Kenso

Are B2B homepages all starting to sound the same?

Yes, slowly. 57.4% of recent B2B software homepages read more like their industry's typical copy than the median homepage of a decade ago. Sounding different was not linked to faster growth.

Cristian Frunze
Founder of Kenso 4 min read
Methodology (PDF)

Key findings

  • 57.4%

    of 2024-2026 homepages sit closer to their industry's typical copy than the median 2014-2016 homepage did. With no change it would be 50%.

  • +0.012

    rise in how alike hero copy is within an industry, from 0.541 in 2014-2016 to 0.553 in 2024-2026 (95% interval 0.009 to 0.015).

  • 8.62%

    of hero copy used "platform" in 2026, up from 2.02% in 2014. "AI-powered" and close variants went from 0.35% in 2022 to 2.41%.

  • -0.82

    percentage points: the gap in median two-year headcount growth between the most distinctive and matched typical companies (95% interval -2.27 to 3.05).

On this page

B2B software homepages have been drifting toward the same message for a decade. We read the headline and subheadline of 42,045 homepage snapshots from 7,633 B2B software companies, one per company per year from 2014 to 2026. 57.4% of 2024-2026 homepages sit closer to their industry's typical copy than the median homepage of 2014-2016 did. If nothing had changed, that figure would be 50%.

The word that spread fastest is "platform": it is in 8.62% of hero copy in 2026, against 2.02% in 2014. And companies whose copy stands out did not grow faster than companies whose copy blends in.

Figure 1Hero copy has grown more alike almost every year
  • 95% interval
Hero copy has grown more alike almost every yearLine chart, 2014 to 2026. Average similarity rises from 0.540 in 2014 to 0.555 in 2026, up in 10 of 12 year-on-year steps.Average similarity of hero copy within anindustry0.530.540.550.5620142017202020232026YearWithin-industry similarity · 2014: 0.540Within-industry similarity · 2015: 0.540Within-industry similarity · 2016: 0.544Within-industry similarity · 2017: 0.545Within-industry similarity · 2018: 0.547Within-industry similarity · 2019: 0.548Within-industry similarity · 2020: 0.547Within-industry similarity · 2021: 0.549Within-industry similarity · 2022: 0.551Within-industry similarity · 2023: 0.551Within-industry similarity · 2024: 0.552Within-industry similarity · 2025: 0.553Within-industry similarity · 2026: 0.5550.555
  • 95% interval
Hero copy has grown more alike almost every yearLine chart, 2014 to 2026. Average similarity rises from 0.540 in 2014 to 0.555 in 2026, up in 10 of 12 year-on-year steps.Average similarity of hero copy within an industry0.530.540.550.562014201620182020202220242026YearWithin-industry similarity · 2014: 0.540Within-industry similarity · 2015: 0.540Within-industry similarity · 2016: 0.544Within-industry similarity · 2017: 0.545Within-industry similarity · 2018: 0.547Within-industry similarity · 2019: 0.548Within-industry similarity · 2020: 0.547Within-industry similarity · 2021: 0.549Within-industry similarity · 2022: 0.551Within-industry similarity · 2023: 0.551Within-industry similarity · 2024: 0.552Within-industry similarity · 2025: 0.553Within-industry similarity · 2026: 0.5550.555

Average similarity in meaning between the hero copy (headline and subheadline) of every pair of B2B software companies in the same industry group, by year. 1 would mean identical meaning, and only the change over time matters. The band is the 95% interval.

Source: Kenso internal data (Kenso's archive of company websites and the company and career records Kenso uses for sales and outreach), analysed by Cristian Frunze.

