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The 70/20/10 Rule That Keeps Your Marketing Budget From Going Stale


Opinions expressed by Entrepreneur contributors are their own.

Key Takeaways

  • Anchor your marketing budget at 10% of projected gross sales, not last year’s revenue, because you can’t market into the past.
  • Split that budget 70/20/10 — 70% to what already works, 20% to promising bets and 10% to true experiments — and rebalance every quarter so proven winners keep climbing into your biggest bucket.

Most marketing budgets are built once and then quietly forgotten. You set the number in January, spread it across the same channels you used last year and check back in December to see how it all went. By then, it’s too late to fix anything. The market moved, your best channel got more expensive and the experiment you were curious about never got funded.

I’ve watched a lot of business owners run their marketing this way, and it almost always produces the same result: a budget that slowly goes stale. The money keeps flowing to whatever worked two years ago, while the opportunities that could actually grow the business sit on the sidelines because nobody set anything aside to chase them.

There’s a better way to think about it, and it comes down to two decisions — how much to spend and how to divide it up.

Start with one number: 10% of projected gross sales

Before you split anything, you need a total. My favorite starting point is 10% of your projected gross sales for the coming year.

Notice the word projected. You’re not budgeting off last year’s revenue, because last year is over and you can’t market into the past. You’re budgeting based on where you intend to be 12 months from now. If you expect to do $2 million in sales, you’re working with a $200,000 marketing budget.

10% is a deliberate number. The U.S. Small Business Administration recommends 7% to 8% of revenue for most small businesses, and Gartner’s 2025 CMO Spend Survey found companies spending an average of 7.7%. I like 10% because it’s a growth number, not a maintenance number. If you want to take market share rather than just hold your ground, you have to be willing to spend a little more aggressively than the company down the street.

If 10% feels like a stretch right now, start lower and build toward it. The point isn’t the exact figure — it’s that you’ve committed to a real number tied to where the business is headed.

The 70% protects what already works

Once you have your total, divide it into three buckets: 70%, 20% and 10%.

The biggest bucket — 70% — goes to what’s already working. These are your proven channels, the ones where you can draw a straight line from dollars in to customers out. Maybe that’s paid search, maybe it’s email, maybe it’s a referral program that quietly outperforms everything else.

Say you run a home-services company and Google Ads brings you a steady stream of booked jobs at a price you’re happy with. That’s a 70% channel. You don’t get cute with it. You fund it fully, you keep it running and you protect it, because it’s paying the bills while the rest of your budget goes looking for the next thing.

The mistake I see owners make is robbing this bucket to chase something shiny. Don’t. The 70% is the foundation on which everything else stands.

The 20% feeds your promising bets

The middle bucket — 20% — goes to the channels that are showing promise but haven’t fully proven themselves yet.

This is where scaling happens. Maybe you ran a small test on a new social platform last quarter and the early numbers looked good. Maybe a content series is starting to bring in leads, just not yet at the volume of your main channels. These are bets worth pressing — pouring a bit more fuel on the fire to see if they can graduate into the 70%.

This bucket is what keeps your budget from going stale, because it’s constantly promoting your best experiments into proven performers. Channels move. The paid platform that prints money today will get more crowded and more expensive over time, and you want a pipeline of contenders ready to take its place.

The 10% funds the experiments

The smallest bucket — 10% — is for true experiments. This is your permission to try things with no guarantee they’ll work.

A new ad format. A platform you’ve never touched. A creative idea that might flop. Most of these won’t pan out, and that’s fine — that’s exactly what the 10% is for. You’re buying information and the occasional breakout winner.

Here’s why this bucket matters even though it’s the smallest: every channel in your 70% started as an experiment. Somebody funded it before it was proven. If you never spend on the unproven, you run out of new things to scale, and a few years down the road your budget is built entirely on aging channels. The 10% is how you keep feeding the machine.

How to keep the split honest

A 70/20/10 budget only works if you actually revisit it. I like to review the split every quarter, not once a year.

Each quarter, ask a simple question of every channel: Is it earning its bucket? A 10% experiment that’s working gets promoted to the 20%. A 20% bet that proved itself moves into the 70%. And anything in the 70% that’s quietly declining gets demoted or cut, which frees up money for the next contender.

