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Most of your visitors start their shopping journey on your homepage. Your site homepage drives a large portion of your visitors shopping journeys but homepages are often built with a static, one-size-fits-all approach. This puts the task of finding relevancy on the shopper by asking them to seek out the content and products that inspire them. A better approach is an entirely personalized homepage for every visitor to your site.
For example, it is critical to understand and distinguish your first time visitor from your repeat customer:
- A first time visitor wants to be educated, inspired, and delighted
- A returning customer is often seeking efficient ways to re-enter a shopping journey
- A visitor in the midst of a shopping decision needs the easiest pathway to convert
Using Evergage, you can create personalized homepages by leveraging:
- Contextual and behavioral data that’s available in real-time for that visitor
- A decision engine that allows you to leverage one to one machine learning algorithms and business rules so you can create this personalized homepage at scale
Typically most home pages have the following four content areas. Personalizing these areas will increase product discovery and shopper engagement:
Hero banner - consider each customer’s lifecycle and affinities when personalizing this area. For example, for a visitor in the midst of a shopping decision, inspire them with a banner that relates to their current shopping interests. Often persona-driven banners both provides inspiration and drives engagement for your visitors. Things to leverage in determining the right hero banner include the customer’s lifecycle, affinities, and other contextual data such as location and weather.
Recommend relevant categories and brands - the homepage often serves as a point of reference for visitors to understand the breadth of your catalog. Relying on the wisdom of the crowd may result in category suggestions that are not relevant to every visitor to your site. Similarly, limiting categories based on previous behavior can be both limiting and, often times, irrelevant. Instead, leverage machine learning algorithms to recommend categories based on a visitor’s historical and current session behavior as well that that of other similar visitors will enable you to both inspire the shopper and serve as an easy reference point.
Content to inspire shoppers - content is becoming more influential in driving online purchase decisions. Leveraging Evergage’s content recommendations, you can automatically recommend inspirational content based on relevancy.
- Recommended products - don’t limit product discovery to one row of five products. Instead, leveraging machine learning provides an opportunity to create more granular recommendations based on the individual and their affinities. For example, use multiple product carousels, with each carousel driving a specific purpose, such as reordering previously purchased products, discovering new products in favorite brands, and cross-selling based on recent purchases.
Use the Evergage segmentation engine based on the specific strategy being implemented
Measure conversion rates against control for each strategy implemented
Select the personalization strategy to see the specific implementation steps:
The following articles from the Evergage Knowledge Base will provide process steps to help you execute the plays in this section: