Collecting Data for a Six Forces Analysis: A Structured Market Research Process
This skill teaches you a repeatable market research process for sourcing, validating, and organizing the specific data points needed to score each of the six forces with confidence rather than guesswork.
Start by listing the specific data points each force requires, such as market share figures for rivalry, switching cost estimates for buyer power, and adoption rates for complementary products. Then layer three source types per force: published industry reports for baseline numbers, public financial filings for company-level data, and primary research like surveys or expert interviews for gaps. Cross-reference at least two independent sources per claim before scoring any force.
Outcome: You produce a fully sourced data workbook where every force has 3-5 validated data points, each with cited origins and a confidence rating, giving your team the evidence needed to score forces accurately and defend strategic recommendations.
Prerequisites
- Basic understanding of the Six Forces Model and what each force measures
- Familiarity with the industry or market you are analyzing
- Access to at least one industry database or report library (e.g., IBISWorld, Statista, or a university library)
- A spreadsheet tool for organizing and tracking data sources
Overview
A Six Forces analysis is only as credible as the data behind it. Teams frequently jump from naming the six forces straight to scoring them, filling cells with gut feelings dressed up as assessments. The result is a framework that looks rigorous on the surface but collapses under the first hard question from a stakeholder. This skill addresses that gap head-on. It gives you a structured market research process for identifying exactly what data each force requires, sourcing that data from a deliberate mix of primary and secondary channels, and validating every claim before it enters your scoring model.
Within the Six Forces Model, data collection sits between the initial scoping phase (where you define the industry boundaries and the forces you will evaluate) and the scoring and synthesis phase (where you rate each force's intensity and draw strategic conclusions). It is the connective tissue that determines whether your analysis produces actionable strategy or decorative slides. The artifact you produce is a data workbook, typically a spreadsheet with one tab per force, listing each data point, its source, the date of the source, a confidence tag (high, medium, low), and any notes on methodology or caveats.
The process works across industries and company sizes, but the mix of sources shifts depending on context. A startup analyzing a nascent market will lean heavily on expert interviews and analogous industry data because published reports may not exist yet. A large enterprise evaluating a mature industry can draw on decades of public filings, trade association statistics, and proprietary customer data. Regardless of context, the principle is the same: layer multiple independent sources per force, flag gaps explicitly, and never let a single uncorroborated data point drive a strategic decision. When done well, this market research process transforms your Six Forces output from opinion into evidence.
Successful data collection also creates a reusable asset. Markets shift, new entrants appear, and supplier dynamics change. A well-structured data workbook with clear source citations can be refreshed quarterly or annually with a fraction of the original effort, turning a one-time analysis into an ongoing strategic monitoring tool.
How It Works
The core idea behind this skill is triangulation. No single data source tells the full story of a competitive force, because every source carries its own bias. Industry reports from market research firms tend to be optimistic about market size because their clients want growing markets. Company filings present the best possible version of competitive positioning. Customer surveys reflect stated preferences, which often diverge from actual behavior. By requiring at least two independent sources per data point, you create a built-in error-correction mechanism. When two sources agree, your confidence rises. When they disagree, you have surfaced a genuine uncertainty that should be flagged in your analysis rather than papered over with a single convenient number.
The market research process for a six forces analysis works in three layers. The first layer is secondary research, meaning data someone else has already collected and published. This includes industry reports from firms like IBISWorld, Gartner, or McKinsey, government statistics from agencies like the Bureau of Labor Statistics or Eurostat, trade association publications, academic studies, and public company filings (10-Ks, earnings transcripts, investor presentations). Secondary research gives you the broadest coverage for the least effort, and it is where you should start. The second layer is structured primary research, meaning data you collect yourself through surveys, structured interviews with industry experts, or analysis of your own company's internal data (customer churn rates, pricing history, support ticket themes). Primary research fills the gaps that secondary sources cannot cover, particularly around buyer behavior, switching costs, and complementary product dependencies that are specific to your market niche. The third layer is observational and informal data: competitor website monitoring, social media sentiment, trade show observations, sales team anecdotes. This layer is the weakest in isolation but valuable for generating hypotheses and catching signals that formal sources miss.
