Creating Impactful Personalization for Digital and Non-Digital Services

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Attrecto team

Creating Impactful Personalization for Digital and Non-Digital Services

As the last installment in this review series of our final webinar from 2021, Leveraging Data to Drive Increased Personalisation and Empathy, we are going to see what is considered a good approach for creating a personalized journey in any industry. 

Our guest speaker for the webinar’s third and final presentation was Armin Gulber, co-founder and Design, and Digitization Lead at Nextwit, an agile start-up that helps companies big and small from a broad spectrum of industries in delivering intuitive data and digital initiatives. 

 And that’s the topic he chose to discuss quite elaborately, providing an insightful perspective on how to effectively personalize digital (and non-digital) services. 



To better illustrate how most companies view personalization schemes, Armin used the analogy of a ship cruising the sea to find a bountiful island where it could land. 

Once it locates such an island on the horizon, it first has to navigate some treacherous, rocky waters before arriving at its destination. 

For a company initiating a personalization journey, the island is a combination of lucrative prospects, increased revenue, and improved organizational efficiency. And the rocks surrounding this place are the challenges and perceived obstacles that, if surmounted, will lead the company to achieve its business objectives. Such “obstacles” also include figuring out appealing offers for customers and designing a UI that users will love. 

However, most ships and their crews can’t see what’s going on under the waves and are oblivious to such factors. 

The same applies to most companies that are ready to embark on a personalization journey but are not mindful enough of such “hidden” challenges and aspects of personalization. 

So, what’s under the water, exactly? 

  • Open-minded culture: It’s one thing to get everybody on board with a personalization project, but people working on it have to feel that this is going to help and be open to change on an organizational level as well. Personalization is not exclusively about number crunching, and to succeed, companies must be perceptive and learn what their customers want exactly.
  • Explored and identified customer needs: Indeed, this is probably the most important prerequisite for personalization, yet it’s also the most overlooked aspect as well. The whole point of the journey is to provide a personalized experience to customers, yet it often occurs that companies focus too much on business goals without considering customer needs.
  • Robust and clean data: Whether our services are digital or not matters little in this regard, as well-structured and clean data sets will always be essential elements for transforming those services into personalized experiences.
  • Future-proof infrastructure: Legacy systems make it all but impossible – or highly difficult, at the very least – to achieve the desired business objectives. A spaghetti infrastructure will make the whole process much harder than it should be. Instead, a streamlined architecture design and some sleek, purpose-built tools of the trade will deliver results. 



Now that we’ve scouted the waters surrounding the island and know what lies beneath, we need a course of action to reach our destination safely. 

Armin brought forward Nextwit’s suggested order for approaching personalization: 

  1. Business objectives: Define early on the “why” of personalization: what are the goals the company wants to achieve through this project? 
  2. Explore customer needs: Learn how your customers go about their business when using your systems right now, how they deal with their challenges, what their exact issues are. Then start mapping out ways you can help them with their problems and come up with viable solutions and alternatives. 
  3. Robust and clean data: Through well-structured data, you can understand what’s needed to build and deliver propositions that your customers want or provide them with trustworthy and relevant information.
  4. Appealing offers: Specific propositions that can be tailored to individual customers from the patterns of collected and processed data. These are the offers the company needs to communicate to its customers in a personalized way. 
  5. Loveable UI / UX: While highly important, and it helps immensely with Customer Experience and customer satisfaction, the deeper layers of system design have to come first. 
  6. Future-proof infrastructure: This stage is all about selecting the right tools and software that best serve the processes you want to create. 
  7. Open-minded culture: Last but not least, you need to figure out how to incorporate the mindset of personalization on an organizational level. What Nextwit discovered is that companies need early successes to change the company’s culture. Without tangible benefits and results that make people open and positive about these changes, it just won’t succeed. 



Armin brought some case studies as well to show how the failure to meet these personalization perspectives can derail, delay, or disrupt a personalization journey entirely. 

Merging Service Providers


Missing aspects: Open-minded culture, Robust and clean data, Explored customer needs

Two service providers went through a merger and began looking for synergies straight away. As a business objective, they wanted to increase revenue by targeting and reaching each other’s customers. They implemented a rushed communication campaign and tried to provide appealing offers but didn’t realize that they’d had raw, unrefined data about their customers’ needs and interests. Offers were dispatched to the wrong segments, some customers received the same offer over and over again, which ruined personalization.

