Retail and impact of emerging technologies on human experience
(10 minute read)
Key ideas and complexities of making things digital in order to solve real world problems

Retail shopping and the customer experience is going through a radical transformative change. Both online, and in stores. The combination of IoT, computer vision, data collection, and machine learning, helps retailers induce cost savings, enhanced decision-making and process automation (Chandran, 2018). But are we losing the feel good human element in the process? Not necessarily!
Artificial intelligence is impacting the world. Any device that has the ability to perceive its environment and takes actions to maximise its chance of success at achieving a goal can be said to have some kind of artificial intelligence. More specifically, AI is classed as when a machine has “cognitive” capabilities, such as problem-solving and learning by example, usually associated with the benchmark human level of reasoning, vision and speech.
In this article I will use explore the retail industry and the impact of emerging technologies. I will focus on Machine Learning (ML) which is a section of AI where we let machines learn from data.
Using Machine Learning can help in modelling and predicting human buying behaviour thereby boosting retail efficiency. The most common approach taken is to identify the next buyer by mining internet data. (Mitra, 2018) This can be done for example by looking at what people are talking about in twitter and then aggregate need projections by those who are searching for a given product or service.
Machine Learning is also an aid for mass personalisation. With the increased availability of artificial intelligence for business combined with marketing automation, sophisticated segmentation is less costly and faster to implement. (Nunes 2018).

Although humans do not follow a well-defined logic, we do have some repeated patterns. Most predictions fail, because most of us have a poor understanding of probability and and uncertainty. (Silver 2012). However, we often buy the same things, behave in a similar way and follow similar intuitions. So with machine learning we can learn the buyer’s pattern and make targeted personalisation, and are able to identify the next buyer too. Computing and classification of consumers, based on the data they actively or passively provide, the so-called digital footprints.
While we have decades of research on how people behave and purchase, machine learning allows us to nudge at scale with models which help detect some patterns that are person-specific and some patterns that are situation-specific so that the right balance can be struck.
When we look at ML algorithms, Neural networks are one of the most widely used ML algorithms. A Neural Network is a computer system modelled on the human brain and nervous system because it can create an approximation of any function. The approximation is based on data, which it learns or is trained with. So neural nets are able to learn similar responses for inputs that are similar in nature. Normally this is done manually and intuitively.
To explain briefly, ML algorithms can identify needs of prospects without meeting prospects.If we solve the same problem via Machine Learning we use Neural Network Classifier.
After the classification, there is feature extraction and matching to buyer personas, followed by labelling the data based on which of the leads took the least amount of time to covert, medium time to convert, maximum time to convert and did not convert. Once labeled, we will use supervised learning algorithm to train a standard Neural Network classifier. Next we test how good the model is with rest to the test data. The final iteration is to execute on the data, which can be done by personalisation communication or for example targeted advertising posts. (Mitra 2018)
“Machine learning improves our ability to predict what person will respond to what persuasive technique, through which channel, and at which time.”
Bernardo Nunes, Growth Tribe Academy — 2018

