Klaviyo vs. uptain: Build or Automate?

This article compares two tools that are leaders in their fields: Klaviyo, the US-based all-rounder for email and SMS marketing, and uptain, the German specialist for conversion optimisation and recovery. The goal is to provide well-founded decision support that is factual, objective, and practical.

Differences between Klaviyo and uptain

In e-commerce, behaviour-based and data-driven marketing is no longer a nice-to-have, but a decisive lever for growth and efficiency.

Online stores and marketing decision-makers are therefore faced with a central question: Do I rely on maximum creative freedom with a high degree of customisation, or on a specialised system that works in an automated way and scales quickly?

Both approaches have their merits

While more manual tools such as Klaviyo offer a very high level of control, they also require more time and technical expertise.

Systems like uptain reduce operational effort by combining AI-powered decision logic with automated processes. This enables data-driven efficiency, consistency, and scalability, without the need for manual intervention.

Which solution is the better fit depends largely on the goals, resources, and setup of each individual online store.

Klaviyo is like a rocket: it can unleash enormous power, but only if an entire team of specialists builds it, steers it, and monitors it continuously.

uptain is also a rocket, but one that automatically navigates and optimises at the critical points. uptain therefore stands for conversion optimisation with a low barrier to entry, essentially marketing on autopilot. This allows any online store to reliably reach its destination: more conversions and more revenue, without the need for its own rocket control centre.

1. Manual vs. Algorithmic

At their core, the two approaches differ significantly: Klaviyo focuses on maximum creative freedom in marketing flows, while uptain delivers true automation.

Klaviyo: Maximum control with high effort

Klaviyo is one of the best-known marketing automation tools in the Shopify ecosystem, partly because Shopify itself holds an equity stake in Klaviyo. It allows complex email and SMS campaigns to be built individually: segmentation, flow creation, trigger logic, and A/B tests can all be defined freely. Teams with clear processes, a strong design focus, and sufficient resources for testing and analysis benefit the most.

The cost of this high degree of flexibility is a significant manual workload. Users must define audiences, plan tests, and evaluate results manually. In practice, this can quickly result in more than 40 individual flows for different audiences and incentives. In addition, US-based hosting introduces data protection uncertainties despite the use of standard contractual clauses. This makes Klaviyo suitable for larger shops with dedicated data or CRM teams, but often too complex for smaller online stores.

The illustration shows: uptain combines a high level of intelligence with ease of use. It is easy to set up, fully automated, and AI-based. Klaviyo, on the other hand, offers a high degree of creative freedom and can be very powerful with sufficient time and resources invested, but it does not reach the same level of algorithmic intelligence.

The reason: critical automations and settings are largely user-driven and therefore often static in practice, whereas fundamentally algorithmic tools like uptain calculate multiple probabilities in real time and dynamically deliver the best option.

uptain: Let the system learn instead of building it yourself

uptain is a German tool that focuses specifically on cart abandonment recovery and conversion optimisation in e-commerce. Its core component is the uptain® ALGORITHM, which analyses user behaviour in real time and automatically delivers the most relevant content, such as trigger emails or behaviour-based popups.

Benefits: automatic segmentation, continuous AI-driven A/B testing, and full GDPR compliance with servers located in Germany. While Klaviyo relies solely on a shop’s own data, uptain additionally uses anonymised data from thousands of online stores. This results in more precise segments, faster statistically valid tests, and significantly higher conversion rates, without additional staffing requirements.

uptain® ALGORITHM

The algorithm analyses signals such as:

• Email domain: @t-online often indicates older users, while @gmail suggests digitally experienced buyers
• Browser: Safari or Edge tend to be more traditional, while consciously installed browsers like Chrome or Opera indicate digital affinity
• Referrer: visitors from Idealo.com are usually price-sensitive, while TikTok traffic is more lifestyle-oriented
• Devices: Android users respond more strongly to discounts, while Apple users expect service-oriented messaging

All factors are combined so that the algorithm decides within milliseconds whether to display a discount, a service message, or a reminder. The technology learns not only from a single shop, but from the entire data pool, and continuously improves over time.

The result: less manual effort, greater scalability, and better conversion performance. uptain is therefore not a traditional marketing tool, but an intelligent, data-driven system that continuously optimises itself. Fully automated and fully GDPR compliant. This represents a clear efficiency gain, especially for growth-focused and performance-driven online stores without dedicated CRM or data teams.

Parallel use: Can both tools run side by side?

