Over the past 8 years, Entytle has had a front-row seat in observing and participating in the Industrial world’s highest margin yet mostly underinvested business: Aftermarket and Service. Specifically, we’ve partnered with dozens of equipment manufacturers (OEMs) in enabling them to drive proactive aftermarket sales. And while our position has generally been as a “complementary solution” to the incumbent CRMs, we can’t help but observe that Aftermarket teams have generally struggled to get the expected return on investment from these CRMs. These challenges typically fall into four buckets: data complexity & quality, customization for specialized workflows, specific needed insights, and collaboration with a wide range of stakeholders across the organization.
An OEM’s aftermarket business is primarily about selling to existing customers. This means prospecting in the pool of people who’ve already bought their equipment and in many cases, aftermarket parts and services. This should be easy, right? Unfortunately, not the case, and here are some reasons why Aftermarket teams struggle with generic CRMs:
First, a wide range of data needs to be unified and available to make these workflows effective. Bringing this data into a CRM is challenging – and can drive significant additional costs from the CRM provider for storage, processing, etc. The challenge does not stop with just ingesting the data – this data is typically of poor quality and incomplete, and often needs significant work to be truly usable. We have all seen CRM implementations where the data quality was not addressed, resulting in a huge mess that discourages user adoption.
Second, these aftermarket sales workflows require significant customization in standard CRMs, which is expensive and time-consuming. For instance, in order to find the right part for a specific machine, the workflow needs to incorporate the bill of materials, parts supersession information, equipment modification records, and prior parts and service transactions – just to get started. Generic CRMs are most commonly used ‘out of the box’ to handle situations with new customers and are not set up to handle this type of workflow complexity regarding the equipment that the customer owns. This customization is incredibly expensive. Recently I have had two separate conversations with customers – one where the customer was being quoted $150,000 for customization and another situation where a customer was being quoted $120,000 to ‘fix’ the customization that had been previously done.
Next, in order to be efficient and cost-effective with aftermarket sales, the team needs to easily be able to identify who to call, for what offers, and when – and prioritize opportunities by which will drive the biggest ROI. Answering these questions requires an understanding of your parts entitlement, your wallet share, which customers are most likely to buy from you, which are at risk, and what do we predict a customer will need. Most modern CRMs have some sort of Artificial Intelligence or other analytics capability built in. While this can likely be somewhat easy to use for lead scoring based on web traffic and social media, it is typically not as easy to use ‘out of the box’ to answer the above questions. These types of analytics will require the CRM user to bring their own data science capabilities in order to get the generic AI to provide a valid output.
Lastly, supporting existing customers and the installed base typically involves a broad range of stakeholders – well beyond just sales. This typically includes support, service engineers, product managers, parts expeditors, marketing, commercial operations, data analysts, etc. Many of these users are often not provisioned with a CRM license as they are not using the CRM often. In many situations, this set of stakeholders extends to channel partners as well. Provisioning all of these different functions with CRM accounts would drive significant expense.
These challenges typically result in a poor outcomes for the OEM. The customization work is high cost, high risk, and often doesn’t deliver the workflow that is needed. The data issues typically frustrate users resulting in less value and low adoption. The analytics capabilities typically sit dormant without scarce data analytics or data science resources to use them. And important users are excluded from using the CRM, which drives workflows back to email, chat, phone, and document sharing – all of the non-scalable collaboration the CRM was trying to replace.
Bringing all this together, an Aftermarket CRM needs to solve these challenges ‘out of the box.’ First, an Aftermarket CRM should be set up to solve the typical OEM workflows: reactive parts sales & support, proactive installed base selling, digital marketing targeting for existing customers, and so forth. The Aftermarket CRM must have a data model that addresses the complexity of the installed base (which accounts for 100% of parts/service sales and typically 80%+ of new equipment sales). However, it cannot just have a data model to bring in the different dimensions of the installed base, it needs to deal with the reality of the data quality issues that OEMs face, especially since transactions that happened 20 or 30 or more years ago, in a legacy system, are still highly relevant. In addition to being able to handle the data realities, an Aftermarket CRM needs relevant analytics. The Aftermarket CRM should be able to help OEMs determine wallet share, loyalty, predictions, recommendations, and propensity to buy without data science expertise while still providing valid output. Lastly, the Aftermarket CRM should have a licensing model that makes sense and aligns with the value being created – not necessarily with the number of users who might need to log in, even if infrequently.
While the leading generic CRMs are very powerful platforms, they often miss the mark on the functionality that an OEM needs, while delivering lots of features that the typical OEM will not be able to drive value from. A simplified, purpose-built CRM for OEMs will enable faster time to value, lower-risk implementation, less customization, and fewer resources dedicated to the CRM (admin, data science, analysts, etc). All of this can enable the OEM to focus on their business and not the CRM.
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