Each of the six forces in the Six Forces Model has a different data profile. Industry rivalry demands market share figures, concentration ratios, growth rates, and exit barrier indicators. Buyer power needs customer concentration data, switching cost estimates, and price sensitivity measures. Supplier power requires input cost trends, supplier market concentration, and availability of substitutes for key inputs. Threat of new entrants calls for capital requirements, regulatory barriers, and recent entry/exit events. Threat of substitutes needs cross-elasticity indicators and adoption trend data for alternative solutions. Complementary products, the sixth force, requires ecosystem mapping data: adoption rates of complementary offerings, integration dependency metrics, and standards or platform lock-in indicators.
The reason you organize collection by force rather than by source type is efficiency and completeness. If you organize by source ("let's read all the industry reports first"), you will end up with rich data for the forces that reports happen to cover well (usually rivalry and market size) and near-empty cells for forces that require more targeted research (usually complementary products and supplier power). Organizing by force ensures you notice gaps early and allocate primary research effort where it matters most, rather than where it is easiest.
Step-by-Step
Step 1: Define the data requirements for each force
Open a new spreadsheet and create one tab for each of the six forces: industry rivalry, buyer power, supplier power, threat of new entrants, threat of substitutes, and complementary products. In each tab, list the specific data points you need to evaluate that force. For industry rivalry, you might list: number of competitors, market share of top five firms, industry growth rate (3-year CAGR), average operating margin, and exit barriers. For buyer power: top 10 customer concentration percentage, average contract length, estimated switching costs, and price sensitivity indicators.
Do this for all six forces before you search for a single source. The goal is a complete wish list, not a perfect one. You will likely add or remove items as you research, but starting with an explicit list prevents you from unconsciously skipping forces that are harder to research. Aim for 4-7 data points per force.
Tip: Reference the sibling skill on evaluating buyer and supplier bargaining power and the skill on assessing threats of new entrants and substitutes. Their scoring criteria tell you exactly which data points matter most, so you can prioritize your collection around the variables that actually influence the final score.
Step 2: Inventory your existing data and identify gaps
Before launching new research, audit what you already have. Pull together internal data your company possesses: CRM data on customer concentration, pricing history, churn rates, sales win/loss reports, supplier contracts, and product analytics. Check what industry reports your team has already purchased or what your library provides access to. ' This inventory step typically eliminates 20-30% of your research list immediately, because companies often have relevant internal data sitting in different departments that nobody has connected to competitive analysis before.
It also focuses your research budget on the true gaps rather than duplicating data you already possess.
Tip: Talk to your sales, procurement, and customer success teams directly. They often have qualitative and quantitative data that never makes it into formal reports. A 15-minute conversation with a procurement manager can surface supplier concentration data that would take hours to piece together from public sources.
Step 3: Source secondary data for each force
Work through your 'need' and 'partial' items force by force. For each data point, identify at least two potential secondary sources. Start with the broadest, most accessible sources: industry overview reports (IBISWorld, Statista, or equivalent), government statistical databases (Census Bureau, BLS, Eurostat), and trade association annual reports. Then move to company-specific sources: public company filings (SEC EDGAR for US companies, Companies House for UK), earnings call transcripts (available free on Seeking Alpha or company investor relations pages), and investor presentations.
For each data point you find, record the source name, publication date, the specific figure, and the URL or document reference in your workbook. If a source is behind a paywall you cannot access, note it as a gap and move on. Do not spend more than 30 minutes searching for any single data point at this stage. If you cannot find it in secondary sources within that window, flag it for primary research in the next step.
Tip: Earnings call transcripts are underused gold mines for competitive data. CEOs and CFOs regularly discuss market share shifts, pricing pressure, supplier dynamics, and competitive threats in Q&A sessions with analysts. Search transcripts for keywords related to each force.