Track this with real numbers — cost per lead, cost per sale and return on what you spent. You don’t need a fancy dashboard. You need to know which dollars are producing customers and which ones aren’t.

That’s the whole system. Start with 10% of projected gross sales, split it 70/20/10 and rebalance every quarter so your best experiments keep climbing toward your biggest bucket.

Do that, and your marketing budget stops being a number you set and forget. It turns into a living thing that gets a little smarter every quarter — and so does your business.

Key Takeaways

  • Anchor your marketing budget at 10% of projected gross sales, not last year’s revenue, because you can’t market into the past.
  • Split that budget 70/20/10 — 70% to what already works, 20% to promising bets and 10% to true experiments — and rebalance every quarter so proven winners keep climbing into your biggest bucket.

Most marketing budgets are built once and then quietly forgotten. You set the number in January, spread it across the same channels you used last year and check back in December to see how it all went. By then, it’s too late to fix anything. The market moved, your best channel got more expensive and the experiment you were curious about never got funded.

I’ve watched a lot of business owners run their marketing this way, and it almost always produces the same result: a budget that slowly goes stale. The money keeps flowing to whatever worked two years ago, while the opportunities that could actually grow the business sit on the sidelines because nobody set anything aside to chase them.

There’s a better way to think about it, and it comes down to two decisions — how much to spend and how to divide it up.

Here’s Why the 2027 Social Security COLA Could Be Lower Than Expected


If you’re hoping for an above-average Social Security boost in 2027, there’s a good chance you could get your wish. The most recent cost-of-living adjustment (COLA) projection from The Senior Citizens League estimates the 2027 COLA at about 3.8%. That’s a whole percentage point higher than what seniors got in 2026.

That said, it’s too early to start celebrating. We won’t know the official COLA until mid-October, and there’s still a chance that changes in inflation could cause the COLA to come in a bit lower than expected.

Image source: Getty Images.

The Social Security Administration calculates COLAs by looking at the change in average third-quarter inflation from one year to the next, as measured by the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W). The percentage increase becomes the COLA for the next year.

We have all the data we need for 2025, but we’ve only just finished the first month of the third quarter of 2026. We need to wait for the CPI-W numbers for July, August, and September to come in before we’ll know the actual COLA.

The COLA estimates are above average right now because inflation has been high over the past several months. But if that changes as we get deeper into August and September, that could result in a lower COLA than current projections suggest.

Fortunately, you’ll have a few months after the official 2027 Social Security COLA announcement to start planning your budget for the next year. That should give you plenty of time to figure out how far your checks will go and how much of your expenses you’ll need to cover on your own.

A veteran broker on what separates the originators who survive a down cycle


Amir Nurani (pictured top), broker-owner at Left Coast Leaders in San Diego, said the numbers are already pointing in one direction. Originator licenses are renewed every year, which makes the headcount easy to track, and he expects to see that number decline.

“I think we are going to see a minimum of a 10% decline in renewed licenses for mortgages,” Nurani told Mortgage Professional America. “The volume is still contracted, and I think this is the normal part of the cycle. The cycle for mortgages is always the same. When rates drop, every lender in the United States starts a hiring frenzy. Then rates start going up, lenders go to layoffs, originators stop renewing their licenses, and you start to see a downtrend.”

AI-driven downsizing

Nurani said the decline is not just about demand. AI is already compressing the workload that used to justify larger originator teams, and if every originator can handle twice the volume with demand staying flat, the math points to fewer originators.

He noted that the same dynamic is playing out across financial services. Last week, Visa announced it was cutting 7% of its staff. Earlier this week, Zillow and Google announced additional layoffs of their own.

“AI is already hitting white-collar jobs,” he said. “Mortgage is not insulated from these types of shifts. AI expands the reach of the originator, but at some point it hits the demand cap. If it took 100 originators to meet the demand in a certain environment, and now every originator can do 2x the volume with demand staying static, the natural order of things is going to reduce that headcount.”

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GUARDD Meets With SEC To Discuss Secondary Trading Of Tokenized Exempt Securities


GUARDD, founded by Sherwood Neiss who is also the co-founder of Crowdfund Capital Advisors (CCA) and one of the authors of the JOBS Act which approved online capital formation, recently met with the Securities and Exchange Commission (SEC) to discuss secondary trading of tokenized exempt securities.