Show the data
Data for figure 1: Hero copy has grown more alike almost every year
YearWithin-industry similarityWithin-industry similarity (95% interval)
20140.5400.537 to 0.543
20150.5400.537 to 0.543
20160.5440.541 to 0.547
20170.5450.542 to 0.548
20180.5470.544 to 0.549
20190.5480.546 to 0.550
20200.5470.545 to 0.550
20210.5490.547 to 0.552
20220.5510.549 to 0.553
20230.5510.549 to 0.553
20240.5520.550 to 0.554
20250.5530.551 to 0.555
20260.5550.553 to 0.557

How we measured sounding the same

The companies are 10,000 B2B software firms drawn at random from the company records Kenso uses for sales and outreach, headquartered in the US, the UK, Canada, Australia, Ireland or New Zealand. For each, we took one homepage snapshot a year from Kenso's archive of company websites and kept the hero copy: the main headline and the line under it. We dropped parked pages, pages not in English, template text ("coming soon", site-builder sample copy) and copy shared word for word by three or more companies in the same year. Each company's own name was replaced with a placeholder, so names do not make companies look different.

We turned each hero into a list of numbers that captures its meaning (a text embedding). Two heroes that say similar things in different words get similar numbers. For every year, within each of three industry groups, we averaged the similarity of every pair of companies.

The average rose from 0.541 in 2014-2016 to 0.553 in 2024-2026, on a scale where 1 means identical meaning: a rise of 0.012 (95% interval 0.009 to 0.015). It went up in 10 of 12 year-on-year steps. This is a slow drift, not a collapse into one voice. The level itself means little, because the embedding rates any two texts as fairly similar. Only the change counts.

The same companies are drifting

Kenso's web archive holds more sites in recent years, so the mix of companies changes. We checked that three ways.

  • Same companies. Among 310 companies captured in every year of both windows, the rise is the same size: 0.013 (95% interval 0.006 to 0.020).
  • Each company against itself. Following 6,722 companies over time, each one's copy moves closer to its industry's average, by 0.00055 a year.
  • Page format. Later homepages more often have a subheadline. The headline alone gives a rise of 0.015, and true H1 headlines alone give 0.012.

We had predicted a steeper rise from 2023, when AI wording spread. It did not appear: about 0.0014 a year from 2014 to 2022, and 0.0010 a year from 2022 to 2026. By group, the rise was 0.014 for software development, 0.013 for security, data and other software, and 0.004 for technology, information and internet, a broad label that also holds consumer apps.

Same claims, not the same words

The convergence is in meaning, not vocabulary. Compared word for word, similarity barely moved, and the interval includes zero. The 50 most common word pairs made up 5.18% of copy in 2014 and 4.92% in 2026. Companies are not copying each other's sentences. They are making the same kind of claim.

Figure 2Stock phrases are several times more common than in 2014
  • 2014
  • 2022
  • 2026
Stock phrases are several times more common than in 2014Grouped bar chart. Platform: 2.02% in 2014, 6.19% in 2022, 8.62% in 2026. AI-powered: 0.0%, 0.35%, 2.41%. All-in-one: 0.22%, 0.67%, 1.19%. Seamless: 0.05%, 0.17%, 0.84%.0%4%8%Hero copy containing the phrase (%)"Platform""Platform" · 2014: 2.02%2.02%"Platform" · 2022: 6.19%6.19%"Platform" · 2026: 8.62%8.62%"AI-powered""AI-powered" · 2014: 0%0%"AI-powered" · 2022: 0.35%0.35%"AI-powered" · 2026: 2.41%2.41%"All-in-one""All-in-one" · 2014: 0.22%0.22%"All-in-one" · 2022: 0.67%0.67%"All-in-one" · 2026: 1.19%1.19%"Seamless""Seamless" · 2014: 0.05%0.05%"Seamless" · 2022: 0.17%0.17%"Seamless" · 2026: 0.84%0.84%
  • 2014
  • 2022
  • 2026
Stock phrases are several times more common than in 2014Grouped bar chart. Platform: 2.02% in 2014, 6.19% in 2022, 8.62% in 2026. AI-powered: 0.0%, 0.35%, 2.41%. All-in-one: 0.22%, 0.67%, 1.19%. Seamless: 0.05%, 0.17%, 0.84%.0%2%4%6%8%10%Hero copy containing the phrase (%)"Platform""Platform" · 2014: 2.02%2.02%"Platform" · 2022: 6.19%6.19%"Platform" · 2026: 8.62%8.62%"AI-powered""AI-powered" · 2014: 0%0%"AI-powered" · 2022: 0.35%0.35%"AI-powered" · 2026: 2.41%2.41%"All-in-one""All-in-one" · 2014: 0.22%0.22%"All-in-one" · 2022: 0.67%0.67%"All-in-one" · 2026: 1.19%1.19%"Seamless""Seamless" · 2014: 0.05%0.05%"Seamless" · 2022: 0.17%0.17%"Seamless" · 2026: 0.84%0.84%

