The Possibilities Of Deep Retail

Data is the new oil. This sentiment has dominated discussions about technological innovation based on efficient data processing, especially in retail. Data is the social lubricant between retailer and customer – always has been. Even before the explosion of new technology, data were leveraged to increase customer satisfaction.

Service, service, service

Waiters know the favorite drinks of their regulars. Hoteliers know which rooms their most loyal customers prefer. Service is all about making customers feel understood. Retailers have to know and predict the wishes and needs of customers for them to feel welcome – and be more willing to spend money.

The foundation for a highly individualized customer experience is data. Thanks to e-commerce and mobile commerce, there’s more data than ever before. Given that customers agreed to the processing of their data, retailers find themselves with a treasure trove of information. For example, customers’ age, their location, the device they’re on, how they found the shop in the first place, which categories are most interesting to them, and how many items they put into their shopping baskets.

All of this information is the ideal foundation to personalize customer experience, for example with personalized offers and recommendations perfectly tailored to their wishes and needs.

Next step: personalization

Personalization is the next big step in retail. Big data, artificial intelligence (AI), machine learning, facial recognition and eye tracking enable retailers to generate, analyze and leverage personal data in a powerful way. This new approach is called deep retail and opens up exciting new opportunities.

Every online activity leaves traces across the world wide web. Customers research products and services and review them on social media, blogs or in online shops. The amount of data is increasing every second, and retailers would do well to take advantage of this treasure trove of information.

By compiling all customer data, retailers can decide which information they really need, such as posts on social media, marketing surveys or customer service requests.

All service activity

When new customers create online shop accounts, they give up valuable information. In a C/4 Hana environment, the data is stored in the SAP Customer Data Cloud.

If the same customers then decide to log in using their social media profiles, retailers can leverage that information as well. In a C/4 Hana environment, all posts, likes and comments are stored and analyzed in the SAP Customer Data Cloud.

The Customer Data Cloud can also handle consent management. This is a valuable addition for service teams. If customers send a request to customer service, the email is transferred to SAP Service Cloud, categorized, stored, and ideally even processed automatically.

Consequently, SAP Service Cloud is at the center of all service activity.

Artificial intelligence

All of these processes create vast amounts of data. Artificial intelligence can help analyze and leverage them.

With Leonardo, SAP offers retailers an AI platform based on machine learning and neuronal networks which supports them in analyzing big data and optimize business processes. Machine learning furthermore enables systems to learn from experience and improve continuously.

AI fully automatically generates the necessary information from unstructured data, like comments and emails. The technology searches for repeating patterns which allow it to make computer-based predictions. The goal is for machines to learn without human interference or help and consequently create the foundation for better decision-making processes.

For example, machine learning systems that analyze customer data can accurately predict if someone is thinking about going on vacation to New Zealand. When this someone visits the shop again, the system can make recommendations based on its prior insights.

Furthermore, retailers can use machine learning to detect anomalies. For example, is there a certain region or target group that is more interested in their product? By analyzing these data sets, retailers can take suitable actions to either continue the trend or expand to other regions or target groups.

Analyzing feelings and opinions

Analyzing comments and reviews has another advantage. Customer feelings and opinions give valuable insights into why they like or dislike a product. Retailers can then use this new information to adapt their offers accordingly.

The system categorizes comments as either positive or negative, enabling retailers to create suitable offers and counter measures. In an SAP environment, this analysis is combined with SAP Cloud Platform and integrated with SAP Marketing Cloud as well as SAP Service Cloud.

In the future, there will be even more opportunities to generate personalized data. Many mobile phones already leverage facial recognition software for unlocking screens, for example.

Facial recognition and eye tracking

It’s only reasonable to think about other uses of this technology. iPhone users are already able to authenticate their identity for Apple Pay with Face ID.

In the future, it will be possible to use facial recognition software to analyze how customers are feeling and give shopping recommendations based on their current mood. This is why many retailers are particularly interested in facial recognition software as its potential only continues to grow.

Walmart has recently patented a technology that identifies the emotional state of customers when shopping in stationary stores.

Retailers also see great potential in eye tracking. Up until now, it was only possible on special screens, like in game testing or for usability tests. Now, it’s easier than ever to install devices on everyday screens to enable professional eye tracking.

The rise of augmented reality contributed to eye tracking becoming part of many different apps by using the phone’s front camera.

If retailers know which categories and offers customers were looking at the longest, the system can further personalize customer experience and recommend the right products.

If implementing this technology, retailers have to take data protection concerns of users seriously. The public, scandalous data breaches of the past years have led customers to be more cautious about data protection.

Data protection in retail

Retailers considering the possibilities of deep retail have to ensure complete transparency when it comes to customer data. They have to clearly state which data they collect, store and analyze and why.

The European General Data Protection Regulation (GDPR) is only one of many legal frameworks attempting exactly that. While it may be daunting to comply with at first, the regulation gives retailers an opportunity to increase customer trust by taking data protection seriously.

Smart retailers should know their customers better than themselves. Customers don’t want to be asked what they want; retailers have to predict their wishes and needs if they want to stay competitive, increase revenue and enjoy sustainable customer loyalty.