Hewlett-Packard Enterprise

126%

Increase in content engagement

207%

Increase in unique clicks

219%

Increase in form fill conversions

In 2017, Hewlett-Packard Company announced plans to separate into two new publicly traded Fortune 50 companies. Hewlett Packard Enterprise (HPE) is comprised of Hewlett Packard Company's enterprise technology infrastructure, software and services businesses. HPE is the global edge-to-cloud Platform-as-a-Service company that helps organizations accelerate outcomes by unlocking value from all of their data, everywhere. Their primary goal and strategic vision is to deliver Everything as a Service (EaaS) by 2022.

Clients include Fortune 500 companies such as American Airlines, Amazon and Facebook.

In order to meet their aggressive goals, HPE realized they needed to provide a seamless, personalized experience of product and content recommendations without having to completely re-invent their digital and corporate taxonomies.  The only way to do this at the pace and scale required, one to three months, was to leverage Machine Learning capabilities in order to match their visitor interests with content already in their taxonomy.

Providing a solution

  • Test Web Content Recommendations’ widget for 90 days on a half dozen pages
  • Run Web Content Recommendations widgets on hpe.com and HPE’s Insights blog know as .NXT
  • The implementation with Web Content Recommendations was straight-forward as HPE's site design follows a consistent template and design standards

For the product recommendations widget HPE needed a smoother transition for the visitor from the researching of products to the actual purchase.  Because the researching motion
(hpe.com) was disconnected from the purchasing motion (buy.hpe.com), Web Content Recommendations worked with the HPE Team to develop the product recommendations widget to index products then deliver them within the .com experience. 

Immediate Impact:

  • The ability to derive meaningful and predictive interest data and the individual level helps Hewlett Packard Enterprise eliminate low value marketing activities
  • Web visitors are more highly engaged with product content. They want to look and touch and feel the product
  • Product recommendations were deployed easily. This drove 3-5x the lift of the content recommendations.

RESULTS:

  • 126% Increase in content engagement
  • 207% Increase in unique clicks
  • 219% Increase in form fill conversions
  • 50-75% consistent lift in content performance
  • 3-5x lift in content recommendations

What’s Next: 

Hewlett-Packard Enterprise intends to expand the range of content used in its recommendations past the 13,000 marketing assets to all available content items (support, learning, news, etc.). Web Content Recommendations’ unique predictive dataset will be leveraged as a foundational capability to dynamically curate experiences across web and email.

Most importantly, as with any digital strategy, we wanted to find speed. We wanted to find the partners, and within our own team, the ability to do this not just at scale but at pace. Inside a digital organization, this is something that would normally take 6-12 months and we wanted to find the 1-3 month motions

Gabrielle Boko

VP Digital Hewlett Packard Enterprise
Gabrielle Boko

[These results] really say the power of a what a personalized serve-up of content does. We know who you are, we’ve understood what you’ve wanted, and this makes it easier for you to decide to fill out a form and want more from the organization

Gabrielle Boko

VP Digital Hewlett Packard Enterprise

Fakta om prosjektet
Hewlett Packard Enterprise benytter

Se resultatet på www.hpe.com