Step 4: Design and execute targeted primary research
Review the remaining gaps in your workbook. These are data points that secondary sources could not fill or where you found only a single source and need corroboration. For each gap, choose the most efficient primary research method. Expert interviews (30-minute calls with industry veterans, former employees of competitors, or industry analysts) are best for qualitative insights on barriers to entry, switching costs, and emerging substitutes.
Customer surveys (even a 5-question survey to 50-100 existing customers) are best for buyer power data: price sensitivity, perceived switching costs, and awareness of alternatives. Internal data analysis is best for complementary product dependencies: analyze which integrations your customers actually use, which partner products appear in your sales pipeline, and what support tickets mention third-party tools. For surveys, keep them short, use rating scales for quantitative data, and include one open-ended question for unexpected insights. For expert interviews, prepare 5-7 specific questions tied directly to your data gaps rather than broad, exploratory questions.
Tip: If budget is tight, you can find industry experts willing to do brief informational interviews for free through LinkedIn outreach. Frame your request as a specific, bounded ask ('I have 4 questions about supplier dynamics in the packaging industry, would you have 20 minutes this week?') rather than a vague 'I'd love to pick your brain.'
Step 5: Validate and cross-reference every data point
Go through each tab of your workbook and check that every data point has at least two independent sources or an explicit justification for relying on a single source. For quantitative figures (market share, growth rates, pricing), compare the numbers across sources. 5B, that is reasonable agreement, and you can use a midpoint or range. If one says $4B and another says $8B, you have a methodology difference to investigate before trusting either figure.
Check the date of each source. Data older than 2 years should be flagged and supplemented with a more recent source if possible, especially for fast-moving markets. For qualitative claims (such as 'switching costs are high'), verify that you have at least one concrete data point supporting the claim, not just an expert opinion. A claim like 'switching costs are high because average implementation takes 6 months and costs $200K' is defensible.
A claim like 'switching costs are high because an analyst said so' is not.
Tip: Create a simple confidence rating system in your workbook. Use 'High' (two or more recent, independent, quantitative sources agree), 'Medium' (one strong quantitative source plus qualitative corroboration), and 'Low' (single source, qualitative only, or data older than 3 years). This rating flows directly into your force scoring and signals where your analysis is most vulnerable.
Step 6: Normalize data formats and fill the workbook
With validated data in hand, standardize the formats across your workbook so the scoring team can work with consistent inputs. Convert all monetary figures to the same currency and year (adjust for inflation if comparing across years). Express market share as percentages, not revenue figures, so they are comparable. Convert growth rates to the same time horizon (annualized is the standard).
For qualitative data, write a 1-2 sentence summary of the finding and the source rather than pasting in raw quotes. Each row in your workbook should now have: the data point label, the value or finding, the source citation, the source date, the confidence rating, and any caveats. Review the completed workbook for internal consistency. If your rivalry tab shows an industry growing at 15% annually but your new entrants tab shows very low entry activity, that is a potential contradiction worth investigating.
Tip: Add a 'caveats' column for each data point. This is where you note things like 'figure includes adjacent market segments,' 'survey sample was US-only,' or 'data predates the 2024 regulatory change.' These caveats are invaluable during scoring discussions because they prevent over-anchoring on a precise number that deserves a range.
Step 7: Document gaps and uncertainty explicitly
No data collection effort is perfectly complete. The final step is to create a summary sheet in your workbook that lists every remaining gap, partial data point, or low-confidence item across all six forces. ), and what proxy or estimate you are using instead. This gap register serves two purposes.
First, it signals to the scoring team where they need to apply wider confidence intervals rather than treating a rough estimate as a precise input. Second, it creates a research backlog for the next refresh cycle, so you know exactly where to focus effort when you update the analysis in six months. Present the gap register alongside the completed data workbook when handing off to the team that will score the forces.
Tip: Frame gaps as explicit assumptions rather than failures. 'We assume supplier concentration is moderate based on three data points from the adjacent European market because US-specific data is unavailable' is a transparent, usable input. A blank cell with no explanation is a liability.
