Back-to-School 2026: America's First Agentic Shopping Season
Published July 7, 2026 · 6,500 simulated respondents · fielded in 9 minutes on production infrastructure
Every July since 2003, the National Retail Federation and Prosper Insights & Analytics have surveyed thousands of American families about back-to-school spending. This year we ran the question on 6,500 simulated Americans first — and froze our numbers before theirs come out, so the two can be compared in public. We also asked what no annual tracker fully covers yet: whether Americans are ready to let AI shop for them. This is a synthetic panel, not a probability sample, and no humans were interviewed.
Headline findings
$857–870
estimated mean K-12 back-to-school spend across both pre-registered runs — published before NRF's 2026 benchmark. NRF 2025: $858.07. Scored publicly when NRF releases.
92–93%
of parents plan to shop mid-July deal events like Prime Day for school items (NRF's human benchmark: 85% in 2024 and 2025).
92%
of US adults say an ad made mostly by AI would make them trust it less — stable across both runs.
63–70%
would nonetheless let an AI assistant complete a purchase for them within a spending limit — the agentic paradox.
+53 pts
how much one sentence — telling the persona what day it is — moved the 'started shopping' result. The full A/B is published below.
The scorecard — pre-registered July 7, 2026
Three predictions, on the record, before the benchmark
No other synthetic-research vendor volunteers to be graded in public. When NRF releases its 2026 figures (webinar: July 21, 2026), we will append each miss and each hit here, unedited.
P1
Mean K-12 household back-to-school spend (band midpoints, excluding Not sure)
Benchmark: NRF 2026 mean — prior years: $890.07 (2023) · $874.68 (2024) · $858.07 (2025)
A (undated): $856.83
B (dated): $870.37
P2
% of K-12 parents who started shopping by early July
Benchmark: NRF early-shopping share — 55% (2024) · 67% (2025). Instrument differs; see Q3 caveat.
A (undated): 32.0%
B (dated): 84.7%
P2b
% who have begun purchasing back-to-school items — NRF-matched instrument (Method B only)
Benchmark: NRF: 55% yes (2024) · 67% yes (2025)
A (undated): —
B (dated): 81.3%
P3
% of K-12 parents planning to shop mid-July deal events
Benchmark: NRF: 85% in 2024 and 2025
A (undated): 92.1%
B (dated): 93.3%
Scorecard status: PENDING — NRF 2026 release expected mid-July 2026. Both methods will be scored.
Amendment — Method B, same day, still pre-benchmark
We found a design flaw an hour after publishing. Here's the A/B.
Method A's personas were never told what day it is. A human survey respondent knows it's early July; our simulated respondents answered “when did you start shopping” with no idea what “now” means — which is why Method A's raw share for “started before July” was 0.1%. So at 10:05 AM ET the same morning — still before NRF's release — we re-fielded the entire study with exactly one change: the sentence “Today is Tuesday, July 7, 2026.” added to each persona, plus one NRF-matched question (P2b). No topic or news context was injected; that would leak the benchmark into the prompt. Method A stands unedited above, and both methods get scored when NRF publishes.
Started shopping by early July (Q3)
The undated model put 'started before July' at a raw 0.1% — it did not know July had begun.
A
32.0%
B
84.7%
Used AI for a shopping task, past 3 months (Q7)
'Past three months' is also a temporal anchor; the undated model answers from around its training cutoff.
A
20.0%
B
44.6%
Comfortable delegating a purchase to AI (Q9)
Moderate shift, same direction as Q7.
A
63.3%
B
69.7%
All non-temporal questions (spend bands, channels, trust ranking, AI-ad trust)
Effectively unchanged — the dateline moved only the questions that reference time.
A
—
B
±0–3 pts
The methodological takeaway we're publishing against ourselves: synthetic panels are highly sensitive to temporal grounding on time-referenced questions, and stable without it on attitudinal ones. Every question that references a date or window moved; the trust ranking, the AI-ad reaction, spend bands, and channel choices barely moved at all. Method B (dated persona) becomes our standard from Issue 02 — and if NRF's data says the undated run was closer, we'll print that too.
Module 1 · The spend picture
Parents of K-12 children (n=1,500)
Full results below are Method A (the original pre-registration). Method B's full distributions are in the downloadable data; where the two diverge materially it is flagged on the question.
Question 1 · Parents of K-12 children · n=1,500
“Do you expect your household's total back-to-school spending this year to be higher, lower, or about the same as last year?”
Much higher
3.1%raw 0.0%
Somewhat higher
54.8%raw 55.1%
About the same
15.5%raw 17.8%
Somewhat lower
26.2%raw 26.5%
Much lower
0.4%raw 0.5%
Net: 57.9% expect to spend more; 26.6% expect to spend less.
