![image1.png](/uploads/image1_fe54179c86.png)

As PZU was looking mainly to gain more behavioral insights from their customers, they used advanced CUX features to find the frustrations and adapt the website to the needs. Before using CUX, they planned that the website analysis and adaptation will take 12 months. However, **thanks to the collaboration, they managed to decrease the time to only 4 months and gain immediate results**. The effects include:

- 70% decrease in frustration on key pages.
- 13x increase in conversion for CTA clicks (from 0.6% to 8%).
- 2.5x increase in interaction with the website.


## Goals {#goals}

![image6.png](/uploads/image6_4c08e074b6.png)

### Customer journey optimization 

PZU aimed to better **understand the steps their customer take on their website** and measure whether their initial hypothesis regarding ideal customer journey was accurate. Afterward, they hoped to optimize the journey to provide a seamless and effortless experience on the website.

### Understanding the users 

Even though PZU is well experienced with data and traffic analytics, they noticed a need for a deeper understanding of their website users, including their behaviors, preferences, and needs. Even though they had previously conducted numerous quantitative analytics, they were hoping **to get more insightful results of WHY the customers behave the way they do**, and if there are any needs that are currently not met. 

### Finding pain points

The company at first didn’t know what elements exactly could be causing issues for the users. They had some understanding of the issues their customers might be facing because of **low activities on contact form and “My PZU” button, but they were looking to deeper analyze the exact steps** that cause these parts to be problematic.

## Scope of work {#scope-of-work}

![image8.png](/uploads/image8_c6ea9b2289.png)

### Heatmaps

- Finding the “hot spots” on the website, focusing on pop-ups and buttons.

### Waterfalls 

- Utilizing the feature for the analysis of complex goals. 
- Finding out why users are not clicking on the desirable elements, and most importantly, what is the reason behind it.
- Analyzing the customer journey (and its complexity) step by step, including the drop-outs and bottlenecks. 
- Using the “Find out” option to see individual session recordings specifically for users that didn’t follow the desired customer journey to understand what caused it. 

### Goal-oriented analytics

### Analyzing the differences in performance between desktop and mobile devices to optimize the user experience on both. 

## Results {#results}

### “My PZU” page clickability increased 13 times

The main goal for PZU was to successfully direct the users to “My PZU” page, where they can log in or create an account and purchase the product. Before using CUX, that button was hidden on the left corner. However, after analyzing the waterfalls and sessions, it turned out that the option wasn’t as intuitive as it seemed. The users were not clicking the “Log in” button, which would take them directly to “My PZU” page. Instead, they were going to Help Center to look for the link there. Therefore, the conclusion was: we have to make the journey easier by making direct, visible link to the page. 

After implementing a prominent box on the top of the page to test the hypothesis, **the clickability increased from 0.6% to 8%, proving that the visibility was a pain point**. 

![image4.png](/uploads/image4_f6eada052f.png)

### Contact form usage increased 2.5 times

By making a strategic change to the contact section on their website, PZU significantly improved user engagement and accessibility. Previously located on the left side, the contact form was not centrally positioned and even disappeared on mobile devices. However, after implementing the modification, **the number of users submitting the contact form increased by more than 2.5 times compared to the previous state**. We can call this a quick win - thanks to a small change on the website, PZU immediately responded to the needs of their clients.

![image2.png](/uploads/image2_0cdb2dd37b.png)

### Customer journey optimization 

PZU’s set a clear goal for the analysis - we want to understand the users and their journey, and ultimately shorten and simplify it. To do so, they set a hypothesis: increased usability and easier navigation will help customers find faster what they are looking for. In other words, if the number of pages opened and session time both decrease, it’d mean that the user was satisfied faster. After CUX analysis, they discovered that by changing the location of the boxes, they were able to change the way customers explore the page. Before, the average number of pages visited before finding the contact form was 2.31. After implementing the changes, it decreased to 1.9 pages per user, which is equal to 17.75% change. Moreover, the average visit time on that page decreased by 8.2%. Therefore, thanks to the changes, the users were able to find quicker and easier what they needed. 

