Steve Jobs once said ‘Customers do not know what they want until we show them.’ To be successful, retailers must know what the customer wants and present it to them before they even know it themselves. And this is only possible by analyzing customer behaviors and preferences.
Customer behavior can be understood as an individual’s buying habits, including frequency patterns, social trends, and other factors that influence their purchase decision. Customer behavior analysis is crucial for retailers as it helps them better understand their intended audience and leads to more significant business sales and profit.
For instance, if a business knows what its customers want or need, it can focus on offering products that precisely appeal to its target audience. Additionally, customer behavior analysis can help businesses reinforce their marketing tactics and respond with more data-driven decisions.
In today’s blog, we will discuss Amazon Redshift and understand how you can use Redshift for retail customer data analysis.
What is Amazon Redshift?
Amazon Redshift is a cloud data warehouse solution offered by Amazon. It is best known for handling massive volumes of data and processing petabyte-scale structured and unstructured data.
Amazon Redshift comprises two critical architectural elements: massively parallel processing (MPP) design and columnar data storage. The two elements enable Redshift to perform SQL-based queries on huge databases and provide enterprises with business intelligence.
Almost every online retailer nowadays has to deal with data coming from multiple sources such as CRM, advertising, customer support, etc. With Amazon Redshift, retailers can build a central repository that stores and processes data from multiple sources in a unified structure, format, and schema, creating a single source of truth. This can then help to derive invaluable reports and analytics on customer behavior and preferences.
Moving on, let us look at how Amazon Redshift can help retailers benefit from customer analytics:
- Customer data analysis enables retail companies to provide targeted communication to their customers. It supports personalization marketing efforts that are known to be 20% more effective than traditional marketing.
- Retail companies can better predict future demands and manage inventory well. With Redshift for customer analysis, they can understand the customer’s purchasing needs and concentrate their efforts on areas that have a huge demand.
- Customer data analytics can enable retailers to drastically improve customer experience and provide a seamless shopping experience. Right from selecting a product to purchasing it, data analytics emphasizes offering personalized attention to each customer. This, in turn, helps to boost customer loyalty.
- Another potential use case of Amazon Redshift in the retail industry is that it helps businesses to identify opportunities with high ROI. Retailers can leverage predictive analytics to measure the response of their prospects to marketing campaigns and even identify their willingness to purchase a product.
- Analyzing customer data with Redshift helps to identify the customers who are not actively engaging with your brand as well as those who are frequent buyers. Based on this info, retailers can introduce attractive offers to engage and retain customers.
Setting up a Redshift for customer data analysis
Amazon Redshift is a fully-managed service maintained by AWS. What this essentially means is that retailers do not have to worry about handling clusters, processing queries between nodes, and other similar tasks within Redshift.
Unlike on-premise data warehouses that are not easily scalable, the Redshift data warehouse can be easily scaled up or down as per your requirements. You can start with a few hundred gigabytes of space and scale up in terms of storage and computation to fulfill your requirements. Additionally, Amazon Redshift’s architecture supports parallel processing and complete data reliability. It allows retailers to leverage all the data and conduct customer data analysis with immense ease and accuracy.
Analyzing customer behavior and preferences
Tools that can be used to perform the analysis
Data wrangling with python code (for very basic insights):
Data wrangling is a process that enables businesses to transform raw and non-resourceful data into valuable data that can be used to derive meaningful insights. Data wrangling with python code enables retailers to perform customer data analysis and obtain valuable information on boosting customer experience.
One of the best ways to visualize your retail data present in Amazon Redshift is by using business intelligence software such as PowerBI. PowerBI is an excellent solution to visualize your data and identify meaningful trends and patterns concerning customer behavior. Retailers can also generate reports of their datasets and visualize data in real time. This enables them to solve customer problems as and when they arise and capture all opportunities in a timely way.
Amazon Redshift and Tableau are two immensely powerful technologies, and when combined together, they enable business users to analyze datasets with unprecedented speed and agility. With this combination, retailers can gain insights from their gathered data and find out crucial details about customer behavior and preferences. They can use the insights to drive strategic actions across their business.
Apart from these, several other analytical reporting tools also connect well with Amazon Redshift. The most widely used tools are Looker, Holistics, Sisense, Ubiq, Mode, and Microsoft SQL Server Reporting Services. You can employ any of these reporting tools to present the customer data in graphical or tabular form that makes it much more comprehensible. This makes big data analysis for customer behavior much easier, helping retailers to make informed decisions.
Here is how the team at Algoscale helped one of Russia’s largest retail chains to benefit from customer data analytics.
From direct marketing to enhance brand positioning and improved customer relationship management, online retailers need customer data analytics to ensure business success. However, they need good data gathering and analyzing systems to capture crucial data from multiple sources and leverage it to obtain meaningful insights.
At Algoscale, we helped a leading retail giant in Russia to make the most of its legacy data and analyze customer behavior and preferences. We deployed effective data cleaning to clean and format all of the client’s fragmented data and structure it into a consistent format. All the final data was compiled using shell scripting and then leveraged for generating a range of reports and dashboards. These reports helped to promote strategic decision-making and facilitate business expansion.
Understanding customer behaviors and preferences are crucial for a business to boost its overall performance. By leveraging customer data analytics, businesses can serve their customers better as well as formulate effective marketing strategies.
At Algoscale, we can help you enhance your business growth by modernizing your data architecture with Amazon Redshift. We help retailers across the world find out more about their customers and make informed and data-driven decisions.