Share of hero copy that contains each phrase in 2014, 2022 and 2026. "AI-powered" also counts AI-driven, AI-native and AI-first. The phrase list was fixed before the data was read.

Source: Kenso internal data (Kenso's archive of company websites and the company and career records Kenso uses for sales and outreach), analysed by Cristian Frunze.

Show the data
Data for figure 2: Stock phrases are several times more common than in 2014
201420222026
"Platform"2.02%6.19%8.62%
"AI-powered"0%0.35%2.41%
"All-in-one"0.22%0.67%1.19%
"Seamless"0.05%0.17%0.84%
  • "Platform" rose steadily, from 2.02% of hero copy in 2014 to 8.62% in 2026.
  • "AI-powered", with AI-driven, AI-native and AI-first, was at 0.35% in 2022 and 2.41% in 2026.
  • "All-in-one" rose from 0.22% to 1.19%.
  • "Seamless" rose from 0.05% to 0.84%. We expected it to stay flat.

Standing out did not pay, or cost

Do companies that write differently grow faster? For each year from 2018 we took the quarter of companies whose copy was furthest from their industry's average, 2,757 company-years in all. We matched each with up to three typical companies of the same industry group, founding period, team size and prior growth, and followed headcount, rebuilt from work histories, for two years.

After 8 quarters, the median distinctive company had grown 0.0% and its matched typical companies 0.82%: a gap of -0.82 percentage points, with a 95% interval from -2.27 to 3.05. The two groups grew alike before the comparison, which is the check that the design works, and a regression across the full range of distinctiveness agrees: no measurable link.

Figure 3Distinctive and typical companies grew alike, before and after
  • 95% interval
Distinctive and typical companies grew alike, before and afterEvent study from 8 quarters before to 8 after. Every estimate stays between -0.003 and 0.0042 log points and every interval includes 0. After 8 quarters the gap is 0.004 (-0.02 to 0.0244).Growth gap, distinctive minus typical (logpoints)-0.03-0.01+0.01+0.03-8-5-2+1+4+8Quarters from the anchor quarterAnchor quarterDistinctive minus typical · -8: -0.0005Distinctive minus typical · -7: -0.0015Distinctive minus typical · -6: -0.0019Distinctive minus typical · -5: -0.003Distinctive minus typical · -4: -0.00003Distinctive minus typical · -3: -0.0021Distinctive minus typical · -2: -0.0009Distinctive minus typical · -1: +0.003Distinctive minus typical · 0: 0Distinctive minus typical · +1: -0.0014Distinctive minus typical · +2: -0.0013Distinctive minus typical · +3: -0.0027Distinctive minus typical · +4: +0.0034Distinctive minus typical · +5: +0.0033Distinctive minus typical · +6: +0.0042Distinctive minus typical · +7: +0.0011Distinctive minus typical · +8: +0.004+0.004
  • 95% interval
Distinctive and typical companies grew alike, before and afterEvent study from 8 quarters before to 8 after. Every estimate stays between -0.003 and 0.0042 log points and every interval includes 0. After 8 quarters the gap is 0.004 (-0.02 to 0.0244).Growth gap, distinctive minus typical (log points)-0.03-0.02-0.010.00+0.01+0.02+0.03-8-6-4-20+2+4+6+8Quarters from the anchor quarterAnchor quarterDistinctive minus typical · -8: -0.0005Distinctive minus typical · -7: -0.0015Distinctive minus typical · -6: -0.0019Distinctive minus typical · -5: -0.003Distinctive minus typical · -4: -0.00003Distinctive minus typical · -3: -0.0021Distinctive minus typical · -2: -0.0009Distinctive minus typical · -1: +0.003Distinctive minus typical · 0: 0Distinctive minus typical · +1: -0.0014Distinctive minus typical · +2: -0.0013Distinctive minus typical · +3: -0.0027Distinctive minus typical · +4: +0.0034Distinctive minus typical · +5: +0.0033Distinctive minus typical · +6: +0.0042Distinctive minus typical · +7: +0.0011Distinctive minus typical · +8: +0.004+0.004

