During the first part of this trilogy, we discussed the key challenges faced by store retailers in the path of Digital Transformation Journey. In this second part, we will discuss how to accelerate retailers’ journey by focusing on the first key area – Enable Digital Shopper Engagement inside stores.
Putting the right products, related content, and recommendations at the right time based on shopper’s buying pattern is the mantra to deliver positive digital shopper experience inside stores. At the same time, new market entrants continue to remain a threat to traditional retailers. Retailers need to put a strong focus on marking their investments and spends around reimagining digital shopper engagement to gain customers’ brownie points and tackle competition.
Reimagining Shopper Engagement Require Adoption of Smart Retail Technology
What is Smart Retail Technology?
Imagine customers receive in-store contextual offers on their smartphone as soon as they enter the store OR they can search, augment, and shop for products on a smart mirror?
Implementing these use cases require a high-speed, real-time technology which can blur the line between physical and digital contextual experience delivery.
We are talking about the buzzword known as Smart Retail Technology (SRT)
SRT is designed to give customers a faster, safer, and smarter contextual experience when shopping in-store. This technology consists of a suite of many interconnected technologies for driving in-store engagement, purchase, and higher customer retention rate.
Let’s discuss these interconnected technologies one-by-one from a step-by-step solution and use case perspective :
With the evolution of sensors especially iBeacons, the adoption of IoT devices has significantly increased in retail and other industries. These sensors can be placed in any physical space to connect seamlessly to other devices, systems for pushing in-moment offers based on context and customer. There are two specific use cases where IoT devices (other than iBeacons) can play a crucial role in understanding and engaging customers inside stores.
- Facial recognition and audience detection technology in coordination with Wi-Fi cameras and digital displays can capture an image of a customer and identify their name, age, and gender.
- Retailers can immediately view a 360-degree customer profile and check the entire shopping history along with customer preferences to tailor individualized experiences fast.
- With Smart Mirrors, customers can browse products and purchase instantly with their mobile wallet configured with the store based on NFC technology.
- Apart from this feature, customers can receive contextual recommendations inside the fitting room on the mirror and use augmented reality to “try” the additional recommendations.
- With a mobile wallet, shopping is fast, seamless, and secure as payments are tied to customers’ personal smartphone.
- If a customer places an item in the basket, a signal can be sent from basket beacon to Smart mirror displaying related items. It increases cross-sell percentage.
Image Credit – Fashion Network
- Finding customers’ footprints inside store helps retailers personalize the experience by placing products at the right space or send sales promotions and discounts. Retailers can place cart and basket beacons ensuring customer privacy and also find most/seldom visited store’s spaces
- IBeacons deliver simple offer messages ensuring there is no hard-to-sales pitch for optimizing in-flow management. This feature is extremely useful because customers might switch off in-app push notifications as they consider this as an invasion of privacy
- Apart from these devices, retailers can install digital price meter and planogram helping customers reach a particular product faster and know real-time prices
In 2016, an estimated 1.61 billion people worldwide purchased products online. Certainly, challenging online commerce with only brick-and-mortar shopping method is not a good idea. Instead, retailers can provide more engagement options to the customer if they bring online commerce into stores. To accomplish this, retailers need to differentiate their service delivery methodology and also introduce new fulfillment options.
Consider these use cases here:
Queueless shopping experience
- If customers arrive at a store for the first time, retailers can send a link to the mobile app with a FREE coupon code or surprise gift.
- Customers can set their preferences in the app to receive appropriate product recommendations along with planogram steps to find the particular product shelf. Customers can scan a product QR code and pay online using a payment gateway or mobile wallet without waiting in a queue. Retailers will get automatic order completion notifications and they do not have to manually verify every customer’s purchase.
- IoT devices like camera, smart mirror, and iBeacons will complement the smartphone experience. Cameras can identify first-time customers and send download link while IBeacons can send personalized offers on an app. Smart mirrors can harmonize the customer experience by displaying recommendations based on interaction data from mirror and shopping data from the app.
- With the launch of cashier-less Amazon Go stores, more opportunities are expected to come at the retailer’s doorstep without breaking their wallet
- Retailers can implement mobile point of sale (mPOS) system that allows consumers to pay for their goods quickly, safely and smartly. They can also use existing popular payment gateways like Apple Pay which allows shoppers to pay by simply presenting their iPhone device in front of in-store contactless readers or via Touch ID.
Smart and Intelligent Shopping
- Intelligent Shopping can be a real game-changer for physical stores. Customers can shop online from any location via the app, select a delivery method (store pick up, home/other location delivery) and payment method (mobile wallet, cash/card on delivery).
