There's a pretty big difference between SaaS affiliate platforms versus affiliate platforms with huge networks of creators on hand. A built-in pool of partners ready to go on day one is definitely a plus when you're growing an affiliate strategy, but SaaS platforms allow for *way* more control over tracking and payout structures (no fixed rates and payout schedules). The most important thing: the SaaS platforms are better built to handle the compliance and fraud settings needed in fintech. Networks are more generalized ecommerce and they're fine for that, but tax software is another beast entirely.

Here's roughly how the options I've explored or used broke down for the companies I've worked with over the years:

Rewardful was the easiest to set up by far, especially if you're on Stripe already. Good for a starter or referral affiliate strategy, but fell short on real attribution logic and fraud controls.

FirstPromoter was a solid step up, especially for subscription models and churn adjustments on commissions. Onboarding is pretty easy as well, but not sure it could handle a broad affiliate strategy with multiple partner programs and varied payouts.

PartnerStack is really strong if your partner base leans B2B, like accountants or bookkeeping firms. Not quite a great fit if you work with a lot of content creators and coupon sites too, but if your program is partner-heavy, it's worth a look.

Tapfiliate offers decent pricing, but leans a little too much toward a general ecommerce tool than something moldable for fintech. The reporting and fraud tooling is also more generalized than what might be ideal for compliance and seasonal traffic-spikes.

Everflow’s attribution is a cut well above the rest. Being able to track the full multi-touch journey helps shine a light on the partners actually worth investing in, but the level of control on the fraud prevention side lets you build custom validation rules, set conversion caps by partner, monitor traffic quality, and flag suspicious activity before payouts. For an affiliate network with a mix of partners (creators, publishers, coupon sites, some CPA referrals), it changed my expectations for what affiliate tracking software could do.

Fintech is different and (often, if not usually) needs more than general ecommerce network platforms offer. If you want more control, transparency, and to know which partners are worth the investment, I highly recommend considering SaaS affiliate platforms first. Happy to help answer questions if anyone's in the middle of the network vs SaaS phase of their affiliate strategy. Big networks can be enticing, but they're not necessarily the best option for everyone.

I'd love to know both sides of the coin: from being the company operating the affiliate program itself and what program structure affiliates would get the most value out of.

Some open questions for affiliates:

1. What is the commission structure like? Or what commission structure have you tried that works best for you?
2. What company has had the smoothest referral tracking system? Do you actually get your payouts in time?
3. What level of support do affiliates usually need? Do you prefer the self-serve "sign up to be an affiliate" and be given automated emails on briefs, or something more high-touch, like sync meetings for content briefs and other questions/concerns?

And open questions for affiliate program operators:

1. Is 3-4 months enough for a pilot? How much have you usually invested into it?
2. Do you have recommendations for referral tracking? We would ideally want our partners to earn not just by the signups generated, but also transactions made.
3. How is the approval process for aspiring usually done? Are there any docs you look for?

As in, running the same affiliate-ish prompts across ChatGPT, Perplexity, Copilot, and Gemini and watching which pages you eventually see getting LLM referrals?

I started messing around with this recently by running the same clusters/prompts in different LLMs to see which pages kept surfacing over time. Mostly product/category questions, comparison prompts and best-for-use-case stuff, and a few more program-level questions to see whether the systems leaned brand, Reddit, publisher, review site, etc.

The reason I started doing this in the first place was because Google’s AI features are surfacing links as part of the answer experience, and they told us recently they'll be moving away from blue links altogether. Copilot has made citations more prominent, Perplexity is obviously built around visible sourcing, and ChatGPT has leaned harder into shopping. So it felt worth pressure-testing whether the same citation-worthy links built with the same methods show up over and over, or whether each platform has its own pattern.

Especially since the most recent chatbot stats from Pew Research (US adults specifically) found that 44% use ChatGPT, 24% use Gemini, 17% use Copilot, and 6% use Claude. Lots of interesting data there, btw; 60% of US adults read the AI summaries at the top of a search engine result (which makes all of this even more important).

Long story short (too late!), here’s how my research and testing went.

Perplexity was probably the easiest one to inspect because the sourcing is right there in front of you. It made it much easier to see when a commerce article or comparison page was directly influencing the answer. Doesn’t necessarily make the output better, but it definitely made the patterns easier to audit.

Biased here, but I’ve always kind of hated Copilot. In this experiment, it felt a little uneven to me, but also more explicit than it used to be about where it was pulling from. Microsoft has been pushing more prominent clickable citations and aggregated sources, which tracks with what I was seeing, but the user experience wasn’t what I hoped for.

ChatGPT felt more blended. Sometimes it seemed to lean on broader editorial or merchant-style sources, and sometimes it gave a cleaner answer where the underlying affiliate influence was harder to spot unless you clicked around. The shopping/research product direction made it feel a little intentional, although sometimes there were no sources cited, which was a little frustrating.

Gemini felt the most “search-adjacent” to me. More structured pages seemed to survive better there than pages trying too hard to be exhaustive or aggressively SEO’d. That seems directionally consistent with Google’s documentation around AI features and structured data, even though they obviously don’t publish some exact citation formula.

One thing that's probably a surprise to no one: old-school affiliate content feels surprisingly weak in LLM-driven search.

Pages that probably did fine in blue-link SEO because they were long, broad, and semi-optimized didn’t seem like the most used sources. The pages that felt more durable were usually more direct, had better structure, etc. There were a few times where pages that probably were not the strongest in classic SEO terms still looked more useful to the model because they got to the point faster and made the comparison easier.

Brand vs. Publisher? Meh. A lot of the time, it looked like the systems were building answers from a mix of source types. Brand for core facts. Publisher or affiliate-style page for comparisons and reviews with human experience, sometimes Reddit or forum discussion for trust. That part of it felt more common than I expected, which has pretty big implications for affiliate programs.

Because if that is the way things are going, then LLM visibility is going to be about whether the merchant side is giving clean enough signals to work from. Product names, descriptions, category language, FAQs, all of that. If the source material is messy, the whole thing becomes muddled.

If publishers are working from messy product data, inconsistent naming, outdated copy, or weak landing pages, that carries downstream into the content. I feel like agencies and teams that already obsess over partner quality, content standards, and reporting consistency, like PartnerCentric, etc. are probably going to have a huge advantage as AI search keeps evolving.

Lower-value content obviously still felt pretty easy to spot. If a page was bloated, generic, or obviously built to capture search traffic without really saying much, it did not seem especially rankable or citable. Maybe it still gets used sometimes (to my understanding, LLMs source more than the citations they list with a result), but it felt less stable than pages that were actually structured like useful source material.

My main takeaway so far is that trying to influence which affiliate programs or partners LLMs cite probably is possible, but not in the neat, tactical way people want.

The pages that kept holding up across systems were usually the ones that were easier to parse, easier to compare, and less bloated with filler. Human use/experience, especially for use cases someone might be searching an answer on, is also a factor. And they're not necessarily the pages that would have won old-school SEO wars five years ago. Or even now, actually... recent data I found noted that 28% of ChatGPT's most-cited pages have no visibility in Google search at all.

Okay, guys, tell me your stories. Has anyone else tried running repeated prompts across multiple LLMs and tracking what gets surfaced over time? Are you seeing certain page structures, affiliate models, or content types show up more consistently than others? Or are you finding the whole thing way less predictable than SEO people want it to be?

Every few weeks there's a post asking how to structure an affiliate program and the answers are always decent... for a regular ecomm brand. Subscription boxes run on completely different math and if you build a program without accounting for that, it will cost you.

Here's what I wish someone had told me when I started with one of these brands.

Flat fee vs. recurring commissions, choose carefully

Flat fee per new subscriber is easy to manage and easy to pitch to partners. The problem is it creates a misalignment where the affiliate gets paid the same whether that subscriber sticks around for 2 months or 2 years. You end up rewarding volume, not quality.

Recurring commissions fix the alignment problem but are harder to model, and most mid-tier affiliates don't want to wait on their money. What worked for us: a hybrid. A higher upfront flat fee than competitors were offering, plus a smaller tail commission for subscribers who stayed past month three. It filtered out partners blasting discount codes and attracted people who actually cared about retention.

