How New Technologies Move From First Users to the Mainstream

An adoption curve can be both bell-shaped and S-shaped. New adopters per period are commonly illustrated with a bell-shaped curve, while cumulative adoption is commonly shown as an S-curve. A declining rate of new adoption therefore does not mean total cumulative adoption is falling. These are conventional views of diffusion, not shapes every product is guaranteed to follow. Rogers’ framework is commonly presented using both views.

For founders, the curve is most useful as a diagnostic and scenario tool. It can clarify what counts as adoption, which customers remain eligible, why uptake is accelerating or stalling, and what assumptions sit behind a forecast. It cannot prove that a product will reach the mainstream.

The adoption curve in one view: rate, cumulative uptake, and remaining market

An adoption curve describes how uptake changes over time within a defined market or social system. The definition of that market matters: a curve for US enterprise deployments will differ from one for individual users worldwide, even if both concern the same product.

The clearest visual uses three synchronized panels:

Illustrative adoption event: completed production deployment
Illustrative eligible market: firms in one defined segment
Shared horizontal axis: quarters since launch

Panel 1 — New adopters per quarter
Adopters
   │            ╭──╮
   │         ╭──╯  ╰──╮
   │      ╭──╯        ╰──╮
   │  ╭───╯               ╰──
   └────────────────────────── Time

Panel 2 — Cumulative adopters
Adopters
   │                    ╭───── Assumed ceiling
   │                ╭───╯
   │           ╭────╯
   │      ╭────╯
   │  ────╯
   └────────────────────────── Time

Panel 3 — Remaining eligible non-adopters
Non-adopters
   │  ────╮
   │      ╰────╮
   │           ╰────╮
   │                ╰────────
   └────────────────────────── Time

Figure note: The three panels share one time axis and one definition of adoption and eligibility. The shapes are illustrative patterns, not forecasts or guaranteed trajectories.

Early uptake may be slow because buyers do not understand the product, credible references are scarce, implementation is uncertain, or the product conflicts with established workflows. Adoption can accelerate as evidence accumulates, peers provide validation, implementation becomes easier, and the installed base generates references and operating knowledge. It may later slow as the remaining eligible population becomes smaller or harder to convert.

Suppose a product adds 50 customers in one quarter, 100 in the next, and 80 in the third. New adoption has declined from its peak, but cumulative adoption has still increased by 80 customers. Confusing the flow with the total can make deceleration look like contraction.

Real paths are often less orderly. Price changes, integrations, mandates, supply constraints, product redesigns, or entry into a new segment can create multiple waves. Churn may reduce active use even while cumulative historical adoption continues rising. Teams should therefore label every curve with:

• The adoption event: trial, purchase, activation, deployment, or retained use
• The eligible population and unit
• The time interval
• Whether the measure is period-specific or cumulative
• Whether it represents historical adoption or currently active use

Without those definitions, a polished curve may combine incompatible events, populations, or time periods.

Rogers’ five adopter groups—and what the percentages do not prove

Rogers’ framework divides a theoretical adopter distribution into five categories. The conventional shares are approximately 2.5% innovators, 13.5% early adopters, 34% early majority, 34% late majority, and 16% laggards. These percentages are model conventions, not measured proportions that every market must reproduce. The conventional category shares are summarized here.

Adopter group	Conventional share	Typical requirement for proof	Product question
Innovators	2.5%	Access, novelty, room to experiment	Can capable users explore the core capability?
Early adopters	13.5%	A meaningful advantage worth accepting risk for	Does it solve an urgent problem substantially better?
Early majority	34%	References, reliability, compatibility, predictable implementation	Can a pragmatic buyer adopt without exceptional risk?
Late majority	34%	Strong support, low disruption, established norms	Is adopting now safer than retaining the old approach?
Laggards	16%	Continuity with familiar practice or loss of viable alternatives	Is there a compelling reason for reluctant buyers to change?

These categories are better understood as context-specific mindsets and behaviors than permanent identities. Someone may experiment early with developer tools while waiting years to adopt home automation. The useful question is not “What type of person is this?” but “How much novelty, uncertainty, and implementation risk will this buyer accept for this decision?”

The framework emerged from agricultural diffusion research before Everett Rogers generalized and popularized it in the 1962 book Diffusion of Innovations. The demographic profiles used in early agricultural research should not be transferred to modern software buyers. The history of the technology-adoption lifecycle traces these agricultural roots and later adaptations.

For a founder, the groups provide shorthand for changing requirements:

• Early users may tolerate manual onboarding if the underlying capability is unusually valuable.
• Mainstream organizations may require integrations, references, predictable implementation, training, and measurable economics.
• Later buyers may move only after the category becomes established, familiar alternatives disappear, or the cost of not adopting becomes clear.

A startup does not enter the early majority simply by crossing a theoretical percentage boundary. Segment definition, customer behavior, and the quality of adoption evidence matter more than the label.