Exempt securities would include securities issued under Reg A, Reg CF and Reg D. Public securities are moving in the direction of becoming digital securities or tokenized assets and private securities are heading that way as well.

Tokenized securities struggle a bit with semantics but the SEC currently defines these assets as: “a financial instrument enumerated in the definition of “security” under the federal securities laws that is formatted as or represented by a crypto asset, where the record of ownership is maintained in whole or in part on or through one or more crypto networks.”

GUARDD is a Fintech platform that helps private companies raising funds under Reg A+, Reg CF, or Reg D, to publish standardized ongoing disclosures so their securities can trade on Alternative Trading Systems (ATSs) while complying with state “Blue Sky” laws. Securities law in the US leans heavily on disclosure.

GUARDD met with the SEC Crypto Task Force to discuss how the SEC’s crypto rulemaking and secondary trading of tokenized, exempt securities through a Qualified Disclosure Publisher.

GUARDD explained that exempt securities offerings “inherit the same structural trap” even if they are tokenized. The proposal is:

“How QDP-published disclosure can anchor uniform national secondary trading — preempting inconsistent state manual-exemption requirements while preserving state anti-fraud authority — and how this interacts with the CLARITY Act’s taxonomy if enacted.”

In a letter sent to the Commission in June, GUARDD lamented the current environment where currently there is an on-ramp but no exit, regarding to exempt securities, specifically addressing difficulties for securities issued under Reg CF.

GUARDD explained:

“Regulation Crowdfunding provides a decade-long natural experiment in what happens when a primary market is built without secondary infrastructure. Per CCLEAR transaction-level data: more than $2.95 billion has been raised across 10,899 offerings by 9,300+ issuers since 2016 — yet less than 1% of issuers have achieved meaningful secondary liquidity. The largest secondary marketplace for crowdfunded securities has quoted only 25 companies, with roughly $1.4 million in total trading volume, against billions raised in the primary market. One issuer spent more than $90,000 and over a year attempting state-by-state compliance solely to enable lawful secondary trading for its investors. Meanwhile, 257 of these companies went on to attract $5.04 billion in institutional follow-on funding — success their earliest retail investors cannot sell into. Tokenized securities issued under new exemptions will inherit precisely this trap unless the proposing release builds the exit alongside the on-ramp.”

Today, investors in private securities typically understand they may hold limited liquidity opportunities beyond an IPO or an acquisition. Yet markets have developed for Reg D securities that have opened up access for early shareholders to sell shares, if they choose to do so. The environment for Reg CF issuers remains underdeveloped.

GUARDD sees an avenue for boosting liquidity via rulemaking to make disclosure standardized to make it easier for secondary transactions to take place.

GUARDD states:

“Disclosure-based secondary market infrastructure is where those goals converge: it protects investors through current, standardized, verified information, and it makes U.S. venues viable by making U.S.-issued tokens tradable.”

Heightened liquidity is a characteristic of crypto markets that have boosted its popularity for investors (speculators). A tokenized asset or digital security, including those which are private, could benefit from improved options for sellers as well as buyers.



GAO Report: Education Dept. Left Student Loan Servicers Scrambling On Major Changes


The Government Accountability Office spent nearly two years auditing how the Department of Education instructs its student loan servicers, and the headline finding is something any functioning leadership team already knows: talk to the people doing the work before you change the work.

The new GAO report found Education routinely skips early coordination with the companies servicing federal student loans, which together manage over $1.6 trillion for about 43 million borrowers.

GAO reviewed 68 change requests (the formal documents Education uses to direct the contractors it pays to service federal loans) issued between March 2020 and December 2024. All four servicers interviewed said instruction would improve if Education looped them in before, or immediately after, requesting a change.

Education acknowledged early coordination has value, then rejected GAO’s recommendation to set formal criteria for when to do it.

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Why This Matters

When Education and its servicers spend months clarifying what a change actually requires, borrowers absorb the delay. One change request triggered six rounds of questions and answers over a 2-month period. Another — a marked-up rewrite of a servicer’s original 300-plus page contract — took more than a year and a half to implement. Borrower advocates told GAO the fallout showed up as long hold times and undertrained customer service reps, exactly when borrowers needed answers most.