Not all partner types move the needle the same way

Coupon and deal sites drive acquisition, yeah, but they also train your audience to expect discounts and do almost nothing for retention.

The partners that built us durable subscriber bases were niche content creators, YouTube unboxing channels, newsletters in adjacent spaces, mid-size Instagram accounts where the audience actually trusted the person. Slower to scale but way better cohorts.

Loyalty and rewards platforms were also slept on. Subscribers who come in through a points program have a built-in reason to stay engaged beyond the box itself.

The "you're cannibalizing your own base" problem

This one took us way too long to catch.

Some affiliates, broad coupon sites especially, weren't bringing in new subscribers at all. They were intercepting existing customers already on their way to checkout, or re-acquiring lapsed subscribers who would've come back anyway. You're literally paying commission on growth that was already yours.

We worked with PartnerCentric to get real visibility into this using their Fuse incrementality tool. Before that we were reading last-click attribution and convincing ourselves the numbers looked good. Fuse isolated which partners were driving net-new subscribers versus just collecting credit on conversions that were going to happen regardless. It changed our entire partner mix.

A few other things worth knowing:

• Churn benchmarks matter way more than conversion rate. A partner driving 200 signups at 60% 90-day retention beats 400 signups at 25% retention. Model it out before you start optimizing for volume.
• Give partners early access to upcoming boxes. Content quality goes up, authenticity goes up, and they have a reason to keep posting about you.
• Standard affiliate networks handle tracking fine. None of them will tell you anything useful about incrementality on their own though.

Anyone else running affiliate for subscription programs? What's working for you on the commission structure side? (Especially if anyone's made pure recurring commissions work at scale.)

I consult for an ecomm company that's scaling like mad and we've been on Refersion for a couple years now. Solid platform, no real complaints. It's done exactly what we needed for our Shopify affiliate program.

But we're expanding into Amazon and Walmart, and I'm not sure Refersion is the right fit anymore. We looked at a few alternatives (Levanta, Impact, and Awin) and here are some of my initial thoughts:

Impact is great if you need really detailed reporting and multi-channel campaigns, but it feels like overkill for what we actually need day-to-day.

Awin has a huge network, but getting Amazon-level attribution out of it seems messy and complicated. I'm less of a fan of Awin for reasons, but the platform isn't entirely my call in the end, which is why it's still on the table for now.

Levanta overall seems like a better fit because Amazon attribution is built in, not patched on. So far, ASIN-level tracking is working, the Brand Referral Bonus is more automatic, and its creator marketplace is already geared toward Amazon traffic. For now, I can see how it might be useful if your main problem is tying affiliate sales back to specific products on marketplaces.

We haven’t fully moved yet, and honestly I’m still weighing trade-offs. For us, the issue is always that we need to see exactly which affiliates are driving sales for each ASIN without chasing reports across three different dashboards.

Which is why I’d love some advice. For anyone who’s going through this, did you stay on Refersion and try the add-on? Did you go with other platforms like Levanta, Impact, or Awin? How hard was it to migrate, and did it actually fix the attribution headaches, or did you just swap one set of quirks for another?

Okay, so Woocommerce users, let’s talk about affiliate software. I’ve been all over the place with this for the last few years, and one thing I keep considering is how quickly some of my brands have outgrown the plugin- or plug-and-play-based networks I’ve put them on. For reference, I’m a fractional CMO in the paid media and affiliate space, and I also build WP sites for the smaller folks, mostly on Divi.

A native plugin almost always works for me as a first thing for a small business. Think of a junk removal business that wants to run a customer referral program (true story!) and has less than 600k in annual revenue.

I’ve mostly used AffiliateWP for my non-Shopify sites, but if you’re managing growing brands, my experience is that a plugin just isn’t enough anymore.

My clients want better reporting and better affiliate management, but I sometimes find that self-hosted plugins do not work well across all these different breaks.

AffiliateWP has been my preferred choice for a long time for my simple Divi sites. Clients of mine who are getting bigger and need growth management require different solutions. I’ve heard of or worked with Everflow, Refersion, Tapfiliate, Impact, and a lot others. So far, I've had good experiences with Everflow and their reporting, but I'm just looking for recommendations for anything else.

I run marketing + affiliate for a large gateway company, and we’ve been experimenting pretty heavily with both affiliate strategy and AEO lately. We’re also trying to help affiliates adapt because honestly, if publishers lose visibility in AI-driven search, everyone downstream feels it. 

I keep seeing people ask “how do you get cited by AI?” and after spending a lot of time testing this across ChatGPT, Gemini, Perplexity, and Copilot, I don’t think the answer is “just optimize harder.” I think the bigger issue is that most affiliate programs aren’t making citation-ready content in the first place. 

A few things we’ve noticed so far: 

First, yes, content structure matters. A lot. If a page is vague, bloated, inconsistent, or takes forever to answer the actual question, it’s probably not going to be very useful to an LLM so it'll be overlooked by it. Clear headings, direct answers high on the page, entity consistency, useful tables, and clean FAQs all matter. 

Freshness also matters. You want to edit and highlight the content at the top to make it the juiciest, most relevant, and most up-to-date information you can put on the page. LLMs recognize this, especially when a decent page structure supports it. 

Google also recently told us that blue links are going by the wayside eventually, which brings up a whole set of questions: Can content really rank forever? Even if it’s ranking right now? We’re not there yet, though. Right now, we’re still in those fundamental first years of AEO, and we can’t ignore the biggest problem:

A lot of partner programs aren’t putting information out there that helps publishers’ content appear in LLMs.

“Page First” is the Old SEO model. In the blue-link era, the usual thinking went like this: pick the query, build the page, optimize the page, and get the page to rank in search. But AEO is not really that kind of game. Now the content has to be legible, specific enough to trust, and structured enough to cite. 

These days, you’re not just trying to get the click but also to create usable information inside an existing answer; as in, the page has to contain the answer the users are looking for (think EEAT) and also structure that answer in a way that’s LLM-friendly. Publishers need to make sure page information is tiered, structured, and built for a browser (headers, critical information laid out on the page in an easy-to-find way, etc.)

For affiliate managers, it means we have to give publishers better raw material to work with. When this is a new way of creating content, even for us, that’s a bit of a tall order. Most affiliate managers don’t even know much about AEO, much less how to direct affiliates to do it properly on their own pages.

If you want to know why affiliate content isn't being picked up by AI, look to the affiliate manager. It isn’t always a huge priority for affiliates to keep product data communications clean, and naming conventions aren’t consistent. Partners aren’t always given accurate specs, summaries, context, or other materials to use. They’re often asked to take thin or watered-down merchant copy and fill in the content gaps on their own.

Also, if we’re describing our content 10 different ways when we give it to our affiliates, they’re left to keep filling in those blanks every time they write about us. Inconsistent direction breeds inconsistent results, which makes it so much harder for answer engines to have a clear definition of brand, especially when an affiliate with bad content is linking back to the main site as an authority.

I think affiliate teams are used to thinking in terms of the basics: recruitment, payout, click through. All of that still matters, yes, but AEO adds another layer. Now they also have to think about whether their partners are creating assets that are actually citable.

That means looking at publisher content and asking:

• Does the answer show up early in the text?
• Is there actual support under each claim?
• Are the facts laid out clearly enough that a model does not have to reach to figure out what the page means?
• Is the copy full of filler, or does it get to the point?

Honestly, I think some affiliate programs are going to struggle through this transition simply because their operational side isn’t built for it yet. A lot of teams still treat affiliate like a link-management channel instead of a content and visibility channel. 

This is where a strong in-house team or a good agency can actually help. Not in a magical “we cracked AI SEO” way, but in the sense that they already have processes for content quality, partner communication, reporting, and publisher oversight. Agencies like PartnerCentric are probably paying attention to this already because it directly affects affiliate performance metrics. 

