Consumers keep altering their behaviors to the changing surroundings in order to fulfill their endless needs best. Their expectations and needs increase over time to reach new levels of comfort and contentment. In contrast to every C-level manager racking their brain over the future of retail, consumers don’t. But in the end, their expectations are shaping the future of retail. They are getting used to on-demand services, demand new product developments on a more frequent level and expect the customer experience to be flawless and consistent over all touchpoints. These high expectations are the true drivers of innovation within the retail market.
It is probably not the first time you hear digitization has impacted the connectiveness between individuals on a global scale more than ever before. Country borders disappeared, which enabled retailers to increasingly operate on a global level. Giants like Amazon, JD.com and Alibaba are rapidly scaling and starting to take over the global e-commerce market. Following this globalization trend, the relationship between retailer and consumer is changing. The effort and time needed to compare and switch between different retailers became negligible because of the increased market transparency due to the rise of e-commerce and comparison websites. This provided the consumer with better insight in competing offers and more freedom of choice. Decreasing brand loyalty and forcing retailers to focus on delivering true value to their consumers, or else they end up in the downward spiral called the price war.
Only few industries are evolving as fast as the retail industry. Resulting in new challenges and innovations on an almost daily basis. In this article we will highlight five trends that are on the retailers' 2019 innovation calendar. More specifically, this article will outline how Data Science and Machine Learning techniques are here to help to overcome these challenges and to be able to fulfil customer expectations in the upcoming years.
In 2019, voice-analytics is going to change the way consumers are interacting with web-interfaces as adoption towards these technologies is increasing. On top of the existing transactional applications of voice-assistant technology, we will see the entrance of conversational voice assistants soon. Technologies in the domain of Machine Learning and Artificial Intelligence are enabling voice-assistants to truly mimic a physical store representative and guide the customer through the online assortment to find the product the customer is looking for.
In October 2018 the news came out that Google Voice Assistant is now available in Dutch. Many retailers are now starting to experiment with voice assistant applications. Most of these projects, however, focus on transactional features of voice assistants. In these cases, consumers give voice commands to the web-interface which in turn executes them correctly. For example, to cancel a hotel reservation or to add products to the shopping card. This changes the way consumers interact with web-interfaces from written to spoken but does not offer new or improved services that help the consumer forward in their shopping experience.
By providing the consumer with advice and make them offers that perfectly fit to their individual needs, customer interactions with voice-assistants will change from transactional ‘Buy the Samsung EF2013912 TV’ to conversational ‘I am looking for a new television, what do you have in store for me?’. The voice assistant can react with relevant advice or ask deepening questions to dive deeper into the individual needs. This will benefit the consumer in several ways, consumers will experience instant personalization based on the context they provide to the smart algorithms and they will save precious time because they don’t need to browse through the endless variety of products an e-commerce web shop has to offer. Making the website experience easier and more convenient.
For retailers to reach this level of instant personalization a profound and clear image has to be created of each individual customer. This can be done by integrating and combining data of customers from various touchpoints in the customer journey. For example, voice-assistants can provide the most relevant recommendations predicted by the recommender engine in use.
Conversational assistance not only will have added value in the online environment, with the use of smart speakers’ consumers are also benefiting from instant personalization in brick and mortar stores.
Augmented reality will become of real added value to the retailer in creating experiences customers will leave their homes for. The possibilities for in-store experiences are endless. The true value is in combining new upcoming technologies such as beacons, RFID and audio-visual technology with Artificial Intelligence and Machine Learning algorithms which enables the retailer to integrate the insights from the online environment and the offline environment.
THE EXPERIENCE CENTER
But what about the relevance of the brick and mortar store? In the first six months of 2018 Dutch consumers have spent 11% more in online shopping than in the same period one year earlier. The Thuiswinkel Marktmonitor expects this trend to continue in the upcoming years as consumers increasingly shop more in digital spaces. On the other hand, we also see large e-commerce parties like Zalando, Amazon and CoolBlue opening physical stores. This seems counter intuitive, but as the online channel is merely focused on transactions, the physical environment of the retailer will become a meeting place where customers can interact with - and experience - the brand and her products in real-life. Focus will shift from transactions to connecting with the customer and establish long-term relationships.