Their personalized eDM campaigns eventually failed due to low conversion rates and bad CX.

Digital Content Provider 

Missing aspects: Future-proof infrastructure, Robust and clean data, Explored customer needs 

The company sought to launch a new app and was very confident about its understanding of personalization. They were fully focused on creating a shiny frontend for the app but didn’t do much with the spaghetti architecture in the back. A jumbled IT stack makes the use of data in personalization schemes difficult and overcomplicates processes of measuring and monitoring results and KPIs. Furthermore, as customer needs were not explored, the company failed to realize that their users were more interested in the quantity of content than how it was presented through recommendations. 

In the end, the management seemed doubtful if the new app would deliver the financial results they’d hoped for. 

Financial Service Provider 

Missing aspects: Future-proof infrastructure, Robust and clean data 

A mammoth financial service provider wanted to increase operational efficiency by moving all its data to digital channels, as it was struggling with its legacy systems that also severely limited customer options. They intended to launch digital self-care solutions which required the presentation of personalized information, however, due to technological debt incurred through said legacy systems, it led to a host of issues, including cases where customers found misleading or incorrect data about services (e.g. billing). The company meant well, but its systems were so obsolete that it made the smooth transition to digitally personalized solutions all but impossible. Instead, they should have started the process with data cleansing; using data virtualization tools that could bundle up all the fragmented data and organize it, which wouldn’t be a huge investment when done smartly. 

Physical Industry (Agriculture) 

Missing aspects: Open-minded culture, Robust and clean data, Explored customer needs 

Slowly, most companies in physical operations are starting to realize they generate enormous amounts of data that can be utilized in their sales processes. Such was the case with a company dealing in agricultural products; at first, they didn’t want to understand that personalization could help their efforts. They knew they had the data and sought to provide a digital solution with great UI but overlooked that they could take it a step further, see what their customers are doing, and tailor the solution to their needs. In this case, by processing data about generations of seeds, soil composition, weather, and more, then comparing such patterns to data gathered from the fields of neighbouring farmers, they could help their customers pick the best seed, soil, equipment, and agricultural technique that best suits their unique circumstances. 

Data can be easily applied to digital surfaces for differentiating experiences , so prototyping with real data can help in these situations to get a better grasp on customer needs. 



All in all, if you consider all seven perspectives of personalization, even those that are “hidden” and can’t be perceived as easily as frontend and coming up with good offers, the journey itself from inception to implementation and launch shouldn’t become a multi-year project.

As a closing, Armin explained Nextwit’s tried and proven approach that they suggest to their clients:

  1. Solution health check: New ideas or existing solutions can be reviewed from business, design, and data perspectives within 2-4 weeks.
  2. Rapid prototyping with data: Test your concept with data-driven prototypes and tools to learn what works well. Modify the concept if needed and plan the implementation.
  3. Launch, measure, and iterate: Create a culture that supports product innovation fuelled by customer feedback and data.

Nextwit can help in all three stages: workshop facilitation, research, and data modeling during the solution health check, by providing prototype construction and data virtualization when prototyping, as well as creating or tweaking decision-supporting tools and providing training post-launch.

If this last blog post on our final 2021 webinar piqued your interest and you’d like to learn more, head over here and watch the presentations at your own leisure!

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Brands Building Human Relationships with Customers

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Attrecto team

Brands Building Human Relationships with Customers

During our webinar, Leveraging Data to Drive Increased Personalisation and Empathy, Lili Kunfalvi, Ynsight’s Market Research professional delved into the depths of the human psyche to see how people derive identity from the products they buy and the brands they prefer.


In this blog post, we’re going to go through some of her enlightening points to see just how human-like our relationships with our favorite (or most despised) brands can be!



At this point, with Artificial Intelligence sifting through an innumerable amount of data every day, it is common knowledge that companies have at least a decent guess about your preferences about the products they offer.

Taking it a step further, however, we can see that today’s technology and understanding of psychology can also enable brands to guess some of their personality traits based on the consumer goods they buy or use.

They do this while most of us still think in a very practical way about our product purchase and use; I bought that car because it fit into my current budget, I ordered that winter coat for the skiing trip next month, I prefer that type of food because it’s healthy, etc.

But the thing is, even when choosing and settling on these products, we also make these choices based on the kind of identity we’d like to possess or show to the world. It can be a very conscious decision or it can hide beneath the practical thoughts on a subconscious level, however, there’s no doubt about the phenomenon that brands and products help us construct, express, and enhance our identities.