Yes, uptain and Klaviyo can be used in parallel without any issues, and the performance of both tools can be easily compared and tested.

• Klaviyo is strong in static campaigns: seasonal newsletters such as new collections or holiday campaigns, regularly scheduled mailings, and design-focused content. In short, wherever brand presentation and content take priority.
• uptain complements this by specialising in performance-driven measures: popups to prevent cart abandonment or to generate newsletter signups, as well as trigger emails for cart abandoners, browse abandonment, and inactive customers.

De facto division of roles

1. Klaviyo becomes the classic newsletter tool, used for campaigns and a few additional features such as birthday emails
2. uptain takes over the conversion-driven use cases: reducing purchase abandonment, recovering cart abandoners, and acquiring new subscribers via popups and targeted trigger emails. For example, as part of smart onsite conversions, uptain automatically transfers newly generated newsletter subscriptions to Klaviyo, seamlessly expanding the recipient base.

2. Economic impact of algorithmic decisions

The following table summarises all subsequent case studies and illustrates how strongly the combination of different algorithmic mechanisms can affect the performance of an online store. The calculation is based on four components:

1. Discount vs. service popup
2. False positive detection
3. Exit intent precision
4. Content personalisation

The cumulative calculation shows the positive effects of all mechanisms combined. This is explicitly an example calculation for a sample online store. Actual results can vary significantly from store to store and from industry to industry, depending on the shop system, design, target audience, and level of optimisation.

Each individual aspect is analysed in more detail and calculated separately below.

*It is important to emphasise that this calculation is based on top-performance values in order to illustrate the potential effects as clearly as possible. In practice, results naturally differ. For example, in the “service vs. discount” case study, we have seen stores using uptain with a service delivery rate of 25 percent, but also stores where only 8 percent was achieved. Both are perfectly valid, as results depend heavily on the target audience, and in both cases the mechanism delivers a positive effect. The same applies to the other scenarios: in the second example, a 20 percent reduction was calculated, but in practice 15 percent may also be achieved.

Explanation of the calculations

An online store has 100,000 monthly visitors. In the traditional scenario, 30,000 users see an exit intent popup. In the algorithmic scenario, this number is initially reduced to 24,000 because 6,000 false positives are filtered out. These are users who do not actually intend to leave the store and are very likely to complete a purchase without any incentive. At the same time, the algorithm identifies 3,000 additional genuine exit situations, resulting in a total of 27,000 relevant popups being displayed.

The summary clearly shows the overall effect that the combination of different mechanisms can have. By identifying around 20 percent false positives, improving the detection of genuine exit intents by 10 percent, increasing the conversion rate through content personalisation, and reducing costs by differentiating between discount and service popups, the example results in an added value of approximately 34,947 euros compared to the traditional scenario. This corresponds to an increase of around 36 percent.

As already emphasised, this is a top-performance calculation based on the best values from the individual components that we have already observed in practice. In reality, lower effects are also possible, for example an uplift of around 11 percent if a store is technically less optimised and has long loading times. The key point remains, however, that the advantage of algorithmic decisions has a positive impact in any case, as results in the traditional scenario would also be correspondingly lower for less optimised online stores.

Algorithmic mechanisms in detail

The following calculation examples illustrate the difference that dynamic, data-driven decision-making can make, and how it can increase revenue while reducing costs.

These are example calculations. Actual results may vary. What matters is that uptain’s algorithmic decisions are data-driven and therefore significantly more precise than rigid rules or flows based on subjective assumptions.

Case study 1: Discount vs. service popup

A user is about to leave a shopping cart with items in it, triggering a popup designed to motivate them to complete the purchase. With traditional technology, every customer would be shown the same popup content with a 15 percent discount.

But what if an algorithm could detect that, in this case, a service-based offer, for example help with a technical question, would have been just as effective in recovering the purchase?

This is exactly where uptain comes in.

The following calculation example from a representative uptain customer shows that simply by using discount codes in a targeted and intelligent way, discount costs can be reduced by up to 25 percent. Actual figures naturally vary depending on the store.

Important: To simplify the calculation, we are comparing only the algorithmic decision between displaying a service popup or a discount popup. In practice, additional factors also play a role.

An online store with 100,000 monthly visitors shows an exit intent popup to 30,000 users.

With a conversion rate of 4.5 percent, this results in 1,350 orders. In the traditional scenario, all orders are generated via a discount popup. With an average order value of 85 euros and a discount value of 15 percent, this results in a discount of 12.75 euros per order. Total discount costs amount to 17,212.50 euros. With revenue of 114,750 euros, this results in a net outcome of 97,537.50 euros.