![image3.png](/uploads/image3_8dd13a403d.png)

### 3x shorter analytics time

The company knew that there is something wrong with the “My PZU” page and the contact forms even before they started using CUX - the numbers were not high enough to be satisfied with the results. It was clear that these parts of the website needed change, but the issue was “how?”. Before discovering CUX, the team was planning to intuitively try different scenarios, hoping that one of them would actually work and increase the clickability and form submissions. **According to the team, this “trial and error” method would take approximately a year to bring the results they wanted**. With CUX, they were able to skip steps and follow the decisions backed up with data and facts from the very beginning. Thanks to that, the analysis revealed the changes they needed to make almost instantly, and **the process of improving the website took only 4 months**.

![image7.png](/uploads/image7_74b14e799c.png)

## Impact {#impact}

KPIs, customer satisfaction, sales etc. Specific measures or results that were improved. 

## Testimonial from the client {#testimonial-from-the-client}


>“CUX enabled us to introduce the customer's perspective to our website.
We've obtained the first analytic's conclusions in just a month!”We've obtained the first analytic's conclusions in just a month!”



>“Previously, we analyzed websites from the Google Analytics perspective. It quickly showed us that certain sessions do not go along quite as we would like them to. We knew something was wrong and worked through trial and error to find the problem. Now with CUX, we can quickly locate those issues.”

## Final Checklist {#final-checklist}
- Enhanced visibility and usability of the contact page, resulting in a 2.5x higher usage rate
- Increased “My PZU” clickability by 13x thanks to changing the location of the button
- 70% decrease in frustration on key pages
- Improved the website 3 times quicker than with traditional methods, saving money and resources 
- Users visited fewer pages (17.75% decrease) and spend less time (8.2% decrease) finding what they are looking for.
- Optimized customer journeys thanks to insights from behavior analytics
- Improved understanding of user behavior on the website
- Combined quantitative and qualitative analysis effectively
- Made informed decisions and optimized website performance based on real user insights.



## Summary {#summary}

PZU Group is one of the largest financial institutions in Poland and in Central and Eastern Europe that was first established in 1803. With previous experience of user analytics through tools such as Google Analytics, they were looking to extend their analyses by meaningful behavior analytics, and combine the previous quantitative measures with qualitative ones. 

large_pzu-logo 1.png

medium_pzu-logo 1.png

small_pzu-logo 1.png

thumbnail_pzu-logo 1.png

pzu-logo 1.png

70% frustration drop in InsurTech – PZU x CUX Case Study

> **Understanding Conversion Rate**: Conversion rate (CVR) is the ratio of conversions to the total number of visitors, expressed as a percentage. It's a crucial metric that indicates the effectiveness of a website's user experience and sales funnel.

## Goals {#Goals}
During a campaign, T-Mobile experienced an unexpected decline in conversion rates. Visitors were proceeding from the landing page to the next step but were abandoning the process at the shopping cart stage, resulting in a significant drop in conversion rates.

![T-mobile conversion drop](/uploads/image001_4_1_700x147_c103195a59.png)
_<center> T-mobile conversion drop</center>_
 
**The main aim was clear**: figure out why this was happening, enhance the customer jurney and bolster conversion rates to restore campaign success.

![mobile summary case study](/uploads/t_mobile_summary_3ce4e38b39.svg)

## Scope of work
T-Mobile used CUX's to deeply analyze user behavior and pinpoint the root cause of the sudden conversion drop. By studying [Visit Recordings](https://cux.io/features/visit-recordings/), they observed the exact actions of users who added items to their carts but failed to complete the purchase.

## Insights
Visit Recordings revealed a critical discrepancy between the offer presented on the landing page and the offer displayed after adding items to the cart. The pricing of the device and the internet package had changed, leading users to abandon their purchases due to the inconsistency.

![T-mobile offer view](/uploads/screen_tmobile_700x433_2ce2129c61.png)
_<center> T-mobile offer view</center>_
![T-mobile cart view ](/uploads/tmobile_screen2_700x358_9d84cfd8d5.png)
_<center> T-mobile cart view</center>_
### CUX and Google Tag Manager Integration
Such efficient issue detection was possible thanks to the integration of CUX with Google Tag Manager. The integration provided T-Mobile with a comprehensive view of both quantitative and qualitative data. 
 
 
![cud google tag manager integration](/uploads/cux_google_tag_manager_fbe831490a_6b1080413b.png)
 
While Google Analytics gave T-Mobile team some hints, the real magic happened when qualitative and quantitative insights blended together. All the data and events from the data layer flowed seamlessly into cux.io. And with a sprinkle of simplicity, using filters based on names and categories, the T-mobile team could pluck out the exact recordings they were after. It's like searching for buried treasure and finding it on the first try.
 

