Customer Experience: how to improve it with Customer Intelligence and new technologies

8 min

Ever since theorists, including Philip Kotler, known as the ‘father of marketing’, highlighted the importance of customer centricity, companies have responded with strategies that focus on analyzing customer needs rather than merely focusing on the product or service. A change of course that has led to Customer Intelligence: support for organizations engaged in the complex path of defining the customer journey and improving the customer experience. What are we talking about? Analyses that make the customer more satisfied and inclined to buy. Read on to discover how valuable their contribution is to increasing sales and customer loyalty.

What is Customer Intelligence (CI), and how can it enhance customer satisfaction?

Customer Intelligence (CI) is a multi-step process that begins with collecting data, which is then analyzed and processed to produce insights. In practice, it gives us aggregated data that outlines the purchasing behavior of customers, current and potential, their needs, and preferences. This work makes use of Artificial Intelligence and its valuable contribution to the process of transforming and interacting data from multiple sources.

Moreover, this approach improves Customer Relationship Management (CRM) which, through the nurturing of long-term relationships, ensures trust and loyalty in the relationship between the customer and the company and enables unprecedented personalization of service. CI makes use of advanced tools and techniques, such as predictive analytics, sentiment analysis, and customer segmentation: valuable elements when trying to predict buyers’ future behavior and anticipate their needs.

This is the only way for companies to speed up decision-making processes, develop products and services more in line with customer needs, and build stronger, longer-lasting relationships. These aspects are crucial considering the speed at which the modern marketplace travels.

What aspects should a company monitor for effective customer relationship management?

Understanding the preferences and buying habits of potential and current customers is the foundation of any successful marketing strategy. A customer-centric organization will utilize technology and resources to gather data and insights relating to interactions with customers.

This approach is known as Customer Experience (CX): the set of techniques and tools deployed by an organization to deliver better customer experiences and value. It is a world that includes emotions, feelings, and cognitive responses. Achieving good CX means having worked well on the path that accompanies the customer along the road to purchase and customer loyalty.

In fact, winning customer acquisition does not end with the purchase, but the goal, which companies that want to last, is customer loyalty. This is achieved by constantly monitoring Customer Satisfaction, i.e. the satisfaction or exceeding of customer expectations regarding the product or service you sell. We can measure this value through surveys, reviews, and other feedback. A commitment that will be reciprocated with loyalty and repeat purchases.

Finally, to be able to say that we really know our customers, it is important to outline a customer journey, i.e. to understand what and how a customer has interactions with your company: from the moment they discover it, through the purchase, to after-sales service. It can start, for example, with simple awareness of a product or service and continue through stages such as consideration, purchase, use, and loyalty. Mapping the customer journey helps companies identify critical points or opportunities along the customer’s journey, enabling them to optimize interactions and improve the overall customer experience.

How to combine strategies and technologies to improve the customer experience?

Today’s digital customers are often more knowledgeable than the ‘analog’ ones about certain products or services. This raises their expectations considerably. Satisfying them is not easy. But companies must try if they do not want to give away potential customers to competitors. Where software or the availability of resources does not arrive, new technologies such as Artificial Intelligence do.

AI analyses huge amounts of data in real-time, allowing companies to anticipate customer needs and customize offers. At the same time, through intelligent chatbots, companies can guarantee assistance and support with fast and relevant answers h24.

In addition to AI, there are platforms such as iPaaS, or ‘Integration Platform as a Service’, which enable the seamless integration of different systems and applications. In this way, the customer will have a seamless experience while the company will have a 360-degree view of the customer, leveraging accurate and up-to-date information. Another software to support the collection and unification of customer data is the Customer Data Platform (CDP) which can create a single, consistent, and complete view of each customer.

If we combine this with an omnichannel strategy, we can truly say that we have placed the customer at the center of our business. Offering customers the opportunity to interact with the brand through various channels while maintaining consistency and quality of service not only improves their overall experience but also strengthens the brand’s reputation.

Customer Experience

How is data collection related to the customer experience?

Let us now look at how we can manage the data we collect from our customers’ interactions. So let’s talk about Customer Data Management (CDM), i.e., the collection, segmentation, analysis, and use of customer data in an efficient and secure way. Management that, with AI, has been greatly enhanced, speeding up the various steps and optimizing the entire process.

How do you get customer data?

Companies that define Data Governance upstream, with the strategies and policy to follow, will achieve a better Data Management strategy. The first point to start with remains the collection of data from various sources.

For a global view of the Customer Experience, we can draw from:

  • internal sources: such as CRMs and all online or recorded conversations of interaction between customer and company, but also data from e-commerce, customer service, and e-mail marketing;
  • external sources: demographic data, psychographic data, market trends, and social media behavior.

This is data that concerns the demographic, behavioral, and psychographic sphere of current or potential customers and that companies collect through emails, phone calls, and IoT devices, but also more advanced technologies such as site heat maps, eye-tracking, surveys and market research, and website cookies. Such raw data, without intelligent software, would take a biblical amount of time to be transformed into useful information.