Why adoption accelerates—or stalls

Rogers’ framework identifies five perceived attributes that can help explain differences in diffusion: relative advantage, compatibility, complexity, trialability, and observability. They concern how potential adopters perceive an innovation, not only its objective technical characteristics. The five attributes are described in this overview of the innovation-adoption curve.

Relative advantage: Is the improvement meaningful to this buyer? A technically impressive product may offer little perceived benefit if it improves a cheap, infrequent, or unimportant task. Express value in customer terms: time saved, risk reduced, revenue enabled, quality improved, or work eliminated.

Compatibility: Does the product fit existing workflows, systems, policies, and incentives? A tool may work well in isolation but stall because it requires infrastructure replacement, new approval processes, or unclear ownership.

Complexity: How difficult is the product to understand, evaluate, deploy, and operate? Complexity includes setup, data preparation, training, governance, billing, troubleshooting, and the cognitive burden of knowing when to use the product.

Trialability: Can customers test a meaningful use case at limited cost and risk? Free access alone is not necessarily an effective trial. Buyers need a credible way to evaluate value without first completing a disruptive migration or exposing critical systems.

Observability: Are the results visible and credible? Prospects may need demonstrations, quantified outcomes, peer references, audit trails, or before-and-after comparisons. For infrastructure and AI products, evidence may need to cover reliability and failure modes as well as headline performance.

Peer influence and opinion leaders can reduce uncertainty when prospects trust references facing similar constraints. The effect is not automatically positive: poor retention and negative references can spread too.

Organizational readiness creates another layer. A user may want a product while the company lacks the skills, infrastructure, procurement route, budget, integration capacity, or approvals needed to deploy it.

An OECD analysis using firm-level data from multiple countries finds that advanced-technology adoption varies by sector and firm size and is associated with human capital, ICT skills, prior digitalisation, and complementary technologies including cloud computing, CRM, ERP, and fast broadband. It also reports productivity differences between adopters and non-adopters, but those associations do not prove adoption caused the advantage. The surveys cover 2017–2023 and do not fully capture the recent generative-AI boom. See the OECD working paper on digital-technology diffusion.

The practical implication is that readiness is a complement stack. Adoption may require willing users, suitable infrastructure, available skills, integration paths, governance, training, and accountable internal ownership.

Rogers, Moore, and Bass: choose the framework for the question

Rogers, Moore, and Bass address different management questions.

Framework	Purpose	Inputs	Output
Rogers	Understand adopter needs, perceived attributes, and diffusion through a social system	Customer research, product evidence, market context	Hypotheses about barriers, proof, product changes, and communication
Moore	Plan a focused move from an early market toward broader vertical adoption	Target use case, references, positioning, implementation needs	A market-entry and whole-product strategy
Bass	Estimate new and cumulative adoption over time	Market potential and external- and internal-influence assumptions	A numerical adoption scenario

Their principal limitations differ:

• Rogers is descriptive rather than a volume forecast.
• Moore’s chasm is a proposed strategic gap, not an inevitable stage in every market.
• Bass is sensitive to its assumptions and omits important market dynamics in its standard form.

Rogers helps founders ask why customers adopt and how their requirements change.

The Bass model, introduced by Frank Bass in 1969, provides a quantitative framework. Its three core parameters are m, the potential number of adopters in the defined market; p, external influence; and q, internal or imitative influence. It estimates both new adoption per period and cumulative adoption toward the assumed ceiling. The model’s history and parameters are summarized in the PyMC-Marketing documentation.

The labels are abstractions. A higher estimated p does not prove that advertising caused adoption, while a higher q does not directly measure causal word of mouth. The coefficients summarize a modeled pattern that may reflect several overlapping mechanisms.

Use Rogers to form customer and product hypotheses, Moore to investigate a possible market transition, and Bass to build explicit numerical scenarios. Then test all three interpretations against customer, funnel, cohort, and usage data.

A practical adoption-curve workflow for an early-stage product

1. Define the adoption event

Choose an event that answers the decision at hand:

• Started a trial
• Purchased a subscription
• Activated a key workflow
• Completed an integration
• Deployed in production
• Remained active after a defined period
• Expanded to another team or use case

Do not conflate these outcomes. A trial indicates willingness to investigate. Production deployment represents a larger commitment. Retained use is different again and does not by itself prove realized business value.

2. Define the eligible market and unit

Decide whether the market ceiling is measured in buyers, firms, users, seats, deployments, locations, or purchases.

A collaboration product might have 2,000 eligible firms, 20,000 eligible teams, or 300,000 eligible seats. Each figure could answer a different question and produce a different curve. Specify geography, sector, company size, use case, and time horizon as well.

3. Chart three synchronized views

Track new adopters per period, cumulative adopters, and remaining eligible non-adopters on the same time axis. Where possible, chart downstream events separately. A spike in trials with flat deployments means something different from rising deployments followed by weak retention.