>Ready to dive in? Create a **free CUX account**, and [integrate it](https://help.cux.io/support/solutions/articles/103000096894-enable-datalayer-capture-integration) with GTM. 


## Results
By leveraging CUX's analytics and insights, T-Mobile was able to:

- Swiftly identify and resolve the inconsistency issue.
- Align the offer presentation across the user journey.
- Restore their conversion rates to desired levels.

### Future-proof conversion rates with Conversion Waterfalls
 
In the realm of CRO, [Conversion Waterfalls](https://cux.io/features/conversion-waterfalls/) emerge as a dynamic force within CUX, offering a vivid visual chronicle of user interactions, sequentially unfolding their journey.

Every step within a Waterfall reflects a user's move, and the succession of these steps crafts a storyline for you to follow. User behavior rarely sticks to a straight line; it frequently takes twists and turns that can seem random or unexpected. This is where Waterfalls become your guiding compass for analysis, helping you decode the puzzle of how users react to particular actions. This thoughtful strategy helps you proactively address situations like the one T-Mobile faced.
 
For instance, picture a Conversion Waterfall: 
Homepage > Product Category > Product Page > Cart > Checkout > Order Confirmation. 

![conversion drops ](/uploads/CUX_conversion_drops_493814933d_3b30d2ae5c.png)
_<center> Waterfall - Conversion Drop</center>_

By mapping out a sequence of actions you can see on which stage the users drop out and immediately identify their issues like broken links or graphics that remind them of call-to-action.

In a nutshell, Conversion Waterfalls become your guardians of conversion rates, guiding you to understand user stories, foresee challenges, and and fortify your digital landscape against unexpected surprises.

## Impact

The T-Mobile case exemplifies the power of analytics in tackling conversion rate challenges. By utilizing CUX's Visit Recordings and Google Tag Manager integration, T-Mobile achieved a data-driven solution that not only addressed the immediate issue but also enhanced their overall user experience. This case underscores the importance of combining quantitative and qualitative data analysis to make informed business decisions and achieve significant improvements in conversion rates.

_Curious for more? For an in-depth exploration of this case study and a deeper understanding of conversion rate, feel free to visit our detailed [blog post](https://cux.io/blog/how-to-boost-your-conversion-rate-using-analytics-as-a-daily-routine-the-t-mobile-case/)._

## Summary

Conversion rate optimization (CRO) is a top priority for e-commerce managers. T-Mobile, a leading mobile operator in Poland, faced a sudden drop in conversion rates during a campaign. Leveraging the power of analytics and CUX, they were able to identify the issue, make data-driven decisions, and swiftly restore their conversion rates.


large_T-Mobile Case Study (1).png

medium_T-Mobile Case Study (1).png

small_T-Mobile Case Study (1).png

thumbnail_T-Mobile Case Study (1).png

T-Mobile Case Study (1).png

From insight to impact: T-Mobile's 68% overnight conversion surge

## Goals {#Goals}

**Optimize engagement**: Elevate ongoing campaigns performance by boosting engagement, conversions, and overall satisfaction during this pivotal period.

**Future strategy development**: Harness insights from user behavior to lay the foundation for upcoming campaigns, ensuring they resonate harmoniously with customer preferences.

## Scope of Work

![Ania Kruk case summary](/uploads/Ania_Kruk_summary_2_d6e733aac2.svg)

**Heatmaps exploration**: The automated [heatmaps](https://help.cux.io/support/solutions/articles/103000126870-heatmaps) generated by CUX provided a visual narrative of user interactions, clicks, and navigation across the website.

**Comparative Analysis**: Leveraging CUX's analytics features, Aniakruk.pl gained access to invaluable insights into user behavior patterns. With retroactive analytics which enables learning from past data, CUX performed a meticulous comparison between the overall Valentine's Day website traffic and the traffic specifically generated by the campaign.

## Insights

In the case of aniakruk.pl, right after Valentine’s Day, we started the analysis by checking how customers generally behaved on the page with products specially selected for the occasion. We used heatmaps for “direct” entries and for visits navigating within the Valentine’s Day offer.

![Ania Kruk Case heat map filtering](/uploads/Ania_Kruk_case_1_700x240_f73d8c0d85_372b611409.png)
_<center>The heatmap applies only to entries on a specific URL</center>_

By analyzing the clicks on the heatmap, we did not notice any unusual or questionable events. We’ve observed a rather natural, diffused flow of customers, without too much involvement in browsing individual products. Most of the clicks accumulate around the option to choose a specific assortment (on the left).

