Skip to content

How an online retailer increased offer response rates by 14% with online customer segmentation

online-retailer-14-increase-reponse-rates

14%

increase in offer response rates

9%

increase in demand per reponse

3.7%

improvement in overall revenue

About the customer

Founded in 1875, this online retailer is the UK's most successful home shopping company. It also offers customers the unique ability to carry shopping bags across its website and complete check out on any site.

Use Cases

  • Customer experience
  • Hyper-personalization
  • Unify cross-channel customer data
  • Maximize marketing ROI

Challenges

As customer behavior evolves in response to the ease and convenience of online shopping, this large online retailer has seen a large shift to online sales. In addition to its continued online success, this omni-channel retailer has embarked on an expansion of its retail estate in the UK as well as growing its footprint overseas.

With millions of active customer accounts, the company has recognized the value of customer data for many years. They use a Teradata warehouse as a single repository for all trading and customer data, with an extraordinary amount of online customer information being captured –some 65GB of data is created every month from which the retailer can derive detailed customer insight and business value.

The retailer wanted to unify their customer data across all channels, including retail stores, while delivering a seamless customer experience. They knew that to do this, it was essential to deliver the right message, at the right time, and via the right channel.

2M+

active online customers

>50%

of sales generated online

50+

highly accurate 

predictive models

online-retailer-targeting

Solution

The large retailer chose Celebrus to handle their digital data capture for all online customer interactions including every click, search, basket change, and purchase. All of this data is instantly fed into the Teradata data warehouse. The Celebrus data is also used to inform customer segmentation to improve the personalization of marketing interactions and increase return on investment.

For example, the retailer created six high-level segments for one of their brands – ranging from ‘interacting online top shoppers’ to ‘not brand engaged’. They built their customer contact strategy around these segments, including which communication each segment of customers should receive, i.e., the marketing channel and type of promotion.

A key factor in determining this segmentation was understanding customers’ online behaviors and ultimately creating online-centric variables through the Celebrus data.

The company is also using the Celebrus data in Teradata Vantage CX to improve its marketing attribution, and eradicate inefficiencies in paid search, which makes up a significant proportion of their overall marketing spend. For example, analyzing the business impact of different paid search keywords by measures such as lifetime customer value (LTV) and credit reject rates will enable the company to refine its search spend and focus heavily on those terms that contribute to a strong customer lifetime value.

dot-quotes-aqua

With over half of all customers buying online, the depth of information provided by Celebrus digital interaction data is compelling, not only in improving our understanding of evolving customer behavior - such as the move towards mobile devices - but also to support both tactical and strategic decision making.

Results

Goal: Maximize efficiency and increase ROI of marketing activities

How was this done?

The power of the Celebrus technology has transformed the retailer’s understanding of the customer journey. It enables them to become increasingly sophisticated in their use of customer data. The company uses Celebrus data to drive behavioral emails such as ‘browse not bought’ and ‘abandoned basket’. Email campaigns are also informed by product preference or the way a customer sorts on site - indicating price sensitivity for example.

In addition to gaining better ROI from their marketing investment and significant cost reductions from improved marketing attribution, the retailer is using predictive modelling to understand the likelihood of a customer making a purchase. The organization has over 50 predictive models. Before including web data, these models were only based on transactional and payment insights. Enriching this with 6 months of Celebrus data, including browsing information which reveals an increased purchasing intent, has greatly improved the accuracy of the models.

Goal: Optimize predictive models to increase response rates

How was this done?

The retailer has created a number of behavioral personas – such as ‘value hunters’, ‘frequent abandoners’, and ‘on-trend customers’. This allows them to create a more relevant and personal experience as customers arrive on site.

For value hunters – those who consistently visit the sales area or sort by price on the navigation – they have a series of triggers that they can action via website personalization. By contrast, ‘frequent abandoners’ can be identified and offered incentives to encourage them to complete the purchase or increase the number of products in the bag, while ‘on trend customers’ – those always looking for new products – can be targeted with the latest items and aspiration emails.

This deeper understanding of online customer behavior is also feeding back directly into the merchandising strategy. With over 50,000 SKUs per site, it can be tough for merchandisers to track sales across categories and turn around slow-moving items.

The retailer now uses Celebrus data to improve insight into product conversions and prioritize activities. For example, comparing individual product conversions week over week can flag several issues – from stock outs in popular sizes, to poor product reviews.

dot-quotes-aqua

Customers can search by product review, so any product with a 2-star rating or less can deter a customer. Revisiting these products can reveal that there is a problem with product copy, or the image is not good enough. Essentially this data is providing the business with both a priority list of products to address, as well as insight to support plans for next season.

TECH INTEGRATIONS

  • SAS
  • Teradata Customer Interaction Manager
  • Teradata Vantage CX
  • Python
  • R