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You’ve heard it before: “Data is the new oil.” But like crude oil, raw data isn’t useful until it’s refined. In the world of customer experience (CX), data alone won’t create loyalty—it’s how you use it to build connection that counts.
As AI and predictive analytics reshape CX, it’s easy to get caught up in tech buzzwords. But we can’t forget the real differentiator: human creativity. No algorithm can replace emotional intelligence, and it’s often a creative leap—not code—that turns data into something truly meaningful.
1. Octopus Energy: Turning Hold Music into Personal Nostalgia
During the pandemic, call centres were overwhelmed. Octopus Energy couldn’t reduce wait times, but they found a clever way to improve the experience.
They discovered that the most nostalgic song for many people is the one that topped the charts when they were 14. So, using just the customer’s phone number and date of birth, Octopus played their summer hit while they were on hold.
It was a simple data match—but it made people smile. Customers even said they were disappointed when their calls were answered too quickly. That’s the power of personalisation through empathy.
2.Target: Predicting Pregnancy Through Shopping Habits
Target wanted to identify pregnant customers early, knowing this life stage could build long-term brand loyalty.
By analysing purchase patterns—like switching to unscented products or buying supplements—they built a “Pregnancy Prediction Calculator.” It could even estimate the trimester.
Initially, this felt invasive to customers. So, Target adjusted, embedding baby product promotions within unrelated offers to make it feel less intrusive. The key wasn’t just spotting the pattern, but knowing how to act on it thoughtfully.
3. A Bank Predicts Death and Divorce
A data scientist for a U.S. bank shared how they used financial behaviour to anticipate major life events.
Spending patterns often hint at terminal illness or relationship troubles—like sudden healthcare expenses or hidden hotel stays. The bank used this data to alert relationship managers, allowing them to build trust with heirs or spouses before money moved elsewhere.
Yes, it walks a fine ethical line—but it shows just how predictive (and powerful) refined data can be when used strategically and creatively.
“Data is the new oil”—a phrase you’ve probably heard a thousand times. But like crude oil, raw data is useless until it’s refined. You can have the biggest data lakes and the smartest algorithms, but if you can’t make a customer feel something, you’re just spinning your wheels.
As agent AI and predictive analytics take centre stage in the customer experience (CX) world, the industry is buzzing with talk of data sets, hyper-personalisation, and quantum-powered algorithms. But in the race toward efficiency, many are forgetting what truly matters: human connection.
The first is from Octopus Energy, a UK based utility company. Selling one of the most commodity-based product categories there is (electricity and gas), they chose to differentiate themselves through branding and a strong CX philosophy.
During the 2020 pandemic, customer contact centres were overwhelmed. Utility companies like Octopus Energy faced increased call volumes and reduced staffing, which meant long wait times—a CX nightmare.
We all know how it feels to be on hold, every 30-seconds feeling like 10-minutes. The brand forces you to listen to either their own aggressive upselling messaging, bland muzak, or the song from their latest corporate campaign, over and over. It is the least positive element of a customer contact experience.
Octopus could not solve the wait times in the short-term. New staff could not be magicked from the air, nor could the inbound call volumes be minimised. All they could do is find a way to make their customers feel better about the wait.
The psychology behind this would be quite simple. At that age, it is a coming-of-age, hormonal, discovery summer. The music that is the backing soundtrack to that long, hot summer would be forever positively coded into your mind.
Google it now, you know you want to. What was the big summer hit when you were 14 years old, open it up on Spotify and see how you feel when you hear it.
Nostalgia is a powerful sensation. And so, the ‘Octopus – What’s Your Jam’ initiative was born. Using a very simple data set and if/then data routing, they made each customer feel special.
Your inbound phone number was instantly harvested, the system immediately matching it to your account. Nothing new there, most customer contact centres do this automatically, cutting out the need to ask you for your account number, making the process more seamless.
But once they had your account identified, all they did was program their system to automatically pull your date of birth data, and then select the relevant hit from their pre-loaded jukebox, one summer hit for every year between 1938-2002.
Every customer waiting was played the most nostalgic music, unique and personal.
And lastly, it is personal, removing the ‘everyone gets the same music’ norm.
The power of this example is in the creative moment where an executive thinks ‘what if we use this basic data to do this’? Every other bank and utility company has had our date of birth data for years, but no one had thought to utilise it in a way to make us feel good.
