Everyone agrees that user experience (UX) matters a lot for digitalisation. Companies like Accenture have created a dedicated practice called Customer Experience (CX) to help their clients get this right. In the machinery sector however, we are seeing a lot of spare parts e-commerce initiatives resulting in much lower customer adoption than expected. In this article, I examine the underlying reasons for this that we have discovered based on user interviews, and 5 tips to build a better web shop experience.
Focusing on the right persona
We have found that there is a fundamental difference between traditional spare parts order handling and spare parts e-commerce. What’s more, a majority of machinery companies have optimised the UX of their web shops for the wrong persona. The dominant user on their customer portals is not the purchasing team that they have traditionally dealt with. Instead, the user is often a maintenance engineer and the key problem that these two very different user personae want solved are not the same.
Traditional offline purchasing of spare parts have been often carried out over the phone or over multiple email exchanges. In the offline channel, the parts to be ordered and the quantities would be discussed and each supplier was instead judged on how quickly s/he could revert with a quote, and the price and the lead times that were offered to the buyer. In the eCommerce channel, the key benefit for machinery companies is the efficiency gains by reducing human intervention to the maximum extent.
Solving the right UX problem
At MachIQ Software, we work a lot with plant maintenance technicians in the context of improving MachIQ APM, our SaaS offering for plant maintenance. Based on our discussions with them, we learnt that the most painful part of spare parts purchasing for them, is identifying the right spare part reference to order. We have seen empty boxes of supplies being left on the maintenance manager’s desk as a way of communicating between team members that a certain part needs to be ordered.
Contributing to this pain is the fact that machinery companies frequently change suppliers for their parts and replace existing spare parts references with new ones. In an automated transaction, it does indeed baffle a buyer when he orders Part No: ABC1234 and receives CDE2345 instead. It might be the exact same part, just produced by another supplier of the machine builder. However without the telephone conversation, there is no way of being sure. The ensuing stress and the lost time is something that the digital buyer shouldn’t have to experience.
The most important UX to optimise for is to help a buyer identify the right part to order, every time.
Protecting IP and preventing price shopping
At the other end of the UX spectrum, we have seen machine builders adapt to this by uploading accurate 3D drawings to help their customers navigate the part ordering puzzle. While this does indeed make the maintenance technicians experience easier, it tips the scale towards the buyer by giving them the ability to take the 3D data to search for after-market parts suppliers. Such web shops see a lot of usage but disproportionately lower purchase orders. The key is to find the right balance of giving enough information to allow buyers to make an informed, accurate choice without giving them so much information that it effectively commoditises the product.
Stream-lining order management with interfaces
Traditional order handling processes developed as a result of the constraints that the IT architecture placed on medium and large machinery companies. Each sales organisation – basically a country or region, had its own ERP instance, separated from the ERP instance of the factory that produced and shipped the parts. Orders needed to be entered in the country ERP system to recognise the revenue and in the factory ERP system to fulfil the order. Price was determined by the first system and availability and lead time by the 2nd system. All of this led to dual order entries, manually done of course, resulting in frequent data entry issues, reconciliation issues and more frustration for customers when they needed to wait to get information on order statuses, etc.
Many of these processes become obsolete in the e-commerce paradigm. Instead, web shops have tiptoed around these potentially difficult, politically charged discussions on how best to get this right. Our advice on this is to link the ordering solely to the factory ERP, ensuring that pricing control still stays with the country teams. Revenue recognition is easier to deal with based on the source of the order and the reconciliation for this is much more easy, especially with an interface to the CRM system of the machinery company.
At one of our customer, we were able to reduce the time and effort to generate a quote by 75%! A whopping 75%! That figure beat even our wildest estimation on the business case for MachIQ Seva‘s Qommerceapp. The secret, we found was in the design of our interface to the customer’s ERP system. The trick was to effectively balance data entry automation with the ability to handle exceptions. The trust that the order handler has in the ability to catch exceptions enable them to allow other quote requests and orders to pass without having to intervene.
Applying intelligence to reduce stress
Another feature that we see often employed was the ability of the Qommerce App to intelligently split an incoming customer order based on the customer as well as the parts in the shopping cart. By routing these split orders automatically to the business unit responsible, we again reduced the stress for order handlers who had to verify each order line manually before. The entire buyer’s purchase order process and the supplier’s sales order process that make up a transaction are replete with many data entry steps that frustrate the users, introduce the risk of data errors and increase the process costs for both sides. Applying intelligence, and here I do not employ loosely thrown buzz words like AI or ML, but simple rules-based automation would go a long way in increasing the probability of success for spare parts web shops.
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