SugarCRM - letting the platform do the work in manufacturing

SugarCRM’s tagline says “let the platform do the work.” At its recent 2023 Connected user event and analyst summit, the company announced new out-of-the-box platform capabilities for generative Artificial Intelligence (AI) in sales, marketing, and service:

• Generative AI for Sales includes capabilities to generate personalized e-mails and sales copy, call scripts, and sales proposals based on Customer Relationship Management (CRM) data.
• Generative AI for Marketing creates marketing campaigns, landing pages and e-mails, automatic translations, and smarter segmentations.
• Generative AI for customer service summarizes case history and service tickets, creates user guides and product documentation for a specific audience, and make it easier for agents and customers to search and find answer and resolve issues.

At the surface level, none of this is particularly different from what other CRM vendors have announced in the space. However, there are some foundational and directional differences between Sugar’s approach to CRM data and AI that differentiate its current capabilities and its roadmap. With a sharpened focus on mid-market manufacturers, Sugar is also leveraging its platform capabilities of flexibility, support for end-to-end processes, and ease of use to appeal to those customers.

Time-aware

Sugar has been talking about time-aware CRM for a long time, and this is particularly evident in its new enhanced forecasting capabilities. Unlike other CRM applications that only provide a snapshot in time, and require a pretty significant data analysis effort to understand historical trends, SugarCRM’s time-aware approach means that users can easily compare quarter over quarter, have visibility into week-to-week changes in pipeline, and understand pipeline creation trends. With Sugar’s latest release, out-of-the-box dashboards highlight the difference between having time-sequenced insights versus only having pipeline data at one point in time. This timestamped data is also used to train the AI. 

Also on the platform front, Sugar has also been investing in integrations, bringing Enterprise Resource Planning (ERP) data into CRM so it can be used to inform the AI can provide sales people with pro-active prompts and alerts based on the time-based data. This is critical for Sugar’s target customers – mid-market manufacturers – because in many cases sales opportunities are driven by ERP data such as unordered complementary products or historical ordering patterns. 

No prompt

The other foundational difference in Sugar’s generative AI is a “no prompt” approach. Sugar believes that rather than training sales and marketing users to write prompts, the CRM platform should do the work for them. Sugar is moving toward this approach, masking the complexity of prompt development to the end user. So, for example, rather than having a sales person have to know how to ask to change the tone of a message, Sugar has coded different prompts into the software so a sales person can use a pull-down menu to select from a given list of “tones” for a particular message.

Although many vendors are moving in this direction, Sugar’s approach – and thinking – show how it’s advanced beyond “our CRM has AI embedded” to a more intelligent application that doesn’t ask for more effort or knowledge from the end user. This approach is also suited for Sugar’s target market, where experienced (and likely less technology-savvy) sales people can benefit from the productivity boost AI provides without having to learn new functionality, while less-experienced sales people get intelligent data-driven recommendations that make them more knowledgeable sellers.

My take

Historically, Sugar’s platform flexibility has been a strength, but also a weakness. Many customers appreciated that they could do almost anything the wanted with the platform, but many built out very custom processes that weren’t necessarily appropriate or effective.

As Sugar focuses its efforts on meeting the needs of mid-market manufacturers, it will need to be more prescriptive in defining what best practices for CRM and AI are in the manufacturing space. Pre-defining what ERP-grounded recommendations look like, what message tones are appropriate, and how time-sequenced data can be used most effectively are good steps in that direction.

Moving forward, I expect it will continue to invest in streamlining the planning and deployment process for manufacturing customers by building out CRM-adjacent areas like Internet of Things (IoT), Configure Price Quote (CPQ), and field service capabilities either organically or through acquisition, as well as continuing to invest in relationships with partners (like Mobileforce, for example, which provides AI-driven revenue operations and field service to manufacturers) that bring sector-specific expertise or functionality. 

In the mid-market manufacturing sector where references and name recognition are important, companies switch CRM vendors less frequently than those in other sectors, and many CEOs are weighing technology investments versus capital expansion or dividends, Sugar still faces brand recognition challenges. It will need to amplify its platform story in manufacturers’ terms, and show how its existing customers in industrial, high tech, life sciences and chemicals, and mill and industrial materials are achieving sustainable value from letting the Sugar platform do the work.