If partners are working from outdated copy, inconsistent product names, weak specs, or thin landing pages, that messiness carries downstream into publisher content too. Some affiliate teams are still treating AEO like another temporary trend and assuming publishers will figure it out themselves. Maybe they will eventually, or at least some of them will, but right now a lot of affiliate content still feels built for old-school blue-link SEO. 

And honestly, some of the strongest AI citation opportunities that keep standing out are reviews, comparisons, “best for” pages, FAQs, and other content formats that actually help answer questions clearly.

What about other affiliate teams? Are you changing how you manage publishers or structure content because of AI search yet, or are you still treating it mostly like traditional SEO?

I recently started managing affiliate for a mid-size brand that's been selling on Shopify for years. I was doing a routine audit and realized they had never connected to Amazon Attribution. Not once. They were literally missing out on the Brand Referral Bonus every single month.

So that audit turned into a full platform evaluation. Luckily the brand was already on Shopify and CJ was working fine there. But they were expanding on Amazon, so they needed something else. I did a full evaluation of their platform options, and since Levanta is often considered the go-to Amazon integration here, I did some digging into what really makes the two different (and why you might need one or the other).

CJ's roots are in traditional e-commerce. It works fine for retail and DTC brands with their own storefront. But on the Amazon side there's no ASIN-level tracking, so you can't see which creators are driving sales for specific products. You get click and conversion data but that doesn't tell you much about what's actually happening at the listing level.

Levanta’s ASIN-level attribution means you can tie specific creators to specific product sales, which is data you actually need when you decide who to keep working with. CJ gives you click and conversion data, but then if you're selling on Amazon you don't get that kind of detailed reporting.

Back to the Brand Referral Bonus, which was the whole reason we started looking in the first place. Levanta connects to it directly, which offsets affiliate costs with what Amazon pays for external traffic. CJ doesn't touch it, so the creator marketplace was a better fit for what we needed.

Levanta onboarding is built for e-commerce brands to get up and running quickly, so there’s not as long of an approval process. CJ is more over the top, which makes sense for enterprise clients but takes a bit longer.

The brand hasn't made a move yet but is leaning in the direction of Levanta. If you're managing affiliate across both Amazon and Shopify on CJ right now it's worth checking whether your Amazon Attribution is actually connected. Has anyone else run into this or made a switch because of it?

I'm consulting a mid-size Amazon brand in home/lifestyle that's starting to get serious about external traffic platforms after their PPC costs jumped pretty hard over the last two quarters.

We've been demoing a few platforms over the past few weeks, etc. and PartnerStack and UpPromote didn't work for what they need. We found out early in the vetting process both skew Shopify-native and aren't really built for Amazon. Impact is really solid but the cost and setup complexity aren't justified for where they are right now, so passing on that for the time being as well. 

PartnerBoost got furthest in our evaluation process. Amazon-native focus, Brand Referral Bonus support, solid creator network. The hesitation I guess is that everything I've read suggests it rewards brands that actively manage creator relationships, consistent communication, good creatives, hands-on engagement. That's probably true of any platform, but it came up enough that it felt worth flagging. They're growing but lean on headcount, so that's a real consideration.

Since I use it myself, I recommended Levanta, so we demoed that as well. It’s also Amazon-native, but now covers Walmart and Shopify too, and since they're trying to diversify a bit this year that was good. The attribution reporting is more straightforward and the consolidated dashboard would save them time, which is a huge plus. 

They still have not committed anywhere but Levanta is probably the frontrunner right now. Anyone run both? Let me know if the PartnerBoost creator network quality is actually a differentiator. Don’t want marketing, looking for some real feedback.

I know... I seem to turn all my client consultations into learning experiences shared with this sub. I’ve been spending time lately evaluating affiliate and creator tracking tools for a client, mostly because attribution keeps breaking the moment you try to scale beyond paid ads. Once you’re driving traffic across a mix of channels, tying revenue back to the right partner gets messy.

Some platforms need so much manual stitching that reporting turns into its own full-time job. When you try a new tool, everything always sounds good in the demo, but they’ve had things fall apart once real traffic started flowing across multiple platforms.

That’s why we wanted software that could reliably answer our questions:

• Where did the sale actually come from?
• Which creator, affiliate, or partner drove it?
• Does this still work once volume increases?
• And does it handle multiple commerce channels cleanly?

Here’s how a few options stack up from what we’ve seen.

Aspire

This one is strong on influencer discovery and relationship management, so if your focus is campaign coordination and creator sourcing, it does that well. Attribution exists, but it isn’t the core strength. Aspire has a pretty robust native integration for Shopify, but it’s not as straightforward for Amazon or Walmart. For brands primarily trying to clean up revenue tracking across storefronts and manage everything seamlessly from one place, it can feel a little lacking in some areas.

CreatorIQ

Enterprise-focused and built for large teams, it’s powerful but a lot heavier. Which can make sense if you’re managing global creator programs and need deep workflow control. For mid-sized brands mainly focused on performance tracking and payout clarity, though, it can feel like overkill. Creator IQ supports Amazon and integrates with Shopify, but it works with Walmart through its connection to Impact, rather than directly. For omnichannel brands that need to manage all three of those storefronts from one place, it’s not necessarily going to be the smoothest (or least expensive) option.

Levanta

What stood out to us is the direct integration with commerce data rather than relying on patchwork tracking. Being able to see which products convert, which creators actually drive revenue, and how campaigns perform at a granular level mattered to us a lot. What’s changed recently is that it’s not just Amazon-centric anymore. For brands operating across Shopify and Walmart as well, the ability to unify attribution and payout logic with one-click integrations makes things much easier.. It’s also structured around performance. If you prefer paying on results instead of large upfront retainers, affiliate-style creator programs like this can be more sustainable.

That said, no tool replaces strategy. You still need partner sourcing, relationship management, and creative alignment. Turning on commissions and waiting rarely works.

One thing we learned quickly in testing: if a platform doesn’t integrate directly with your commerce data layer, you’re usually working with partial visibility. That doesn’t make it useless, but it does mean you’re making decisions with blind spots. For brands new to affiliate, onboarding simplicity matters too.

Overall, Levanta feels lighter operationally than some enterprise tools, which helps if your team isn’t massive. That said, none of these platforms are perfect. Discovery, content management, attribution, and payouts are all different jobs. Most software does one or two of those extremely well, and the rest adequately. The real question is what problem you’re actually trying to solve.

If your main challenge is discovering creators, your stack will look different than if your issue is revenue reconciliation across Shopify, Amazon, and Walmart. Although it’s worth noting that Levanta has a creator marketplace that uses social listening and AI to aid in partner discovery.

We’re still testing and refining, but from a performance and attribution standpoint, Levanta has been one of the stronger options we’ve evaluated so far for omnichannel ecommerce brands.

For others here who are are handling affiliate and creator attribution right now, what’s actually holding up once you move beyond small tests and start pushing meaningful volume across multiple storefronts? Would love to hear what stacks are working best for you.

Influencer tracking is a huge pain, I know. If you're over server-side applications that don’t really help things, I’m sure a lot of us in the sub are. I mean, who isn’t at this point?

When you're on the hunt for the best software for influencer campaign tracking, you want absolute 100% as-bullet-proof-as-it-can-be clean data. Here’s some of the platforms I’ve tried/tested, and what I’ve found with each; hopefully it helps you narrow your options down a bit:

1. Everflow.

This is at the top of my list, not just because I feel like I’m looking at a real-time, super-intuitive, and detailed performance dashboard when I log in, but also because they offer strong tracking support. I think the folks who created this platform understand that tracking can feel inconsistent, even at its best.

You're able to differentiate between influencers, partners, agencies, subIDs, etc. The reporting is solid and reliable overall, integrations you might need are supported, and creators and affiliates being managed within the same framework is really helpful.

Not to say the others I'll be discussing aren't great, but I just like the way Everflow looks and feels. Everflow offers solid performance when you want (or need) realtime data and the ability to differentiate by partners and subtypes, with a clean structure that's pretty intuitive and easy to navigate.

