#048: Insights_ Top 10 Adobe Analytics implementation best practices for retail

With high customer expectations, the retail industry is on the edge to deliver seamless shopping experiences that stand out. With that in mind, Adobe Analytics empowers retailers to be more creative in gathering and analyzing data to gain in-depth insights into consumer behavior. However, simply implementing Adobe Analytics to your website won’t deliver results. You must make the platform robust via high-end customizations to utilize its capabilities fully. 

In this blog, we will understand what challenges retailers face while implementing data analytics in their business and what are the Adobe Analytics implementation best practices.

What challenges does the retail industry face? 

The retail industry is quickly adopting data-centric technology to increase sales, leveraging massive data across its supply chain and at diverse customer touchpoints. Here are a few challenges retailers face before implementing analytics:

• Businesses need data that provides more customized information to gain insights, segment customers, and deliver relevant marketing messages. But disparate data from multiple sources often results in profiles too segmented to be actionable. 
• Most companies know the importance of data security, but many ignore it due to the complexity it creates. With bulk data gathering in the retail industry, the lack of big data security protocols results in internal and external risks affecting customer loyalty. 
• Organizations need data from all the touchpoints to manage stock reliably and support marketing and sales teams in demand forecasting. Enterprises find this challenging to collect data from individual stores and online stores. Hence, Adobe Analytics tool is required for measuring valuable data. 
• Retailers reach customers online or offline both ways, which makes customer experience challenging. That’s why providing customers with a smooth experience on the website becomes a priority. This is only possible when visitor tracking, mapping user behavior, measuring the impact of landing pages and banners, etc., come in handy to let users find information a lot faster.

Adobe Analytics best practices for retail 

Let’s discuss Adobe Analytics implementation best practices that are essential for the retail industry to make customer tracking smoother:

#1. Merchandising variables

One of the most complex best practices of Adobe Analytics is eVars (merchandising variables). Adobe Analytics collects data in multiple ways, which can be used to gain insights. Merchandising variables allow you to tie the value of a variable to a specific product for a particular event. It can help you to find : 

• How are visitors finding the products with the highest margins
• Are we getting value from recommendations or a live search
• Which products have lost the most revenue due to discounts

#Product String: Adobe Analytics Product String Builder extension automatically sets the products variable for you by looping through your data layer, grabbing all the product-related data, and formatting it in the proper syntax. 

There are two types of eVars: 

1. Product Syntax: It is the standard approach for tracking and reporting in Adobe Analytics for eCommerce tracking and reporting. It stores contextual information about products like color, size, name, quantity, shipping, discounts, price, etc. You can choose product syntax when merchandising value is available at the same time. 
2. Conversion Syntax: It allows you to set a value other than the product string. You need to choose conversion syntax when the attributes you want to bind are not necessarily available in the same hit as the product you want to bind them to. 

#2. Event serialization

Sometimes data does not match in back-end systems, be it purchases, submissions, checkouts, add to cart, or any other conversion event. It works by combining each instance of an event with a unique ID. Adobe Analytics features eliminates double counting. Retailers can flexibly align various systems by successfully implementing event serialization analytics. 

#3. Segmentation

Adobe Analytics lets you create, manage, share, and apply influential, focused audience segments to reports along with other Adobe Experience Cloud tools. Segments give better audience insights by following values: 

• Visitors-based on attributes: browser, device, number of visits, country, gender
• Visitors-based on interactions: campaigns, keyword search, search engine
• Visitors-based on exits and entries: social media, landing page, referring domain 
• Visitors-based on custom variables: field, defined categories, customer ID

Adobe Analytics segmentation lets you identify patterns of consumer spending and associated behavior. 

#4. Adobe Launch

Launch makes integrating easier with Adobe Analytics to Adobe Experience Platform, Adobe Target, and Adobe Audience Management. You can connect Adobe Launch with multiple technologies and turn data into action so you can deliver powerful, memorable experiences. Adobe’s current method is through tags in the Adobe Experience platform. Tags in Adobe Experience Platform is a tag management solution that lets you deploy analytics code alongside other tagging requirements. 

#5. Set up alerts

Adobe Analytics tool lets you create and manage alerts to track and monitor data 24/7. Retailers can monitor data anomalies and maintain data integrity. 

#6. Create calculated metrics

Calculated metric tools in Adobe Analytics offer a highly flexible way of building and managing metrics. They allow you, as marketers, product managers, and analysts, to ask questions about the data without changing your analytics implementation. Conversion and bounce rate metrics are the two most important calculated metrics in digital analytics for retailers. But with huge audience segmentation, retailers need to apply additional calculated metrics to enrich the analysis.

#7. Import product costs

Retailers must analyze which products are earning the most revenue to upscale the product performance. Adobe Analytics has the feature of bringing product costs into the platform to identify your most profitable products. 

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