Examples
Example: B2B SaaS startup entering the project management tools market
A 30-person SaaS company is preparing a Six Forces analysis of the project management software market to inform its product positioning and pricing strategy. The team has two people available for research, a modest budget (no expensive report subscriptions), and needs results within one week. The market is mature with well-known incumbents (Asana, Monday.com, Jira, Trello) and extensive public data.
The team starts by listing data requirements for each force. For rivalry, they need: number of active competitors, market share of the top five, industry growth rate, and average pricing per seat. For buyer power: customer churn rates, average contract length, and price sensitivity (willingness to switch for a 20% discount). For the complementary products force: which integrations are most used, and how dependent are users on ecosystem connections.
They inventory existing data and find that their own CRM has win/loss data against 12 competitors, and their product analytics show which integrations customers activate most. This fills several buyer power and complementary product cells immediately. com is public) for revenue and growth, G2 and Capterra for competitor counts and review sentiment, and LinkedIn for headcount growth as a proxy for new entrant activity. For primary research, they send a 6-question survey to 80 existing customers about switching costs, integration dependencies, and substitute tools they considered.
They get 35 responses in 4 days. The completed workbook has 28 data points across six forces, with 22 rated high or medium confidence and 6 rated low confidence. The low-confidence items are concentrated in supplier power (their cloud infrastructure costs are straightforward, but they have limited data on how competitors negotiate hosting contracts). They document this gap and proceed to scoring with an explicit assumption that supplier power is low to moderate, subject to revision.
Example: Large manufacturer evaluating the industrial packaging industry
A Fortune 500 packaging manufacturer wants to assess whether to expand into sustainable packaging. The team has a dedicated strategy analyst, access to IBISWorld and Euromonitor, and 3 weeks for the analysis. The industry is mature but undergoing a regulatory-driven shift toward recyclable materials, creating new entrant activity and changing supplier dynamics.
The analyst builds the data workbook and immediately identifies that the complementary products force is unusually important here: sustainable packaging depends on recycling infrastructure, compostable material suppliers, and brand certification programs (like FSC or cradle-to-cradle). Standard industry reports cover rivalry and market size well but say little about these dependencies. For secondary research, the analyst pulls IBISWorld data on the packaging industry (market size, concentration ratios, growth rates) and Euromonitor data on sustainable packaging specifically. Public filings from Amcor, Berry Global, and Sealed Air provide competitor revenue breakdowns and capital expenditure data relevant to entry barriers.
EU regulatory databases provide data on upcoming packaging regulations that affect threat of substitutes and new entrant incentives. For primary research, the analyst conducts four 30-minute interviews: a recycling facility operator (supplier power for sustainable inputs), a CPG brand sustainability officer (buyer power and willingness to pay premiums), a startup founder in compostable packaging (new entrant perspective), and an industry association director (complementary products ecosystem). These interviews fill 9 data gaps that no published source covered. The final workbook has 42 data points, with the complementary products tab being the richest because the analyst planned primary research for it from the beginning.
The gap register notes that supplier power data for bio-based resins is thin because the market is nascent, and the team will monitor this quarterly.
Example: E-commerce brand analyzing the direct-to-consumer apparel market
A mid-stage DTC apparel brand (50 employees, $15M annual revenue) is conducting a Six Forces analysis to decide whether to expand into a new product category (activewear). The team has one marketing strategist leading the research, no industry database subscriptions, and two weeks. The market is fragmented with both large incumbents (Nike, Lululemon) and hundreds of small DTC brands.
The strategist defines data requirements with a focus on the forces most relevant to a DTC expansion: buyer power (brand loyalty, switching costs in apparel are nearly zero), threat of new entrants (low capital requirements for DTC apparel), and rivalry (extreme fragmentation). For secondary research, the strategist uses free sources: Shopify's annual commerce reports for DTC market trends, Nike and Lululemon investor presentations for market sizing and growth rates, SimilarWeb free tier for competitor traffic estimates, and Reddit's r/activewear and fitness communities for consumer sentiment on switching between brands. For supplier power, the strategist contacts three contract manufacturers via email with standardized questions about minimum order quantities, lead times, and capacity constraints. Two respond within a week.