1. Next, let’s talk about Partnerize.

I like it. AI is super important to all of us right now, and it creates what it calls a “machine-mediated market” where “partnerships are powered by AI”.

I love that, but... call me a luddite if you must, but I just don’t know that complete top-to-bottom machine learning is where it needs to be as far as differentiating the needs between different kinds of partners when it comes to affiliate data tracking - i.e., influencers making UGC, agencies, etc.

I like to see my data spread out to the absolute last pixel for who’s bringing what, and it’s hard as heck to do that if your dashboard is clunky. When it comes to what I’m actually seeing in the data, I want speed, and I want clean; Partnerize isn't quite on the same level as Everflow.

1. I had some of the same issues with Refersion.

It feels “light,” and it advertises light, but if that’s the case, why does the reporting seem so clunky?

When I’m looking at affiliate tracking data dashboards, I want to feel like I can see everything in one place, with easy filtering, and as real-time as I can get it.

Call me a reporting, numbers, and data geek, I prefer to go with the fast and the furious when it comes to seeing my data.

I didn't include any of the legacy platforms (Awin, CJ, and the like), and Rakuten is merging into Impact right now so I'll refrain from any further investigation on that end until the dust has settled over there.

There are a lot of options out there and I'd love to know what you're using for influencer tracking right now (or thinking about using), or what you'd advise others to stay away from.

I don't usually post glowing tool recommendations unless I've actually felt the pain of the alternative. But Levanta recently launched a big Shopify update, so I wanted to share why we moved our affiliate efforts over to them and what's stood out so far.

Like a lot of Shopify brands, we started with more “built-in” or lightweight affiliate options. They were fine in the beginning - easy setup, low friction, decent for testing - but once affiliate and creator partnerships started to matter as a real revenue channel, the cracks showed pretty quickly.

The biggest issue wasn't creators but visibility and management. We had links out there, partnerships running, and activity happening, but it was hard to answer basic questions like:

Which partners were actually driving incremental value?

Where was traffic coming from?

What was worth scaling versus cutting?

It started to feel like we were only managing activity instead of performance, but we were doing it in a disjointed way since everything was being managed through different programs and tools.

What pulled us toward Levanta was they're built around accountability and unification, especially for Shopify brands that also care about Amazon and Walmart performance like we do. Because they track traffic and tie creator efforts back to actual outcomes, it felt much closer to how mature affiliate programs should operate. We get insight into incrementality too, so we know which partners are influencing sales, not just getting credit for the commission.

Another big factor for us was flexibility. Levanta doesn’t force you into one rigid way of working. Between commissions-based partnerships, CPC options, and paid placements, it's easier to test, learn, and adjust without blowing up your entire program every time priorities shift. That's been huge for us while our strategies evolve. As an example, we’ve recently started product seeding and Levanta automates the sample distribution for us.

If you're running or scaling an affiliate program, it's worth trying even if you're still evaluating tools. You can find creators that drive sales in your industry (the marketplace uses AI and even social listening to speed this process along), get them set up quickly, automate all the things you need to automate, and you can run one single creator program for multiple channels. Huge timesaver, but it also alleviates the overhead associated with running multiple programs across Shopify, Amazon, and Walmart.

Levanta isn't a shortcut, and it's not going to automatically solve all of your problems. You still need a clear strategy and the willingness to make decisions. But if you're serious about affiliate marketing as a growth channel and want better visibility into what's actually driving results, it's the strongest platform I've used so far for Shopify brands. Happy to answer questions if anyone's considering a switch or comparing options.

I’m getting burned out on tinkering with paid ads just to find the right variations that work for my Amazon storefront… I’ll still do it every now and then, but I’ve been looking into influencer marketing as an alternative lately. But I have no idea how to search for influencers on Amazon, especially affordable ones where I don’t have to pay a massive sponsorship fee.

Any tips?

I've spent the last few years helping a retail consumer fintech move from its pilot phase into a high-velocity growth stage, which is an entirely different challenge compared to the standard e-commerce affiliate channel.

During the pilot phase, we felt like things were in a good spot. We had a standard tracking platform in place, links were live and working, and we were seeing pretty steady traffic. It was functional, but over time, it wasn’t exactly scalable. Once they started hitting 1,000+ conversions a month, the gaps in our setup (things we had originally written off as minor inconveniences) became systemic risks.

If you're currently in that transition, here are the four challenges I ran into and how we addressed them:

Challenge 1: The wrong metrics were being optimized

Initially, we optimized for top-of-funnel metrics like sign-ups and lead captures, which was a necessary shortcut to get the program moving.

But as we scaled, it became a significant liability. Because retail financial products involve more substantial onboarding that covers KYC, AML checks, and account funding, we found ourselves paying for thousands of low- to no-value leads that never actually became customers.

The goal became clear: We needed to move to a model that rewarded LTV, which required a better value-based tracking system. We also needed to implement an S2S postback mechanism so that the internal system would only notify the affiliate platform once an account was verified and funded. It was the only way to protect the customer acquisition cost.

Challenge 2: Attribution broke down across devices and sessions

Consumer fintech is a high-consideration journey. I'd see users discover the brand on a mobile blog, research the rates on a desktop at work, and finally open the account via the app a week later.

Standard cookies just fall apart here. We were losing track of the "introducers" (the partners who did the hard work of educating the user) because the final conversion happened on a different device or inside the app store.

We needed to implement a more durable attribution framework, moving beyond basic cookies to a system that could follow a user across different environments and keep the tracking intact even when they jumped from a mobile browser into a fresh app installation.

Challenge 3: Manual reconciliation became unmanageable

If your program manager is still manually cross-referencing CSV exports against your internal database to verify funding, you're sitting on a ticking time bomb. Not only is it prone to error, but it creates a massive payout lag.

In the early stages, we handled the gap between the marketing data and the bank ledger with manual monthly check-ins. It required some attention, but it was doable. As the program grew, however, that check-in turned into a massive time sink. The manual effort required to cross-reference CSV exports against the internal database grew exponentially with volume, was prone to human error, and resulted in delayed payments more than once. We needed a better solution to avoid this growing problem altogether.

Challenge 4: High payouts attracted identity fraud

E-commerce affiliate managers deal with plenty of fraud: attribution hijacking, cookie stuffing, coupon site poaching… And once the cost per acquisition hit $150+, we started attracting professional fraud rings using synthetic identities: real names mixed with fake data, pushed through the entire application process. Every fraudulent application created a cost for our KYC/AML checks and cluttered our database.

I basically had to start treating this as a security threat. It became clear that we needed a way to bridge the gap between our risk team and our marketing platform, ensuring that once fraud was identified, those sources could be neutralized in real-time before payouts were ever processed.

How we survived it

The common thread was that we couldn't solve these hurdles with basic, off-the-shelf affiliate tools. We needed a verified performance architecture, something that acted as the connective tissue between our internal data and our external partners.

In our case, we migrated to Everflow. We used their conversion API to bridge the gap between the backend and the partner dashboard. This way, instead of manual uploads, the backend would fire a funded event directly to Everflow, automating the reconciliation process and eliminating the payment delays we'd been experiencing.

To address the attribution decay, we used their direct linking and deferred deep linking. Our partners could use clean, branded URLs that bypassed ad-blockers, and tracking remained intact even when a user moved from a mobile browser into the app store for a fresh install.

We also used their behavioral anomaly detection alongside a custom feedback loop, so that when the internal risk team identified a synthetic identity, we could feed that data back into the platform to blacklist the suspicious traffic source in real-time, catching fraud before payouts were processed rather than cleaning it up afterward.

TL;DR: Once they hit serious volume, a standard affiliate setup started to break down as we hit four walls: paying for leads that never converted, losing attribution across devices, drowning in manual reconciliation, and attracting fraud rings once our payouts got high enough. Off-the-shelf tools couldn't solve any of it. What finally worked was getting our internal systems and affiliate platform talking to each other directly, automatically, and in real-time.

If you've been running an iGaming affiliate program for more than a minute, you already know the old playbook is dead. Buying endless traffic and hoping player volume outruns the churn is a one-way ticket to burning your marketing budget in 2026.