In the algorithmic scenario, uptain differentiates between discount and service popups. 75 percent of orders, or 1,013, are incentivised with a discount, while 25 percent, or 338, are completed via service popups without any discount. As a result, discount costs are reduced to 12,909.38 euros. Revenue remains at 114,750 euros, while the net outcome improves to 101,840.63 euros.

In this example, the online store saves 4,303.13 euros per month by using uptain, without any loss in performance, as service popups convert just as effectively as discount popups in the respective cases.

This was just one single example of an intelligent decision. Across the entire process of delivering popups and trigger emails, the AI makes dozens of data-driven decisions regarding timing and content, always in relation to known user data and behaviour. This level of precision could not be replicated even with countless manual flows and rule-based connections. In addition, the algorithm can adjust its decisions in real time, something that is simply impossible to achieve manually.

Case study 2: Not every click is an exit

In Klaviyo and comparable tools, triggers such as scroll depth, session duration, page views, mouse-out, or tab switching can be configured. However, these triggers are based on static rules that apply equally to all users. At first glance, this may sound flexible, but in practice it is not very intelligent. An arbitrarily defined threshold of 20 seconds can interrupt users in the middle of reading. A scroll depth of 50 percent is not an abandonment signal, but often an indicator of genuine interest. And simply moving the mouse cursor toward the top of the browser window is not a clear signal of exit intent.

This requires a more granular analysis, for example how far and how quickly the cursor moves within a given time window, and whether it is actually an intention to close the tab or merely a tab switch. Fine details also play a role, such as the fact that Windows users typically navigate toward the top right, while macOS users move toward the top left. These differences and contextual factors must be taken into account to clearly distinguish real exit signals from normal interactions.

Freely configurable triggers ultimately remain subjective hypotheses, which inevitably leads to a high number of false positives. This means that behaviour is incorrectly interpreted as a relevant event even though no actual exit intention exists.

With uptain, the situation is different:

The algorithm does not analyse tab switches in isolation, but in combination with additional behavioural patterns such as mouse movements toward the browser bar, periods of inactivity, or scroll and hover sequences within defined time frames. This allows the algorithm to detect subtle movement and timing patterns in real time and determine whether an action truly represents an abandonment or merely an interaction.

Up to 20 percent more accurate triggers with False Positive Detection

The system automatically detects when the right moment has been reached and which types of user behaviour actually indicate exit intent. Through this intelligent pattern recognition, the precision of exit intent detection increases by up to 20 percent, significantly reducing the number of so-called false positives.

In this context, a false positive occurs when a popup is triggered even though the user had no intention of leaving the shop. In other words, the system incorrectly interprets behaviour as an intention to abandon. The result would be unnecessary interruptions and discounts, which would also be shown to customers who would have completed their purchase without any additional incentive.

Thanks to improved detection, this is precisely what is prevented. Popups are shown only to users who are genuinely about to abandon the purchase process, while purchase-ready customers are not disturbed.

This is an example calculation. Actual results may of course vary. What matters is that uptain’s algorithmic decisions are data-driven and therefore significantly more precise than rigid rules or flows based on subjective assumptions.

Example calculation:

An online store with 100,000 monthly visitors shows an exit intent popup to 30,000 users in the traditional scenario. With a conversion rate of 4.5 percent, this results in 1,350 orders. With an average order value of 85 euros and a discount value of 15 percent, this corresponds to a discount of 12.75 euros per order. Total discount costs therefore amount to 17,212.50 euros.

Using uptain’s algorithm, unnecessary popups are avoided for users who would have purchased anyway. As a result, the number of relevant popups is reduced from 30,000 to 24,000. With the same conversion rate, this leads to 1,080 orders, which is 270 fewer than in the traditional scenario. However, these 270 orders are not lost: the affected users are likely to purchase even without a popup, and this is recognised by the algorithm. Accordingly, only 1,080 discounts are granted. Total discount costs are therefore reduced to 13,770.00 euros.

This results in savings of 3,442.50 euros per month, with the same overall performance.

Case study 3: Exit intent precision

The exit intent precision scenario highlights the advantage of more accurate detection of cart abandonment through the analysis of back navigation. The algorithm uses the so-called history back trigger, which identifies whether a user is navigating back within the shop or whether they are actually leaving the shop via the back button and switching to a different website. In this way, around 10 percent more exit intents can be identified and addressed in a targeted manner.