In other words, when we buy or use the products from a particular brand, the associations related to that brand are automatically transferred to us.

For example, when money is not a limiting factor, and someone is ready to buy a new car, he or she can consider a jeep or an elegant city car, like a Mercedes for purchase. The jeep generally exudes an aura of adventurousness; it is built for off-road environments, traditionally masculine features are associated with it, and it implies that its driver is probably going on exciting trips quite often.

On the other hand, the Mercedes looks sleek, elegant, and stylish. When we look at it and see its driver sitting inside, we can easily imagine the person having a remarkable record of successful businesses or career paths. And whether the driver is indeed a successful businessperson or not is kind of irrelevant in this consideration, since what matters is that he or she successfully projects that identity through his or her car.

All in all, the key takeaway here is that we as consumers take up roles and identities using the products we buy. But this is not a static phenomenon. Just because someone buys a Mercedes, it doesn’t mean she’s not adventurous. Once again, putting financial constraints aside, a consumer could go to work in a Mercedes during the week as an elegant businessperson, but on the weekend she hops into a jeep and goes to explore the countryside or drive up mountainous regions.

One additional note to make here is that the connection between our identities and our possessions can sometimes be so strong that an unfortunate loss of such object or possession might feel like a loss of identity as well. 



To better understand which areas can be improved by the inclusion of AI technology, we have to first know what we mean by customer experience and what we can do about it to make it better for our target audience.

CX is the combined interactions and emotions that a customer has in connection with a company. It is the combination of both pain and pleasure when a consumer is using a product or service – or is inquiring about one.

And what we often see (as customers) from what CX is, we usually only perceive (or encounter) the very tip of the iceberg. And in the case of the technology involved, it is mostly the front end of it.

But there’s a lot more to CX below that we don’t see, even when we’re already using the product. Customer service, billing, technical support, account management, logistics, and many more can be found below the surface.

UX is still just one of these elements. It needs to be well planned out and executed, but in no way it is the only determining factor that can result in great CX.

And it’s very important to note that the majority of customer data necessary to discover impactful insights are hidden there, but AI provides the capacity to mine this data and leverage it in the delivery of superior customer experiences.

There are three ways products exert a psychological function over our identity:

  • Symbolic self-completion: Consuming products in order to compensate for self-perceived inadequacies in identity.
  • Symbolic group membership: Signifying group affiliation, e.g. first-year students buying university merchandise or mascots to show that they belong there, but can be associated with any kind of affiliation (political party, social class, sex/gender).
  • Symbolic differentiation: Picking products that reflect the person’s uniqueness.

Brands exploit these symbolic functions in how they portray and advertise their products. They create different associations that will attract specific customers. For instance, one watch brand speaks to more adventurous, young people, another for affluent, elegant individuals that want to show off their wealth through luxurious products, and another that combines a bit of both world: elegant, sleek, but can be worn in any setting.

The key here is that these brands want to portray their products as items that are integral to their consumers’ identity, where each watch symbolizes a different identity.



A brand schema is a set of associations linked to a brand. In other words, they are the mental representation of brands; the qualities, values, and beliefs associated with the brand itself.

They work in the way of attaching highly human attributes to a product and its brand to show how they “behave” in the world. Touchpoints that these attributes are communicated through are varied; from advertisements through scandals to price and logo, it can be virtually anything that tells something about the product or brand.

For instance, when we buy a bottle of Coca Cola, we don’t know the people who produced that or decide on the company policies, but we can glean information about them through the packaging, the quality, the colors, the logo, as well as any news related to them.



When making our minds up about people, we usually do it by measuring them through two perceptual dimensions: warmth and competence.

And we do the very same in the case of brands. With human-like traits attributed to brands, it becomes easier for our brains to judge those the same way as we measure other people.

So, warmth is connected to the intentions of a person or brand; how trustworthy, kind, generous, helpful, nice do they seem?

And the dimension of competence is their ability to act on these intentions. Subconsciously, we’re assessing a person’s or brand’s strength, status, resources, etc. compared to our qualities.

Interestingly, we use these dimensions to “judge” everything around us, including pets, friends, lovers, family members, strangers, politicians, celebrities, and the list goes on.

And where these dimensions meet, they together stimulate a predictable set of behaviors and emotions, which can also be directed not just towards people (or living beings in general), but concepts like brands as well.

