Understand and Engage your customer(s) with more AI power

Today we are happy to release Solver AI Suite 2.0. You can read more about it in this post. The focus of the past three months was on collecting and working on clients’ feedback. We decided to ask more and work more to boost the revenue of our clients. Several key things popped out from this process. The first one – our users want to know their customers(s) even better and easily understand them while having all customer-related data in one place. The second thing is how to engage them quickly and at the right time. Okay, it sounds like everyone’s wishes and that it is the promise of all similar tools, but we focused on making the whole process simple, in a couple of clicks, while maintaining top quality. We want to follow the way of simplicity with power. Like the great Steve Jobs once said, “That’s been one of my mantras – focus and simplicity. Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple. But it’s worth it in the end because once you get there, you can move mountains.”  

Key new features

New Portal interface – we redesigned the entrance app to be more seamless and more informative. We are now offering two unique views, depending on whether you are a new user and want to read more about our offer, or you are a veteran of tools, and you need one click to start your daily job.

Campaign Studio interface – now focused on channels. We pulled the audience build process as a separate app. Now Campaign Studio is more focused on marketing departments, allowing marketing people to use the tool on their own easily. You can create and schedule a marketing campaign in 5 steps.


Campaign Studio Viber channel upgrade – imagine that you want to send personalized messages to your customers to get different sets of products in one click. You need to have pictures of your products, and pricing and the tool can do the rest.   


Creative Studio Beta, we make the first steps to offering a template engine inside our tool but do not worry, we will support other email template engines too.


Audience Studio Interface – our superstar of this release. We improved this process so that you can create ANY audience in 5 steps. You can have all filters you want under your click and fit audiences perfectly to your business needs. You choose to power up the selection with AI model(s) output. Unique, we designed one step focused on the product, allowing the users to find the most appropriate/best audiences for selected products. Here we engaged our complex and powerful Recommendation Engine, but as stated before – you can use it with just a couple of clicks. What else:

  • You can use the created audience for the campaign 
  • You can download the audience as excel or CSV file  
  • You can choose the audience to be static or dynamic 


As you can see, Audience Studio can be a self-service insight tool. 

Audience Dynamics or Statics – the audience can be static, and you can use it now or want to look at insights. But nowadays, things are changing. Behavior is changing. That’s why we introduced dynamic audiences, which AI models automatically update. You can sit back and relax, knowing you can use this kind of audience for Marketing Automation or repetitive campaigns. You can be focused on creativity and not on overloading your IT department with requests for data. 

Recommender Engine update – we are constantly upgrading our Recommendation models, now we can support better results to push leaflets personalized to your customers. Second and very important, we shorten the process from data to recommendations.  

Profile Studio, we renamed Personalizer to a new name, and we cleaned some things. More importantly, we can add items dynamically. Imagine you have additional information you want to see in one place or show to your colleagues – you do not need to do anything. Just provide us data, and we will add it in a couple of seconds. 

Smart Segmentation interface, we released a new interface which is more informative and can support multiple segmentation reports.


  • As you might know, we are focused on API first strategy. Everything we mention our clients can leverage by using REST API and embed these functionalities into existing apps or processes
  • We released Events API for easier integration
  • Privacy and Security, we improved our process and APIs to make sure that there is no room for this kind of problems 

What we plan for the future releases

Here are some things related to the topic that we are working on:

  • Push notification channel via firebase
  • Digital Edge by ASEE as a channel.  
  • Assceo Live deeper integration.
  • Reporting page for selected audience.
  • Open selected Audience in BI tool.  
  • Campaign Studio improved reporting. We want to add more things so you can make better decisions for the future.
  • Creative Studio for easy email template creating.
  • Create an Audience from Profile Studio, choosing similar customers based on the selected customer. 
  • Look-alike model implementation, so you can extend target groups with a move of a slider. 
  • Create an Audience from Smart Segmentation Studio directly choosing customers by special rules.  
  • Product Studio for the product manager, create an audience starting from the product or check KPI’s before making a decision.
  • Campaign Studio A/B testing functionalities.
  • AI model improvements 
  • Customer Journey detection.


  • Forecast Studio for doing forecast in couple clicks
  • Anomaly Detection model update
  • Atlas update

We are focused on providing what our clients need. If you have any feedback or you want to schedule a demo, please drop us a message at ai@thingsolver.com

Stay tuned for more. 

How banks can leverage AI: Segmentation as one-two-three!

Segmentation is a widely known term, especially within the marketing sector. It is an insightful, effective and actionable approach to better understanding a customer or client base. 

What is so attractive about leveraging AI specifically within the banking sector is the amount and variety of data. Segmentation per se is not the final outcome of the analytics process. If is often used as the initial step or auxiliary step in the process of “customer 360”. Insights about customer segments can be used to get closer to the tailored marketing strategy and personalization. 

RFM segmentation is one of the most popular approaches used by marketing and business analytics teams, in order to group customers with similar descriptive features – recency (R), frequency (F) and monetary value (M). 

Years before machine learning has seen the light of the day, RFM segmentation has been performed by binning and ranking the customers based on these three features. Namely, each feature is tiered into four or five bins, where the bins are assigned ranks from 0 (lowest/worst) to N(highest/best) in terms of the feature being analyzed.

In regards to all said: the higher the final segment rank – the better. Impressive! I really like the simplicity and mightiness this approach possesses!

Nevertheless, there are some drawbacks. We are obliged to always use fixed bins, and assign ranks that could result in rough separation between segments, and also, what if we have two pretty similar clients that have fallen into different segments due to the slight difference between their recencies (e.g. customer with final rank 455 and customer with final rank 555)? 

There is a solid ground for this from the business perspective – clients are best identified by their spending habits. If we know how much they spend and how active they are, we can anticipate whether there is a fertile ground for building strong and loyal relationships. 

RFMT is one of our first and most commonly used methods for performing a specific customer segmentation based on the activity level and spending habits. “T” in RFMT stands for tenure, the duration of the client’s lifecycle. We have introduced one additional modification more, in regards to the standard RFM model. We’re not binning the data and using ranks – but we use clustering techniques based on machine learning algorithms. 

One of the things I’m especially proud of within our platform is the perseverance of simplicity – all our modules can be easily objectified, understood and utilized by non-tech users.

The only thing we need is the anonymized data – and all the magic happens within our platform – no manual work or additional involvement of the client is needed! (unless the opposite is especially requested by the client). A brief overview of what you can expect from our “Customer importance” module is depicted below.

You may as well track client’s transitions through the segments over time – and thus model the customer journey and define triggers for marketing automation when some specific event happens, e.g. falling from “champions” to “sleepy” segment. 

Within our Solver AI Suite – segmentation is an integral part of Solver Personalize pillar (which besides includes recommender system and customer lifetime value estimation). By combining outputs from these three modules, we support marketing automation through audience creation and campaigning (campaigning will have its shining moment in our future posts).

What about the results? – you might ask. We’ve got 30% higher conversion rates by targeting a specific segment of customers, which we have identified as potentially loyal, by a tailored offer. On the other hand, we have proven that the so-called “champions” will come either way, and that they don’t require a special discount or often targeting. This further implies the increase of sales and reduction of unnecessary costs. 

So, let me ask you a question – what are you waiting for? Drop us an email, and we’ll help you out. 🙂