In the traditional scenario, 30,000 exit intent popups are displayed. In the algorithmic approach, the number of users who actually qualify for a popup increases by 10 percent, as browsing history is identified more precisely. As a result, the number of displayed popups rises to 33,000. The reason is that the algorithm distinguishes more accurately whether a user truly intends to leave the shop. For example, if someone clicks the back button but remains within the shop, no popup is shown. If the last page interaction clearly indicates an exit, a popup is displayed in a targeted way. This allows the conversion rate to be utilised more effectively.

The result: Thanks to more precise detection, more potentially lost customers are reached at the right moment, and in this example the net outcome improves by 9,753 euros.

Case study 4: Content personalisation

The content personalisation scenario shows how significantly the conversion rate can be increased when popups are not only displayed at the right moment, but also tailored in terms of content. This includes factors such as tone of voice, form of address, and incentive. Whether formal or informal language is used, whether a discount popup or a service popup is shown, or whether contact is offered via email or phone, the algorithm ensures that the message matches the individual user.

Compared to static popups that are shown to all visitors in the same way, personalised content increases relevance and therefore the likelihood that an abandonment is converted into an order.

In the traditional scenario, 30,000 users see a uniform exit intent popup. With a conversion rate of 4.5 percent, this results in 1,350 orders.

With content personalisation, popups are dynamically adapted to each individual user. Tone of voice, form of address, and the appropriate incentive are delivered on a personalised basis. As a result, the conversion rate increases to 5.5 percent.

Result: This leads to a monthly uplift of 21,675.00 euros compared to the traditional scenario. The increase is driven solely by the higher conversion rate, from 4.5 to 5.5 percent. Discount costs remain unchanged, while the additional revenue improves the overall outcome accordingly. If, in practice, a portion of the popups is additionally delivered as service popups without discounts, the outcome improves further due to lower discount costs.

3. Cost logic

The two pricing models follow different philosophies and are therefore not directly comparable.

While uptain operates on a purely performance-based model and charges fees only for verifiably additional revenue generated, Klaviyo relies on a volume-based subscription model.

The key difference: With uptain, costs are incurred only when additional revenue is actually generated. This can be tracked transparently in the dashboard and down to each individual order, clearly showing which email or popup contributed to a conversion or recovery.

With Klaviyo, on the other hand, monthly fees apply regardless of the actual outcome. Anyone who keeps a close eye on ROI should therefore compare both models based on their effectiveness and economic impact within their own online store.

How costs are calculated:

• uptain costs = commission × additional revenue
• Klaviyo costs = monthly fee (based on profiles) + optional add-ons or SMS

With Klaviyo, the monthly fee depends on the number of active contacts (profiles) in the account. The more contacts are managed and the more additional modules or SMS credits are used, the higher the monthly costs become, regardless of whether these contacts actually receive emails or generate revenue.

With uptain, the commission is based on the additional revenue generated. Alternatively, a monthly flat fee is also available. The commission can be calculated transparently on the pricing model page. Costs scale with volume as well, but not with usage. They scale with success. More conversions and recoveries mean more revenue for the store and therefore a proportional contribution to uptain.

Billing: paying for revenue or just for usage?

uptain follows a clearly results-oriented approach. Only when the store is successful does uptain benefit as well. For this reason, it is inherently important for uptain to work proactively on the success of its customers, supported by highly rated customer support, in addition to the automated software processes that continuously drive revenue growth.

A key component of this approach is the high level of automation and the content logic controlled by artificial intelligence. Decisions are not made based on intuition, but on data, behavioural patterns, and real-time detection, often putting the system one step ahead of human decision-making. This technological approach is complemented by a personal component: every uptain customer has a dedicated contact person who is available at any time as an expert.

With Klaviyo, the responsibility for success lies with the user. The tool offers maximum creative freedom, but also requires the corresponding expertise, personnel resources, and ongoing maintenance. Whether campaigns perform well or not has no impact on the costs. They are incurred in any case.

4. Technical implementation and effort

This section explains how trigger emails and popups are technically set up in both tools. We show which settings are required, how the implementation works in detail, and where the tools differ.