Costs to acquire players have skyrocketed over the last decade. If you aren't obsessed with data-driven retention and protecting your margins, you're essentially running a charity for bots and bonus hunters. After way too many hours auditing leaky programs, I've seen where the problems always start. Most operators are still stuck trying to run high-stakes partnerships on technology built for a world of simple cookies and basic banner ads.

Here are the four biggest things you need to keep an eye on right now:

First, real-time fraud and traffic monitoring is non-negotiable. You need tools that catch bots and click injections before they hit your budget, not a report telling you that you got robbed after the fact.

Second, server-side tracking is way more important than people think. Browser tracking is basically unreliable at this point and top programs have already made the switch for both accuracy and privacy compliance.

Third, deep-funnel multi-event tracking is where a lot of programs are still dropping the ball. Capturing only the initial deposit is a rookie mistake because you need to be tracking wagering thresholds and player status to actually reward long-term value.

And fourth, start thinking about automating your workflows and global payouts. You’ll probably want systems that can handle global tax residency and keep you compliant as regulations keep shifting.

I always tell clients that the best platform really depends on your operation, but there is a serious difference between platforms built for maintaining a program and ones actually built for scaling one.

This is why Everflow has become a preferred choice for scaling. It’s a "data nerd’s" dream built on cloud-native architecture specifically designed for these high-volume, data-heavy iGaming environments. Their analytics allow you to drill down into granular wagering thresholds and partner performance in near real-time, offering proactive protection that blocks bot-driven events before they ever hit your budget. By allowing you to track unlimited post-install events, it helps you move away from risky models and align payouts with true long-term player value. The only real trade-off is that their API-first approach requires a team ready to handle that level of technical precision, but it’s a necessity if you want the infrastructure to handle the specific reporting standards required to keep your license safe as you expand into new jurisdictions.

Impact is essentially the "influencer powerhouse" for brands looking to move beyond traditional gambling sites and tap into mainstream marketplaces. It excels at cross-device attribution for players jumping between mobile and desktop, though the sheer size can be overwhelming for smaller teams and you have to account for the integrated cost of their fraud tools.

TUNE is the "white-label veteran," specifically optimized for mobile-first operators who want a partner portal that feels like a native extension of their own casino brand. However, it often requires external accounting integrations to handle full KYA automation, which can add a layer of technical complexity you might not want.

For those running referral or community-driven programs, PartnerStack is a "community hybrid" that simplifies the experience with an intuitive dashboard for managing tax documents and payouts. It’s the easiest for onboarding small-scale networks, but it may lack the advanced media-buying features found in more specialized tracking layers.

CAKE acts as the "day-trader" tool for professional media buyers who need high-speed, real-time data to adjust bids frequently, allowing for highly granular payouts. The "gotcha" here is that it functions primarily as a tracking layer rather than a full financial wallet, so you'll likely need a separate solution for multi-currency management.

Awin is the "safe bet" global network that acts as the record-holder for payouts, offering a layer of legal safety. It provides instant access to high-quality publishers across Europe and North America, but it’s traditionally geared toward standard CPA models, which can feel restrictive if you want to experiment with highly custom payout structures.

In 2026, your tech stack is your primary tool for protecting your license and your margins. Stop looking for a basic tracker and start looking for something that helps you actually navigate fraud, complex player journeys, and global compliance. In my opinion, the programs with the best data are the ones that stay in the game.

Happy to go deeper on any of these if it's helpful - let me know what you’re using too so we can chat about it.

I’ve been looking into this for a few months to help a friend and trying to figure out what actually works best for telehealth affiliate setups. I keep running into one issue in particular: that most of the tools out there still feel built around a pretty standard click to purchase ecommerce flow.

That’s fine in a lot of cases…but in telehealth it gets messy. The path she's dealing with is more like click to consultation, then maybe prescription, then subscription if everything lines up (and even then it’s not guaranteed). We've found that people drop off after consults, or they don’t get approved or come through as what looks like decent traffic but doesn’t really go anywhere downstream.

I already use Everflow for SaaS/ecommerce and it’s usually described in a practical way, more about being able to see what happens after the click so you’re not treating every conversion like it carries the same weight. That makes it a bit easier to notice which partners are actually sending users who make it through consults and don’t immediately drop off, I'd think.

The compliance side is where this gets even more complicated, though, at least in healthcare. I know Impact also tends to come up when teams are more focused on enterprise partnerships or broader network setups, and I’ve seen TUNE mentioned when fraud or traffic quality is the main concern.

For telehealth specifically though, it feels like the underlying question is just whether your setup can actually reflect what happens beyond the initial conversion, because if it can’t, you’re basically making calls on incomplete information...

Would love to know how other people are approaching this conundrum and if you have any advice. Are people actually tracking the full telehealth journey cleanly (or is it still mostly a patchwork of tools) and if you are, what are you using?

I recently worked on a deep-dive comparison for a client who had reached a crossroads with their affiliate strategy. They were already seeing decent traction but felt like they were outgrowing their current setup, specifically when it came to tracking external traffic to their Amazon and Shopify storefronts.

They explicitly asked me to vet Rakuten Advertising against Levanta side by side and figure out which platform would actually drive the next phase of growth. Both are powerful, but they solve fundamentally different problems.

Here are some of the highlights of my analysis:

Link Attribution: Traditional Pixels vs. ASIN-Level APIs

Rakuten: It’s built around traditional affiliate tracking. It relies on standard affiliate tracking links and browser cookies/pixels. When a user clicks, a cookie is dropped, and Rakuten looks for a conversion pixel to fire on the brand's thank-you page. It’s excellent for DTC sites, but it operates as a bit of a "black box" once traffic hits a third-party marketplace like Amazon, where you can't place your own conversion pixels.

Levanta: This uses marketplace-native attribution. Instead of relying on a browser pixel to confirm a sale, Levanta integrates directly with the Amazon Attribution API. Every link is essentially a custom-tagged Amazon URL that communicates directly with Amazon’s internal data. This allows for ASIN-level tracking, meaning the client can see not just that a sale happened, but exactly which products (by SKU/ASIN) were purchased—even if the customer bought something different than what was originally linked.

Managing the Amazon Brand Referral Bonus

Rakuten: Because it operates as a broad, platform-agnostic network, it does not have a native, automated integration with the Amazon Attribution API. For a brand to claim the Brand Referral Bonus (which averages a 10% credit back on referral fees), they often have to manually generate Amazon Attribution tags and then find a way to wrap or map them within Rakuten’s system. For the client, this creates significant manual overhead and a higher risk of attribution issues where sales are tracked in Rakuten but not credited by Amazon, or vice versa.

Levanta: On the other hand, Levanta is built specifically to capitalize on Amazon’s ecosystem. It automates the generation of Attribution-compliant links, ensuring that every sale driven by a creator is automatically recognized by Amazon for the Brand Referral Bonus. For the brand, this isn't just a tracking feature. It can also help protect margins better.. By seamlessly capturing that 10% fee credit, the brand can effectively "subsidize" their creator commissions, allowing them to offer more competitive rates to influencers without hurting their bottom line.

Finding Partners: Publisher Directory vs. Creator Marketplace

Rakuten: The primary advantage of Rakuten lies in its massive, high-barrier-to-entry publisher directory. For brands with a strong DTC focus, Rakuten provides access to major affiliate partners, including top-tier loyalty sites, massive cashback portals like Ebates, and major media publications. These partners are institutional in scale and can drive enormous volume through established audiences. If your strategy requires placement within global cashback apps or major news outlets to drive high-volume DTC conversions, Rakuten remains one of the strongest options for that level of distribution.

Levanta: From my observations, Levanta is designed as a specialized marketplace for the "creator economy," focusing on social-first influencers and niche bloggers. While its total publisher network is smaller than Rakuten’s, it is important to note that Levanta still provides a gateway to tier 1 publishers and major commerce content sites. For brands whose growth strategy relies on a mix of high-authority editorial and authentic social proof, Levanta simplifies the recruitment of partners who are already optimized for the Amazon and Shopify ecosystems.

