The Value of Happy Customers

Happy customers are cheaper to service, less price sensitive and less likely to churn. Customer happiness goes beyond customer satisfaction by creating an emotional connection with a brand’s products and services. The challenge lies in understanding what makes customers happy and how much value this brings to the business?

Most organisations have developed extensive surveys and tools to analyse and assess customer satisfaction. CSAT and NPS provide time tested metrics for assessing satisfaction from customers willing to participate in a survey. CSAT surveys typically ask customers to rate experiences by the following criteria:

  • Very satisfied
  • Somewhat satisfied
  • Neutral
  • Somewhat dissatisfied
  • Very dissatisfied

But customer happiness is very different to customer satisfaction. The ability to define and measure happiness is far more challenging, though the potential benefits far exceed the benefits gained from customers who are merely satisfied.

The difference between satisfaction and happiness

Customer satisfaction implies that customers feel ok about their experiences with a brand. It’s an emotionally neutral state where customers’ expectations are being met. Satisfaction does not mean there is an emotional connection with the brand or its products and services

An article from Gallup highlights how satisfying customers without creating an emotional connection has no real value. For satisfaction without an emotional connection has no impact on loyalty or churn reduction. A customer may be satisfied with the last interaction they had with a company but would readily switch to a competitor for a better deal.

Creating an emotional connection

Cliff Condon from Forrester comments, “If brands want to break away from the pack and become CX leaders, they must focus on emotion. Best-in-class brands average 17 emotionally positive experiences for every negative experience, while the lowest-performing brands provided only two emotionally positive experiences for each negative one. Emotion is critical to a brand’s bottom line.”

To establish an emotional connection between a customer and the brand, means designing customer experiences that tap into emotional motivators such as a desire to feel a sense of belonging, to succeed in life, or to feel secure. So customers are not just satisfied with a transaction or an experience, but that sense of satisfaction is matched with a deeper emotional connection.

The benefits

It’s happiness not satisfaction that drives customer loyalty and engagement as well as the propensity for customers to recommend the brand to others. Research published in HBR demonstrates that emotionally connected customers are more than twice as valuable as very satisfied customers.

Emotionally connected customers will buy more products and services, be less price sensitive and recommend the brand and its products to others. In 2018 research from Adobe found repeat customers:

  • Are buying nearly 30% more items per order than first-time shoppers
  • Are 9 times more likely to convert than first-time shoppers
  • Generated three to seven times more revenue per visit

Other statistics include:

  • A 5% increase in customer retention correlates with at least a 25% increase in profit (Bain & Company)
  • Customers with an emotional relationship with a brand have a 306% higher lifetime value and will recommend the company at a rate of 71%, rather than the average rate of 45%. (Motista).

The statistics are compelling. Maximising customer happiness is a powerful means for maximising customer value.

Turning the tide on churn with AI, big data and your service team

Customer churn, sometimes called attrition, is when a customer switches to another supplier.

Let’s face it, times are tough for businesses, particularly energy retailers.  Retail margins are under pressure, they are at the end of the supply chain and carry the high cost of customer churn. 

While competition is a good thing, constant churn between providers is significantly detrimental to profitability and reputation.

There are a couple of misconceptions about customer churn:

  1. Price alone drives customers to leave
  2. It’s too costly to try to keep customers.  It’s cheaper to let them go and replace them with new ones

Price alone drives customers to leave

If it’s not about price, then why do customers leave?

Forbes says that 68% of all people leave a business, because of ‘perceived indifference’

While Bain & Co says a customer is 4 times more likely to defect to a competitor if the problem is service related than price or product related.

If we reflect on our own experiences and feelings as customers to large suppliers.  We can probably relate to both these statements.  As humans, we relate to being understood and appreciated and if we feel that we are being serviced too, then we are not even likely to entertain a conversation with a new supplier, let alone actually churn.

If we feel our supplier is looking after us, we stay, even at a price premium.  Who wants the hassle of moving suppliers?

Now if we get an exceptional offer from another supplier at a good price, and we are unhappy with our current supplier, then we might consider switching if the price is attractive. 

So, businesses need to focus on making us feel appreciated and on service, rather than on price.  More on this later.

It’s too costly to try to keep customers.
We often hear – “It’s cheaper to let them go and replace them with new ones.”