For buyer power primary research, the strategist runs a 4-question Instagram poll to their brand's 40K followers asking about brand loyalty, price sensitivity, and substitute products they consider (yoga studios, fitness apps as alternatives to buying new gear). For complementary products, the strategist maps the ecosystem: fitness tracking apps, gym memberships, nutrition brands, and wellness influencers that DTC activewear brands partner with. The team's own affiliate data shows which complementary brands drive the most referral traffic. The completed workbook has 24 data points.
Confidence is highest for rivalry and buyer power (abundant public data plus primary research) and lowest for supplier power (only two manufacturer responses). The strategist flags supplier power as the area needing the most attention in the next research cycle and proceeds to scoring.
Example: Healthcare technology company analyzing the telehealth platform market
A Series B healthtech company ($8M ARR, 80 employees) needs a Six Forces analysis to support a board presentation on market expansion. The VP of Strategy leads the effort with support from a business analyst. They have access to Gartner and CB Insights, strong relationships with hospital system CIOs, and four weeks for the project. The market is heavily regulated with high barriers to entry.
The team recognizes that this market's distinctive features are extreme regulatory barriers (threat of new entrants), high switching costs due to EHR integration requirements (buyer power and complementary products), and a small number of large health system buyers (buyer concentration). They structure data requirements accordingly, allocating extra data points to these critical forces. For secondary research, Gartner provides market sizing and vendor landscape reports that cover rivalry comprehensively. CB Insights provides funding data for new entrants and M&A activity as an exit barrier indicator.
CMS (Centers for Medicare and Medicaid Services) data provides reimbursement policy changes affecting substitute threats. FDA databases provide regulatory timeline data relevant to entry barriers. For primary research, the VP leverages existing relationships to conduct six 20-minute interviews with hospital CIOs about switching costs, integration dependencies, and vendor evaluation criteria. The business analyst surveys 30 of the company's current customers about which complementary products they use (EHR systems, patient engagement tools, billing platforms) and how dependent their workflows are on these integrations.
The final workbook has 38 data points with unusually high confidence in buyer power and complementary products (thanks to direct access to buyers) and moderate confidence in supplier power (cloud infrastructure and data hosting costs are well-documented, but specialized healthcare AI talent supply is harder to quantify). The board presentation includes the gap register as an appendix, and the board specifically commends the transparency about data limitations.
Best Practices
Organize your data collection by force, not by source type. If you start by reading all available industry reports, you will end up with deep data on rivalry and market size but near-empty tabs for complementary products and supplier power. Working force by force ensures you notice and address gaps early, distributing your research effort across all six forces proportionally to their strategic importance.
Set a time box for secondary research before escalating to primary research. Allocate no more than 30-45 minutes of searching per data point in secondary sources. If you cannot find a credible source in that window, the data point probably requires primary research (an interview, a survey, or internal data analysis). Without a time box, researchers spend hours chasing published data that does not exist while neglecting the primary research that would actually fill the gap.
Record the full source citation at the moment you find the data, not after the fact. This means the document title, author or publisher, publication date, page number or URL, and access date. Researchers who plan to 'go back and add citations later' routinely lose track of where specific figures came from, which undermines the entire validation process and creates embarrassing moments when stakeholders ask for the source behind a key claim.
Refresh your data workbook on a regular cadence tied to your strategic planning cycle. For most industries, a quarterly scan of key metrics (market share shifts, new entrant activity, pricing changes) plus an annual deep refresh is sufficient. Without a refresh cadence, your Six Forces analysis degrades into a historical artifact within 6-12 months, and teams default back to gut-feel decision making.
Include at least one disconfirming search for each force. After you have assembled data supporting your initial hypothesis about a force's intensity, deliberately search for evidence that contradicts it. If you believe buyer power is low, search for 'customer churn [industry]' or 'price war [industry]' to see if you are missing signals. This practice counteracts confirmation bias, which is the single most common source of error in competitive analysis.