![Ania Kruk case heatmap](/uploads/Ania_Kruk_case2_700x381_3db23b7c1c_fe3c2318fb.png)
_<center>Heatmap for the aniakruk.pl Valentine’s campaign</center>_

### Filtering: Campaign only traffic
After the initial analysis, we’ve decided to check what the same entrances looked like in the case of customers who clearly came to the site from external campaigns. To do this, all you have to do is filter the entries after the given URL. The address should have an additional “tail” to recognize where a given client came from, e.g._ fbclid._

Using filtering, we have prepared a list of all addresses of interest to us. Then we’ve generated a [grouped heatmap](https://help.cux.io/support/solutions/articles/103000126870-heatmaps#Grouping-Heatmaps) for them (the function is in the upper right corner of the page). Such a heatmap allows to generate a collective image for many URLs. URLs under which there are pages with the same structure – e.g. product pages or just campaign entrances. Those addresses, in the basic analysis, will be only visable in the form of separate URLs.

![Ania kruk case 3 grouped heat maps](/uploads/Ania_Kruk_case_3_700x278_bff833d7e8_38d09da45f.png)
_<center> Grouped heatmap allows you to see the same page displayed by visitors using different entry points. </center>_

In this case, the situation is slightly different from the case of “direct” entries. First, we see a clearly greater involvement of users who clicked more often (more red spots). The grouped heatmap also allows us to observe that customers did not click on the products themselves as eagerly as on the filtering options. They were trying in all possible ways to narrow down the range of products! The item in the top menu under the name “Czego szukasz” [eng. What are you looking for] was also very popular.
 
### Seaching for frustration
Interestingly, we did not see an increased or significant number of clicks in the menu after its expansion. Just a quick heads-up – a heatmap is essentially a snapshot of a single webpage. So, if people start clicking around in an expanded menu, those clicks might seem like they're floating in mid-air on the classic heatmap. However, they could actually be related to pop-ups, widgets, or just sliding menus.

Now, let's talk about the grouped heatmap, which is like the star of the show here. It gives us a bird's-eye view and tells us that the customer journey kind of hit a roadblock. It's like visitors didn't go much further on the site. This could signal that they didn't quite find what they were after. And if we dive deeper into the details, that hunch becomes even more evident.

![Ania kruk case grouped heatmap](/uploads/Ania_Kruk_case_4_700x401_7dd7c655a3_1530072094.png)
_<center>Grouped heatmap of the jewelry brand’s Valentine’s Day campaign</center>_

Changing the data display type to “rage clicks” only confirms our hypothesis. Customers were disappointed after clicking on the aforementioned category. In addition, it gave us valuable information about existing problems. Including the info about usefulness of a slider that allows filtering by prices.

What's intriguing is that we caught a glimpse of customer hesitations – it was as if they were standing at a crossroads, unsure about the best way to have the products presented before them.

## Impact

Our analysis revealed that customers lean towards refining their e-commerce experience by narrowing down their search results. This inclination becomes evident through their interactions on the website – behaviors like gravitating towards filtering options, engaging with the "What are you looking for" menu bar, and expressing frustration through "rage clicks" around the price-adjustment slider.

Considering these insights, we've crafted a recommendation for your upcoming campaign: 
- **strategically center it around segmented offerings, such as products within designated price ranges**. 
- Furthermore, we also recommended further investigating the usability problems of the price slider. Working out a new solution for it could significantly improve conversion.

**Contextual targeting**: With a thoughtful strategy in play, Aniakruk.pl strategically segmented their campaigns based on distinct product categories. This approach unlocked a remarkable outcome — **threefold increase in conversion rates**. This success wasn't a stroke of luck; it was the result of deliberate decision-making grounded in user behavior insights.

_Interested in delving deeper into the CUX and Ania Kruk partnership? Discover the detailed story [here](https://cux.io/blog/valentines-campaign-analysis-aniakruk-case/)._

## Summary 

In a quest to refine their campaigns strategy, Aniakruk.pl, a renowned Polish jewelry brand, joined forces with CUX. Their aim? To unlock insights that would spark immediate campaigns improvements and shape future initiatives. This partnership was all about blending the art of data-driven decision-making with a grasp of user preferences.