HBR says “Acquiring a new customer is anywhere between 5 and 25 more expensive than retaining an existing one”

The truth is, the main focus on retention is when a customer has already decided to leave, and are in the process of changing suppliers.  The existing supplier will often then attempt to win us back with an offer of a lower price and usually a gift or voucher to entice us to stay.

The cost of this retention activity, so late in the game, is high, and if it retains the customer there is usually some ill-will created.  Why wasn’t I offered this price before now?  It took someone else to pay attention to me for my existing supplier to notice me.

Retention effort at this late stage is expensive and doesn’t always work.

The trick is to be pro-active and get in early with service because it’s cheaper and more effective at retaining the customer.

Providing timely customer service is cheaper and makes happier customers

Large businesses often have huge numbers of customers and it’s hard to see who is in need of service attention.  So what should a business look out for?  What factors reflect or impact customer sentiment?

There are 3 groups of data which, when combined, are quite effective at predicting customer churn:

  1. Operational data – how the business is delivering for its customers
  2. Behavioural data – what actions customers are taking that might give us a measure of how they are feeling (their sentiment)
  3. External data – There are external data sources that could provide an early indicator that a customer may consider looking around


There is a lot of data across many systems and it’s constantly changing.  What’s the best way to monitor the situation for every customer and predict when to take action to service those in need in real-time?   

Most large businesses have data analytics programs to leverage their data to gain insights to take action.  But is this may not be the best approach for churn prediction.

An alternative approach is presented in a recent HBR article – Alibaba and the Future of Business.  This new approach has been labelled “Smart Business” and proposes running a continuous process of collecting data, analysing, learning and taking action in real-time. 

In this example, for measuring individual customer health and predicting churn, the AI does the discovery and takes action based on findings.  Historical data is collected to train the AI to predict customer sentiment and churn risk, then Operational, Behavioural and External data is collected real time to continuously assess the risk for every customer.

The AI alerts the business to when customers are ‘at-risk’ of churning and a Treatment Plan is initiated automatically to reach out to service the customer.

The Treatment Plans are designed in conjunction with the customer service team to ensure effectiveness and are optimised to ensure the cost of treatment is minimised and well below the replacement cost for that customer.

Smart Business is where all players/systems are coordinated in an online network and use machine-learning technology to efficiently leverage data in real time

When an ‘at-risk’ customer is treated, the results are fed back into the AI to continuously learn and optimise costs.

The objective with a solution like this is to leverage all that existing data for the good of the customer AND for growing the business’s revenue.

Just imagine a business knowing their customers at an individual level, knowing if they’re happy or not.  And if they’re not, knowing they have the systems in place to reach out and service individual customers BEFORE they think about leaving.

At SmartMeasures, we have built a software solution that does all this.

If this is something you would like to hear more about, please message me.

Libby Dale
Co-founder, SmartMeasures

[email protected]
0400 633 729

A pragmatic approach to getting benefits from AI

In the enterprise world, data is never clean.  Data is never complete.  Data is never without errors and it is never all in the one place.  Worse still, often the meaning of the data changes over time as business processes change or the systems that store the data are upgraded.

There is an age-old saying in the computer industry that goes:  “Garbage in, garbage out” Or in other words, if you feed a computer data with issues, don’t expect an answer without issues.

All this does not bode well for AI, which likes to consume vast amounts of data.

Traditionally, the data science approach has been to prepare the data first – to plug the gaps and correct the data as much as possible so that you are comparing apples with apples.

I’d like to suggest a different mindset.  A communication engineer’s view of the world is from the perspective of signal and noise. There will always be noise.  The goal is to maximise the gain of the signal in the presence of noise.

So, rather than over-invest in data prep (particularly since the value of the data is uncertain in the early stages), instead prioritize AI approaches that:

  • are resilient to noise and agnostic to the meaning of the data
  • can start as a small pilot then quickly scale to your entire customer base
  • continuously learn as your data changes

As we demonstrated to an Australian energy retailer recently for predicting customer churn, this approach can achieve unexpectedly high levels of prediction accuracy.

But prediction alone is not the answer. 

A system that just predicts doesn’t deliver a business outcome.  Instead you need two engines – one to constantly predict based on changing data and a second “treatment engine” to decide when and how to act or not-act on the prediction. Where is the cut-off point that maximises the benefit we receive?  In the case of customer churn, how do I minimise the combined cost of either keeping my customers or replacing them?  How do I maximise the total number of customers?  Can my system learn from my attempts to reduce my churn?

These are some of the principles we hold dear in our software.