4. Segment before interpreting the shape

Break down evidence by customer type, sector, company size, use case, geography, digital readiness, acquisition channel, and deployment model. A product may be nearing saturation among technology startups while barely entering regulated enterprises. A blended curve can conceal both patterns.

5. Locate the bottleneck

Map awareness, trial, activation, deployment, retention, and expansion. Use CRM records, funnel data, cohorts, referral attribution, and product usage to identify where momentum stops.

Typical diagnostic patterns include:

• Awareness without trials: unclear value, weak targeting, or excessive perceived risk
• Trials without activation: complexity or poor onboarding
• Activation without deployment: integration, security, procurement, or stakeholder friction
• Deployment without retention: weak recurring value or poor fit
• Retention without expansion: a narrow use case, pricing friction, or low internal visibility

6. Turn Rogers’ attributes into hypotheses

Translate each suspected bottleneck into a testable explanation. Buyers may not perceive enough relative advantage; the product may conflict with their stack; setup may be too complex; evaluation may require too much commitment; or results may be difficult to demonstrate internally.

Test a specific product, implementation, packaging, or evidence change against a defined metric rather than assigning customers to an adopter category and stopping there.

7. Monitor decision-relevant indicators

Useful indicators include trial conversion, time to first value, deployment cycle time, reference-sourced pipeline, cohort retention, post-onboarding engagement, expansion, implementation effort per account, and loss reasons by segment.

The following guidance is diagnostic, not a universal causal rule:

Market situation	Primary requirement	Product and go-to-market focus	Decision signal
Early market	Insight and meaningful advantage	Learn rapidly and narrow the problem	Repeated use and strong problem evidence
Moving toward a majority	Credible proof and lower risk	Improve reliability, compatibility, references, and implementation	Repeatable deployment beyond founder-led accounts
Broader adoption	Predictability and operational fit	Standardize onboarding, support, integrations, and economics	Consistent conversion, retention, and deployment time
Mature or constrained market	Low disruption or replacement economics	Reduce switching costs or redefine the eligible segment	Profitable expansion or evidence of a new market

Forecasting: make the assumptions visible

The Bass model combines an assumed market ceiling with external and internal influence. A common discrete-period approximation is:

New adopters = [p + q × (existing adopters / m)] × remaining non-adopters

Here, m is the eligible market in a consistent unit, p represents external influence, and q represents internal or imitative influence. Existing adopters are counted at the start of the period. This approximation needs a suitable time step and parameters; it should not be allowed to forecast more new adopters than remain eligible. The Bass model documentation explains the underlying formulation.

Before launch, use bottom-up market sizing, relevant analogues, and clearly labeled assumptions to create a range of scenarios. After launch, compare them with actual adoption and test forecasts against later observations that were not used to fit the model.

The ceiling is conditional: retained enterprise deployments in one industry are a different market from individual trials worldwide. A large assumed ceiling can dominate the forecast even when the other inputs look plausible. Neither coefficient proves that advertising or word of mouth caused the measured adoption.

Where the classic curve breaks down

Myth: Every technology follows a smooth curve. Evidence-based interpretation: Adoption may pause, restart, split into segment-specific waves, or jump after a mandate, viral event, distribution change, supply release, or redesign. The familiar shapes are baseline patterns, not laws.

Myth: Every market contains a chasm. Evidence-based interpretation: The chasm is a proposed strategic risk for some discontinuous innovations. Other products move gradually between segments or face several gaps created by different workflows, regulations, and buying processes.

Myth: A plateau proves saturation. Evidence-based interpretation: A plateau may reflect saturation, but it can also indicate limited supply, high pricing, procurement friction, weak onboarding, poor retention, insufficient product-market fit, or an overly narrow market definition.

Myth: Trial proves product-market fit. Evidence-based interpretation: Trial, purchase, activation, deployment, retention, expansion, and realized value are separate outcomes. Publicity can generate trials without creating durable behavior.

Myth: Adoption proves productivity impact. Evidence-based interpretation: Adoption data record uptake; they do not isolate causal effects. More capable firms may adopt earlier, while skills, infrastructure, training, and complementary investment may explain part of any observed advantage.

The standard Bass formulation assumes fixed market potential and does not directly model competition, switching, churn, repeat purchases, abandonment, changing market size, price changes, supply constraints, regulation, or major shocks. Mandates, structural changes, viral events, and winner-takes-all network effects can produce paths that differ materially from the classic curve.

Move beyond a simple diffusion model when the decision depends heavily on retention, repeated purchases, competitive entry, switching, network structure, marketplace liquidity, pricing interventions, capacity constraints, or regulation. Cohort, retention, competitive-diffusion, network, agent-based, or explicit funnel models may then be more useful.

Define the adoption event and eligible market first. Use the curve to locate questions worth testing, then revise the explanation as real customer behavior arrives.