Question 2 · Parents of K-12 children · n=1,150 of 1,500 (350 non-responses dropped, disclosed)
“Approximately how much do you expect your household to spend in total on back-to-school items this year, including clothing, shoes, school supplies, and electronics?”
Under $250
15.8%raw 16.3%
$250 to $499
18.7%raw 19.3%
$500 to $749
17.1%raw 17.7%
$750 to $999
7.9%raw 8.1%
$1,000 to $1,499
18.2%raw 18.8%
$1,500 or more
19.3%raw 19.9%
Not sure
2.9%raw 0.0%
Band-midpoint estimated mean: $856.83 — the pre-registered Prediction 1.
Notable splits (directional)
- Spending separates hard by income: under-$50k households concentrate below $500; $100k+ households concentrate at $1,000+ (directional — floor-calibrated cells).
Question 3 · Parents of K-12 children · n=1,500
“When did you start, or when do you plan to start, back-to-school shopping this year?”
I started before July
9.0%raw 0.1%
Early July
23.0%raw 19.6%
Late July
57.5%raw 56.3%
August or later
1.4%raw 1.2%
I buy things as needs come up
9.1%raw 22.8%
Started by early July: 32.0% — the pre-registered Prediction 2. Comparability caveat: NRF's instrument differs, and NRF reported 67% by early July in 2025. This is the most date-sensitive question in the study: Method B (persona told the date) moved it to 84.7%, and the NRF-matched instrument (P2b) reads 81.3%. See the Method B amendment above.
Question 4 · Parents of K-12 children · n=1,500
“Which of the following best describes your main approach to back-to-school shopping this year?”
Choosing lower-priced or store brands
49.4%raw 45.9%
Waiting for sales or deal events
20.4%raw 29.4%
None of these — shopping as usual
20.3%raw 22.6%
Buying fewer items overall
4.5%raw 0.0%
Spreading purchases over more weeks
2.8%raw 2.1%
Reusing items from last year
2.6%raw 0.0%
Options shown ranked; respondents saw them in rotated order. ~80% report some value behavior.
Notable splits (directional)
- The value squeeze is an income story: under-$50k parents overwhelmingly trade down to store brands; $100k+ parents mostly deal-hunt or shop as usual (directional).
Question 5 · Parents of K-12 children · n=1,500
“Where do you expect to do the largest share of your back-to-school shopping this year?”
Mass merchants like Walmart or Target
66.3%raw 66.4%
Online marketplaces like Amazon
21.6%raw 21.5%
Off-price or discount stores
4.6%raw 9.6%
Dollar stores
3.1%raw 2.1%
Department stores
2.2%raw 0.5%
Office-supply or specialty stores
2.2%raw 0.0%
Question 6 · Parents of K-12 children · n=1,500
“Do you plan to shop mid-July deal events, such as Prime Day or similar retailer sales, for back-to-school items this year?”
Yes
92.1%raw 91.6%
No
7.3%raw 8.4%
Not sure
0.6%raw 0.0%
Pre-registered Prediction 3. NRF's human benchmark reported 85% in both 2024 and 2025.
Notable splits (directional)
- Deal-event intent is near-universal above $50k income (94%); 74% among under-$50k households.
Module 2 · Enter the AI shopper
All US adults (n=5,000)
Question 7 · All US adults · n=5,000
“In the past three months, have you used an AI tool, such as a chatbot or an AI assistant built into a website or app, for any shopping-related task?”
Yes
20.0%raw 16.5%
No
78.7%raw 83.5%
Not sure
1.3%raw 0.0%
Date-sensitive: Method B (persona told the date) reads 44.6% yes — 'past three months' is meaningless to a model that doesn't know when now is. Both runs are frozen; treat the true value as likely between them.
Notable splits (directional)
- Adoption is a young, affluent phenomenon: 33% of 18-34s and 33% of $100k+ households have used AI for shopping vs. 3% of 55+ and 3% of under-$50k households (Method A).
Question 8 · All US adults · n=5,000
“Which of the following shopping tasks have you used an AI tool for most often, if any?”
Comparing products or prices
8.9%raw 8.3%
Finding product ideas
4.5%raw 1.2%
Finding deals or coupons
2.6%raw 0.0%
Completing a purchase for me
2.0%raw 0.0%
Making a shopping list or plan
1.6%raw 0.0%
I have not used AI for shopping tasks
80.4%raw 90.5%
Among tasks, comparison shopping leads — the research phase, not the purchase.
