Customer Relationship Management (CRM) has been, and continues to be, a common thread in my personal history, weaving its way through much of my career as a technologist and entrepreneur. You could call it “an old friend,” I suppose, but the reality is that it has simply proven to be inescapable. For me, it began as a young programmer in the 1990s when CRM became a “buzzword.” Imagine a 20-year-old, who didn’t know much about the world yet, eagerly writing application code to match emerging CRM and Salesforce Automation principles to the very specific needs of automotive retail professionals across the United States - all while stacking Mountain Dew cans and pasting labels on 3.5 floppy disks to do on-premise installations for auto dealers. Those early experiences with CRM, noted on my CV, gained the attention of a regional financial institution who sought to implement CRM for its 3-bank holding company. That ultimately led me to cofound of a FinTech company in 2012, specializing in the delivery of affordable CRM and Marketing Automation solutions for banks and credit unions. That concern was ultimately acquired in 2020. In many ways, the landscape has changed significantly over the years, especially with really good SaaS-based alternatives to traditional vendors appealing to startups and SMEs. But in other ways, I find the CRM landscape very familiar, in part because (a) CRM is really intended to be rather broad in its application when fully expressed, (b) there always seems to be a disconnect between largescale Enterprise CRM solution providers and SMEs who tend to favor smaller CRM vendors with features tailored to their sector, and (c) because the promises of CRM remain unchanged: (1) acquire more clients, (2) make them more profitable, and (3) retain them if profitable or drop them if not. So, I think it’s “how” those promises are fulfilled and by “whom” where we will continue to see the influence of emerging AI as we move further into the 2020s.
All of the familiar Enterprise CRM providers have already launched and will continue to launch AI initiatives and AI- enabled features in the 2020s. Salesforce, Microsoft, Oracle, SAP, SugarCRM, and HubSpot are familiar faces, and all have integrated AI into their platforms in similar ways. Salesforce launched its “Einstein” platform in 2016, and earlier this year, in fact, a partnership was announced between C3.ai, Microsoft, and Adobe with the promise of “re-inventing CRM” with AI. This week, I tuned-in to a webinar provided by Salesforce that included Mark Cuban speaking on the subject of AI’s influence. One thing is generally agreed upon: AI will transform CRM by doing “better” the same activities that have previously been achieved manually or with dumber, traditional algorithms. Here are a few of those activities, but this is certainly not an exhaustive list:
To be clear, Expandigo is “not” a CRM platform, but as I noted earlier, CRM is very broad so there are certainly some core characteristics that do overlap. As a “Business Expansion Platform,” Expandigo is working to deliver a more focused application of AI that matches “Company A” to companies “X,” “Y,” and “Z” who can help “Company A” meet both stated - and inferred - business objectives. So, where CRM strives to deliver and score “sales” leads for example, Expandigo strives to deliver and score “strategic business connections.” To put it more simply, Expandigo wants to be the gold standard for AI-powered networking based on what an organization seeks to actually accomplish over time. In practice, those objectives are usually more dynamic than simply “selling and retaining,” and may include “raising capital,” “achieving liquidity,” “expanding markets,” etc. In a similar way, Expandigo strives to algorithmically suggest data and other resources that are equally well-matched to this dynamic landscape of corporate objectives.
AI takes data. Lots of data. Unbelievable amounts of data. It requires data from external sources, but it also requires data from internal sources. As successful strategic matches are confirmed within Expandigo, the algorithms responsible for matching get smarter to make better future suggestions. No single user teaches Expandigo how better matches are made, but instead, users who share similar characteristics and objectives across the collective ecosystem iteratively make Expandigo better. That sounds cool, but it’s hard. Really hard. It can also sound scary, particularly for organizations who pride themselves on the same type of strategic matchmaking as paid consultants. As a “Business Expansion Platform,” Expandigo would fall short of it did not consider the importance of nurturing and expanding upon “existing” relationships, and by serving consultants instead of replacing them. Allowing an organization to import, curate, and manage existing client relationships is viewed as both integral to providing value to Expandigo users near-term, and to teaching Expandigo algorithms how to best serve those relationships long-term. As such, Expandigo shares some CRM characteristics, and may even be used as a lite-CRM by SMEs who have not adopted CRM to date, or who pay for Enterprise CRM solutions that might be overkill given their size and sector.