Assessing a brand as warm and competent, triggers emotions of admiration and pride, which makes us attracted or want to be affiliated with a brand. While a cold but competent company brings forth envy and jealousy, which can either lead to the obligatory association or a form of sabotage. For instance, luxury brands often evoke such behaviors in consumers, and in this case, sabotage means talking about the brand as a symbol of affluent, uncaring wealth.

On the other hand, the emotional response of warm incompetence is sympathy and pity, which can make people approach a brand patronizingly. Ultimately, it either transforms into an intention (and action) to help that organization somehow or into social neglect. Many non-profit or international organizations (relief, wildlife protection, etc.), unfortunately, suffer from this brand image.

And lastly, there’s cold incompetence that evokes contempt and disgust in people. They will reject and avoid the products and services of such brands, wanting nothing to do with them.



Just like in the case of any other connections we create over our lives, our relationships with brands are malleable and dynamic. Brands can befriend or offend us, and then they can either do something that will make us forgive them or antagonize us further until our behavior is all about enmity to that particular brand (never doing business with it).

But what’s important to note here is that a brand is not a passive object of marketing transactions, but actually is a contributing member of a relationship dyad, where the brand acts through the actions (or inaction) of the decision-making executives as well as the marketing team communicating those decisions and the brand image itself.

We’ve collected an exciting sample of meaningful relationships a person can have with a brand:

  • Casual friendship: Sporadic but pleasant interactions with a brand with a few expectations for reciprocity.
  • Arranged marriage: Non-voluntary use of the brand, imposed by the preferences of a third party (e.g. spouse prefers a different coffee brand and we keep buying from that brand).
  • Secret affairs: While on a diet we’re supposed to drink only water, but we ‘cheat’ on the diet by sometimes drinking Coca-Cola.
  • Flings: Trying out something new for a short-term period (e.g. beauty products offered as gifts in malls).
  • Childhood friendships: When we were small children and loved a particular product that we’re not using anymore, however, seeing them again evokes nostalgia.



In conclusion, Lili presented the key takeaways of her presentation, the premise of which was that customers choose brands that match their ideal identity.

But to create brands that construct, narrate, and enhance their customers’ identities, they need successful targeting to collect relevant psychological data about them: if they have valuable information of their audience’s preferences, personality, attitude, and beliefs, they can draw insight about their needs, motivations, and even desires. Ultimately, it helps cement the brand as a cultural icon by empowering related associations between it and the identity the consumer wants for him or herself.

Lili recommended every participant think about their brand as a person and use social media to strengthen the likeness between brand and person. Last but not least, she had a great piece of advice on how brands can maximize customer loyalty!

Interested to learn this secret? Watch the presentation right here!

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How Can Machine Learning Technology Improve Customer Experiences?

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Attrecto team

How Can Machine Learning Technology Improve Customer Experiences?

In our most recent webinar, Leveraging Data to Drive Increased Personalisation and Empathy, our speakers discussed a great deal relating to relationships between brands and customers, personalisation, as well as the increased role of technology in driving CX initiatives.

Our very own CEO, Gergely Kiss was the one to speak about the vital role AI and Machine Learning play in shaping customer experiences. So, without further ado, let’s see what his thoughts are on this topic!



To better understand which areas can be improved by the inclusion of AI technology, we have to first know what we mean by customer experience and what we can do about it to make it better for our target audience.

CX is the combined interactions and emotions that a customer has in connection with a company. It is the combination of both pain and pleasure when a consumer is using a product or service – or is inquiring about one.

And what we often see (as customers) from what CX is, we usually only perceive (or encounter) the very tip of the iceberg. And in the case of the technology involved, it is mostly the front end of it.

But there’s a lot more to CX below that we don’t see, even when we’re already using the product. Customer service, billing, technical support, account management, logistics, and many more can be found below the surface.

UX is still just one of these elements. It needs to be well planned out and executed, but in no way it is the only determining factor that can result in great CX.

And it’s very important to note that the majority of customer data necessary to discover impactful insights are hidden there, but AI provides the capacity to mine this data and leverage it in the delivery of superior customer experiences.


Currently, there are two major trends in CX that are in the focus for technological developments:

Individual customer management and predictive personalization.

And these trends create a cycle. With more and more customer data being generated because of increased computer and smartphone use, it becomes easier to provide personalized services. And because of smoother and more efficient customer management and the successful anticipation of customer needs (which are then subsequently met), companies receive more interactions from which they can gain more customer data to be processed and analyzed.