Software not only integrates data from different sources to give us a unified view of the customer, but it also cleans the data. When we eliminate repetitions, inaccurate, incomplete, obsolete, or irrelevant data on a huge amount of data, it is obvious that the effect will be a considerable time-saving in classification. Not only that, but we will also obtain a statistic that is much closer to reality. The next step, after collection, is the segmentation and classification of the data.


Knowing the customer is essential for an effective marketing strategy; a company geared towards this will invest in AI resources and technologies to improve the customer experience. Share on X

How we classify and segment the data we receive

Any customer we wish to acquire or who is already loyal to our services or products may be classified according to:

  • Demographic data: age, gender, education, occupation or income;
  • Geographical data: information about geographical location;
  • Psychographic data: information about the customer’s values, interests, and personality;
  • Behavioral data: information about buying patterns, preferences, or brand loyalty;
  • Transactional data: relating to transactions carried out with us.

Finally, we can know how they interact with our business through the channels we provide.

This information is stored in data warehouses, data lakes, or other storage solutions that have robust IT security systems and comply with all data protection laws and regulations (such as GDPR).

What is the purpose of this classification and archiving work?

Here is an example. If you are planning to launch a new product nationally, it is important to monitor key data to maximize the effectiveness of the campaign. For example, you should look at demographics such as age, gender, and income to identify your target audience. At the same time, you should analyze purchase history to understand which products have been successful with specific segments. Geographical information, such as city or region, can also help inform logistical and marketing decisions. Finally, by monitoring customer interactions with previous campaigns, such as email open rates or social media interactions, you can adapt your communications to make them more persuasive.

Therefore, in addition to the classification, it is useful to perform a similar segmentation of customers according to data relevant to my business. The main segmentations are based on:

  • Demographic: Based on factors such as age, gender, income, education, etc. For example, a cosmetics brand might want to target women in their 20s and 30s.
  • Geographic: Focuses on geographic location. A segmentation that might interest, for example, a municipality for a public communication campaign in a particular region or city.
  • Psychographic: Focused on values, lifestyle, or personality. For example, a campaign for eco-sustainable products might target individuals who are sensitive to environmental sustainability.
  • Behavioural: Based on purchasing behavior, brand loyalty, usage rate, etc. A useful subdivision for promotions: discounts for loyal customers, bonuses for reaching a purchase threshold, and word-of-mouth rewards for friends and family.

The effective combination of data classification and segmentation in CDM will allow us to better target our marketing, sales and customer service strategies. We will thus be able to offer a more personalised and relevant experience to the customers we already have or want to win over.

The data analysis phase to generate insights

Once we have classified and segmented the data, we have more structured information, but that is not enough. If we want to gain insights, we need to extract buying patterns, preferences, or customer behavior from our stored databases. These insights will guide our future strategic decisions.

At this stage, Artificial Intelligence will show its full potential by aggregating information based on the results we would like to obtain. Analyses will be:

  • Descriptive: i.e., an overview of our customers’ behavior, able to highlight the purchasing patterns and interactions chosen to interface with our products or services.
  • Predictive: this is where the information we gather is used to predict future behavior and trends, allowing us to anticipate customer needs.
  • Prescriptive: where we go beyond simple prediction to obtain suggestions for specific and useful actions to maximize opportunities or mitigate risks, thereby guiding business strategies into the future.

In summary, this process will transform raw data into actionable information for real-time decision-making; it will provide a comprehensive view of our customers and their behavior; it will give us insights that will help us build stronger and more lasting relationships.

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Using and Sharing Data and Collecting Feedback

This is the phase we could call the harvesting of the seed. The one that justifies all the previous work and makes the data collection worthwhile. Indeed, the application of the knowledge gained will make business strategy, marketing, sales, and customer service precise and timely.

For example, suppose we want to make sales forecasts. To do this, we need to look at historical sales data and analyze customer behavior patterns. This step will help us to forecast future sales, but also to manage inventory and resources efficiently. We will need to track how our customers navigate between different touchpoints, such as the website, mobile app, or physical store, to optimize their experience. Indicators will show us how satisfied customers are and whether they would recommend our company to friends or colleagues.

These examples show how the data obtained will be useful to multiple departments and should therefore be shared once the analysis phase is complete. To ensure that everyone has a clear and up-to-date view of the customer, this information should be presented in a clear and concise way (dashboards, alerts or customer journey maps).

Now the flow seems complete, but there is actually one last link that closes the circle: feedback. Whether it comes from our customers or from a failed process, feedback is a powerful tool for optimizing future strategies and actions. In e-commerce, for example, we find that customers sometimes abandon shopping carts. Finding out why and developing strategies to limit this is crucial.

From all this, we can deduce that AI will enable companies to anticipate customer needs, personalize interactions and respond to requests in real-time. The precision and speed with which AI analyses customer data will turn every experience into a unique and meaningful moment. Looking ahead, the integration of AI and customer management will not just be a competitive advantage. It will become a necessity for those who want to continue selling in markets that will be crowded with competitors.