Loyalty, advocacy and referrals
Asked as “customer loyalty and referral platform”, and as “customer advocacy and referral program tool”, on behalf of a mid-market B2B software company. 41 first choices recorded across the direct, paraphrase, budget and scale prompts, six models each.
01The standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all six models, for a mid-market B2B software company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
Show the nine products at 0%, ordered by negative rate
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its vendor page.
Recommended versus criticized
Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
Criticized challengerCriticized default
Negative label rate →
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18Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative40%
Key
01Smile.io24%
Smile.io24%02Referral Rock22%
Referral Rock22%03Influitive10%
Influitive10%04GrowSurf5%
GrowSurf5%05Ambassador5%
Ambassador5%06Referral Factory5%
Referral Factory5%07SlapFive2%
SlapFive2%08Open Loyalty2%
Open Loyalty2%09Zinrelo2%
Zinrelo2%10Talon.One0%
Talon.One0%11Annex Cloud0%
Annex Cloud0%12LoyaltyLion0%
LoyaltyLion0%13Extole0%
Extole0%14Salesforce Loyalty Management0%
Salesforce Loyalty Management0%15Friendbuy0%
Friendbuy0%16ReferralCandy0%
ReferralCandy0%17Yotpo Loyalty & Referrals0%
Yotpo Loyalty & Referrals0%18Yotpo0%
Yotpo0%02What they warned about
A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Smile.io19%
7 of 36 labels negative · 6 of 6 models · 1 hard negative
“**Billing complaints**: Multiple merchants report being charged even after cancelling their free plan” GLM 4.7 FlashX, negative prompt
Friendbuy23%
5 of 22 labels negative · 5 of 6 models · 1 hard negative
“**Outdated customer service**: Described as "stuck in the 80s" with slow response times” GLM 4.7 FlashX, negative prompt
LoyaltyLion25%
7 of 28 labels negative · 5 of 6 models · 1 hard negative
“Better for larger/scale brands; minimal plans start around $100+/month” DeepSeek V4 Flash, budget prompt
Yotpo38%
8 of 21 labels negative · 5 of 6 models · 1 hard negative
“What to Avoid on a Tight Budget - **Yotpo** — Quote-based pricing, historically more expensive” DeepSeek V4 Flash, budget prompt
03What they cite
Citations exist only for the models that return a source list: four of the six in this edition.
Sites the answers cite
21 of 72 answers in this category came back with a source list, from 4 of 6 models: citations where the model returns them, or the search results it consulted. 6 of those lists are Google grounding redirects that name no site and are left out of the counts. 208 links across 122 sites, every framing counted. Ranked by the number of answers carrying the site or page. The other 2 models searched through a gateway that returned no source list on this edition.
vendor site · Open Loyalty9 answers · 14 citations · 2 models
vendor site · CustomerGauge5 answers · 6 citations · 3 models
vendor site · Yotpo5 answers · 5 citations · 2 models
vendor site · Extole4 answers · 6 citations · 3 models
4 answers · 5 citations · 2 models
vendor site · Voucherify4 answers · 5 citations · 1 model
4 answers · 4 citations · 2 models
vendor site · Bubblehouse3 answers · 4 citations · 2 models
3 answers · 4 citations · 1 model
3 answers · 4 citations · 1 model
vendor site · HubSpot3 answers · 3 citations · 1 model
vendor site · Capterra3 answers · 3 citations · 2 models
Pages the answers cite
The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.
5 answers · 2 models
4 answers · 3 models
3 answers · 1 model
3 answers · 2 models
3 answers · 1 model
3 answers · 2 models
3 answers · 1 model
3 answers · 1 model
3 answers · 2 models
3 answers · 1 model
04How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ModelDirect“What is the best customer loyalty and referral platform for a mid-market B2B company?”Paraphrase“Which customer advocacy and referral program tool would you recommend to a mid-sized B2B company?”Comparative“What are the top loyalty, advocacy and referral platforms and how do they differ?”Budget-constrained“What is the best customer loyalty and referral platform for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a customer loyalty and referral platform. What should we look at?”Negative“Which loyalty, advocacy and referral platforms should I avoid or be cautious about?”
Claude Opus 5no first choiceno first choiceno first choiceno first choiceno first choicenothing named
Claude Opus 4.8no first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-6 Astrano first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-5.6 Solno first choiceno first choiceno first choiceno first choiceno first choicenothing named
Gemini 3.1 Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Perplexity Sonar Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Bold is the first choiceAlternatives are counted; the count opens them.What the answer argued against
05The record
One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Zero rows: every prompt, every model, every answer.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
Category-scoped readings
Salesforce read as Salesforce Loyalty Management
Unresolved, counted raw
AnnexCloud
Elementary POS
EverAfter
ExpertVoice
FiveStars Loyalty (Fivestars)
Loloyal
LoyaltyMethods (ReactorCX)
LoyaltyPass
Loyverse
Muster
OneClickPolitics
OpenLoyalty
Oracle Siebel
PC Optimum
Physical Punch Cards
Points.com
Rivo.io
Salesforce Loyalty Cloud
Stamp Me
SumUp
Toast
VoterVoice
Webloyalty.com / Reservation Rewards
Xoxoday Loyalife
Yotpo Referral
impact.com / Advocate
meed
Discontinued, still offered
No shut-down product was recommended here.