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Personalization in the retail industry

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Today's digitally savvy consumers know they can have it all. From product research to purchase convenience, shipment tracking to return optimization, customers use digital means wherever possible. Businesses are then expected to react to all of their needs and wants for personalized services. But shoppers are increasingly concerned about privacy in the wake of corporate and social data breaches.

As consumer behavior and expectations continue to evolve, retailers must find new ways to understand and meet them. Delivering personalization across multiple channels to increase the relevance of offers and improve customer experiences is no longer just a competitive advantage. It's now imperative to survival in the retail business.

Business outcomes of personalization

Deep personalization gives retailers what's needed to thrive in today's market. By developing a single view of customers, they gain the tools to engage customers at the right time and place. This view also allows retailers to provide the right content, context, messaging, and offers. The positive impacts have the potential to reach many areas of the business.

  • Democratize insights: Identify customer needs with analytics and digital behavior triggers. These insights empower retailers to put products in front of the people most likely to buy them.
  • Grow revenue and customer lifetime value: Deliver relevant real-time offers and insights to help cross-sell products based on unified data.
  • Enhance customer loyalty: Detect and respond to changes in customer behavior and tailor interactions to individual needs to drive enhanced loyalty.
  • Improve marketing effectiveness: Generate a deep understanding of customer needs and interact with them according to their unique preferences to boost engagement.

Top challenges for personalization

Integrating the mechanisms required to deliver personalization into existing retail processes is complex. It exposes significant challenges for many organizations.

  • Data silos and lack of data governance make it hard to get a 360-degree view of consumers across channels.
  • A lack of insights acquired through AI and machine learning model development. This lack limits the ability to respond to changes in customer behavior and tailor interactions to individual needs.
  • Ineffective targeting of advertisements and product information negatively impacts conversion, cross-sell effectiveness, and customer satisfaction.

It's important to integrate the data across all applications, systems, and external data signals into a single view of the customer. This view maximizes the value of modern customer engagements. Algorithms and heuristics can then be applied to determine the best actions.

How Microsoft Azure supports retailers in delivering personalization

Along with our partners, Microsoft enables retailers to overcome the challenges associated with personalization. Azure makes it possible to provide consumers with coordinated, personalized messaging. Azure also provides offers through their preferred channel mix and touch points. These offers build deeper brand connections and extend customer lifetime value.

Here's how our offerings use and augment retail data to enhance personalization:

  • Determine data inputs: Azure can use a wide variety of both structured and unstructured data.
    • Customer data
    • Sales or point-of-sale (POS) data
    • Online transaction data
    • Campaign or offer data
    • Product data
    • External data, like social media
  • Ingest data: Once inputs are determined, use Azure Data Factory to ingest data, and then Azure Databricks to prepare it. Ingested data is stored using Azure Data Lake Storage.
  • Accelerate insights: After data has been ingested and prepared, the Azure Synapse Analytics engine acts as a data warehouse. From this warehouse, data can be modeled and then served up as insights. Azure Synapse Analytics is up to 14 times faster and costs 94 percent less than other cloud providers. These advantages enable retailers to accelerate insights needed to support personalized offers and experiences while managing costs. The machine learning capabilities of Azure Databricks are also used at this step. These capabilities continuously train data and run up to 10 times faster than Spark.
  • Visualize personalized recommendations: Integrating with Power BI enables retailers to visually surface insights as personalized recommendations and promotions. These insights empower employees across the business to enhance customer service.
  • Discover patterns and monetize strategies: Azure's AI and machine learning capabilities provide intelligence across large data sets, enabling retailers to discover patterns and monetize strategies. This intelligence provides the knowledge that retailers need to continuously grow revenue, enhance customer loyalty, and improve marketing campaign effectiveness.

Personalization success stories

  • Discover how Home Hardware used Azure to unpack its data and create personalized customer experiences.
  • Read about how ASOS used Azure Machine Learning to surface targeted recommendations, combine solutions, and develop a collaboration structure for its data science teams.
  • Learn how Walgreens Boots Alliance found the sweet spot for reaching customers.

Next steps

To learn about another strategic driver for retail customers, see Omnichannel optimization.

The following articles can guide your cloud adoption journey and help you succeed in the cloud adoption scenario for the retail industry.