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Connectivity is increasingly becoming the main driving force behind the digital and mobile transformation in retail. Above all, the pace of change in the retail industry is accelerating rapidly – and showing no sign of slowing down.
Innovative Concept Studies as a Procedural Model
Allgeier‘s SAP Retail Innovation Lab is an innovative and agile environment that has set itself the goal of exploring and developing new ideas and solutions for retail in collaboration with its customers.
New process strategies for the digital and mobile transformation show how new, disruptive technologies can be put to profitable use for customers.
To take just one example: Customers can be identifi ed via their smartphone as they enter a store. Using indoor navigation technology, they can then be guided to products in which they have already shown an interest online.
Returning customers can also be recognized, and information about their purchase history and preferences helps in-store staff provide more focused and relevant advice.
IoT: Opportunities for Retail?
It was obvious at the EuroShop 2017 retail trade fair that the trend toward the Internet of Things – and retailers‘ interest in this trend – is gaining momentum. In retail in particular, the digitization of local stores and the ability to „connect“ diff erent objects to each other and to users opens up vast innovation potential that can add substantial value.
From connected, smart shelves to smart lighting to networks of store technologies and predictive systems: In a retail context, the Internet of Things should help consumers buy what they want and need.
It should not distract them. Indeed, they shouldn‘t even notice it. Specifically, smart sensors should mean that customers fi nd staff available precisely where their advice is needed.
Alternatively, customers could use mobile devices or wearables to call for an assistant. Using SAP resources and aided by technologies such as beacons, RFID, cameras and smart lighting systems, it is already possible to operate location-based marketing in real time.
Camera systems recognize a person‘s age, gender and/ or mood. Based on a recognized customer profi le, the system then works out what advertising would be suitable and can change the display on digital signage screens near to the customer in real time. Smart shelves automatically identify the stock situation.
Fitted with temperature sensors, they can also monitor refrigerators (e.g. sending alerts to a smart watch) and track withdrawals, error situations and shelf lives.
Out-of stock situations can be avoided based on predictive analytics. Customers have the advantage of being able to query exactly what is locally in stock, and can also use their smartphone to reserve products at any time.
Motion sensors, infrared sensors and beacons can be used to produce customer movement profi les, based on which product placement and store structures can be optimized. Real-time navigation in large shopping malls is also enabled by these new technologies.
Smart home technology will gradually move the point of sale into the home. In the future, smart refrigerators will automatically compile a shopping list as soon as stocks of products run low.
That may sound like science fi ction, but is actually not very far off in reality: Witness strategies such as „Amazon Dash“, a physical button that allows users to automatically reorder everyday essentials literally at the push of a button.
Mini-IoT retail store based on S/4
Based on S/4 Hana and not NetWeaver, Allgeier‘s miniature IoT store provides an attractive and intuitive illustration of some of the most promising solutions, including data-based in-store promotion and advertising that uses facial tracking.
The future of pitches to the customer at the point of sale lies in individualized off erings and the ability to display advertising content that is genuinely of relevance (and therefore interesting) to a given customer.
This future can be based on products with embedded RFID tags, smartphone or app profile data, and age and gender data. Another concept concerns itself with voice interfaces such as Amazon Alexa, Microsoft Cortina, Apple Siri, and so on.
These new technologies open up a completely new communication channel to the customer, making it much easier for customers to find and order products. Retailers, however, will have to come up with new concepts in order to keep up with fi erce competition from online retailers.
Our in-store pick-up prototype shows what this transformation might look like: It lets customers initiate an order via Alexa and then browse through a big data cluster looking for recipes on the basis of preferences, keywords and ingredients.
A customer order is created based on the chosen recipe – naturally factoring in what the customer already has in stock at home, as calculated from smart home data and their purchasing patterns.
The order is forwarded to the store of the customer‘s choice, where the various ingredients are put together. As soon as the customer approaches the store, his or her order will be placed where it can be picked up easily without waiting in line.
In the Allgeier model, the car number plate is recognized by a camera and assigned to customer orders in the SAP system. This information is then forwarded to a shelf robot, which automatically selects the goods prepared for the customer and makes them ready to take out.
Big Data and Privacy
The more historical and pre-crunched data is available to draw on, the more individually both off erings and process support can be designed. That is why we even incorporate weather data and local sporting and cultural events, for example.
This data serves two purposes: It helps the retailer to prepare tailor-made off erings, but it also helps store assistants to make informed decisions.
If the full potential of these technologies is to be exploited, it is important to understand that a high degree of transparency toward the customer is imperative – especially with a view to sensitive issues such as privacy and data security.
Customers will only disclose their data if it is handled with the greatest care and if transparency gives them a sense of security. It is also important not to use the collected data merely for „dashboarding“. All the treasure buried in big data must be dug out so that it can infl uence processes too.
In a predictive-data-driven enterprise, employees can, for example, receive automated support for personnel scheduling, procurement processes and promotion planning.
Decisions no longer have to be based on „gut feelings“, but are rooted in predictive models and backed by reliable statistical data.
A Bridge Between Inﬁnite Variables
As far as the technology is concerned, a clever architecture is needed to keep supposedly heavy investment to a minimum.
In cooperation with technology providers and working with generic middleware, smart predictive analytics and machine learning algorithms, Allgeier has built a bridge between two infi nite variables: the world of SAP and retail processes.
The potential of Retail 4.0 is huge – but only if retailers succeed in placing customers at the center of everything and genuinely adding value for them.