Have businesses really understood what it is all about?
At Wirecard we like to name this whole process of measuring continuous streams of data coming from users to identity the decision-relevant attributes and enable our business partners offer suitable solutions, customer centricity. Why is this relevant?
A study conducted by Janrain revealed that 41.7% of respondents feel that online ads are too aggressive in following them on every device or browser, or they are fed up with being presented with annoying and irrelevant content (51% users surveyed by GlobalWebIndex in Q3 2017).
Also, nowadays, customers not only prefer to communicate with their favourite brands in a personal way, but they have come to expect personalization and they are willing to share more information in exchange of better shopping experience. Interestingly, the majority of them prefer a trusted personal relationship with a retailer, with over 66% of shoppers being more likely to respond to a personalized offer, while 53% agree to spend more time with a retailer they feel understands them, according to our internal data.
But in reality, on the other side, most retailers struggle with a consistent customer approach across all their channels, as customer data is not used effectively to personalize marketing messages, with 43% shoppers feeling that retailers provide an inconsistent shopping experience across channels.
To solve this problem, big names like Starbucks, Amazon Go, ebay, Disney and more have started analysing their smart data.
What is smart data?
By measuring continuous streams of information such as time of purchase, brand preferences, advertising reach, purchased products, payment method, coupon usage, time spent in store, social engagement, research behaviour, location, etc. retailers can get ready to analyse data – smart data.
If this isn’t enough, to get richer and more actionable insight at a granular level, some companies have adopted a variety of advanced technological capabilities, such as machine learning, deep learning, and artificial intelligence (AI). Thus, supported by these emerging techs, these early adopters are providing front-end staff with in-depth views of customers, including information such as their product-acceptance probabilities, price sensitivities, propensities to churn, and lifetime values.
This data is then structured in sets that follow the customer journey – awareness attraction, product search/comparison, purchase/payment, repurchase. Also, customer segments and ID with their relevant characteristics are underlined.
Is it real?
Take for example Amazon Go – the location is packed with hundreds of cameras on the ceiling and sensors in the shelves, which record what each person picks up. Shoppers only need to scan a QR code in the Amazon Go mobile app to be able to enter the store and buy something. From the outside, it is almost like shoplifting. But the reality is that anything that you pick up, anything that you put back, is kept track of and Amazon trained machine-learning algorithms recognize when items have been picked up using thousands of hours of footage of people grabbing items from shelves.
Also, there are some technology providers, such as Standard Cognition, that can offer retailers solutions based only on overhead cameras, not shelf sensors, making it easier to deploy in existing stores. These systems can log things/data like how often people pick up a can of Pepsi, look at its nutrition information, or whether they put it down and buy a Coke instead.
The learning point is that cashier-less systems can collect impressively detailed data on shoppers that can be analysed and translated into customised offers.
Again, machine learning algorithms are used by the German online retailer Zalando to offer a virtual fashion assistant to customers. The online assistant is named the Algorithmic Fashion Companion and can generate outfit recommendations in real time. The algorithm identifies an ‘anchor’ product based on the customer’s personal preferences, such as their wish list or previous purchases. It then builds a new outfit around this product.
By analysing 200,000 existing outfits, the machine learning solution can make recommendations to customers. However, Zalando is not willing to trust a machine entirely with style matters, as its stylists regularly tweak the algorithm to keep it up to date with current trends.
But if you want to go the extra mile for your customer, after analysing internal data specialists discovered that what customers really want is (different types of) rewards for purchases, personalized (recommendations) discounts, earn bonuses by specified activity, rewards for sharing content on social media, store-specific loyalty programs, integration with 3rd partly loyalty apps.
For example, food delivery and loyalty rewards apps can offer value to customers seeking to get more out of their engagement with a restaurant.
According to a survey, Starbucks' app is the most popular among several well-known restaurant loyalty rewards apps, with almost half (48%) of app users surveyed regularly using the Starbucks loyalty app, compared to 34% who use the Domino's app and 30% who use the Pizza Hut app.
Starbucks was an early mover in adopting mobile technology to help customers manage their loyalty programs and place orders for in-store pickup. Thus, the Starbucks app is tied to its rewards program. For instance, Starbucks Rewards members receive two stars for every USD 1 spent, which can be redeemed for free food and drinks, as well as birthday rewards and free in-store refills, in addition to the ability to order ahead and pay by phone.
There is a lot of great behavioural data in commerce and a large array of technologies, such as machine learning, artificial intelligence, mobile apps, that help us capture this data and transform it in ways never seen before. And the benefits are numerous; not only for customers, who can earn bonuses and have their needs met, but also for retailers, who can increase profitability of sustainable customer relationships.