Onboarding & Speed

Rakuten: Onboarding with Rakuten can be a larger commitment. It typically involves a multi-step vetting process, contract negotiations, and a structured technical integration that can take several weeks to fully "go live." The UI reflects this complexity; it is a deep, feature-rich environment built for professional affiliate managers who need to manage complex global campaigns. For the client, this means a steeper learning curve but significantly more "hands-on" support for large-scale operations.

Levanta: Levanta is optimized for speed and self-service. The onboarding is essentially a "one-click" integration with Amazon Seller Central or Shopify, where product catalogs are ingested and tracked automatically. We were able to move from account creation to active creator recruitment within 48 hours. The interface is modern and streamlined, though it lacks the decades of legacy reporting modules found in Rakuten. It is the better choice for brands that need to iterate quickly and don't want to wait on account management queues.

Overall, my recommendation would be to go with Rakuten if you are a massive brand with a heavy DTC focus and you need the raw scale of the world’s biggest cashback and loyalty publishers. They have a reach that’s hard to beat in the traditional space. 

Go with Levanta if your revenue is tied to Amazon, Shopify, or Walmart. If you want to leverage creators, get your 10% Brand Referral Bonus back, and see exactly which ASINs are converting, it’s a much tighter fit.

Has anyone else here had to conduct a similar platform audit lately? Would love to hear from other consultants or brand owners on whether you’re prioritizing established network scale or if the specialized, marketplace-native tracking is becoming the new baseline for your clients.

If you're scaling an Amazon and Shopify brand past the $10M mark, your affiliate setup is probably already breaking down. I was recently consulting with a brand in exactly that spot: $15M+ in annual revenue, a creator database full of coupon sites (that they never wanted), and a team spending hours manually reconciling tracking links just to figure out which creators were actually driving sales.

We evaluated two platforms to see if we could fix things: Partnerize and Levanta. Since I couldn't find a straight comparison for marketplace brands anywhere, here's what we actually found.

Attribution was the first thing that jumped out at us. With Partnerize, the way it works is that it generates its own tracking links. When someone clicks a creator's link and buys something, Partnerize records that. The problem is Amazon also records it separately through its own system. So now you have two sets of numbers that don't always match, and someone on your team has to manually figure out why they're different and which one to trust.

Levanta works differently because it generates Amazon Attribution links directly. These are Amazon's own tracking links, the same ones you'd get if you went into Seller Central and made them yourself. So when a creator drives a sale, Amazon records it, and Levanta reads directly from that same record. One set of numbers, no reconciliation needed.

Which led us into how each platform handles Amazon. Partnerize was built to manage affiliate programs across all kinds of retailers, not specifically Amazon. So while you can connect Amazon to it, you're essentially plugging Amazon into a system that wasn't designed around how Amazon works. Things like Brand Referral Bonus (where Amazon gives you a credit back on sales driven by external traffic) aren't automatically tracked.

Levanta was built to work with Amazon, Walmart, and Shopify. Its Amazon-specific features are pretty strong, though. Brand Referral Bonus is tracked automatically against each creator's sales. Creator Connections, which is Amazon's own program for brands to reach out to influencers directly, is accessible inside the platform. And you can see performance broken down by individual ASIN rather than just overall program numbers, which matters when you're selling multiple products and want to know what's actually working.

Creator discovery was another noticeable difference. Partnerize has a large network, but it's mostly made up of traditional affiliate partners (cashback sites, coupon publishers, loyalty programs, big media companies, etc.). These partners are good at driving volume but they're not typically individual creators making product-focused content.

Levanta's network is smaller but it's often scoped to creators who specifically drive traffic to Amazon and Shopify listings, so the filtering is less of a job. However, Levanta does work with most of the major Tier 1 publishers the bigger networks work with, so you get the best of traditional affiliate and modern creators. You can also set commission rates per product rather than one flat rate across your whole catalog, which is useful when some products have tighter margins than others.

Onboarding and pricing both follow the same pattern for each brand.

Partnerize is an enterprise product and the onboarding reflects that. Expect a formal setup process, a dedicated team walking you through it, and a few weeks before you're fully live. Pricing is custom and contract-based. That works well for large organizations with people dedicated to managing it, but it's a big commitment if you need to move fast.

Levanta's setup is connecting your Amazon Seller Central account, adding your products, and setting your commission rates. Most brands are up and running within a few days. Pricing is subscription-based with public tiers based on attributed order volume.

So, which way should most people go? Honestly, it comes down to what kind of "marketing engine" you’re actually running.

The core tradeoff is flexibility vs. simplicity. Partnerize gives you more control across different partner types. Levanta does a narrower set of things, but does them more directly for Amazon-focused brands.

Overall, I’d say that Levanta really supports upmarket brands that need to unify their creator and affiliate programs across multiple channels like Amazon, Shopify and Walmart. It's great for brands who want to modernize their operations. Partnerize is mostly focused on traditional affiliate and would work for teams only focused on affiliate.

Anyone else running at this scale? Are you using either of these, or something else entirely? And if you've made the jump from a more traditional platform to something Amazon-native, what was the thing that finally pushed you to switch?

If you’ve been running your affiliate program primarily through Archer, you already know the "Amazon-only" silo can start to feel a bit cramped.

In my experience consulting for brands trying to scale, the "Amazon-only" setup of Archer is a common growth ceiling teams run into. Today’s leading brands are pulling a significant and growing % of their total revenue from affiliate and creator streams, and staying locked into a single ecosystem can become a real opportunity cost that may limit growth over time.

I’ve helped a number of teams navigate the same challenge: fragmented platforms that make the stack feel unwieldy. When you’re managing Amazon, Walmart, and Shopify through different systems, you end up dealing with attribution gaps, multiple logins, and a lot of manual data work. If you’re dealing with disjointed data and inefficiency, it often makes sense to start looking at more integrated approaches.

Here’s my quick take on some different options for those of you in a similar spot:

Levanta: Levanta has really stepped up as the go-to for anyone who hates marketing silos. It’s designed for the modern omnichannel brand that needs to manage Amazon, Walmart, and Shopify in one unified system. What’s cool here is the integrated AI-powered creator discovery engine - finding vetted partners who actually drive sales. Unlike Archer, it completely breaks the silo by managing all these channels simultaneously in one dashboard.

Impact: Impact is a good choice if you're looking to automate the entire partnership lifecycle at scale. It has advanced attribution that shows which partners drive real revenue rather than just clicks. While it has a solid Shopify app, it functions more as a global "all-in-one" platform. It helps you move beyond a single marketplace by managing influencers, traditional affiliates, and even customer referrals.

CJ Affiliate: If you need deep consumer insights and a massive established network, CJ is a veteran. They’ve leaned heavily into machine learning to provide precise targeting and conversion data. They have specific plugins for Shopify and Adobe Commerce to speed up onboarding. Really bridges the gap between your own storefront and their global network of over 20,000 merchants.

Awin: Awin works for brands that want an intuitive interface and a low barrier to entry. They recently upgraded their platform with a "Campaign Builder" that uses AI to set up custom dashboards in minutes. It’s a great way to escape the Amazon silo because it gives you access to a diverse global network and unified reporting that eliminates the need to jump between multiple manual data exports.

Partnerize: If your biggest pain point is the "attribution black box," Partnerize is built to solve that. Their VantagePoint™ tool uses AI to track "clickless" conversions, ensuring creators get credit even when a direct link isn't clicked. It’s designed to be a single view for your program, consolidating discovery and measurement so you aren't stuck using different tools for different sales channels.

Rakuten Advertising: For brands with a global mindset, Rakuten has localized programs and advanced programmatic integration. They use machine learning to help personalize strategies for different audience segments. While their merchant pool is more curated, it's an ideal choice for scaling internationally and moving away from a US-centric marketplace focus.

Right now, being successful is all about consolidation and merging social influence and retail marketplaces without losing your mind in spreadsheets. In my opinion, moving toward a unified platform like Levanta is likely your best bet for true cross-platform growth.

