Most business executives will advise you to get a good customer relationship management system (CRM). They will tell you that it is essential for your business to function effectively. It is widely regarded as the key system for a better and deeper understanding of customers and building a strong relationship with them. It also helps in making data-driven decisions that maximise customer satisfaction and lifetime value.
But what if I told you that CRM systems are bound to become obsolete in the near future? The growing understanding that CRM systems cannot truly deliver on their promise of enhanced customer interactions will be the cause of CRM’s strategic demise. In fact, CRM systems are a major contributor to the mismatch that most businesses have between their sales, marketing, and customer success teams. Simply said, CRM solutions are incapable of performing the tasks that businesses require. For example, these systems are now used by businesses as real cloud-based spreadsheets of upcoming deals. On the other hand, companies require a system of record that analyses activity data and gives guidance on how to enhance the chances of closing a deal as won.
CRM systems lack these features, and a new approach is quickly gaining traction based on powerful advances in emerging technologies such as artificial intelligence, machine learning, computation, and storage delivery.
To comprehend why CRM systems are so problematic, you must first comprehend how CRM systems function and how they were created in the first place. In contrast to an activity streaming approach, CRM solutions are developed upon an object-oriented data architecture. A CRM object, according to Hubspot, can be defined as the various interactions and processes an organisation engages in, with common examples including contacts, companies, deals, tickets, and custom objects created by businesses. Each object record in a database is analogous to one line with several fields in an Excel file—also known as structured data rows and columns. The split between sales, marketing, and customer success teams is due to the fact that each is looking at separate objects, which is inherent in an object-oriented paradigm.
The focus of a sales team is on opportunities, or “the deal.” Marketing is less concerned with opportunities than with another CRM object, leads, which are used to establish a pipeline. Both opportunities and leads are unimportant to the customer success team. Its main concentration is on accounting. And the support staff is solely interested in tickets. What a modern system of record for prospects and customers really needs is a strong focus on employee activity—not just objects as the lowest common denominator that applies to all teams. Activities document what team members perform in their interactions with clients and prospects on a daily basis. Identifying activities at scale can help us identify the patterns and trends effectively and better cater to our clients.
CRMs lacked the computing and storage capability to manage the enormous volume of activities when they have first constructed decades ago—for instance, any particular contract may have 10,000 linked activities, and a company could have 10,000 deals in progress. Because SQL databases, which were the leading technology when CRM systems were developed and are still used by every CRM system today, an emphasis on objects rather than actions were imposed.
Furthermore, there was no context-aware technology to link activities to the appropriate items, allowing teams to gather relevant data. AI-powered matching skills, such as determining that this phone call is relevant to this specific contract (out of 20 ongoing ones) with this prospect (out of 10 other accounts with the same domain and company name), were decades away. It is now possible to build an activity-centric system that learns from the behaviour of all customers and sellers, thanks to the advent of cloud storage, cloud computing, and cloud distributed AI, as well as legal systems getting closer to allowing cross-customer AI learning (similar to how Google trains their AI across all users’ searches).
Data pipelines and streaming systems, which are required to scale action-based systems of record such as Kafka, have only recently become available. After its IPO last year, Confluent, which supplies auxiliary tools that make Kafka easier to use, surged to a $16 billion market capitalisation, indicating that systems of record that process events are displacing systems of record that process objects.
CRMs, being object-oriented systems of record, are currently in an unfixable state. Therefore, object storage will continue to be required by businesses. However, the current method of storing objects is not adaptable to human actions or occurrences. As a result, an events layer will be constructed on top of the legacy CRM systems, spanning instances and data silos.
Every object-oriented system of record will have an AI-enabled events data pipeline on top of it within the next ten years, produced by a new generation of third-party vendors with experience in these new technologies. (An object-oriented vendor who also does SQL will struggle to design a Kafka events data pipeline.)
What is our recommendation to today’s business and IT leaders? Consider your record-keeping system plan. To begin, determine where you only have object-oriented solutions, such as CRM, ATS, ERP, and so on. Then consider the data and insight you’re missing at the event level. Are you missing out on sales opportunities? Are you missing the linked BDR actions when you have a marketing activity? Finally, identify the data gathering and storage gaps so you can address them in your future systems of record plan. CRM systems are on the verge of transitioning to an “invisible” storage and integration layer. Companies that plan for the transition to AI-enabled data pipelines now will be in a much better position to exploit data in the future to maximise customer pleasure and lifetime value.