Gap in headcount growth between companies with the most distinctive hero copy and matched companies with typical copy, in log points (0.01 is about 1%). Quarter 0 is the last quarter of the year before the homepage snapshot. 0 means no difference; the bars are 95% intervals.

Source: Kenso internal data (Kenso's archive of company websites and the company and career records Kenso uses for sales and outreach), analysed by Cristian Frunze.

Show the data
Data for figure 3: Distinctive and typical companies grew alike, before and after
Quarters from the anchor quarterDistinctive minus typical95% interval
-8-0.0005-0.0025 to +0.0015
-7-0.0015-0.006 to +0.0033
-6-0.0019-0.0072 to +0.0033
-5-0.003-0.0069 to +0.0009
-4-0.00003-0.0015 to +0.0013
-3-0.0021-0.0061 to +0.0013
-2-0.0009-0.0052 to +0.0036
-1+0.003-0.0006 to +0.0062
000 to 0
+1-0.0014-0.0071 to +0.0039
+2-0.0013-0.0113 to +0.0073
+3-0.0027-0.0155 to +0.0082
+4+0.0034-0.0108 to +0.0158
+5+0.0033-0.0137 to +0.0175
+6+0.0042-0.0166 to +0.0215
+7+0.0011-0.0218 to +0.0212
+8+0.004-0.02 to +0.0244

We had predicted a small growth edge for distinctive copy. The data does not show one.

What this data cannot show

  • Distinct is not good. Distance from the average measures difference in meaning, not quality. A sharp homepage and a confusing one can be equally far from the norm.
  • Headcount is slow. Copy could still help pipeline or win rates in ways that do not show up in hiring two years later.
  • The embedding's view. An embedding trained on recent web text may place recent marketing language closer together. Word overlap shows no rise, so the finding is about meaning as the embedding measures it.
  • Coverage. The archive does not capture every site every year, the sample is companies that exist today, and industry labels do not separate B2B from B2C software.
  • Association, not cause. The growth result is a matched comparison, not an experiment.

What it means for B2B teams

  • Read your hero against your category. If your headline leans on "platform", "all-in-one" or "seamless", a growing share of your competitors says the same.
  • Specific beats different. Standing apart carried no growth penalty and no measurable reward. Say what you do and for whom, rather than chasing novelty.
  • The AI label is getting crowded. "AI-powered" and its variants went from 0.35% to 2.41% of hero copy in four years. On its own, it no longer sets you apart.
  • Judge copy by outcomes. Distinctiveness alone did not predict growth, so test a new headline on demo requests or replies, not on how different it sounds.

Data and method

Data
Kenso internal data: Kenso's archive of company websites, one homepage snapshot per company per year, and the company records and work histories Kenso uses for sales and outreach, reused by Cristian Frunze for this study.
Sample
42,045 homepage snapshots from 7,633 B2B software companies, out of a random sample of 10,000 with 11 to 5,000 employees in the US, the UK, Canada, Australia, Ireland and New Zealand.
Date range
One homepage snapshot per company per year, 2014 to 2026. Quarterly headcount from 2015 to March 2026.
Method
Pre-registered. Hero copy compared by meaning (text embeddings) within industry group and year, with same-company and within-company checks. Growth compared by matching distinctive and typical companies. Headline numbers recomputed by a separate analysis.
Excluded
  • Parked pages, pages with no usable headline and pages not in English
  • Template text, and copy shared word for word by three or more companies in a year
  • Any group, year or cell with fewer than 50 companies
Privacy
Aggregates only. Any group smaller than 50 people is suppressed, and no individual is named.
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