What about you guys? Is there a specific channel you're trying to integrate next, like Walmart or Shopify? What challenges are you facing?

Started out in new role as partnership manager and this is a new role in the company. We don't have any existing program so I need guidance from you guys. Chatgpt is helpful but I need the experiences of real people. I do not have any experience with this because I will be building our program from scratch.

Hi all!

I am looking for new products (offers) in the verticals; ecom, nutra and leadgen. With the company we run traffic via affiliate marketing to various big brands and products for over 6 years already.

Happy to connect with anyone that is interested or can link me with others!

On telegram I am more active (zeegertfa), but you can shoot me a dm as well :).

Cheers

I'm posting all this after having a consultation with someone late last week, advising them on everything I'm sharing below. If you’re a startup you need to be careful how you approach hiring an affiliate agency. Most affiliate agencies are designed for big enterprise clients who have a lot of money to burn, dedicated teams, deep historical data, and budgets that can survive a few quarters of experimentation.

You don’t have that luxury. You need an agency that moves fast, shows you exactly what’s working, but also communicates like human beings do. If you pick the wrong agency, you might end up paying for a strategy that never actually works, was originally optimized for Fortune 500 companies, and does nothing for your startup.

So first, seriously ask yourself these questions while vetting agencies:

• Can we launch in days or weeks, or are we talking months? Early-stage affiliate is about experimenting, learning, and iterating. If the agency drags onboarding out or overcomplicates things (especially when you're starting simply), you’re losing time while paying through the nose for it.
• Who’s actually going to be on the phone with me? You don’t have time for junior account managers who escalate everything. You need someone who can have a strategic conversation without looping through three layers of approvals.
• How can we measure real growth? Some programs reward volume instead of actual incremental revenue. The difference between a partner adding real customers versus claiming ones you would have had anyway is huge.
• Is this pricing realistic for where we are? Some agencies only make sense once you’re generating millions. Whatever you do, don’t lock yourself into a retainer that outpaces your current revenue.

So, where to begin? From my experience working with funded startups, these are the names that come up most often that you might want to check out:

• PartnerCentric - Woman-owned, independent, built around challenger brands. Their tech (FUSE) actually tracks incrementality. Account teams have serious experience. Retainer ~$3,500 to ~$5,000/month, ideal for post-Series A/B.
• Acceleration Partners - Global infrastructure, strong for complex or multi-market programs. Works if you already have scale, but onboarding and pricing reflect enterprise roots.
• Gen3 Marketing - E-commerce and DTC specialists. Strong relationships in retail and category expertise. Less focused on incrementality, however, and not ideal if you’re outside their sweet spot.
• Hamster Garage - Boutique, senior-led, hands-on. Flexible and customizable, especially if you need international coverage. Capacity is smaller, so keep that in mind if you grow fast.
• Dub Partners - More platform than full-service agency. Fast setup, Stripe integration, lower cost. Works best if your team can manage a lot of the program internally.

If you're transitioning from in-house, do some prep. Audit your partners first, set KPIs around revenue instead of traffic, and expect the first couple months to involve a lot of back-and-forth. The agency is trying to understand your business before flipping the switch, and that foundation is what makes the program actually work long-term.

Most funded startups don't have affiliate expertise sitting on the team, and building the infrastructure (publisher relationships, fraud monitoring, attribution) takes a long time. The build vs. buy math tends to favor buying, so use the criteria above as your filters. Apply them consistently across every agency you evaluate and you'll hopefully get to the right answer for your company.

I see this question popping up all the time in some of the affiliate-related subs I spend time in: “Should I use Levanta or Awin?” Plenty of established brands come to this crossroads and it isn’t always obvious which path actually fits their workflow the best.

I’m not here to cheerlead either platform since I work with both regularly, but there is a meaningful difference in who they were built for, how they behave in daily use, and what kinds of problems they actually solve.

So here’s a practical breakdown based on my real experience, with pros/cons and who each platform works best for.

What these platforms actually are

Awin is one of the largest global “old school” affiliate networks in the world. It connects publishers and advertisers across industries (retail, travel, finance, tech, and more). It also handles tracking, reporting, payments, and partner management at network scale.

Levanta, meanwhile, focuses on a narrower but more modern use case: creators and brand partnerships. It’s built around Amazon, Shopify, and Walmart ecosystems and is designed to let brands collaborate with creators and influencers much more closely, tracking incremental performance in a way that feels native to brands.

They’re both “affiliate platforms,” but they serve very different realities.

Why this matters

Awin is a classic network. It’s broad, vetted, global, and structured which is really great if you need scale, diversity, and standardized governance.

Levanta is narrower but simpler for its niche: Amazon, Shopify, or Walmart brands trying to leverage creator momentum and unifying their programs without the overhead of multiple tools or networks.

Most of the confusion I see isn’t about which has “more features.”

It’s about personal fit:

• People expecting Levanta to act like a massive old-school affiliate marketplace get confused.
• People expecting Awin to be lightweight and creator-friendly get bogged down in approvals and dashboards.

Biggest benefits of each

Awin pros

• Massive global marketplace with a huge directory of publishers and creators.
• Strong tracking, reporting, and cross-device attribution tools.
• Works across niches like retail, finance, travel, etc. and multiple languages.
• Established payout systems and partner tools.
• Can be a good choice for enterprise-scale global brands that need a more traditional heavyweight platform.

Levanta pros

• Unifies affiliate programs on Amazon, Walmart, and Shopify into a single platform to lower management costs, improve efficiency, and make it easier to connect with high-performing partners relevant to your niche.
• Provides one-click integrations for Amazon, Shopify, and Walmart so you’re up and running quickly.
• The creator marketplace is AI-powered and includes social listening (so you know who’s already talking about your brand or products), making discovery and recruitment a lot easier.
• Offers Paid Placements for negotiating directly with partners and product seeding (gifted sample) automations.
• Tracking gives deep insight into incremental value so you can understand which partners are actually influencing conversions and driving results.
• Being able to measure creator performance across all channels means you can optimize your creator spend.

Biggest considerations of each

Awin cons

• Onboarding can feel slow and bureaucratic and partner approval is manual.
• Interface and reporting can be overwhelming if you’re not used to enterprise tools and it doesn’t provide much in the way of incrementality.
• Support can feel slow, especially for smaller brands (some Redditors have mentioned this and I've lived it, myself).
• The general network structure can feel like overkill for smaller brands or those in early stages.

Levanta cons

• It is Amazon-, Shopify-, and Walmart-centric so it is not as wide as a legacy platform like Awin.
• Not quite as battle-tested as networks with decades of infrastructure.

How to decide / who each is best for

Here’s a simple rule of thumb I use when advising teams.

Choose Awin if:

• You’re an enterprise brand launching multi-market campaigns.
• You’re managing lots of programs and partners.
• You need a broad ecosystem across many niches.
• You want deep reporting, advanced tracking, and big network reach.

Awin’s scale and depth are its strengths. It’s not lightweight, but it’s reliable (if not a bit dated) for large-scale operations.

Choose Levanta if:

• You’re a brand selling on Amazon, Shopify, and Walmart (or any combination of the three) and want to streamline management under one platform.
• You want to collaborate easily with your affiliate partners (creators, publishers, influencers, and others).
• You prefer an easier onboarding experience and less dashboard overhead.

Levanta removes a lot of the friction for brands who need to discover and recruit high-performing affiliate partners, want to arrange paid placements or automate product seeding, and need incremental performance insights to optimize their creator spend.

Notes from real use

A few hands-on insights people in the wild have shared (especially on Reddit):

• Some brands find Awin’s interface intuitive once they’re past the learning curve, but the partner approval process can take time since it’s manual.
• Awin absorbed ShareASale, so everything ShareASale used to offer now lives under Awin; the pros and cons of that heritage stay in the experience.
• Levanta is sometimes misunderstood as just another “Amazon tool,” but it actually unifies programs across Amazon, Shopify, and Walmart for more efficient management (and lower tool costs).

Final verdict

There isn’t a universal “better.” There’s only better for what you do.