As for why companies are doing this is because they have realized that treating customers well is the best way to retain the ones who are already doing business with them, improve their satisfaction levels (also needed to bring in new customers), and increase cross-selling and up-selling. No wonder that these are the top three reasons for businesses that are proactively investing in improving their CX initiatives: if all three areas are addressed appropriately, the bottom line and ROI will be impacted very positively.

As for personalization itself, it helped with bringing in a new approach to customer segmentation. Instead of groups of people based on demography, we can now have a single person be a separate, unique segment, enabling much greater personalization.

Predictive analysis helps with this, as it anticipates what users will do within the system, and then provide them with useful interactions that they want and need at that particular point of their journey.
In other words, the company is trying its best to find out who you are and what you’d like to achieve… and then deliver that to you without having to tell it yourself



There’s an insane amount of data being generated every day. Several years ago, it was estimated that 2.5 quintillion bytes of data was the daily figure and that got only ever higher since. Much of it is customer data, and whether we like it or not, hundreds of thousands of businesses could be storing data about us in data silos across the world. Data that is aimed at finding out who we are and what we are doing on a website.

While unfortunately, it’s easy to use this data for bad purposes, for now, let’s focus on how an ethical business can utilize it for understanding their customers better, building better relationships with them, and delivering them both the products/services and CX they prefer.

And that is why they need AI and its Machine Learning (ML) functionalities. To make sense of impossibly huge sets of data and use those for predictive personalization, for instance, ML is vital.

Let’s approach this from a practical standpoint and see some great examples of how ML can improve CX.

Credit Card Companies’ Anti-Fraud Algorithms

Visa or MasterCard, for example, are using online learning algorithms to detect fraud. They go through millions upon millions of data points each day with neural network systems running in the background. And these networks can ‘feel’ (or rather, compute) when something fishy happens. How? It basically ‘sees’ that the data points of a fraudulent transaction do not match the ordinary patterns that were compiled from those billions of data points.

Although the companies won’t know what the problem is in an instant, they will be alerted to the problematic incident and then investigate.


Netflix’s or Amazon’s Recommendations Engine

There was a story some years ago, where Netflix offered $1 million USD to the team that could best predict what the customers wanted to watch next after finishing a show.

Today, 75% of programs watched on Netflix are watched because they were recommended.

While at Amazon, 35% of purchases also happen because of recommendations either based on the customer’s previous choices or other people’s purchases who also bought the same item. Given the insane amount of revenue generated by purchases at Amazon, just imagine how much can be attributed to those suggestions!

Disney’s Magic Band

A physical example, but in Disney parks you simply put this band on and then use it for virtually any interaction: you can enter and pay with it, you know where you are at the moment, and you’ll also be recommended nearby programs and routes based on where you checked in previously

Progressive’s Telemetry System

By installing this system in your vehicle, you get a shot at driving down your insurance costs. It will monitor and analyse your driving habits, taking that into account when calculating your insurance premium. Drivers who engage in risky and haphazard behavior won’t see a decline in their costs, but if you drive carefully and mindfully, you will get to pay less!

Chatboss Chatbots

Attrecto sister company, ChatBoss develops chatbots for HR departments to help with recruitment and internal communication. Although chatbots are not the most sophisticated pieces of AI tech in the world yet, they can be a great help for more introverted job seekers who are more at ease chatting than making calls. This broadens the horizon for HR as well, as they get more applicants and with less work involved.



Gergely closed his presentation with these takeaways:

CX is becoming increasingly important for companies, as a major differentiating factor that makes them stand out as businesses is how they treat their customers. The focus of investments and technological advancements in all about treating the customer right, as not only that will result in satisfied customers who buy more often, but create a loyal brand image that is attractive to current and prospective clients alike.

While automation was a big thing in the last decade, in CX it wasn’t the best trend to follow. Fortunately, instead of going in the direction of over-automation, companies are moving towards making personalized experiences as unique and widespread as they possibly can.

If you’ll allow this personal recommendation, then we suggest checking out Gergely’s presentation and hearing him discuss this at length. And while you’re there, listen to Lili Kunfalvi as she talks about the way brands can build relationships that makes their customers happy, satisfied, and loyal, and Armin Gulbert as well as he dives deep to show what an effective personalization journey looks like.

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