Algorithms and (Artificial) Intelligence: Hype vs. Reality
ALL THAT has transpired over the past year has hugely affected how business is done—so much so that it can be hard to make rhyme or reason about technologies and how and why people engage with them. Common knowledge going into last year was that if you empower good people with good processes and the right technology, you end up with good outcomes. What we have come to realize, especially with CRM, is that it is critical to understand what you are deploying and what the end game looks like.
With your business’s tech stack, you must understand what the underpinning technology is attempting to achieve, whether it solves a problem you have or is just another loud flashing buzzer at the fair that gets your attention but does little else. The key here is to understand the difference between code and intelligence.
Circa February 2020 many companies started to see exactly how bad their CRM data was and quickly spent crazy amounts of money trying to address it. When these companies began to solicit enablement tech stacks, one thing became clear, as Michael Lewis paraphrased in Liar’s Poker: “Warren Buffett is fond of saying that any player unaware of the fool in the market probably is the fool in the market.”
What does this mean vis-a-vis code vs. intelligence? A code-based tool relies on an algorithm. In other words, the code will move forward, based on a specific use case, to bring you data points. Those data points could be bad or irrelevant—or wonderful, deep, and expansive. Either way, it is a crap shoot that’s dependent on the market you serve, the algorithm’s relation to that market, and the frequency of data updates.
If all of this is starting to make your head spin, welcome to the club. That’s why enablement solutions are starting to focus more on intelligence. An intelligence tool presents data in a way that considers the information you’ve gathered and how you use it; learns from the data to get better at making recommendations; and then refines it. The byproduct will generally result in less data, updated less frequently, but will be more impactful to your business.
THE “SALES INTELLIGENCE” EXAMPLE
When consulting with a client recently, I took part in an intense boardroom talk about sales intelligence, how it differed from CRM intelligence and how it fared against data intelligence. It was a fun exercise to dissect these concepts and come to some conclusion. And we found interesting examples of how this plays out in real time.
For those unfamiliar with sales intelligence tools, they often combine information from platforms such as email, websites, and conference calls to provide sellers with insights or data points that can help drive results. Their advantage is that they easily integrate into popular CRM tools, but their potential drawback, it turns out, is that they often operate with the purpose of sequencing information for sales organizations. Example: How many conversations did we have with a C-suite executive; how long were those conversations; and what were the percentages of each person speaking? These items are collections of data, but do they help answer the question “How is this going to help me win a deal?”? Not always.
Closely related to sales intelligence is CRM intelligence. And while there is intrinsically lots of value in combining transactional information with people information, the intelligence from AI solutions based on CRM data is only as good as the model and the underlying data. When the latter’s quality has not been addressed, how good can the model be, and therefore the CRM intelligence?
Finally, there’s data intelligence—a solution that scrutinizes data, learns from itself, and provides only valuable data points to other platforms, like CRM, that can influence outcomes. A data intelligence solution would provide insights on key decision makers beyond just their title, how many times one of your employees had an interaction, and for how long. The ability to get more and better insights, combined with operational, transactional, financial, and even people-engagement data, can truly flip the script on how you approach solutions that work on top of your CRM or CX platform.
Before wading through the acronym alphabet soup of AI, CPQ, ACV, CLV, BI, MDM, and ERM, maybe an exercise in understanding what outcomes you need will make the process of evaluating solutions much easier.
Danny Estrada is Director, Enterprise Solutions, at Introhive, and has spent more than 25 years helping organizations implement and adopt CRM platforms. Throughout his career, he has been an author and thought leader on adoption, as well as a speaker for many industry leaders like Salesforce.com and Microsoft. His experience includes leading a CRM consulting practice and serving as a management consultant across hundreds of CRM implementations. He was recently featured as a thought leader in Harvard Business Review’s “Winning with Client Relationships” white paper.