In one case, Education gave servicers a single business day’s notice before publishing IDR payment counts on StudentAid.gov — leaving call centers unprepared for the wave of borrower questions that followed.

The Numbers

Five student loan servicers manage the portfolio:

  1. Nelnet – 12.07 million borrowers
  2. Aidvantage – 9.21 million borrowers
  3. MOHELA – 6.73 million borrowers
  4. Edfinancia l- 6.52 million borrowers
  5. CRI – 2.93 million borrowers
  • 36% of change requests from fiscal years 2023 through 2025 were designated “emergency” or “quick pace”, which are rush classifications. In fiscal year 2025 alone, it was 42%.
  • Emergency status can cut a servicer’s response window from 10 days to 2.

How This Connects

This is the same agency GAO recently flagged for halting its servicer oversight reviews and that the Inspector General found had cut 40% of its staff. The timing is not great: servicers are in the middle on the largest repayment overhaul in decades, including the court-ordered end of the SAVE plan and the launch of the Repayment Assistance Plan under the One Big Beautiful Bill Act.

Education says formal coordination criteria would slow it down, but the GAO is keeping the recommendation open. The real test is how cleanly servicers execute the repayment changes that took effect in July 2026 and whether the 2028 deadline to move off sunsetting plans arrives with fewer surprises than the last transition.

Education did hold a multi-day summit with servicers in September 2025 on the new repayment plans, so someone is reading the memos.

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Is artificial intelligence making us more productive? What the UK industry data show – Bank Underground


Sandra Batten

Unlike previous waves of automation, machine learning and generative AI (Gen AI) technologies can perform non-routine cognitive tasks, such as those involving written or spoken language, and have the potential to affect a wider range of occupations. By augmenting or replacing workers in these tasks, these technologies promise to deliver significant productivity gains. AI adoption, while still limited, seems to be linked to productivity gains across industries in the US, although it can only explain a small fraction of the aggregate pick up in US productivity. This post examines the emerging evidence from UK industry data and finds some indication that AI is contributing to productivity growth following a similar pattern to previous key technologies.

Key technologies, industrial revolutions and productivity growth: a historical perspective

New technologies differ in their economic impact: some ‘General Purpose Technologies’ (GPTs) have a central role in economics because they tend to have a large and long-lasting impact on productivity growth. They are characterised by (a) Pervasiveness: they are widely used and spread to most sectors; (b) Improvement: they are capable of ongoing technical improvement; and (c) Innovation spawning: they make it easier to invent and produce new products or processes. Each past Industrial Revolution was associated with a new GPT: the First Industrial Revolution (Britain, 1760s–1830s) with the creation of steam engine, the Second (US, 1870–1914) with the invention of electricity and the Third (US, 1960s–2000s) with the introduction of Information and Communication Technologies (ICTs).

In addition to a new GPT, each Industrial Revolution was also accompanied by the ‘Invention of a Method of Invention’ (IMI), a significant change in the way new ideas are generated, which raises the productivity of the technological process itself. In the First Industrial Revolution, the new method was based on systematic empiricism and experimentation. In the Second, the IMI was the creation of the industrial R&D laboratory. In the Third, rapid developments in computers (ICT) provided a new method for innovating. Importantly, GPTs and IMIs are different concepts, and ICT is the only example of a previous technology that was both a GPT and an IMI.

Artificial intelligence as a ‘revolutionary’ technology and its impact on productivity

Many authors agree that AI, more specifically Gen AI, has the characteristics of a GPT: it can be used for a very broad set of tasks and therefore has the potential to spread widely across the economy; it generates innovation, for example in user interfaces or product design; and its capabilities have been expanding dramatically since its introduction. It also enhances the research process, confirming it is also an IMI.

Gen AI seems to be advancing at an unprecedented pace, with potentially large impacts not only on productivity but also on human labour. For these reasons, it is crucial to monitor its diffusion and impact on the economy. This post introduces a possible framework to think about the impacts of AI as a supply-side shock in the first instance. It then examines the evidence of AI impacts on UK labour productivity, measured as output per hour worked. A change in labour productivity can come either through higher output or through fewer hours worked, possibly due to lower employment. The current evidence on AI impact on labour demand is presented in a companion post.

Figure 1 depicts two broad transmission channels of the impact of AI on the macroeconomy.


