The Need for Real-Time Personalisation in CX

In today’s experience-driven market, customers not only appreciate personal touches – they expect them. Research shows that 71% of consumers expect companies to deliver personalised interactions, and 76% feel frustrated when this doesn’t happen. Personalisation has evolved far beyond using a customer’s name in an email or ad. Modern consumers want companies to understand their needs in the moment and respond accordingly. In fact, delivering tailored experiences “so they make customers feel uniquely seen, valued and understood” now requires leveraging real-time data and AI. Brands that consistently meet these expectations are rewarded with loyalty and repeat business, while those that don’t risk customers switching to competitors in a low-loyalty environment.

For marketers, this raises a critical challenge: how can one identify and act on each customer’s needs at exactly the right moment, especially when dealing with thousands or even millions of customers? Traditionally, companies have relied on surveys and aggregate metrics like Net Promoter Score (NPS) to gauge customer experience. However, surveys are infrequent, reactive, and capture only a tiny sample of opinions. They often fail to flag emerging issues or opportunities in real time and can introduce bias or incomplete insights. To truly personalise experiences at scale, marketers need a way to continuously “listen” to customers and glean actionable insights in the moment. This is where a new AI-driven approach, Observational Customer Experience (oCX), is changing the game.

From Surveys to Observational CX: Listening “In the Wild”

Alterna CX’s Observational Customer Experience (oCX) methodology represents a shift from soliciting feedback to observing it. oCX is an AI-generated customer experience metric that gauges the quality of a company’s CX without using any surveys at all. Instead of asking customers how they feel, oCX leverages AI to analyse unsolicited feedback that customers are already sharing “in the wild” – in social media posts, online reviews, complaint forums, live chat logs, and more. In essence, oCX passively measures customer sentiment from natural, organic interactions, rather than actively measuring via questionnaires.

How does this work? Advanced AI algorithms scan and decode the sentiments and emotions behind written comments that customers make about their experiences. For example, if a customer tweets “The app is super easy to use and I love the quick delivery!”, the AI can interpret this as a highly positive sentiment. Another customer’s review might read “It works okay sometimes, but often I get errors” – a mixed sentiment. A third might post “Terrible service, I’m never using this again!” – clearly negative. Alterna’s oCX engine assesses each of these pieces of text and predicts the rating that the customer would have given if asked the classic NPS question (“How likely are you to recommend…?” on a 0-10 scale). Highly enthusiastic comments might be scored as a 9 or 10 (Promoter-level), lukewarm ones around 7 (Passive), and angry rants as 0–6 (Detractor-level). By doing this for countless comments, oCX computes an NPS-like score for the brand without ever issuing a survey, and notably, these scores have been found to closely mirror actual survey results. In other words, the AI is essentially estimating your NPS continuously by listening to what customers say organically.

Sample of Alterna CX’s oCX engine in action: the AI analyses real customer comments from social media/reviews and assigns each a 0–10 score (rightmost column) predicting the customer’s likely survey rating. These individual scores feed into an overall “Observational NPS” metric.

This approach offers several big advantages. Firstly, it captures far more data than traditional feedback programs. Customers today leave a wealth of unstructured feedback online – indeed, 80–90% of all data available is unstructured text, and it’s growing by over 50% annually. Observational techniques tap into this rich vein of feedback that would otherwise be missed by only relying on surveys. Secondly, the feedback is real and authentic, not constrained by survey questionnaires. Customers tend to be more candid on social networks or review sites, providing detail on what they loved or hated, which yields deeper insight into their true feelings. As CX expert Shep Hyken notes, whenever a customer gives feedback – good or bad – “it’s a gift”, and finding a way to analyse and measure that gift is crucial for improving future experiences. The oCX metric is exactly such a tool, one that Hyken finds “intriguing” for its ability to operationalise unsolicited feedback into actionable CX improvements.

Crucially, oCX overcomes many limitations of surveys. There is no risk of survey fatigue or bias in who responds, since no one is being directly asked. There’s also no delay – the AI can continuously process new comments as they appear, providing real-time insight. This real-time aspect is vital for personalisation. If a spike of negative tweets starts trending this morning about a new feature, a company using oCX can detect the sentiment shift immediately. By afternoon, marketing and CX teams can already be working on targeted responses or fixes, rather than waiting weeks for a survey report. In fact, Alterna CX emphasizes that oCX will become an indispensable asset for extracting genuine insight from unstructured customer data, especially as younger, digital-native consumers take centre stage. Generation Z, for instance, are “dialoguers” who constantly consult and broadcast opinions on Instagram, YouTube, TikTok and more – they have fluid expectations and make decisions heavily influenced by what others say online. Traditional feedback loops struggle to keep up with this pace, but observational AI can present a clearer way to understand and serve these vocal consumers in real time.

How AI-Driven oCX Enables Right-Moment Marketing

The ultimate promise of AI-driven oCX is that it empowers marketers and CX teams with timely, granular insights to act on – essentially turning customer feedback into a real-time alert system and decision engine. By analysing every comment, review, or message as it comes in, the AI can surface critical signals: spikes in frustration about a process, emerging product issues, trending desires or praise for certain features, and so on. This enables a shift from reactive to proactive customer engagement.