AUGMENTED REALITY IN RETAIL
Augmented reality will enhance the way businesses communicate with consumers and display products in the offline environment. Smart displays are an outstanding example that can be used for endless use cases. Smart displays make it for example easy to dynamically change the products promoted in stores. Based on the offline and online shopping behavior from customers the products promoted in-store can be adjusted on a shop level. Do not let the marketer decide which products should be promoted in every store, but let the algorithm decide which product should be showed in which store at which time frame to match them with the needs of your shoppers in place and optimize conversions.
As personalization in the offline environment will become more and more personal, smart mirrors can also be leveraged for providing in-store recommendations. Smart mirrors can for example be used for providing instant personalized fashion advice. The smart mirror has a profound understanding (based on online clicking and conversions data) on what’s often bought together and is able distil a ‘taste profile’ just as the store representative can do.
Another use case comes from Alibaba which has partnered with the shopping mall Intime to launch a smart mirror by which customer can virtually try different make-up looks, and if customers like the product, they can purchase the products via a vending machine by scanning a QR code. This use case also shows what true omni-channel stands for, not only integrating the online and offline environment, but looking for opportunities to sell your products to your customers in places that were previously untouched. Where do your customers spend their time? Where might your brand presence be of added value? These questions should be in the mind of the modern retailer.
Augmented reality will pave the road for deep retailing in which smart retailers will know their consumers better than themselves. In the near future technological advancements will make it possible to get access to more meaningful personal data sources, such as emotional data, eye tracking and even brainwaves. Based on intelligent sensors a smart mirror can for example recognize facial expression to estimate age, gender and mood. By using this information, targeted and personalized ads can be showed. Another inspiring example comes from Ebay’s experimental shopping experience ‘The Art of Shopping’. Visitors in the experiment wore brainwave monitors while watching the selected artworks for the experiment. Their brainwave responses to each piece of art was measured. Based on the insights from their subconsciousness the participants received a personalized report and a digital shopping cart containing the items that were best matching their individual profile.
As new technological developments come with revolutional new applications that could be of great value to the retailer, we should bear in mind the ethical consequences and privacy of the consumer. Consumers should always be aware of these new forms of advertisement as they are highly influenceable, especially as they target the subconscious. On the other hand, the modern consumer seeks for personalization and these technologies can truly live up to that. So, implementation should always be beneficial to the customer and respect the privacy of the customer.
By now, we start to realize that we are not competing with chatbots and that it’s not as simple as ‘human agent or bot’. Bots and human agents really need to work together and utilize their own strengths. Bots are good in analyzing vast amounts of data and are able to increase the efficiency and effectivity of their human colleagues and give the human agent a good starting point by suggesting answers, follow-up actions and even predict the next question that will be asked to be able to help the customer proactively. But still, human agents need to give a human touch to interactions dealing more complex and emotional situations. Customer service centers should find the optimal balance between efficiency and personalization.
As a result of the increased workload (growth of e-commerce) and complexity (Increase in number of channels), Gartner predicts that 25% of customer service operations will use virtual customer assistance by 2020 (Gartner Customer Experience Summit, 2018).
We’ve already seen chatbots taking over simple first line customer interactions, automating the conversation between consumer and company on a basic level. In contrast to traditional chatbots, that provide pre-defined answers based on triggers and/or cues, intelligent assistance can offer your employees a helping hand in efficiently handling interactions while maintaining a personal touch by suggesting answers based on the collective memory off all previous service tickets. In this way the algorithm does not take over the conversation but is providing the human agents with relevant input to enhance the speed and accuracy of the conversation.