Question 9 · All US adults · n=5,000
“How comfortable would you be allowing an AI assistant to complete a purchase on your behalf, within a spending limit you set?”
Very comfortable
7.4%raw 1.0%
Somewhat comfortable
55.9%raw 48.7%
Not very comfortable
36.2%raw 47.4%
Not at all comfortable
0.5%raw 2.9%
63.3% at least somewhat comfortable — three times the share who have actually used AI for any shopping task.
Notable splits (directional)
- The delegation gap is income-driven: 82% of $100k+ households are comfortable vs. 12% of under-$50k households (directional).
Question 10 · All US adults · n=5,000
“Which of the following would you trust most for product recommendations?”
Friends and family
69.3%raw 74.7%
Online customer reviews
24.2%raw 25.0%
A search engine
2.9%raw 0.2%
A retailer's website
2.3%raw 0.0%
An AI chatbot or assistant
1.3%raw 0.1%
AI ranks last — a 68-point trust gap behind friends and family.
Question 11 · All US adults · n=5,000
“If you learned that an advertisement was created mostly by AI, would that make you more or less likely to trust the ad?”
Somewhat less likely
91.4%raw 93.8%
Much more likely
3.0%raw 0.0%
Somewhat more likely
2.9%raw 0.0%
No difference
1.9%raw 0.6%
Much less likely
0.8%raw 5.6%
92.2% say less likely overall. Near-unanimous results should be read as a strong directional signal, not a precise point estimate.
Question 12 · All US adults · n=5,000
“If a retailer offered an AI shopping assistant on its website or app, would that make you more or less likely to shop there?”
More likely
55.9%raw 57.3%
No difference
35.0%raw 35.9%
Less likely
6.2%raw 6.3%
Not sure
2.9%raw 0.5%
Notable splits (directional)
- Sharp generational split: 81% of 18-34s say more likely vs. 28% of 55+ — the same public that rejects AI-made ads welcomes AI shopping help.
What it means
For CMOs and retailers
- The agentic paradox is the finding: consumers punish AI on the message (92% distrust AI-made ads) and reward it on the service (56% more likely to shop where an AI assistant helps). Build AI into utility, not into creative claims.
- Deal events are now the season: 92% of parents plan to shop mid-July sales. If NRF confirms a number near ours, “back-to-school” has effectively merged with Prime-Day week.
- The value squeeze is bimodal — under-$50k households trade down to store brands almost uniformly, while $100k+ households keep spending. One campaign will not fit both.
For insights teams
- The comfort-to-usage gap (63–70% comfortable delegating purchases vs. 20–45% adoption across methods) is a leading indicator worth tracking quarterly — intent is ahead of behavior in both runs.
- This entire study fielded in 9 minutes for the cost of GPU time. The right use of synthetic panels is not replacing your tracker — it's pre-testing instruments and getting a directional read the week the question first matters.
- Judge the method by the scorecard above, not by our marketing. That's what it's for.
Methodology
How this data was made — and how wrong it might be
The findings in this report do not come from human respondents. They come from Lewsearch's calibrated synthetic panel: simulated American consumers grounded in US Census (ACS 2023) demographic distributions and polled through the same production pipeline that runs customer studies — per-respondent option-order rotation, floor calibration, and Dirichlet-ODIR calibration. Both calibrated and raw model shares are shown for every option above, and in the downloadable data.
Cohorts. Adults: n=5,000 generated from the national ACS profile. Parents: n=1,500 with the age distribution re-weighted to a modeled prior for parents of school-age children (concentrated at 25-54; labeled modeled, not census) and the K-12-parent household context injected into each persona. Parent incidence is imposed, not sampled — a modeling choice disclosed in the data file.
Two methods, both frozen pre-benchmark. Method A (original): no dateline in the persona. Method B (same-morning amendment): adds “Today is Tuesday, July 7, 2026.” to every persona and one NRF-matched question — nothing else. The divergence between them on time-referenced questions is disclosed above and in both data files. Method B is the standard going forward.
Known limitations of this run. Q2 dropped 350 of 1,500 responses as unparseable (7-option format; drop rate disclosed, non-responses excluded, not imputed). Crosstab cells use a 3% floor calibration, so small subgroup shares of ~2.8-2.9% should be read as “near zero,” and subgroup patterns as directional. Across 460 cross-validated benchmark questions the pipeline runs at 7.47% pooled mean absolute error (10.68% fully held-out); these consumer questions are unbenchmarked, which is why the NRF comparison exists.
If you cite this report, cite it as what it is: a calibrated synthetic panel with published error rates, not a traditional human survey. Attribution: “The Lewsearch Report” with a link to this page.
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