Tag every data point with its original context and scope. A market share figure for 'the global CRM market' is not interchangeable with one for 'the US mid-market CRM segment.' Mixing scopes is a subtle error that compounds when you combine data points to score a force. Your workbook should make scope visible at a glance, so the scoring team never accidentally compares apples to oranges.
Keep raw data and interpreted data in separate columns. Your workbook should have one column for the literal figure or quote from the source and a separate column for your team's interpretation or adjusted estimate. This separation preserves the audit trail and allows a new team member to review the raw inputs independently rather than inheriting someone else's interpretation as fact.
Common Mistakes
Relying on a single industry report as the sole data source for multiple forces
Correction
This happens because one comprehensive report feels like it covers everything, and researchers stop looking once the cells are filled. The problem is that a single report carries a single methodology bias. Market sizing firms may define industry boundaries differently than your strategy requires, or they may exclude segments that matter to your analysis. You can catch this early by checking whether your source citations column shows the same publisher appearing in more than 40% of rows.
If it does, deliberately seek alternative sources for the data points that report covers, even if the original numbers seem reasonable.
Skipping the complementary products force because data is harder to find
Correction
The sixth force (complementary products) rarely appears in standard industry reports, so teams leave it blank or fill it with speculation. This defeats the purpose of using a six forces framework instead of the classic five. The fix is to plan primary research for this force from the start. Map your product's integration ecosystem, survey customers about which adjacent products they use alongside yours, and check platform marketplace data (app stores, integration directories, partner program listings).
These sources are not traditional market research, but they yield concrete data about complementary product dependencies.
Treating qualitative expert opinions as equivalent to quantitative data
Correction
This occurs when a single interview quote like 'switching costs in this market are very high' gets entered into the workbook and treated with the same confidence as a measured figure. The problem surfaces during scoring, where the team anchors on the opinion and assigns a high rating without probing further. Prevent this by requiring every qualitative claim to be accompanied by at least one supporting fact. 'Switching costs are high' should be paired with a data point like 'average implementation timeline is 9 months' or 'contractual lock-in period is 3 years.' If you cannot find a supporting fact, rate the data point as low confidence.
Collecting data at the wrong level of granularity for the strategic question
Correction
Teams often default to the broadest available data (global market size, total industry revenue) when their strategic question is about a specific segment, geography, or customer tier. This happens because broad data is easier to find. You can catch this mistake by revisiting your original analysis scope before filling in any data point. If your strategy question is about the European mid-market, a global market share figure for the entire industry is misleading and should be flagged.
Record the broad figure as context but note that segment-specific data is still needed.
Spending all research time on rivalry and neglecting the other five forces
Correction
Rivalry data (market share, competitor counts, growth rates) is the most abundantly published and therefore the easiest to collect. Teams naturally gravitate toward what is available, and the result is a workbook with a richly detailed rivalry tab and sparse tabs for everything else. Time-boxing by force (allocate roughly equal research time to each force initially, then redistribute based on gaps) prevents this imbalance. Check your workbook midway through the process.
If rivalry has 15 data points and supplier power has 2, you have fallen into this trap.
Failing to record the date and methodology of each source
Correction
Without dates, you cannot judge whether a data point is current or stale. Without methodology notes, you cannot explain discrepancies between sources. This mistake usually manifests months later, when the team tries to refresh the analysis and cannot determine which figures need updating or why two sources disagreed. The fix is simple but requires discipline: add a 'date' and 'methodology notes' column to your workbook template from the start, and make it a rule that no data point is complete until both columns are filled.
Other Skills in This Method
Selecting Tools and Templates for Six Forces Research
How to choose and configure market research tools, visualization software, and scoring templates to streamline and standardize a Six Forces analysis.
Mapping the Complementary Products Force
How to identify, evaluate, and analyze the sixth force—complementary products and services—that distinguishes the Six Forces Model from Porter's Five Forces.
Synthesizing Six Forces into Strategic Recommendations
How to combine findings from all six forces into a cohesive strategic positioning map that guides investment, partnership, and competitive strategy decisions.