The initiative was so successful, callers were often disappointed when their call was answered and their nostalgia interrupted.
Our next story is from Target, and a classic.
There are several key life stages where behavioural norms are established, and in retail, locking in a customer around these moments results in a profitable pipeline. The birth of a baby is one such moment. There are a lot of products bought during pregnancy, immediately after the birth, not to mention that shoppers tend to stick to stores they know during stressful life events.
So, Target wanted to know which of their shoppers was pregnant and they asked Andrew Pole, one of their analysts, if there was any way this could be done. If they could market to them early in their pregnancy, there was every likelihood they could significantly deepen the relationship with a profitable future customer. Customer Retention 101.
With millions of data points, Pole built a surprisingly accurate ‘Pregnancy Predictor Calculator’, capable of scanning current purchase patterns for any given shopper and offering a percentage prediction, basically data-mining their way into customer’s wombs. He even had it down to identifying a shopper’s trimester.
What this case shares with the Octopus Energy example above is the genius of human creativity in aligning varied data points to a cohesive actionable insight. Data is nothing until it is refined, in this case refined so that the brand can deepen the relationship with their customer during a critical life stage.
The answer was in the data but it took human creativity and ideation to turn that data into CX insight and action.
He had spent some years working for a large American retail bank. Like the Target case above, key life stages and events also effect customer retention and profitability in financial services, particularly in the High-Net-Worth individual sector.
This segment, highly profitable for financial institutions, was his focus.
An interesting data insight with high-net-worth individuals, is that their adult children often have multiple banking relationships, banking with brands outside their parents to maintain their autonomy. The motivation is that they like to hide some of their spending from their parent, to avoid those awkward ‘I see you have bought another sports car David?’ conversations at Thanksgiving.
The problem with this, is that when that parent dies, their significant inheritance can often then be withdrawn and invested in their own institutions, causing a succession and customer retention issue for the bank.
When a high-net-worth individual dies, the bank often hears sometime after the event, often from government sources or other bureaucratic means, perhaps too late to build a deeper relationship with the next-of-kin and siblings.
If only there was a way they could know when that death was imminent? Well, there was.
Without getting into specifics, the bank was able to analyse and identify which individuals were likely to die in the next 12-months based on the data alone, and relationship managers would then be tasked with reaching out to the family members who would likely inherit.
Relationship building and early trust conversations would allow the bank to best increase the likelihood of those funds staying with them. No longer would they learn weeks or months after the death but be able to predict and invest in relationship before the event.
A similar project was initiated around divorce, another key life stage which results in significant fund withdrawal. After a divorce, one of the parties will usually take their share and bank it elsewhere, often for no other reason than ‘this is their bank, and so I will go elsewhere’.
Like the inheritance example, if the relationship managers could deepen the relationship with the non-core customer spouse in advance of the divorce, building trust and connection, then it was more likely those funds would stay with the bank post-divorce.
But how could a bank predict divorce? Well, it turns out that is all there in the data too.
Incompatibility and infidelity are the two most common causes of divorce. While predicting the first one is complex, identifying infidelity in financial spending patterns is apparently relatively easy.
While the creepiness of all this does not sit well with anyone (thus the reason I have not named the bank), this happens to you every day. Every time a product or service pops up on your Instagram feed, the one you were just having a WhatsApp conversation with a friend about yesterday, is targeted data at work. This is the world we live in, data harvested and used by brands to boost and protect their revenue and profits.
What all of these examples share is the input of human creativity in using data to make an impact to the Customer Experience.
If Octopus Energy can make their customers feel good just by using their date of birth, surely you can take a look into all the data you hold on your customers and get creative too?
Yes, AI makes things faster and we can deliver both scale and depth of connection for the first time, but ultimately true customer connection will come from the creative application of the data in making a customer feel good.
That is always the objective, better and more meaningful connections with our customers and employees.
Connection is Everything.
Human Creativity in CX
No matter how advanced your tech stack, it can’t replicate the emotional intelligence of a skilled CX strategist. Data remains inert until someone has a spark of genius—an insight that transforms ones and zeros into a moment that makes a customer feel valued. Let me share three powerful examples of how human creativity, not just technology, turned data into meaningful customer connections.1.Octopus Energy: The Power of Nostalgia
The first is from Octopus Energy, a UK based utility company. Selling one of the most commodity-based product categories there is (electricity and gas), they chose to differentiate themselves through branding and a strong CX philosophy.
