Retail promotions can generate a quick increase in sales, attract new buyers, and increase engagement with existing customers, but these promotions often don’t reach their full potential. Poor personalization, suboptimal product pricing, or the inability to accurately predict demand result in missed opportunities. Today, data analytics plays an important role in helping marketers optimize their retail promotions, providing them with the insight they need to create more profitable campaigns. In this article, we’ll explore the value of modern data analytics in retail and the cloud infrastructure required to support an effective analytics program.
Why Data Analytics Is Crucial for Optimizing Retail Promotions
Data analytics reveals the truth of a situation and offers vital clues to how a marketing team can improve its results. With deeper insight into the needs and preferences of customers, an accurate picture of customer behavior along the buying journey, and the performance of past campaigns, retailers can more effectively optimize their promotions. Here are just a few ways data analytics can help retailers.
Big data makes it possible to create a highly personalized profile for individual customers. Sometimes referred to as the Customer 360, this type of profile is built using in-house data, such as past purchases and level of engagement with prior promotional campaigns, as well as external data including social media activity, data exchanged with ad partners via data clean rooms, and data purchased via third-party data marketplaces. Using data analytics tools, retailers can extract the insights required to create hyper-personalized, highly effective retail promotions.
More accurate targeting
Advanced data analytics tools allow marketers to segment customers with fine-grained precision based on a variety of factors. By taking into account demographics, geographic location, interests, patterns of engagement with the brand, average purchase amount, and other factors, retailers can better identify the individuals who are likely to respond to a promotion.
Advanced data analytics helps marketers optimize promotional pricing, ensuring retailers maintain healthy profit margins. By properly accounting for a range of variables, including customer price sensitivity, competitor pricing and promotional strategies, and company-specific factors, marketers can optimize their promotional pricing at the individual product level.
Predictive analytics powered by machine learning can aggregate and analyze massive amounts of historical and trending data, helping retailers better understand when demand is likely to increase for any given product. This knowledge helps marketers optimize the timing of promotional offers. Beyond this, retailers can use predictive analytics to spot emerging trends, allowing them to capitalize on new opportunities.
Real-time performance monitoring
Data analytics allows businesses to optimize their marketing ROI by enabling them to dynamically track the performance of in-progress campaigns. By monitoring performance in real time, marketers can dynamically adjust their targeting, marketing channels, and other factors to maximize the campaign’s effectiveness.
Key Data Infrastructure Requirements for Retail Analytics
The modern analytics programs behind today’s highly optimized retail promotions rely on the power and performance of the cloud. The following four components are essential for creating a reliable retail analytics strategy.
A single source of truth for all relevant data
When data is spread across multiple source systems, it's impossible to gain a clear, cohesive picture of individual customers and their behavior. Traditional approaches to data storage weren’t designed to handle the volume of data required for powering today’s retail promotions. As a result, actionable data ends up unused and locked away in databases or software programs. Siloed data is arguably the most significant barrier to creating coordinated, highly effective retail promotions.
A cloud data platform solves this problem, providing marketers with a single source of truth for all their data. With the ability to store and process structured, semi-structured, and unstructured data from a variety of sources, businesses can capitalize on relevant data that are often overlooked, including social media conversations, videos, and images.
Easily scalable data storage
With flexible, on-demand scalability, a cloud data platform allows retailers to store their data at the scale required to meet their marketing objectives. With near-limitless storage, a cloud data platform can easily accommodate organic growth over time without the added expense and hassle of maintaining and scaling traditional data storage solutions.
Elastic performance engine for rapidly scaling compute power
Aggregating and analyzing data at scale requires significant compute resources. Too often, analytics solutions compete for these resources with other business-critical systems, creating costly bottlenecks. A cloud data platform is designed to provide the lightning-fast, rapidly scalable compute power required to run modern analytics programs. No longer held back by resource contention issues, marketers can use their data more effectively.
Access to third-party data
Supplementing in-house data with data sourced from a third-party data marketplace provides retailers with additional insights into their customers' preferences and behaviors that would be impossible to uncover otherwise. Retailers now have the ability to buy prepackaged data sets, including demographic, psychographic, and location data from vetted providers.
Creating High-Performing Retail Promotions with Snowflake
The Snowflake Retail Data Cloud helps retailers adjust to a rapidly shifting consumer landscape, enabling them to easily access, govern, and share data. By eliminating data silos and the limitations of legacy infrastructure, retailers can capitalize on new opportunities to improve personalization, leverage data-driven merchandising, and create more effective retail promotions.
Read How Retailers and CPGs Can Leverage Third-Party Data in Their Analytics to learn how retailers and their CPG partners are harnessing third-party data to achieve more powerful segmentation and targeting and personalizing customer experiences.