Trigger emails and popups intervene at critical moments in the purchase process to win back interested users or motivate them to complete a purchase:

• Cart abandonment emails: reminders sent to customers who added products to their cart but did not complete the purchase
• Comeback emails: messages sent to previous customers who have been inactive for a longer period, aimed at reactivation
• Browse abandonment emails: emails sent to visitors who browsed products but did not add anything to the cart
• Popups are displayed in the shop at specific moments when certain behaviour is detected, usually abandonment-related behaviour
  • Exit intent popups: with suitable incentives, cart abandonment can be prevented directly by motivating the user to complete the purchase
  • Newsletter popups: used to acquire newsletter subscribers by actively requesting the visitor’s email address

4.1 Trigger-Mails: automated recovery with impact

A key application area for behaviour-based marketing is the recovery of abandoned purchases, and this is exactly where trigger emails show their strengths. While in tools such as Klaviyo these emails must be set up manually using rules, segments, and test variants, uptain handles this process fully automatically.

The uptain software operates on an AI-based foundation. The uptain® ALGORITHM detects in real time when a user is about to abandon the purchase process and sends a personalised email at the optimal moment, using the appropriate tone of voice and argumentation.

What makes this special: With the additional TrueMatch Mails feature, this intelligence is further refined. The technology analyses individual user behaviour and then generates the most psychologically effective email content, precisely tailored to the respective customer type. As a result, each email is unique and directly aligned with the customer and their current situation.

The best part: Trigger emails are ready to use immediately. No flow builder, no complex configuration required. Only an initial check is needed, during which basic parameters such as tone of voice, branding elements, or exclusions are defined. After that, the system takes over.

The visual appearance adapts to the shop’s corporate identity. Colours, logos, fonts, and style guidelines are taken into account. uptain continuously learns, re-analyses user behaviour, and adjusts in a performance-driven way. This ensures that emails remain consistent with the shop’s look and feel while continuously evolving.

The impact is measurable. Many online stores achieve significantly higher open rates, click rates, and recovery rates with uptain than with manually created campaigns. This often turns an abandoned purchase into a completed order, with minimal effort and maximum impact.

With Klaviyo, the responsibility for success lies with the user. The tool offers maximum creative freedom, but also requires the corresponding expertise, personnel resources, and ongoing maintenance. Whether campaigns perform well or not has no impact on the costs. They are incurred in any case.

How are trigger emails implemented in Klaviyo and uptain?

Klaviyo

In Klaviyo, trigger emails such as “comeback /” or “abandoned cart” are built using a classic flow builder.

The process typically looks like this: you define the trigger, set a waiting period, and then add conditions and segmentation rules. After that, you create the individual email templates, manually insert the content, and configure sender details, sending times, and additional settings. Optionally, further channels such as SMS or webhooks can be integrated.

The result is highly flexible, but requires significant manual effort. Separate templates and flows must be created and continuously maintained for different customer segments. Testing and variant management are possible, but must be actively set up and managed. In short, Klaviyo offers extensive creative flexibility, but requires personnel resources and ongoing operational control.

uptain

uptain automates the entire process. After a brief initial configuration in which branding, tone of voice, and no-gos are defined, the software takes over trigger management in real time. You can either preset a waiting period or let the AI decide automatically when, for example, a comeback email makes sense. The uptain® ALGORITHM identifies the optimal timing and the most effective messaging for each situation.

With the TrueMatch Mails feature, the AI generates individual, psychologically effective content tailored to each user profile. Templates are automatically embedded into the corporate design, and discount codes or service elements are inserted dynamically. The result is ready for immediate use. The copy is proven in practice and continuously optimised, without the need to create separate flows for each target group or event. By comparison, achieving this in Klaviyo would fundamentally require expertise in marketing, design, UX, psychology, and data science, either through a specialised team or through employees who cover these disciplines.

Summary

• Setup effort: Klaviyo requires manual creation of flows and templates, while uptain requires only minimal initial setup.
• Personalisation: Klaviyo enables segment-based personalisation, whereas uptain automatically generates personalised content using AI, tailored to each individual user.
• Timing: Klaviyo works with static waiting periods or manually configured triggers, while uptain can determine the optimal sending window or trigger automatically using AI.
• Templates: While Klaviyo relies on manually created templates, uptain uses dynamic, corporate-identity-compliant templates that automatically and contextually integrate discounts or service offers. These templates are continuously optimised based on anonymised insights from thousands of other customer shops, as well as ongoing background A/B testing.
• Scalability: With Klaviyo, effort and complexity increase as the number of segments grows. uptain scales automatically, as decisions are made in a data-driven way.

What this means for you

With Klaviyo, you gain maximum control, but you need resources for setup and ongoing management. With uptain, operational effort is significantly reduced while personalisation and automation increase. Trigger emails are ready to use immediately and are continuously adapted to behavioural data, leading to improved open rates, click rates, and recovery rates.