- With machine learning, retailers can find new micro-business opportunities previously not available to them. Machine Learning (ML) driven text and voice shopping assistants use Natural Language Processing (NLP) and Artificial Intelligence (AI) to automate shopping experience.
- From building shopping lists, showing present offers, generating a daily/weekly.monthly ration list to planning monthly shopping budget through a series of chat or voice commands, an AI driven shopping assistant can revolutionize the entire retail shopping model. With more data and learning, you can get strategic notifications on when to buy a particular product and save more.
- Shopping assistant bots can seamlessly integrate with the mobile app to provide next-gen shopping experience to customers at their fingertips
- With mPos payment feature integrated inside an AI driven Shopping Assistant, retailers can give a more immersive, fast, and engaging experience to customers. Customers can make a quick and secure checkout with 1-step OTP/Touch ID/Voice authentication method through the shopping assistant present inside the mobile app.
- With amalgamation of AI driven shopping assistant, smart mobile commerce, and IoT devices, retailers can engage customers with more innovative monetization methods not available in a typical online-only commerce site model
- How can retailers reward loyal customers? They can add gamification features like encouraging customers to take part in fun quizzes and win coupon codes, extra savings points. Retailers can also send surprise gifts to customers on special occasions like birthdays, anniversary etc.
How to implement Smart Retail Technology?
After discussing the above, the important question is how these technologies can be unified to work cohesively without any downtime and performance issues?
Managing a smart retail technology stack requires an in-memory computing platform that can process millions or billions of transactional data sets in a fraction of seconds and minutes.
Why do we need such computing engines?
Relational database drove computing engines process data in a sequential manner. Processing millions and billions of records in parallel may cause system failures due to excessive server load and internal memory limitations.
In-memory computing engines only scan columns in a data record which means they only look for the exact data value matching with the query. They do not scan the complete data string as compared to relational computing engines. As a result, business continuity and customer experience are unaffected.
SAP HANA is a future-ready in-memory data computing platform with hybrid transactional and analytical processing capabilities (both OLAP and OLTP). It was developed by SAP with the core idea to help businesses become part of Industry 4.0 through technology transformation.
SAP has successfully build enterprise IoT projects utilizing the power of SAP HANA. ARI, a global fleet management system provider is managing its entire vehicle fleet operations with SAP HANA and SAP Leonardo in coordination with the Internet of Things (IoT) technology.
SAP has a suite of industry and technology relevant products which can be easily configured, customized, and extended to build innovative CX solutions.
Further, these products can connect with each other through RFC middleware to send and receive transactional data seamlessly.
IoT/Machine Learning/AI driven Shopping Assistant Implementation
- SAP provides multiple technology products starting from its flagship IoT product known as SAP Leonardo. Smart retail technology solutions can be developed on SAP HANA Cloud Platform with an RFC integration from SAP Leonardo for a particular use case.
- SAP HANA Cloud is an in-memory PAAS which provides a framework for developers to build business-centric individualized solutions on top of SAP HANA database. By utilizing SAP HANA AppServices capability, developers can build complete technology stack (IoT/Machine Learning Algorithms for smart shopping and NLP/AI driven shopping assistant)
- Through SAP HCI solution, it’s possible to integrate the IoT/Machine Learning/AI driven Shopping Assistant stack with Mobile Commerce or smartphone app.
- With SAP CoPilot solution, users can chat, ask questions, and give commands just like Siri. The internal engine contextualizes, analyzes and process informal and unstructured speech to present a user with appropriate human responses in a simple and conversational style.
- SAP Hybris Commerce Mobile Module provides accelerators, tools, development toolkit, and libraries to build mobile applications for iOS and Android devices rapidly.
- With pre-configured sample apps which leverage all important mobile phone features (GPS, Camera, NFC, QR-code scanning), all the basic commerce modules like product search, recommendations, cart management, and checkout can be customized to meet in-store digital shopper engagement model.
- SAP Hybris Commerce Mobile Module also provides new fulfillment options like buying online/store pickup which is critical to implement in-store customer engagement. From UI/UX perspective, SAP Fiori ensures the customer can find and shop products through the mobile app easily.
- SAP developed SAP Fiori UI framework keeping design thinking and material design in mind. With this simplified UI/UX, customers can reach a particular screen quickly and checkout fast.
The Conclusion: Still, work needs to be done to complete store transformation!
Reimagining in-store shopper engagement is great, but retailers still need to address few important aspects to build a sustainable and profitable future-ready retail business.
First Image Credit – Mofluid