Leading companies are already reorganising around this kind of real-time personalisation. McKinsey research finds that top performers in personalisation use AI-driven decisioning to respond to customer signals in real time – they leverage predictive models to determine the next best action or offer for each customer and deliver “the right content through the right channels at the right moments in a customer’s journey.” This is exactly what oCX makes possible by supplying those customer signals continuously. Rather than generic one-size-fits-all campaigns, marketers can tailor their outreach based on what customers are actually experiencing or feeling in the moment.

For example, imagine a scenario: a telecommunications company notices through oCX analysis that many customers in a certain region are posting complaints about network downtime this morning. Instead of sending a standard marketing newsletter, the company could immediately personalise its engagement for that segment – perhaps pushing out an apology message with real-time updates on the fix, or offering affected customers a bill credit for the inconvenience. Conversely, for customers who just tweeted praise about a service, the marketer might automatically send a thank-you and perhaps a personalised offer for an upgrade or referral program. These are the kinds of “next best actions at the most relevant moment” that AI enables. As the team at Genesys describes, AI can synthesize vast data – every interaction, the customer’s sentiment history, their recent actions – to build a dynamic profile and then anticipate what that customer might need next. It could be proactive support (reaching out before a minor issue becomes a major complaint), or a timely suggestion that genuinely helps the customer (as opposed to a random upsell). The engagement feels helpful, even “invisible” in the sense that the customer doesn’t have to ask for it – the brand just knows and responds in the flow of the experience.

This kind of right-moment personalisation is not only about fixing problems, but also about seizing opportunities. A classic marketing challenge is identifying when a customer is open to a cross-sell or when they are at risk of churn. With AI-driven CX analytics, signals like a surge in positive sentiment about a product can cue the marketing team to amplify that momentum (maybe by highlighting those testimonials in ads or sending related product recommendations). On the flip side, signals of frustration or repeated issues can trigger immediate retention efforts (such as a personalised outreach from a customer care rep). Equipped with AI-driven insights, front-line staff can offer tailored solutions “at the right moment instead of generic pitches,” turning service interactions into opportunities to delight or recover customers. Over time, consistently getting these moments right boosts satisfaction, loyalty and ultimately revenue – which is why companies that excel at personalisation see 40% more of their revenue come from those efforts compared to average players.

In summary, AI-driven oCX provides the listening and analytical backbone for moment-by-moment personalisation at scale. It scans the innumerable “voices” of customers across the digital world, interprets what they’re saying and feeling, and translates that into clear metrics and alerts. This gives marketers unprecedented situational awareness of customer experience quality. When experience metrics dip or spike – even within a specific touchpoint or customer segment – the team knows about it right away and can act. It transforms CX metrics from quarterly lagging indicators into a live feed of customer sentiment, empowering businesses to treat each customer interaction with the context it deserves.

Case Study: Turning Feedback into Action at Koçtaş

To illustrate how AI-driven oCX can empower personalisation at the right moment, consider the experience of Koçtaş, a major home improvement retail brand. Koçtaş (part of Kingfisher Group in Europe) serves millions of customers across 50+ large stores and an e-commerce platform, handling over 10 million transactions per year. As a B2C retailer with such scale, Koçtaş’s leadership recognised that delivering a consistently great customer experience would be critical to success. Their vision was to become a fully omnichannel, customer-centric company – meaning measuring and improving every customer touchpoint in real time across the entire journey, from initial purchase through delivery and after-sales.

The challenge, however, was that their traditional Voice of Customer programme couldn’t keep up with this vision. Feedback was collected only periodically and in limited quantities, often via surveys, so insights came infrequently and with delay. Open-ended comments from customers (for example, free-text survey responses or emails) had to be read and analysed manually, which was time-consuming and made it hard to spot systemic issues quickly. In practice, this meant store managers and support teams often learned about customer pain points too late to take timely action. As Koçtaş’s Chief Marketing & Digital Officer, Ebru Darip, later put it: “We can now identify the root cause for satisfaction and dissatisfaction almost in real-time… observe trends at each touchpoint and take real-time action” – implying that previously, such real-time insight was lacking. The company needed an omnichannel CX management capability to engage the entire organisation in a continuous improvement process and not just review historical KPIs.

The solution came in 2021 when Koçtaş partnered with Alterna CX to implement an AI-driven oCX platform. In less than a month, they designed and rolled out a seamless NPS measurement programme across 10+ touchpoints – including in-store interactions, delivery, online shopping, and call centre – with multiple feedback channels (in-store surveys, email, SMS, etc.) feeding into one system. Crucially, this platform combined solicited feedback (transactional surveys) and unsolicited feedback. Koçtaş was now capturing customer input from all angles and analysing it using machine learning for sentiment and topic trends. All front-line teams – over 50 store managers, the e-commerce and delivery teams, and call centre agents – were given access to live customer experience dashboards relevant to their domain. Instead of waiting for monthly reports, staff on the ground could see up-to-the-minute feedback from their customers and see how they were performing.