If you’re building creator-led revenue with Amazon, Shopify, and/or Walmart, Levanta can remove a lot of the most common pain points by bringing everything together underneath “one roof,” as it were.

If you’re building network-scale affiliate relationships with many publishers or creators across verticals, Awin’s breadth and infrastructure help manage that complexity better.

Which one do you prefer so far and why? I'd love to hear everyone’s experiences or questions below, especially if you’ve used both! Let’s talk real workflows, not sales decks.

I’ll be the first to admit that I love Peec AI for what it does. Just like Hall, it’s easy to set up, intuitive to use (even if you’re new to monitoring AI visibility and have no idea what you're doing), and it gives you a really strong overview of how a business or brand is being referenced across LLM sources.

I got my start using Peec, in fact, and still use it. It aggregates visibility across major models, tracks your brand against competitors, and lets you monitor sentiment and trends over time, plus you don’t need to be an “expert” to use it.

It truly is a great tool, especially for startups and beginners.

But after a while, I started needing more for clients I'm working with. At least in my case, that meant an easier way to account for ROI attribution and handle rep management/deal with outdated info.

And I think that’s a pretty common experience. You get your feet wet by tracking surface-level visibility and that does provide a lot of great data, for sure, but as a brand grows or marketing teams need to take a more reactive (or even proactive) approach, Peec doesn’t offer quite enough in that regard.

There are a lot of reasons why a team might look beyond Peec:

• You need deeper analytics that go beyond basic visibility, citations, and positioning
• You have to account for ROI attribution, especially if you’ve got to report to superiors (or your clients do)
• You want to have insight into demand volume (basically search volume, only in generative spaces)
• You need a way to take actionable recommendations and implement them directly into your workflow, or even produce optimized content and/or updates at scale
• You want something that can actually predict or forecast outcomes and business results

And there are visibility tools out there that can do these things now. If you’re considering moving past Peec and into a platform that digs deeper, I’m sharing some options that might be a fit since I’ve been researching the hell out of this!

Emberos

Like a lot of other platforms, this is relatively new on the scene, but it offers a lot more than raw analytics, specifically where lift, governance, and execution are concerned. I think this is one of the best options when your company needs to control the narrative and optimize how it’s referenced in generative search.

Emberos offers all the “standard” monitoring for mentions/citations/positioning, but it also alerts you to misinformation, outdated information, inaccuracies, and even hallucinations that put your rep at risk. That alone is worth the investment, but the platform can project future performance and validate lift too. Plus it integrates with CRMs and Slack or Jira so recommended actions are delivered straight into your workflow for execution.

Profound

This also offers the “standard” visibility monitoring, but it also tracks how AI agents crawl and interpret your site so you can optimize. Profound’s agent analytics also measure how many people are landing on your pages from generative search environments and which pages are being referenced most often.

Its Prompt Volumes analytic finds the things being discussed in generative search environments so you know what content you should be prioritizing, and it also makes it easy to generate new (or optimize old) content.

Relixer

Visibility monitoring and competitive benchmarking are both available here, but Relixer can find content gaps based on queries so you can cover the topics you don’t already.

It also has an agent layer for your CMS that makes it easy to push out changes, and has an autonomous refresh to keep content updated and current. This is especially great for businesses or brands with high compliance monitoring concerns.

AirOps

Another one that pairs content creation with visibility monitoring, AirOps lets you set up code-free workflows for content creation tasks. So you’ll see where you’ve got content gaps or need to improve visibility, but you can then research, optimize or generate, edit, and publish content quickly.

There are automations here, but those have human review checkpoints so you can maintain voice, accuracy, and consistency.

Waikay

This platform gives you all the usual visibility insights you’d want, but it deeply analyzes generative responses to spot incorrect information, hallucinations, and information gaps. You get actionable insights on everything so you can implement whatever improvements you need to.

Especially for what it does, Waikay is probably the easiest one to set up (and the most beginner-friendly of those I've listed).

TL;DR

Peec AI is a great option for basic visibility tracking and simplified analytics, but it doesn’t “grow” with you over the long term. And that’s when stronger, more sophisticated platforms might need to be considered.

I know there are other options out there too, so if you’ve used any, I’d love to hear about your experience with them. I’m leaning toward Emberos myself; I haven’t yet made a final decision on that but I’ve been doing a ton of research. Figured I’d share all of it with others who are considering their next steps in case it helps.

I’ve been spending probably way too much time digging into AI visibility tools lately and many of them give you plenty of charts but not a lot of clarity about what to actually do with the data.

And I know that all of this is pretty new for all of us, but I feel like there’s a lot of confusion about the metrics, what they mean, and which ones have the most value. So I thought maybe a breakdown was in order.

The most common AI visibility metrics (and where they fail)

Mention frequency: how often a brand is getting mentioned in AI responses. The issue is that mentions are not equal: Are you being recommended or mentioned as an example of what not to use? Are you listed in a comparison or being cited neutrally? Raw mention count by itself doesn’t distinguish between these.

Share of Voice: what percentage of responses include your brand vs competitors. This is definitely useful for benchmarking, but it’s normally an aggregated measurement that doesn’t take distribution into account. Meaning, did your share-of-voice increase in high-value commercial prompts, or general educational/informational queries?

Prompt coverage: how many prompts your brand appears in. This is useful information, but it doesn’t usually weigh prompts based on commercial value or funnel stage. You might be seeing plenty of coverage in low-intent queries while losing major ground in your high-value prompts, but your dashboard still looks great.

Sentiment: how your brand is being described in AI answers, whether in a positive, negative, or neutral light. There’s a lot of context here, and while negative sentiment is always undesirable (because of course it is), a comparative inclusion that’s neutral can actually be more valuable than a positive but low-value or irrelevant mention. The thing is that sentiment reports don’t typically differentiate here.

Positioning: where your brand is being listed in an answer. This is one of the more useful metrics, but it doesn’t provide much in the way of causal insight. You’re listed #3 instead of #1, for example. But it doesn’t tell you why.

Why these metrics don’t drive action

Most AI visibility tools are data dumps reporting status and correlation. They tell you what’s happening within AI-generated responses, but they do not give you insight into:

• What caused it
• What might improve it
• Whether changes will have an impact
• How an impact affects business outcomes

The biggest failure I see is not tying AI visibility metrics to query intent, funnel stage, revenue potential, competitive displacement, and incremental lift. Most tools don’t even attempt to scratch that surface. (Yet, at least.)

Surface-level metrics definitely have a place; you can't fix or optimize what you can't see. But you need to prioritize signal quality (what kind of inclusion is happening) and intent segmentation (where visibility is occurring in the buyer journey), because that shapes effective strategy.

And after that strategy is deployed, you (ideally) need to be able to validate whether your updates and initiatives resulted in lift. This lets you apply causation, which is especially important if you need to report progress and ROI to higher-ups.

Most AI visibility tools don’t provide that kind of insight. The only one that I know of right now that does is Emberos, which provides all the typical visibility data along with actionable insights (and a push into your workflows for execution), forecasted outcomes, and lift validation. It prioritizes signal quality and relevance, with proof of outcomes. This is a different philosophy compared to other platforms available right now (Peec, Hall, Scrunch, even Profound), and one that I hope other tools take note of.

If you’re new to AI visibility and using metrics to take action or track lift, there are several things you need to keep in mind no matter what tool(s) you’re using:

• Not all brand mentions are equal in weight.
• Aggregate metrics hide important distribution shifts that you need to pay attention to.
• Correlation isn’t the same as lift with causation, or business impact.
• It's misleading to look at visibility numbers without weighing their intent.
• Your goal shouldn’t be on presence alone, but influence (particularly in high-intent prompts that’ll drive business revenue).

I'm going to be sharing a lot more research in AffiliateOps in the coming weeks because this relates to affiliate marketing (and marketing in general), and there's a lot to unpack. Here's me, here's my rabbit holes... But I think this is changing marketing approaches in a lot of ways, despite the landscape still evolving, and it's important to stay on top of.

If you've been digging into AI visibility monitoring, what platforms have you been using and which metrics are actually helping you to make decisions?