4.2 Popups and newsletter opt-ins: the conversion moment

Klaviyo: classic timing and standardised triggers

Popups are part of Klaviyo’s standard feature set, typically using exit intent triggers. This means that as soon as a user intends to leave the shop, a window appears with a discount code or a newsletter sign-up. This is technically easy to implement, but often psychologically ill-timed. The timing is not based on actual behaviour, but on a fixed pattern. At the decisive moment, users are often interrupted rather than convinced, which leads to high irritation rates and low opt-in rates. Someone who is about to leave a website is usually not interested in signing up for a newsletter.

In addition, Klaviyo detects exit intent only very roughly, typically based solely on the mouse cursor moving upwards. uptain differentiates far more precisely here. On Windows devices, an exit usually means movement towards the top right, where the browser close button is located, while on macOS it is towards the top left. In addition, uptain takes into account further signals such as specific time windows, tab switches, use of the back button, app switches on mobile devices, or AI-based behavioural analyses. As a result, popups are truly behaviour-based and appear at the right moment, convincing rather than irritating.

Particularly critical: if the message does not match the user profile, the negative effect is amplified.

For many shops, the potential of popups therefore remains unused. The moment of conversion is wasted, even though the channel itself can be highly effective.

uptain: intelligent timing and psychological triggers

uptain takes the use of popups one step further. Intelligent triggers deliberately take individual user behaviour into account. Instead of reacting solely to mouse movement, the system analyses various behavioural patterns in real time. Only when a clear signal is detected does a popup appear with appropriate messaging. The applied psychology is subtle but effective. Users feel understood, not disturbed.

What does this mean in practice?

Other tools such as Klaviyo usually work with static, freely configurable trigger rules, for example based on a certain scroll depth, session duration, or simply when the mouse cursor moves upwards. This may sound flexible, but it is not intelligent. An arbitrarily chosen threshold of 20 seconds can interrupt users in the middle of reading. A scroll depth of 50 percent is not an abandonment signal, but often a sign of genuine interest.

uptain, by contrast, uses AI-powered pattern recognition to differentiate between such situations and deploy popups selectively only when they actually make sense.

Key data points used to evaluate exit intent:

• Mouse-out (smart): not a static “mouse up” trigger, but analysis of complete mouse movements. uptain more precisely detects whether movements indicate exit intent, for example top right on Windows or top left on macOS
• Tab switch and app switch: switching to another browser tab or app, analysed in the context of specific time windows and behaviour
• History back: detection of whether a user actually intends to leave the page or is simply navigating back within the shop. This enables the identification of up to 10 percent more genuine exit intents
• Scroll-up and inactivity: abrupt backward navigation or prolonged inactivity as abandonment signals
• Hover patterns: movements over elements provide insight into uncertainty or purchase intent
• AI behavioural analyses: artificial intelligence models continuously analyse user behaviour and calculate abandonment probability
• Referrer: visitors from price comparison sites such as Idealo are usually price-sensitive, while visitors from TikTok or fashion blogs tend to be more impulsive
  Device type and browser: Android users respond more strongly to discounts, Apple users more to service. Browser choice such as Chrome vs. Edge or operating systems like Linux indicate technical affinity
• Additional data: for example master data such as the email domain (e.g. @t-online vs. @gmail) or information from purchase history helps to infer the user profile

The added value:

The AI does not only decide when a popup makes sense, but also how:

• Price-sensitive? → discount
• Service-oriented or uncertain? → service popup
  No genuine exit signal? → no popup

In addition, differentiation is made based on user preferences or hotline availability. This allows the system to distinguish, for example, between a service popup offering email contact or telephone support.

Result:

Higher conversion, less frustration, and significantly better opt-in rates compared to standardised popups. uptain turns the critical moment of abandonment into a targeted opportunity for customer retention.

• Fewer intrusive popups
• Up to 20 percent more accurate exit detection, meaning fewer false positives (fewer users who would have converted even without a discount)
• Up to 20 percent lower discount costs by identifying whether a discount or a service offer is the more appropriate option

How are popups implemented in Klaviyo and uptain?

In Klaviyo, you have to design and maintain your popups entirely yourself. This offers maximum creative freedom in terms of layout and messaging, but at the same time increases ongoing effort. Separate designs, tests, and maintenance tasks are required for each target group, variant, or campaign. Scaling usually means more templates, more A/B tests, and more manual effort.

