Omnichannel marketing and smart retail

Marketers have long understood the importance of providing customers with seamless, highly personalized retail experiences, regardless of what route the customer journey takes. Today’s retail journey is exceedingly complex, winding its way through multiple channels, including ecommerce sites, mobile apps, social media and physical stores. This makes it difficult for marketers to provide customers with the right message at the right time. Modern retailers are now using AI and big data analytics to cut through this complexity to improve omnichannel marketing. Let’s explore how AI is being used to craft highly targeted campaigns, optimize customer journeys and deliver seamless experiences across online and offline channels. 

How AI is modernizing omnichannel marketing

AI and big data analytics are changing what’s possible with omnichannel marketing. These technologies allow marketing teams to analyze complex, diverse data sets at scale. This data can be used to create superior retail experiences for customers and increase market share for brands. 

Advanced customer segmentation

Modern retailers have access to vast quantities of customer data, including purchase history, online behavior and demographic information. Deep learning (DL), a branch of AI, can analyze diverse data at scale and use it to produce accurate insights and predictions. AI-driven customer segmentation removes human preconceptions, looking at the data objectively rather than through a lens tainted by long-held assumptions of who makes up the target audience. AI algorithms take segmentation capabilities to the next level, spotting patterns in the data that humans overlook, creating customer segments of unlimited size and quantity and dynamically adjusting them to reflect changing market conditions. 

Augmented and virtual reality experiences

AI-driven AR and VR technologies help customers make more informed purchasing decisions. Computer vision, a type of AI, powers the immersive experiences that allow customers to interact with products in real-world settings before making a purchasing decision. These applications enable a variety of experiences, from virtually trying on eyewear to previewing paint shades in a living room and help boost customer engagement, increase confidence in purchasing decisions and decrease returns.

Social listening

Social listening tools help companies understand how their brands are perceived by customers. Natural language processing (NLP) is a type of AI that can detect emotions and subtle nuances of meaning in written language. NLP-powered sentiment analysis tools continuously monitor social media, online reviews, and customer feedback, allowing marketing teams to gauge customer sentiment about the brand, products, or marketing strategies and respond appropriately. 

Predicting customer purchase intent, preferences and churn

Predictive analytics is used to anticipate customer behavior, including purchase intent, product preferences and the likelihood of churn. A combination of data analysis and AI, predictive data modeling analyzes data gathered from in-house and third-party data sources to uncover patterns, outliers and other key indicators to identify likely outcomes in various scenarios. Marketing teams can use these insights to optimize their campaigns to meet behavior, event-based or revenue goals.

Multi-touch attribution modeling

In the past, it has been difficult to accurately track customer interactions across various touchpoints and attribute conversions to the correct channels or marketing campaigns. ML-enabled multi-touch attribution captures every touchpoint and dimension, including campaign, publisher and offer, and then assesses the likely impact of each in driving the conversion before assigning fractional credits accordingly. By calculating and assigning credit in this manner, marketers gain a more accurate and detailed view of the conversion process, equipping them to optimize their campaigns and marketing spend.

Real-time personalization

As customers interact with brands across websites, mobile apps, social media and in-store, they create a trail of data that can go cold quickly. Real-time data analytics analyzes this data as it is created, empowering marketers to dynamically personalize content, offers and recommendations based on in-the-moment insights. By instantly responding to customer behaviors and preferences, retailers can provide a richer, more responsive retail experience. 

Enable the use of AI in omnichannel marketing

The integration of artificial intelligence and big data analytics into omnichannel marketing programs has helped brands remain relevant in this fast-changing, highly competitive industry. A modern cloud data platform provides the foundation businesses need to realize the full potential of these powerful, resource-intensive technologies. 

Bring together siloed data

The cloud data platform allows retailers to unite data previously stored in various systems across the business in a single source of truth. With data silos cleared away, marketing teams can more effectively connect the dots, tracking and responding to customers at every stage of the retail journey. 

Support for diverse data types

Today, data is generated by a diverse and rapidly changing set of sources, including application logs, web interactions, mobile devices and more. That data frequently arrives in flexible semi-structured formats, such as JSON or Avro, at highly variable rates and volumes. Cloud data platforms natively support and optimize diverse data, including structured, semi-structured and unstructured data.

Analyze streaming data

Streaming data helps marketers provide a more responsive retail experience and act on customer-generated data shortly after its creation. The latest data pipeline technology simplifies the creation of streaming data pipelines and eliminates the traditional separation of streaming and batch data. Data engineers and developers no longer need to stitch together different systems and tools to work with real-time streaming and batch data in one single system, making it easier to capture time-sensitive insights. 

Enable secure data sharing

Marketing teams work closely with other departments, including sales, R&D, accounting and production. Secure data sharing enables teams across the organization to seamlessly collaborate on relevant data. Modern marketers can access live data across the organization, control governed access, and publish data for discovery and controlled access.

Create and deploy Gen AI and LLMs models

Using the latest technology, today’s teams can efficiently and cost-effectively develop and deploy AI-enabled omnichannel marketing tools using ready-made libraries and industry-leading AI models, LLMs and vector search functionality. Teams can now build features, train models and deploy them into production quickly and easily. 

Elevate the retail experience with Snowflake 

Data-driven retail organizations are transforming their omnichannel capabilities to meet the increasing demands of modern retail. Snowflake provides the data infrastructure required for developing and deploying powerful AI and retail data analytics solutions. With the Snowflake Retail Data Cloud, retailers can provide their customers with a seamless and fully integrated customer experience across multiple channels, increasing customer satisfaction and brand loyalty.