How Retailers Can Leverage Predictive Marketing to Drive Connected Customer Experiences

As retailers become more skilled at harnessing big data to build predictive intelligence capabilities, they will learn to create more personalized customer experiences and drive operational efficiencies throughout their organization. Savvy retailers already employ artificial intelligence (AI) to create distinctive and transformative shopping experiences.

For example, the Amazon Echo, a hands-free, voice-controlled digital assistant, lets you do everything from play your favorite music to reorder toilet paper. At The North Face, an IBM Watson-enabled search engine helps you find the perfect jacket by analyzing the weather, your activity level, your gender and whether you’re in a remote or urban location — all from a few pieces of information.

Predictive technologies can be useful across many different applications, like powering product recommendation systems and providing product assortment, using information from shopper radio frequency identification (RFID) and beacons to customize digital and physical shopping experiences. Predictive technology can also automate mundane retail tasks by combining advanced technologies in drone delivery services, mobile payments, checkout optimizers and connected supply chain platforms.

With these technologies, retail marketers will capture unique insights across different devices that will allow for new opportunities to message individual customers in meaningful ways. There are three significant macro trends in retailing that will disrupt the industry and will push the boundaries of what’s possible 
in business operations and customer service.

Predictive Marketing Through Machine Learning and AI

Predictive marketing moves away from broad segmentation and towards informed messaging decisions that amplify the customer journey. This is done through propensity models (also called likelihood models), which are core tenets of predictive intelligence and analytics. These models make educated estimations about a customer’s future behavior based on their previous interactions — e.g., browsing behavior, previous purchases and interests, as well as metadata about their devices and thousands of other variants. All these aspects combined paint a holistic picture of the entire customer journey. Propensity models can be used not only to maximize moments of influence, but also to provide a snapshot of a company's most valuable customers.

Agile Operations: Leveraging Dynamic Intelligence to Become a Predictive Enterprise

An Italian pasta producer needed
 to improve visibility across its entire production chain, from field to finished product. This higher visibility would not only improve food safety, but would increase transparency with customers who wanted to know where their food comes from. The company piloted track-and-trace technology on 
a limited number of its pastas and sauces. By scanning a 
QR code on the package for these products, consumers could see information on everything from where the ingredients for that particular batch were grown, to a detailed view of the item’s journey through the production process. The new tracking process provided significant marketing value and added increased compliance and quality control capabilities, as the company could trace foods throughout the whole chain of production.

Functional Integration: Converging Digital and Physical Consumer Experiences

The key to a connected, relevant experience is in the customer’s mobile device. This is where retailers can integrate both online and offline data at the user level. Customers’ smartphones or tablets link purchase paths in the home to physical stores, providing a wealth of data that can be analyzed to optimize customer intent — whether that intent is to seek advice, buy
 an item quickly, pick up or deliver, or find a different or complementary solution. Data flowing through these touchpoints allows marketers to meet personal preferences and increase convenience. Relevance and resonance will exist wherever shopper experiences can be accessed and specific customer demand can be met.

Nikos Acuña is director of innovation at Rocket Fuel Inc, a big data predictive marketing platform that leverages artificial intelligence to create meaningful brand experiences that drive results.