At uptain, you define your CI guidelines once, including colours, fonts, tone of voice, and no-gos. uptain then automatically generates multiple popup variants, dynamically inserts discounts or service elements, and tests them simultaneously across different user groups.

Data instead of guesswork

Choosing the right type of discount is crucial and anything but trivial.

Should it be a 15 percent discount or a 5-euro voucher? Conversion rates can vary significantly depending on the customer segment. In classic tools such as Klaviyo, each store has to test variants individually, evaluate them, and repeatedly adjust them. In addition, what works well in summer may perform very differently in winter or in another industry.

uptain can take this effort off your hands. The algorithm automatically determines the optimal values, based on continuous background A/B testing and anonymised data from all stores in which uptain is integrated. If you wish, you can still define and deploy your own values at any time.

Did you know that 3 euros less can lead to a 15 percent higher conversion rate? That is correct. Analyses show, for example, that a 7-euro popup converts significantly better than a 10-euro popup. Likewise, a discount popup that displays 12 euros instead of 10 euros can already achieve a 13 percent higher conversion rate, with only 2 euros of additional discount. The data is based on more than 30 million real user sessions from stores using uptain, not on secondary sources, surveys, or indirect data collection. Of course, the exact effects also depend on the individual store and its target audience.

Real-time decisions

The uptain® ALGORITHM decides in real time which variant and which delivery timing is most likely to succeed for each user. You can define the timing manually or leave the decision to the AI. Successful variants are automatically prioritised and scaled, based on anonymised learnings from thousands of online stores and ongoing A/B tests. The result is brand-compliant popups with lower maintenance effort, higher conversion rates, and targeted use of discounts.

The infographic below shows how this works in practice: the uptain® ALGORITHM recognises that the user “Jürgen, 68 years old” has low technical affinity but a high need for service, with a preference for direct conversations and low price sensitivity. Based on this data, uptain automatically decides on tone of voice and content: formal address, warm wording, a service popup instead of a discount, with direct phone support. Every component, from wording to the button, is the result of previously captured behavioural and profile data.

Summary:

• CI compliance is maintained without manual maintenance effort
• Continuous improvement of the conversion rate through automated variant testing
• Lower operational effort, as you do not need to build separate flows and templates for each target group
• Better targeting and timing, because the AI decides in real time whether a discount or a service offer is more appropriate
• Scalability effects through the collective learning model that leverages insights from thousands of online stores

5. Segmentation and A/B testing: learning with a system

Klaviyo: rule-based segmentation

Klaviyo offers extensive options for manual segmentation.

Users can define target groups based on fixed criteria, for example: “Viewed at least 3 products in the last 7 days, located in Germany, has not purchased yet.” These rule-based segments can then be addressed with specific campaigns.

A/B tests are also possible within individual flows. However, the responsibility for test design, implementation, evaluation, and optimisation lies entirely with the user. This makes the process error-prone and maintenance-intensive, especially when many variants are in use.

A central problem is that each shop starts from scratch. There is no overarching learning logic, no benchmarking, and no shared collective insights. Every A/B test is an isolated project with a corresponding investment of resources. This can work for data-driven teams with strong analytical expertise. For many shops, however, it is a challenge.

uptain: segmentation through collective behaviour

uptain can fully automate segmentation, based on actual user behaviour. The uptain® ALGORITHM identifies which user type is active in the shop and delivers appropriate content in real time. Integrated A/B tests run continuously and are controlled by AI.

The special aspect: learning effects from all uptain customer shops flow into the optimisation process. When certain messaging styles or triggers prove successful for similar target groups, all users benefit from these insights.

The result: faster testing cycles, lower traffic requirements for validation, and demonstrably higher conversion rates.

uptain not only makes testing more efficient, but also opens up new possibilities. An anonymised, self-learning data system creates a knowledge advantage from which all shops using uptain benefit.

6. Data protection and trust

Klaviyo: data protection with compromises

Klaviyo is a US-based provider, which automatically means data transfers to a third country. While Klaviyo uses standard contractual clauses (SCCs) to ensure an adequate level of data protection in line with the GDPR, a legal residual risk remains even with these measures in place. For many online stores, especially those with European target audiences, this represents a factor of uncertainty that is critically questioned both internally and externally.

uptain: clear conditions through German infrastructure

uptain processes all data exclusively on servers located in Germany, fully GDPR compliant and without data transfers to third countries. For shop operators, this means not only legal certainty, but also operational clarity. There are no uncertain grey areas and no complex additional agreements with third-party providers.