Conducting an Industry Rivalry Assessment
How to systematically research and evaluate the intensity of competitive rivalry within an industry using both qualitative and quantitative market research methods.
Evaluating Buyer and Supplier Bargaining Power
How to gather primary and secondary market research data to assess the bargaining power of buyers and suppliers as distinct forces shaping industry profitability.
Assessing Threats of New Entrants and Substitutes
How to use consumer market research and competitive intelligence to quantify the threats posed by new market entrants and substitute products or services.
Frequently Asked Questions
How long should the full market research process take for a Six Forces analysis?
For a focused analysis with a clear industry scope, expect 4-8 hours of active research time spread across 2-3 sessions. The first session covers defining data requirements and inventorying existing data (1-2 hours). The second session covers secondary research (2-3 hours). The third session covers primary research follow-up and validation (1-3 hours). If you compress this into a single day, you will likely skip validation and primary research, which are the steps that differentiate a credible analysis from a superficial one. If your industry is complex or you lack database access, add an extra session.
Should I collect data for all six forces simultaneously or one at a time?
Work through them one at a time, completing each force's data requirements before moving to the next. This prevents the common trap of spending all your research time on rivalry (because data is most abundant there) and running out of time for the other forces. Start with the force you expect to be hardest to research, which is usually complementary products or supplier power, because you will need to plan primary research for those and primary research has lead time. Finish with rivalry, where published data is most plentiful.
What do I do when I cannot find reliable data for a specific force?
Document the gap explicitly rather than filling it with speculation. Note what sources you checked, why the data is unavailable, and what proxy you are using instead. Proxies from analogous industries or adjacent markets are acceptable if clearly labeled. For example, if you are analyzing a nascent market with no published market share data, you might use website traffic share (from SimilarWeb) or app download share as a proxy for market share. The key is transparency: your scoring team needs to know which inputs are measured and which are estimated so they can adjust confidence accordingly.
How do I collect data for the complementary products force when no reports cover it?
This force almost always requires primary research because standard industry reports rarely analyze ecosystem dependencies. Start with your own product data: which integrations do customers activate, which partner products appear in your sales process, and what third-party tools do support tickets mention. Then survey customers about their full technology or product stack. Check platform marketplaces and integration directories (like Zapier's integration pages or app store listings) for adoption indicators. Expert interviews with ecosystem partners or platform operators can fill remaining gaps. The sibling skill on mapping the complementary products force at /skills/mapping-complementary-products-force provides detailed guidance on structuring this research.
Can I use AI tools to speed up the secondary research process?
AI search tools like Perplexity and ChatGPT with web search can accelerate the initial source discovery phase significantly, especially for finding industry reports, locating public filings, and identifying relevant trade associations. Use them as a starting point to find sources, not as sources themselves. Always trace every AI-surfaced claim back to its original publication, verify the date, and confirm the figure matches what the original source actually says. AI tools sometimes hallucinate statistics or combine figures from different scopes and years. Treat AI-generated answers as research leads, not as data points for your workbook.
How many data points per force is enough?
Aim for 3-5 validated data points per force as a minimum viable baseline. Fewer than three typically means you are relying on a single perspective, which makes your force rating fragile. More than seven per force often introduces diminishing returns and analysis paralysis. The right number depends on the force's strategic importance to your specific question. If your entire strategy hinges on whether buyer power is high or low, invest in 5-7 robust data points for that force. If a force is clearly not a major factor (for example, supplier power for a pure-software company), three solid data points confirming that assessment are sufficient.
Should I complete data collection before or after selecting tools and templates for the analysis?
Select your tools and templates first. The sibling skill on selecting tools and templates at /skills/selecting-tools-for-six-forces-research helps you set up the workbook structure, scoring criteria, and output format before you start collecting data. If you collect data without a structured template, you will gather information in inconsistent formats, miss required fields, and spend extra time reformatting later. The template tells you exactly which cells need filling, which makes your data collection focused and efficient rather than exploratory and scattered.