With AI text analytics automatically processing every comment, Koçtaş could pinpoint what was driving satisfaction or frustration at each touchpoint. More importantly, they set up automated triggers to enable real-time personalisation in response to feedback. For instance, if a customer gave a low score or a negative comment on a survey, the Alterna CX system would immediately flag it and launch a workflow: alerting the relevant manager, creating a case for follow-up, and even scheduling a call-back to that unhappy customer. This closed-loop system ensured that no detractor was ignored – every complaint or poor experience got a quick, personalised response to try to recover the customer’s goodwill. Such responsive outreach at the right moment can turn a failing experience into a saved relationship. Internally, Koçtaş also tied this into performance management: each store or team’s NPS and detractor follow-up rate fed into individual scorecards, incentivising employees to pay attention and act fast. Positive feedback, too, was shared in real time – giving teams a pat on the back and reinforcing what was working well.

The impact of this AI-driven, observational CX approach was dramatic. Within nine months, Koçtaş’s Net Promoter Score shot up by 60%, reflecting a huge increase in customer satisfaction. Rapidly addressing issues as they arose led to fewer complaints – Koçtaş reportedly reduced certain types of service complaints by 20% – and improved customer sentiment. The real-time insights also enabled continuous improvements: for example, if many customers mentioned difficulty finding a product or confusion with a process, Koçtaş could swiftly adjust store layouts or rewrite instructions on the website. Essentially, the organisation became far more customer-centric, weaving customer feedback into daily operations. “ML-based text and sentiment analytics” allowed Koçtaş to find root causes and trends almost instantly, and that meant problems could be fixed (or opportunities seized) almost as soon as they were identified. This case demonstrates how, with the right tools, a large B2C company turned the firehose of customer feedback into actionable intelligence – and used it to deliver personal, timely responses as well as systemic enhancements.

Koçtaş is not alone. Across industries, leading brands are using AI-powered CX platforms to similar effect. In insurance, for instance, Aksigorta’s Head of Marketing remarked that “taking timely actions with our AI-enhanced Voice of the Customer program” has been a key success factor – by monitoring all customer needs and loads of reviews, they can act quickly and see the results reflected in rising NPS. In digital finance, European lender IuteCredit unified its feedback across five countries with Alterna CX; as a result, it achieved a 10-point NPS uplift in six months (and over 18 points within a year) at alternacx.com by systematically addressing pain points that the AI analysis of detractor comments revealed. One such insight led IuteCredit to change how they notify customers about contract completions – switching to friendly SMS reminders instead of a previously problematic method – thereby avoiding unnecessary frustration and improving the experience at a critical moment for customers. These examples underscore a common theme: when organisations can clearly “hear” what their customers are saying in real time, they can intervene with the right response at the right time, often turning a poor experience into a positive one and driving significant gains in customer loyalty.

Conclusion: oCX as a Game-Changer for Marketers

In the age of empowered consumers and instant feedback, the ability to personalise engagement at precisely the right moment is becoming a cornerstone of competitive advantage. AI-driven oCX provides marketers with a powerful lens into customer experience quality, minute by minute, across all channels. Instead of flying blind or relying on outdated survey data, companies can now continuously measure the pulse of customer sentiment and act on it immediately. This means marketing can be far more agile and context-aware – tailoring messages or offers not just to the right person, but at the right time based on that person’s current experience. It’s a shift from periodic, generic campaigns to always-on, event-driven personalisation.

The business impact of getting this right is considerable. Personalisation done well doesn’t just improve metrics like NPS; it directly drives sales and retention. Customers reward brands that make the effort – over three-quarters of consumers say that receiving personalised, relevant communications is a key factor in prompting them to consider or repurchase from a brand. On the flip side, failing to meet expectations can quickly erode loyalty, especially when it’s so easy for unhappy customers to voice their discontent publicly (potentially influencing many others) or switch to alternatives. By embracing tools like oCX, marketers are essentially arming themselves with an early warning system for customer issues and a treasure trove of opportunities to delight. Each piece of feedback is a chance to either mend or strengthen the relationship, and AI ensures none of those chances slip through the cracks.

As we have seen, Alterna CX’s oCX methodology is at the forefront of this revolution. It reflects a broader trend in CX management: moving from measuring to listening, and from listening to doing. The technology to analyse vast amounts of text and sentiment in real time is here now – and it’s proving its value in the field by empowering companies to be more responsive, empathetic, and personal than ever before. Marketers, in partnership with CX teams, can leverage these AI-driven insights to ensure that no customer feels like just a number. Instead, each customer encounter can be met with understanding and relevant action, delivered exactly when it matters most. This ability to personalise in the moment will define the next era of customer experience leadership. Brands that harness observational AI to truly understand and serve their customers are poised to build the kind of loyalty and trust that competitors will struggle to match. In short, AI-driven oCX is enabling marketers to operationalise the old ideal of “right message, right customer, right time” at a scale and precision never before possible – a true game-changer in how we craft customer experiences.