Potential Disruptive Impacts of current and future technology in retail on society
ML algorithms can be used to model and predict human buying behaviour. However, Nobel prize winning author Thaler (2015) explains ‘no economic model has been successfully built to predict human behaviour as humans do not behave according to economic models.’
“Retail is well-placed to benefit from the intersection of Artificial Intelligence, machine learning and big data. There is a need to manage and track a large number of items across various categories, track consumers’ shopping habits and above all, maintain a compelling brand that keeps consumers coming back. Today’s consumer wants to keep up with the latest trends, but also craves convenience; hence, the popularity of subscription boxes and online shopping.” (Chandran, 2013)
While it is easy to consider the retail shifts with emerging technology, of note, the in- store experience that is possible today using near field communication and bluetooth allows retailers to greet customers by name as soon as they walk-in and make relevant promotions on screens personalised for the individuals. Although this possible, it is not being widely used.
A disruptive impact can be on the legacy stores, which are not applying the optimising search categorisation and online listing for search and voice SEO. For example customer are using voice technology search, not just to buy, but to find stores. If a physical store does not know to set their key words in voice technology search, they will not be found by the person driving in their car and using Siri to find the nearest shoe store, for example.
Another example of impacts to society based on use of machine learning can be seen in the luxury travel market. The travel industry actually makes use of text mining (a form of natural language processing that derives high-quality information from text) to create and test recommendation systems based on the similarity of destinations. As the online travel industry picks up, and this search and presentation is used, we may be driving all travel to the same locations, in a discovery way that would not have happened organically, thus causing environmental disturbances to high socially shared areas.
Ethical considerations of solutions, data and personal privacy
“People don’t always behave logically when it comes to privacy. For example, we often share intimate details with total strangers while we keep secrets from loved ones.“ Harvard Business Review (2018)
We may remember 2018 as the year when technology’s dystopian potential became clear, from Facebook’s role enabling the harvesting of our personal data for election interference to a seemingly unending series of revelations about the dark side of Silicon Valley’s connect-everything ethos. (O’Brien 2018)
What we know, is that on average, just 65 liked Facebook Pages allows behaviour analysts to understand someone’s personality traits better than their friends do, 120 to understand them better than their family members, and 250 to understand them better than a partner or spouse. (Champion 2018).
Correct timing, relevance and trust is of utmost concern for the impact of the personalisation technology.
Personalisation value = ((relevance + timeliness)/Loss of privacy)* Trust (Nunes 2018)
Bias is a dark concern. More awaits us, as surveillance and data-collection efforts rapidly increasing and artificial intelligence systems have started sounding more human, reading facial expressions and even news stations are generating fake video images so realistic that it will be harder to detect malicious distortions of the truth.
Internet pioneer Vint Cerf said he and other engineers never imagined their vision of a worldwide network of connected computers would morph 45 years later into a surveillance system that collects personal information or a propaganda machine that could sway elections. (Fry 2018)
Contrary to futuristic fears of “super-intelligent” robots taking control, the real dangers of our tech era have crept in more prosaically — often in the form of tech innovations we welcomed for making life more convenient. Too much regulation may bring its own undesirable side effects. As is the case with profiling. (Fry 2018)
Economic value of technology
Seeing the number of big retail chains that are closing stores such as JC Pennys, Macys and Payless, it an appear that retail is dying. But that could not be further from the truth. The industry is booming in the digital space. The revolution started by companies like Amazon and eBay has led to huge challenges for the traditional retail business model, but also massive potential for retailers and consumers alike. (Fagella, 2018).
The first economic gain can be reached through increased sales. Retailers can take advantage of the AI-personalised browsing and searching experiences to tailor products to a shopper based on their activity and other shoppers.
Secondly, they can use the data to order just in time, and reduce surplus production.
This can also allow the retailer to show their best, most relevant products to a shopper from the first moment of interaction and increase conversion.
Another economic advantage is through reduction of cost and retained happy customers. We as humans have inherent needs that can not be fulfilled purely through digital means. And with the help of information sourced from AI, brands can host physical world pop-up stores where and when needed, and lower their fixed costs. This also means that there is an increasing shift towards optimisation and efficiency, and a shift away from excess and waste. Also, chatbots and virtual assistants can be used to help users find a specific item or to answer comment product or order questions quickly to putting the customer on a fast track for purchase while saving in their service department.
Reduction of content costs is significant and necessary in order to fulfil the personalisation that is possible with the deep learnings. Alibaba has an A.I. visual design platform (Alibaba LuBan) that’s capable of generating 8,000 different banner designs per second and designed over 400 million banners for a wide array of products during 2018 Single’s Day Event which is China’s version of Black Friday. (Xu 2019).
“If we assume it takes a human designer 20 minutes to design one single banner, then we will need100 designers to work non-stop for 150 years to produce the same amount”. ‘ Xu on AI created Banner Content for 2018 Singles Day
Conclusion
Machine learning can be leveraged to boost efficiency and customer engagement in retail and therefore provide greater utility towards customers and owners. Data driven results and new retail technologies are providing customers with what they have longed for, relevant products, informed offerings, fast customer service, and the ability to experience products before they arrive on their doorsteps.
Those who succeed in retail, will no longer just center around products, but need to be increasingly focused on the human-centric customer experience. The customisation can make us feel as if we are starring in our own movies, with scenes curated just for us. Using machine learning, the retailers are able to create, optimise and and scale the immersive customer centric modern retail experience — when and where we can most take use. Pop Up shops are marrying off line and online shopping experience and I believe will be a big opportunity for Brands to be closer to their customers and provide experiences.
Central to many privacy concerns myself and others have is the loss of control. Consumers may not object to information being used in a particular context, but they worry about their inability to dictate who else might get access to it and how it will be used down the line (John 2018).
In conclusion, we see that personalisation is an efficient way of influencing consumers, especially when it is powered by artificial intelligence. Retail is well-placed to benefit from the intersection of Artificial Intelligence, machine learning and big data. There is a need to manage and track a large number of items across various categories, track consumers’ shopping habits and above all, maintain a compelling brand that keeps consumers coming back. (Fagella, 2018). However, Trust is a key factor in dealing with the new world of shared information and data gathering. (Risdon, 2018).
On the whole, I am excited by the future of retail personalisation and the opportunities that customised online and pop up stores can create and I`m especially hopeful with the implications that the knowledge and availability given to us as consumers will enable more sustainable conscious consumption.
About the Author: Leslie is studying at HyperIsland Stockholm, completing her Master in Digital Media.
Biography
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