‘Customer data is the holy grail to understanding customers and winning their loyalty!’
Today, I would hardly find someone willing to argue the statement above. And with good reason.
It is no secret that in this age of the customer, data creates business value, especially to us marketers. Each data point is an opportunity to know customers better and engage with them in what we call “a relevant dialogue”. But this opportunity is also a challenge. With the upsurge of technological advancements happening today, there is a heap of customer data piling up at an exponential pace and in most cases, just sitting there. Brands too are grappling to keep up with this wave of ‘big data’. Today, companies have more data than ever at their disposal and less time than ever to identify how to use it. This conundrum raises the question: How can marketers effectively use data as a strategic resource to surpass customer expectations and increase profitability?
Two words – Machine Learning.
Once perceived as a daunting and complex ‘thing’ that could only be mastered and used by few, machine learning today has the power to become your golden ticket to better customer experiences leading to increased customer loyalty.
Exciting, right? Sure, but how?
According to Forrester, 40 percent of loyalty marketers struggle with personalization. So as marketers, it is only natural to be apprehensive and struggle with questions like ‘How can I use machine learning for my business?’ or ‘Sure, artificial intelligence sounds fancy but can it really drive better results and growth?’
Yes, it can.
Customers today are less patient than they were ten years ago, they demand a data value exchange: hyper-personalized marketing in exchange for their personal data and a better customer experience. And until a few years ago, this seemed like a distant dream. But now, sophisticated machine learning technologies and deep learning algorithms can make this happen; so much so that these tools are powerful enough to become enablers of relevant, personalized experiences leading to customer delight.
So exactly how can machine learning use customer data to provide personalized experiences?
Here’s how –
The page you see when you log into your Netflix account is different from the one I see. Ever wonder how and why this happens? Netflix’s machine learning algorithms identify my behavior patterns from my previous interactions and cross-reference those data points with other historical data of similar customers. As a result, the algorithm knows before I do that because I watched Stranger Things, I might also enjoy Black Mirror and places that in my recommendations (Thank you Netflix, you just delivered relevancy and personalization!)
Amazon and Netflix are undeniably the leaders in this space. Their algorithms do not need human (read, employee) interaction to assess customer needs and guide them. Thanks to their phenomenal results, other brands are also realizing how pervasive machine learning can be. International fashion retailer Club Monaco started using machine learning algorithms to understand changes in customer behavior. As a result, the company witnessed an almost 5% increase in per-session revenue with personalized product recommendations and more than 70% boost in email sign-up.
Improve customer relationships
The primary premise that we at rDialogue abide by is that customer loyalty comes from a brand paying attention to the customer and acting accordingly. Thereby creating a rewarding customer experience and trusted relationship with your brand. Research firm Gartner predicts that artificial intelligence will drive 85% of customer relationships by 2020. But we don’t need to wait till 2020 to see the results, do we? Even as you are reading this, conversational commerce is making relationships better. Chatbots, one of the most powerful tools of the artificial intelligence arsenal, can have real-time conversations with customers without human intervention, learn from the data through their neural networks and convert customer pain points into opportunities. Facebook understood this trend early on and currently has more than 33,000 bots up and running in its messenger; that number will only grow. And ongoing advances in machine learning, like natural language processing, continue to make chatbots even better listeners.
A superior product is no longer persuasive enough. It needs to be coupled with better experiences to reap the benefits of an improved relationship. Brands need to understand this and start working with machine learning to support this vision.
Many times, companies are guilty of running irrelevant, generic blanket promotions that are based on guesswork or intuition rather than data-driven insights. This happens because it is often difficult to assess what a customer wants on this day at this time. Is it the free shipping? Or maybe a discount? Perhaps a free gift could do the trick? Who knows?
Well, artificial intelligence does. The beauty of machine learning lies in eliminating guesswork and identifying the right incentives for the right customers. Evolution Slimming, a health supplement company achieved exactly this by implementing artificial intelligence in its incentive strategy to generate personalized offers which resulted in a 28% potential revenue uplift.
Supplement for human intelligence
While machine learning can analyze and track way more data than humans can, we would argue that a human touch is still required. To quote Microsoft’s CEO Satya Nadella, ultimately, it's not going to be about man versus machines. It is going to be about man with machines.
Customer loyalty is at stake here and while machine learning is certainly an emerging technology, we believe that human interface is a necessity. Companies that realize this and combine the power of both are soon going to lead the race. One example is the work Performance Bicycle is doing with IBM. IBM’s Watson identifies content that satisfied customers looked at before they made their purchase and gets that information to the store associates who then uses this form of persistent personalization to attract potential customers. New Balance too has installed 3D foot scanners which use machine learning to transform feet information into data points that help store associates determine customer’s ideal shoe model.
With the onset of Loyalty 3.0, the marketing landscape is changing. It’s time we extend conversations beyond transactions. The one-size-fits-all approach doesn’t work anymore, personalization is key and brands need a data science approach to unlock its potential. Data always has an important story to tell, machine learning just gives that story a voice through improved relevance.
As Sherlock Holmes says, ‘It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to suit facts.’
It’s time we agree with him, don’t you think?