Figure 1: Transmission channels for AI macroeconomic impacts

Sources: Author’s figure based on Acemoglu and Restrepo (2019), Aghion et al (2018), Bresnahan and Trajtenberg (1995), Crafts (2021), Brynjolfsson et al (2025), Baily et al (2025) and Haskel et al (2025).


The efficiency channels increase workers’ or capital’s productivity, as automation expands the set of tasks that can be executed by capital. Through this channel, AI predominantly affects the labour market, by either substituting labour (extensive margin automation), complementing it (task complementarity) or creating new tasks. AI can also make already automated tasks more productive (deepening of automation).

The innovation channels boost productivity growth by changing the nature of the scientific process, are related to the role of AI as a GPT and an IMI and affect the technology parameter (A) in the production function.

These two channels represent the initial impact of AI on the (real) components of the supply side of the economy: the overall effect of AI on the macroeconomy will ultimately depend on general equilibrium effects through the demand side of the economy. For example, changes in employment or wages could affect household consumption and changes in the desired level of capital stock could boost business investment. Expectations about future returns or income from AI could affect both consumption and business investment.

Is Gen AI improving UK productivity?

The experience of previous GPT eras – particularly the ICT revolution in the US – can help answer this question. In the first phase of the ICT revolution, the ICT-producing sector – semiconductors, hardware, software and communication equipment – contributed strongly to labour productivity growth, whereas in the second phase labour productivity was driven by industries outside of the production of information technology, particularly the highest ICT-using industries

Chart 1 compares industries’ contributions to UK labour productivity growth for two different time periods: the ‘post Gen AI’ period (2023–25) and the ‘Pre-Covid 19’ period (2010–19). Chart 1 shows that the major contributor over the most recent period has been the ICT sector, the ‘AI-producing sector in the UK. While the ICT contribution was higher in 2010–19, this was driven mainly by technology improvements in the telecommunication industry in that decade, for example increased coverage and broadband speeds, reflected in a quality adjustment in published statistics.

Of the AI-using sectors, expected positive contributions come from Administrative and support activities and Manufacturing. Financial and insurance activities, instead, show a negative contribution, despite being large and a strong AI adopter. Possible explanations include output mismeasurement and other negative factors offsetting positive AI impacts. 


Chart 1: Industry contributions to labour productivity growth

Note: Average contribution to year-on-year output per hour growth in percentage points.

Sources: ONS output per hour and author’s calculations.


Productivity in the AI-producing industry

AI-producing activities are not explicitly identified in the current Industrial Classification (SIC 2007). Of the five layers of the AI supply chain – hardware, cloud computing (AI infrastructure), training data, foundation models and AI applications – UK AI activity is concentrated in AI infrastructure and AI applications.

Cloud computing services are included in ‘Data processing, hosting and related activities’ (SIC code 63110). AI applications include both AI products the development, customisation, and licensing of AI-powered software (‘Business and domestic software development’, SIC code 62012) and AI services – the provision of expert advice and technical services (‘Computer consultancy activities’, SIC code 62020).

Chart 2 shows that the contribution of both AI-producing industries stands out: ‘Computer programming, consultancy and related activities’ (SIC 62) increased its contribution to annual productivity growth tenfold, from 0.01 percentage points to 0.10 percentage points between the two periods, and ‘Information services activities’ (SIC 63), which was dragging on productivity pre-Covid, switched to a positive contribution of 0.06 percentage points.


Chart 2: Contribution of the ICT sector to aggregate productivity growth, by section

Notes: Average contribution to year-on-year output per hour growth in percentage points. The telecommunications sector is excluded, since its quality improvement in the pre-pandemic decade greatly affects the contribution of the ICT sector summarised in Chart 1.

Sources: ONS output per hour and author’s calculations.


The increase in the contribution of these two industries suggests that AI might be already boosting the productivity in the GPT-producing sectors, similarly to what happened during the ICT boom.

Productivity in the AI-using industries and across sectors

Is there any indication that AI-using industries are also becoming more productive? The second highest contributor to recent labour productivity growth shown in Chart 1 is ‘Administrative and support services’. Within these, ‘Office administrative and business services activities’ have contributed the most to aggregate productivity growth most recently, while dragging on growth in the past (Chart 3). Business service activities seem to be highly exposed to AI automation, and therefore these findings suggest that AI might have started to improve productivity in these (AI-using) areas.


