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Discovery

In August 2019, Wittner Shoes selected Matter Design & Digital as the agency to deliver their next generation ecommerce system. This appointment was the culmination of a competitive bid process between a shortlist of hand-selected agencies, and became the genesis of a creative and productive process to develop a complex system that would deliver outstanding customer experience for Wittner Shoes.

Our discovery process included the following objectives:

  • Create an omni-channel solution to give Wittner’s customers a unified experience across online and offline (in-store) channels
  • Create a platform that provides the customer with the options for delivery, payments, and communication methods that suits them
  • Create a strategy to personalise interactions with customers by ‘knowing’ them better – their preferences and behaviours and how they like to shop
  • Innovations that make shopping at Wittner special and unique
  • UX/UI design that is high quality, beautiful and functional
  • Data integrity across systems
  • A platform that delivers on short-term requirements and provides the opportunity to grow and innovate in the future

 

Strategy

The strategy from the beginning was customer-first – empowering women to walk tall.

Wittner needed to showcase who they are, what they stand for and what they can offer their target audience. 

In order to achieve this there were three main considerations:

  • Consistency of brand experience regardless of customer touchpoint
  • Personalisation and appropriateness of product merchandising
  • An integrated system of technologies that enables growth 

The new website, powered by BigCommerce, would introduce Wittner’s belief system and connect with consumers on a more emotive level. The objective was to engage consumers in a new way, setting Wittner apart from the competition. 

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Creativity

The Wittner customer is an intelligent, confident and independent woman, who is forever 30. She deserves to shop on a site that works for her, gives her control and clear communication at every step. She wants to know she is valued and understood, and so the digital experience was thoughtfully designed to achieve this.

Matter was involved in the design of the website from the get-go. We prototyped, workshopped and refined the user interface of a mobile-first design that emanated confidence and elegance. We wanted to design a website that was easy to interact with, incorporating a stylish touch that reflected the tone of voice of the Wittner Shoes branding.

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Technology

The Wittner technology stack is built to unify the online and offline systems so that the customer’s experience is seamless no matter where they choose to shop. 

The stack consists of 16 individual components working together to perform specialised functions that enable the customer experience. These functions include:

Stock availability

Wittner has 90 stores across Australia and New Zealand. This acts as a distributed network of warehouses making the product accessible to the customer. Knowing which stores have the pair of shoes the customer is looking for gives confidence for the user to visit the store to try them on, or to click and collect. To enable this experience, accurate data from the ERP is surfaced through the website interface.

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Fulfilment methods

To confidently provide a choice of shipping methods, the system relies on accurate store inventory data. This data tells the system whether the customer can click and collect their order, whether they can get same-day delivery, or if their order is better suited to a standard or express delivery method.

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Post-order stock check

Online orders must deliver unblemished product to the customer to achieve the optimal customer experience and reduce returns. For standard and express deliveries orders are routed to the store location with the most stock and therefore the best chance to have stock that can be sent to the customer. If that store can not fulfil, then the ERP will reassign the order to another location.

In the case of click and collect orders and same-day delivery orders, the stock must be checked by specific stores – the store the customer selects to click and collect from, or the stores within range of the customer so a same-day delivery can be achieved. To achieve this requirement we developed a custom system that would run the stock-check processes to efficiently fulfil those orders.  

StarTrack Courier API integration

Same day delivery is achieved with StarTrack Courier service. This service is driven by a logistics system that relies on automated consignments without manual data entry. We built a custom integration to automatically lodge new orders into the system and book a pickup. This dramatically reduces the order processing time which enables this fast delivery method. 

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Tiered loyalty

As a Wittner Co member, you qualify for discounts based on how much you spend in a 12 month period. The calculation of these tiers was already working using the ERP and CRM. However, what we did was strategically surface the loyalty tier data into the new UI to incentivise the customer to maintain their current membership status or encourage them to increase their status to the next tier. Data integrity around online and offline order history gives the customer transparency on the program.

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Gift Vouchers

As an omnichannel retailer, gift vouchers need to be able to be purchased online and offline and redeemed online or offline. These vouchers represent cash, and so referencing a reliable source of truth for these balances is critical to not losing money. In this system, the ERP maintains the voucher balance. Gift vouchers purchased online generate new vouchers in the ERP and vouchers being redeemed online check the balance against the ERP.

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Personalisation

Driven by machine learning, the personalisation engine will make personalised product recommendations that help the user have a more focussed browsing session. The system utilises data from millions of browsing sessions to associate products together – if one million users that looked at product A also looked at product B, then there is a high chance future users will also want to browse both products. The machine learning detects this algorithm and implements it into the recommendations database. 

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