research techniques.

  • Brand funnel measures tracking awareness, consideration, preference and purchase
  • Concept testing assessing relevance, differentiation, credibility and purchase intention
  • Product feature prioritisation using Kano model categories and customer evidence
  • Price sensitivity explored through Van Westendorp price sensitivity meter
  • Conjoint analysis principles estimating feature trade-offs and willingness to pay
  • Usage and attitude studies linking category behaviour, needs and brand choice

Participants can collect and interpret competitor evidence within ethical boundaries.

  • Competitor profiles covering offerings, customers, channels, capabilities and strategic moves
  • Win-loss analysis extracting reasons for selection, rejection and switching
  • Battlecards comparing claims, proof points, objections and response guidance
  • Public-source monitoring tracking filings, tenders, launches, hiring and partnerships
  • Competitor benchmarking using comparable criteria, evidence dates and confidence ratings
  • Early-warning indicators identifying likely pricing, channel or product changes

Participants can combine commercial data with research evidence to inform action.

  • Sales funnel analysis locating conversion losses by segment, channel and stage
  • Cohort analysis comparing retention, repeat purchase and value over time
  • Customer lifetime value components linking acquisition, margin, retention and service costs
  • CRM data quality checked for duplicates, missing fields and inconsistent definitions
  • Research findings joined with sales data through common segment identifiers
  • KPI trees connecting operational measures to revenue, margin and customer outcomes

Participants can design decision-useful dashboards and visual evidence displays.

  • Dashboard audience defined by decisions, cadence, thresholds and required actions
  • KPI definitions documenting formula, data source, owner and refresh frequency
  • Chart selection matching comparison, trend, distribution, relationship and composition questions
  • Data-ink ratio improved by removing decorative elements and redundant labels
  • Annotations explaining inflection points, exceptions, targets and decision implications
  • Dashboard governance controlling versioning, access, validation and change requests

Participants can turn multiple evidence sources into prioritised business recommendations.

  • Insight statements connecting evidence, interpretation, implication and recommended action
  • Triangulation matrix comparing findings across research, operational and external data
  • Issue prioritisation using impact, confidence, urgency and implementation effort
  • Assumption register separating validated conclusions from unresolved decision risks
  • Recommendation roadmaps assigning actions, owners, milestones and success measures
  • Executive storyline structured around decision, evidence, options and requested commitment

Participants can build, review and present a complete market research and business intelligence recommendation pack.

  • Decision brief defining sponsor, decision, objectives, scope and success measures
  • Evidence plan combining secondary sources, primary research and commercial data
  • Sampling, questionnaire or interview guide selected for the stated decision
  • Market, segment and competitor analyses consolidated into defensible findings
  • Dashboard and executive narrative presenting priorities, risks and recommended actions
  • Peer review challenging assumptions, evidence quality, feasibility and decision relevance

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

What you take back to work

  1. Decision-focused research brief template
  2. Research design and method selection matrix
  3. Sampling and questionnaire quality checklist
  4. Market sizing and forecast model
  5. Competitive intelligence profile template
  6. Executive market intelligence recommendation pack

Who should attend

  • Market Research Managers — improve study design, supplier briefing and insight delivery
  • Marketing Managers — base campaign, brand and portfolio decisions on evidence
  • Business Intelligence Analysts — connect commercial data with customer and market evidence
  • Product Managers — test needs, concepts, pricing and adoption barriers
  • Sales Directors — identify account opportunities, buyer priorities and competitor threats
  • Customer Experience Managers — convert feedback into service improvement priorities

Skills and tools covered

Research brief designCustomer segmentationSurvey analysisMarket sizingCompetitive intelligenceInsight communication

Tools and standards

PESTLE analysis, Porter's Five Forces, STP marketing framework, SWOT analysis

How the course runs

Training methodology

The course uses short briefings to introduce methods, followed by case work using market reports, customer evidence, survey data and commercial performance data. Participants assess research briefs, compare study designs, interpret findings and make decisions under realistic constraints. Group exercises include questionnaire review, segmentation, competitor analysis and dashboard critique. Simulations test how evidence holds up when challenged by executives. Individual application planning connects each module to a current work question, culminating in a reviewed market intelligence project.

Requirements and prerequisites

Participants should have practical experience in a marketing, sales, product, customer experience, strategy or analytics role. They should be comfortable reading tables, percentages, charts and basic business reports, and should understand their organisation’s customers, offerings or markets. Experience commissioning research, using statistical software or building dashboards is helpful but not required. The course does not require prior training in statistics, coding, data science or advanced mathematics. Participants will gain more value by bringing a current market, customer, product or competitive question from their work.

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 have experience in a commercial, marketing, product, customer or analytics role and be comfortable with basic charts and percentages. You do not need prior statistical training, coding skills or formal market research qualifications.

Bring a current market, customer, product, pricing or competitor question from your work where possible. You may also bring non-confidential reports, dashboards or research briefs for reference.

It is designed for professionals who commission, conduct, interpret or act on market and customer evidence. It suits both research specialists and commercial managers who need to challenge research quality and use findings in decisions.

This course focuses on deciding what evidence is needed, gathering it ethically, evaluating its quality and converting it into market decisions. Data analytics courses usually concentrate more heavily on software, statistical modelling or coding, while marketing strategy courses focus more on campaign and portfolio choices.

Participants build a decision-focused intelligence project during the course using a relevant business issue. The templates support research commissioning, supplier management, survey review, market sizing, competitor monitoring and executive reporting.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours. The first week establishes research design and evidence collection, while the second week develops analysis, intelligence reporting and the final capstone 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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