Chart 3: Contribution of the administrative and business services sector to aggregate productivity growth, by section

Note: Average contribution to year-on-year output per hour growth in percentage points.

Sources: ONS and author’s calculations.


What can be said across a wider cross section of industries? Chart 4 shows the change in the contribution to aggregate productivity growth between the two periods across industries, relative to their AI adoption rate.

The regression line has a positive slope, and, although adoption only explains a small part of the variance, it is suggestive of an association between higher AI adoption and an increase in the industry’s contribution to aggregate productivity.


Chart 4: Industry contribution to productivity growth and AI adoption

Notes: Change in the average quarterly industry contribution to annual labour productivity growth over the period 2023–25 relative to 2010–19 by AI adoption. AI adoption is the percentage of companies in the industry that used any form of AI up to December 2025. Fitted line y = 0.105x – 1.3918 (slope coefficient p-value=0.065; R-squared= 0.1028).

Sources: ONS output per hour by industry; ONS Business Insights and Conditions Survey and author’s calculations.
This work was undertaken in the Office for National Statistics Secure Research Service using data from ONS and other owners and does not imply the endorsement of the ONS or other data owners.


Conclusion

The previous General purpose technology era – the ICT era – was characterised by an improvement in productivity of the GPT-producing sectors, followed by faster growth in the GPT-using sectors, following widespread adoption of the technology. Similarly, with AI as the new GPT, we highlighted some evidence of an increasing contribution of AI-producing industries to aggregate productivity growth. We also showed a tentative association between AI adoption and an increase in the contribution to aggregate productivity across industries. This is of course only suggestive of a correlation, not causation. To help us understand whether AI is indeed causing productivity improvements it will be important to continue monitoring these productivity indicators, as well as developments in labour markets and in the measurement of AI adoption, together with additional and more granular analysis.


Sandra Batten works in the Bank’s Structural Economics Division.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Why 84 Percent of Managers Don’t Trust Your Summer Sick Days (And What Half Are Secretly Doing)



A new survey reveals the hypocrisy driving summer productivity slumps—and why the boss suspecting your “sick day” might be using the exact same excuse.

After Michigan, the Democratic Party’s Civil War moves next door to Wisconsin



Sarah Miller, a software engineer from the liberal stronghold of Madison, has heard all of the chatter about a civil war in the Democratic Party between progressives and moderates. But all she cares about is winning.

That’s why Miller said she is voting in Tuesday’s gubernatorial primary for David Crowley, the Milwaukee County executive endorsed by two-term Gov. Tony Evers. He faces democratic socialist Francesca Hong in a contest that will be the latest test nationally of how far Democrats are willing to swing to the political left.

Wisconsin’s primary comes a week after progressive Abdul El-Sayed narrowly defeated Rep. Haley Stevens in Michigan’s U.S. Senate primary, setting up a crucial test for progressives in November in a key battleground state. Also on Tuesday, voters in Minnesota will decide another U.S. Senate contest featuring a centrist and a progressive.

In a year where voters upset with the Democratic Party mainstream have propelled outsider candidates to victory across the country, Crowley is hoping to buck the trend in Wisconsin. He’s banking on the support of Evers, one of the state’s most popular elected officials, to make up ground in the waning days of the chaotic primary.

Crowley said that El-Sayed’s slim margin of victory “does give me a little bit of hope.”

“We’re going to focus on those undecided voters,” Crowley said.

Wisconsin Democratic voters grapple with electability, chaotic primary

“The divide is real,” Miller, 49, said of the struggle between progressives and more mainstream Democrats. But her focus remains on November, when the winner of the primary will face Republican U.S. Rep. Tom Tiffany, a staunch supporter of President Donald Trump and one of the most conservative members of Congress.

Miller, who described herself as a progressive, called Hong a “very, very weak candidate” and described her popularity as “a little bit of a mirage.”

“I don’t think she can win,” Miller said at a Crowley campaign stop a week before election day at a winery in the rolling hills of New Glarus.

Crowley is banking on more voters feeling that way. A Marquette University Law School poll of Wisconsin Democratic primary voters conducted in late July, before former Lt. Gov. Mandela Barnes dropped out, showed Hong with a significant lead over the other candidates. More than 112,000 absentee ballots had already been returned by the day the poll was released.