Especially in the sensitive area of behaviour-based recovery, for example through reminder emails after cart abandonment, this data protection advantage is crucial. To ensure legal certainty in this context, uptain has developed a reviewed information sheet together with Händlerbund. It clearly explains under which conditions email marketing after a cart abandonment is permissible, for example under the existing customer exception (§ 7 para. 3 UWG).

This enables uptain users to act not only with technological confidence, but also with legal certainty, without the risk of warnings or loss of trust. In a market where data protection is increasingly becoming a differentiating factor, this advantage translates directly into tangible value.

Conclusion

Making efficiency measurable: when does each tool make sense?

Marketing activities should not only look good, but also deliver calculable performance. This is often where the wheat is separated from the chaff, especially when it comes to recovery and conversion. While Klaviyo provides full access to all control levers, one key question remains:

How much revenue is actually generated? And with what level of effort?

uptain takes a clear ROI-focused approach here. Automated messaging, supported by behaviour-based triggers, continuous A/B tests, and AI-driven optimisation logic, demonstrably leads to more completed purchases.

Even when uptain is used in addition to an existing email tool such as Klaviyo, the system pays off economically in a very short time,** because it complements rather than replaces**. Klaviyo is ideally suited for classic newsletters and campaigns, such as announcing new products or seasonal promotions. uptain, on the other hand, takes over the conversion-driven use cases: the concrete reduction of purchase abandonment, the recovery of cart abandoners, and the acquisition of new newsletter subscribers.

Both tools, Klaviyo and uptain, therefore have their place. The decision depends heavily on the resources available to a shop and which objective takes priority.

Those who prefer to control everything themselves, feel at home segmenting, testing, calculating statistical significance, and optimising, and have a dedicated CRM, data, and marketing team, will find Klaviyo to be a powerful toolkit. It offers flexibility, but also complexity.

Those who, by contrast, want to automate conversion without getting lost in endless flows and configurations are better advised to use uptain. The combination of behaviour-based messaging, continuous optimisation, and full GDPR compliance delivers not only efficiency, but also quickly measurable revenue impact.

Our recommendation: start with a controlled A/B test, for example by using uptain as a complement to existing campaigns. The numbers will speak for themselves.

In the end, it is not about gut feeling, but about facts. Those who test, win.

Frequently asked questions about Klaviyo and uptain

What is the difference between Klaviyo and uptain?

Klaviyo offers manual control over marketing campaigns, while uptain relies on automated, AI-driven processes with a strong focus on conversion optimisation and data protection.

Can both tools be used together?

Yes, both tools can be combined without any issues. Klaviyo is particularly suitable for campaigns and newsletters, while uptain takes over performance-driven use cases such as popups to prevent cart abandonment, trigger emails for cart abandoners, browse abandonment, and inactive customers. Newly generated newsletter subscriptions are automatically transferred to Klaviyo, allowing both systems to complement each other seamlessly.

Why does Klaviyo have so many installations?

Klaviyo is installed so frequently because it is closely integrated with Shopify, one of its main investors. This makes the app especially quick and easy to set up and use. Experience shows that many shops install Klaviyo, but only a few use it efficiently. While the creative possibilities are extensive, the effort required for maintenance, testing, and data protection is equally high.

How does Klaviyo’s pricing model work?

Klaviyo uses a volume-based pricing model. Monthly costs depend on the number of active contacts in the system. Additional fees may apply for add-ons such as SMS marketing or extended support. These costs are incurred monthly regardless of campaign success, which can quickly become expensive if the tool is used inefficiently.

Is uptain GDPR compliant?

Yes, uptain stores all data exclusively on servers located in Germany and is fully GDPR compliant, without the risk of legal uncertainty.

Is Klaviyo GDPR compliant?

Klaviyo uses servers located in the United States, which involves data transfers to a third country. While the company works with standard contractual clauses (SCCs) to meet GDPR requirements, a legal residual risk remains. For European online stores, this can be a factor of uncertainty, especially when using personal data for marketing purposes.

Which tool is better suited for small online stores?

uptain is suitable for online stores of any size. Smaller and medium-sized shops benefit in particular, as they often lack large teams or deep technical expertise. uptain automates many processes such as segmentation, testing, and optimisation. At the same time, uptain is also relevant for larger shops, as A/B tests based on aggregated data from all shops often work more efficiently and effectively than individual in-house solutions.

How does uptain’s pricing model work?

uptain only charges fees when additional revenue is generated. Transparent flat-fee alternatives are also available.