By Peter Hoffman, Senior Sales Engineer

My last post focused on the basics of big data, the challenges associated with collecting and extrapolating information and the simplified steps of leveraging big data to acquire actionable information.

Now we’ll look at how this affects the retail space and the role Demandware plays in enabling and facilitating the capture and use of big data.

eCommerce merchandising strategies are hard to predict due to the complex relationships between data and the activities that influence success. Big data can help drive better customer engagement in ecommerce by answering questions like:

  • How many ecommerce customers live close to the store and shopped online but not in the store? Once targeted, will that customer segment shop in store if given store-only promotions? If so, which promotions work?
  • Which keywords are used in search terms? Which keywords should be feed back into the platform to improve search indexes? How do customers interact within their social networks? Can this information be used to improve merchandising?
  • How can we improve traditional functionality from information such as “Customers also bought?”

For a long time, Demandware has supported big data under the name Active Merchandising. This is a comprehensive set of selling tools designed to automate the merchandising value chain with control over tasks and processes.

An overview of the process flow of Active Merchandising data through the Demandware Commerce platform can be seen below:

In the previous blog, we identified 4 challenges that companies need to master to get the most out of big data. Let’s map these to Demandware’s Active Merchandising:

  1. Efficiently aggregate the data – based on years of experience, the Demandware platform has been designed to focus on the data elements that are key for ecommerce success.
  2. Extract important data in order to understand the state of the business – Demandware customers can choose from a vast array of KPIs which are the most important for their business model.
  3. Quickly identify customer trends – the customer experience dynamically updates based on how your customers are using your site.
  4. Define subsequent actions – Demandware customers can monitor how the platform is evolving to meet customer expectations and adjust as required.

Retailers can enrich Active Merchandising with other data specifically relevant to them (e.g. data from web analytics tools, social networks, OMS, CRM) to provide a more complete and accurate view of their customers, leading to more effective engagement.

Looking to the future, this enriched data, coupled with Demandware’s cloud infrastructure, makes for a compelling big data opportunity. As a single cloud-based commerce platform, Demandware is uniquely positioned to monitor and understand shopping trends across hundreds of different sites around the world, enhancing the pool of anonymized data, which retailers can use to inform their strategies.

The Demandware mission is to be the global backbone for digital commerce between brands and consumers – access to, analysis of and action on data is an important part of delivering robust, comprehensive consumer engagement.