During the 2020 pandemic, customer contact centres were overwhelmed. Utility companies like Octopus Energy faced increased call volumes and reduced staffing, which meant long wait times—a CX nightmare.
We all know how it feels to be on hold, every 30-seconds feeling like 10-minutes. The brand forces you to listen to either their own aggressive upselling messaging, bland muzak, or the song from their latest corporate campaign, over and over. It is the least positive element of a customer contact experience.
Octopus could not solve the wait times in the short-term. New staff could not be magicked from the air, nor could the inbound call volumes be minimised. All they could do is find a way to make their customers feel better about the wait.
Making Hold Music Nostalgic
Here is the creative genius moment. One of their executives had come across an article that outlined that the most emotive song you can hear as an adult, the one with most nostalgia potential, is the summer hit when you were 14 years old.
The psychology behind this would be quite simple. At that age, it is a coming-of-age, hormonal, discovery summer. The music that is the backing soundtrack to that long, hot summer would be forever positively coded into your mind.
Google it now, you know you want to. What was the big summer hit when you were 14 years old, open it up on Spotify and see how you feel when you hear it.
Nostalgia is a powerful sensation. And so, the ‘Octopus – What’s Your Jam’ initiative was born. Using a very simple data set and if/then data routing, they made each customer feel special.
Your inbound phone number was instantly harvested, the system immediately matching it to your account. Nothing new there, most customer contact centres do this automatically, cutting out the need to ask you for your account number, making the process more seamless.
But once they had your account identified, all they did was program their system to automatically pull your date of birth data, and then select the relevant hit from their pre-loaded jukebox, one summer hit for every year between 1938-2002.
Every customer waiting was played the most nostalgic music, unique and personal.
Simple Data. Big Emotional Payoff
I love this example for many reasons. Firstly, it is deliciously simple. Using a single provided identifying data point (a phone number), they then match the account number and date of birth and use that to provide a unique experience. In our complicated AI world, this is a good example that it doesn’t have to be overly sophisticated to have significant impact. Secondly, it is data being used to make a customer feel good. That should always be the point. Data is nothing without emotive impact.
And lastly, it is personal, removing the ‘everyone gets the same music’ norm.
The power of this example is in the creative moment where an executive thinks ‘what if we use this basic data to do this’? Every other bank and utility company has had our date of birth data for years, but no one had thought to utilise it in a way to make us feel good.
The initiative was so successful, callers were often disappointed when their call was answered and their nostalgia interrupted.
2.How Do You Know I’m Pregnant?
Our next story is from Target, and a classic.
There are several key life stages where behavioural norms are established, and in retail, locking in a customer around these moments results in a profitable pipeline. The birth of a baby is one such moment. There are a lot of products bought during pregnancy, immediately after the birth, not to mention that shoppers tend to stick to stores they know during stressful life events.
So, Target wanted to know which of their shoppers was pregnant and they asked Andrew Pole, one of their analysts, if there was any way this could be done. If they could market to them early in their pregnancy, there was every likelihood they could significantly deepen the relationship with a profitable future customer. Customer Retention 101.
I’m Watching You
In the Target system, every shopper is given a unique guest ID, and this is then matched to their credit card and every purchase. The result is that even those shoppers outside of the loyalty programme have their every purchase tracked. Target also have a baby register, and Pole looked at the historical purchase data of shoppers, as well as modelling the purchasing of those currently buying infant diapers. Simply put, he worked backwards from those he knew had babies, to see what they were buying while pregnant. Some of the findings were obvious. Women suddenly swapping out of the scented shampoo they regularly bought for an unscented variant. Women loading up on magnesium, zinc and calcium supplements, buying a purse large enough to double as a diaper bag.
With millions of data points, Pole built a surprisingly accurate ‘Pregnancy Predictor Calculator’, capable of scanning current purchase patterns for any given shopper and offering a percentage prediction, basically data-mining their way into customer’s wombs. He even had it down to identifying a shopper’s trimester.
Powerful but Creepy
With this information, shoppers were then targeted (pun intended) with promotional offers and circulars. But they didn’t like that. It was all a bit creepy. How did Target know they were pregnant when they were yet to even share this news with friends and family? In the end, Target learned to disguise the offers and coupons for baby products amongst items that were of less interest to those shoppers (lawnmowers and wine glasses), so that it just looked coincidental.
What this case shares with the Octopus Energy example above is the genius of human creativity in aligning varied data points to a cohesive actionable insight. Data is nothing until it is refined, in this case refined so that the brand can deepen the relationship with their customer during a critical life stage.
The answer was in the data but it took human creativity and ideation to turn that data into CX insight and action.
3.Death & Divorce: We Know They Are Coming
This last story (well two similar stores in one) come compliments of a data scientist passenger I sat alongside on a recent flight. We were talking all things CX and data, and he shared the following with me.
He had spent some years working for a large American retail bank. Like the Target case above, key life stages and events also effect customer retention and profitability in financial services, particularly in the High-Net-Worth individual sector.
This segment, highly profitable for financial institutions, was his focus.
An interesting data insight with high-net-worth individuals, is that their adult children often have multiple banking relationships, banking with brands outside their parents to maintain their autonomy. The motivation is that they like to hide some of their spending from their parent, to avoid those awkward ‘I see you have bought another sports car David?’ conversations at Thanksgiving.
The problem with this, is that when that parent dies, their significant inheritance can often then be withdrawn and invested in their own institutions, causing a succession and customer retention issue for the bank.
When a high-net-worth individual dies, the bank often hears sometime after the event, often from government sources or other bureaucratic means, perhaps too late to build a deeper relationship with the next-of-kin and siblings.
If only there was a way they could know when that death was imminent? Well, there was.
Predicting Death to Preserve Wealth
Financial data and spending patterns are like those magic eye pictures famous in the 1990s. Stare at them long enough and the image is there for you to see. While there is no way of predicting sudden death, modelling changes in investment strategy and healthcare related spending can identify likely terminal illness.
Without getting into specifics, the bank was able to analyse and identify which individuals were likely to die in the next 12-months based on the data alone, and relationship managers would then be tasked with reaching out to the family members who would likely inherit.
Relationship building and early trust conversations would allow the bank to best increase the likelihood of those funds staying with them. No longer would they learn weeks or months after the death but be able to predict and invest in relationship before the event.
I Know What You Did Last Summer
A similar project was initiated around divorce, another key life stage which results in significant fund withdrawal. After a divorce, one of the parties will usually take their share and bank it elsewhere, often for no other reason than ‘this is their bank, and so I will go elsewhere’.
Like the inheritance example, if the relationship managers could deepen the relationship with the non-core customer spouse in advance of the divorce, building trust and connection, then it was more likely those funds would stay with the bank post-divorce.
But how could a bank predict divorce? Well, it turns out that is all there in the data too.
Incompatibility and infidelity are the two most common causes of divorce. While predicting the first one is complex, identifying infidelity in financial spending patterns is apparently relatively easy.
Following the Financial Footprints
Everything from a new second credit card being requested to a different address, increased spending on hotel and short vacations, the difference in the geo-location of transactions of each spouse’s credit card on such short vacations are all calculated. Like the Target pregnancy calculator, the bank was able to identify likely infidelity in the data. While not a guarantee of divorce (unless the bank called the other spouse ;), it allowed the relationship managers to prioritise the customer contact and depth of relationship with the other party, in advance of what may happen in the future.
While the creepiness of all this does not sit well with anyone (thus the reason I have not named the bank), this happens to you every day. Every time a product or service pops up on your Instagram feed, the one you were just having a WhatsApp conversation with a friend about yesterday, is targeted data at work. This is the world we live in, data harvested and used by brands to boost and protect their revenue and profits.
The Future is Connection not Automation
Should those banking relationship managers have been building deep, meaningful and trustworthy connections with all their customers anyway? Of course. But with limited resources, I guess focusing on those that may take funds elsewhere soon makes sense. Did the pregnant women shopping in Target enjoy getting coupons for baby products they needed? Definitely. Context is King. Did those customers on hold enjoy singing along to their teenage nostalgia while waiting to discuss payment plans? Absolutely.
What all of these examples share is the input of human creativity in using data to make an impact to the Customer Experience.
If Octopus Energy can make their customers feel good just by using their date of birth, surely you can take a look into all the data you hold on your customers and get creative too?
Yes, AI makes things faster and we can deliver both scale and depth of connection for the first time, but ultimately true customer connection will come from the creative application of the data in making a customer feel good.
That is always the objective, better and more meaningful connections with our customers and employees.
Connection is Everything. 