As Natural Language Processing technologies are developing at a rapid pace, bots will become increasingly better in understanding context, reasoning and remembering. Resulting in human like answers, that will be hard for the customer to distinguish from an answer given by a human agents. Because of this rapidly evolving technology it is expected that in the coming year bots will play an increasingly important role in customer service centers, taking also care of more complex customer interactions.
In this blog about customer service we dive deeper in the how this technology is going to revolutionize the customer service centers in the near future.
Next day delivery was a couple of years ago unthinkable. Nowadays, next day delivery is the market standard and timeframes of delivery getting shorter and shorter. Retailers are fiercely competing on shorter delivery times, because it is a crucial decision-making attribute while shopping online. It can make the difference between a sell or no sell. Retailers are fiercely competing on shorter delivery times, because it is a crucial decision-making attribute for consumers while shopping online. As aforementioned expectations of consumers are on the rise, consumers don’t want to wait for long for their products bought online, especially when they need a product last-minute for example when they forget to buy a present for a good friend. In 2019, more and more retailers will offer same day delivery to gain a competitive edge and meet the expectations of the modern consumer.
Advancements in Data Science and Machine Learning are closing the technological gap to actually make same day delivery possible. Retailers are already able, by deploying intelligent replenishment, to adapt the assortment (every single SKU) and order quantities on a shop level to match the needs and behaviors of the customers in a specific geographical area based on consumer predictions. These innovations will help e-commerce retailers to bring their logistic processes to the next level. If consumer predictions can be leveraged, it is able to predict in which regions which products will be sold. Distribution center replenishment can be optimized on a local level based on these predictions. This enables the e-commerce retailers to have their products closer to the customer and offer same day delivery for these products.
Now the question arises how this trend will evolve in the coming years. We already see that retailers are searching for innovative ways to become less dependent on buyers being at home at the moment of delivery, as consumers don’t want to wait for their product to be delivered. An innovative application that is solving this problem is in-home delivery. In-home delivery is based on digital locks, where the deliverer gets a temporal code to unlock the door of the customer.
What will become the role of the retailer based on these innovations. Can we expect that retailers for example deliver our groceries all the way to the refrigerator? As consumer expectations are constantly changing, these developments aren’t that unrealistic.
The past years, the topic of an integrated customer journey is widely discussed by many retailers. Practice, however, often tells another story. Business decision-making is often still centralized among the different silos in the organization and based on the information available within these silos. Pricing is still the responsibility of the Pricing Manager, assortment of the Purchasing Manager and promotion of the marketing Manager.
THE ORGANIZATIONAL MASTER ALGORITHM
We have seen improvements in data warehousing and orchestration. At many retailers all relevant incoming data from each customer touchpoint is centrally available to each part of the organization. Which is a major step forward compared what we saw five years ago, since all departments now make use of the same data for decision-making. Nevertheless, Data Science and Machine Learning reached a level of maturity, in terms of scalability and intelligence, that breaks down these traditional and old-fashioned silos. It now becomes possible to optimize business decision-making on an organizational level, focused on revenue, margins and profits instead of the KPI’s of individual department managers. All algorithm-based solutions from the different silo can be connected to each other in such a way that there is only one ‘master’ algorithm that prescribes the optimal actions to optimize the selected KPI’s on an organizational level. This ‘master’ algorithm takes into account the influence that the different outcomes of the different solutions have on each other, such as price on demand, recommendations on assortment, assortment on demand et cetera.
This will not only significantly optimize business decision making, in the end the retailer is able to deliver a consistent customer experience, in which every consumer touchpoint is aligned with his or her individual needs and behavior. In sum, this organizational shift in thinking is making a truly customer-centric business model reachable.
EMBRACE TECH DEVELOPMENT
The retail industry will keep evolving at lightning speed, to stay relevant as a retailer it is from utmost importance that you will embrace these new technologies right now. These 5 trends will guide you with insights and capabilities to grow as a retailer in 2019 and beyond. Interested in how we can help you leveraging the Data Science and Machine Learning opportunities? Get in contact with one of our retail experts and start now.