Complicating the path for Crowley is the fact that he withdrew from the race on July 8 and only got back in 10 days later, with the backing of Evers, after Lt. Gov. Sara Rodriguez ended her candidacy. Then, 12 days later, Barnes also ended his campaign amid allegations of inappropriate behavior.

Barnes and Rodriguez, who were both seen as potential front-runners, remain on the ballot and votes already cast for them can’t be undone.

“We’ve created a mess,” said Michael Gebben, a 64-year-old Democrat who voted absentee for Rodriguez before she dropped out. “Hopefully we can get out of it.”

Hong bats down controversies as Crowley makes a final sprint

As Crowley sprinted across the state to capture undecided voters, Hong was batting down controversies.

Hong, a single mom and former restaurant cook, faced questions about past social media posts where she called for canceling Thanksgiving because “we should stop celebrating colonialism,” disparaged Valentine’s Day as “another day capitalism tells you how to show love” and repeatedly called for defunding the police.

She has backtracked or clarified many of those past statements in recent days, now saying that she does not support abolishing the police.

Tiffany pointed to Hong’s comments on Thanksgiving and said she wants “to destroy the traditions and culture that unite us.” Tiffany, who had 10-times more cash on hand than either Hong or Crowley did entering August, has portrayed the race as a contest between chaos and common sense.

“Wisconsinites can’t afford for November to come down to the most extreme things Tom Tiffany and Francesca Hong have said online,” Evers’ spokesperson Britt Cudaback said this week in a social media post explaining why the governor endorsed Crowley.

Crowley’s mention of the Evers endorsement drew a round of applause at both of his campaign stops in New Glarus and Madison this week, but similar votes of confidence haven’t always been decisive in other primaries. In Michigan, Democratic Gov. Gretchen Whitmer endorsed the moderate Stevens who came up short.

Even with Evers’ backing, Crowley insists he is not an establishment Democrat despite having served more than three years in the Legislature and as the top elected official in Milwaukee County, the state’s largest, since 2020.

“When people try to call me the establishment, I look at that as a slur, particularly as an African American candidate,” said Crowley, who is looking to become Wisconsin’s first Black governor. “I have never run for office to protect the establishment. I’ve always been here to protect people.”

Voters say message from progressive candidates resonates

Hong supporter Chris Vestin has heard the concerns about electability before.

To him, the fears about Hong’s chances against Tiffany in November sound like a repeat of those who pushed Hillary Clinton over Bernie Sanders in the 2016 presidential race. Sanders defeated Clinton in the Wisconsin primary by 13 points that year, before losing the presidency to Trump. Trump carried Wisconsin in 2016, lost it in 2020 and then won it again in 2024.

“It frustrates me when they say that person is unelectable,” Vestin said of Hong, who is vying to become Wisconsin’s first woman governor and first Asian American governor. “A lot of times it’s racist, it’s sexist.”

Vestin, 54, called Evers’ endorsement of Crowley “counterproductive.”

“Establishment Democrats want to take the choice away,” Vestin said.

On Tuesday in Minnesota, voters will choose between moderate U.S. Rep. Angie Craig and progressive Lt. Gov. Peggy Flanagan in the Democratic primary for U.S. Senate that’s playing out in the aftermath of Trump’s aggressive immigration enforcement surge in the state.

But the stakes are even higher in Wisconsin.

Democrats are trying to hold onto the governor’s office and flip both chambers of the Legislature to have full control for the first time since 2010.

Republicans transformed Wisconsin by enacting a wide array of conservative priorities, including effectively doing away with public worker collective bargaining, when they had full control from 2011 to 2018.

“We need a trifecta,” Crowley told his supporters at the New Glarus rally. “And who is at the top of the ticket matters.”

Crowley supporter Lindsey Lee, 61, of Madison said before the rally there that he believed previously undecided Democrats were coalescing around Crowley.

“It’s like the last 50 yards of a horse race,” Lee said. “Hong is obviously in the lead but Crowley is coming up on a side lane. I think it will be a photo finish.”

Bilt and Qatar Expand Collaboration with 3X Avios on Rent for Qatar Airways Privilege Club Credit Cardholders


Bilt and Qatar Expand Collaboration with 3X Avios on Rent

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