Summary: This week on the AI in Sales podcast I talk with Chris Orlob of Gong (http://www.Gong.io) as we discuss using artificial intelligence to enhance and make more effective, sales conversations with clients. Chris gives us a peek to what Gong is doing to change how salespeople converse and engage clients in today's market. All this and an interesting study on the use and timing of the words "We" versus "I" in a conversation.
Summary: This week on the AI in Sales podcast, I speak with CEO Nick Nikolaiev and CTO Alexander Leonov who co-founded the company TaskPace who provides an AI Sales Management Assistant that helps managers focus sales teams on result-oriented activities and keeps them on pace through automated task management. In this podcast Nick and Alex talk about some of the shortcomings in CRM systems and how their application can complement a CRM and augment a salesperson's performance using a Task Oriented Interface.
As the new General Data Protection Regulations (GDPR) go into effect today, companies are scrambling to either comply or deny service to subscribers in the European Union (EU). There is a lot of ambiguity in the GDPR and many subject to interpretation that I’m sure will be a boon for the legal industry.
There are two things you need to be aware of with GDPR:
If you're a U.S. company doing business with folks or companies in the EU, this includes you.
This weekend saw the marriage of Prince Harry and Meghan Markle. I was at the airport on my way to a speaking event so I got to see it on the overhead monitors. The number of people that were glued to the screens was more fascinating to watch than the wedding itself. It's estimated that 18 million people tuned it to get a glimpse of the nuptials.
As all this was going on, my mind was focused on another royal wedding, the marriage of Artificial Intelligence (AI) and Sales. Okay, maybe not royal but certainly its impact in the business world and our economy has already exceeded the 18M people. It's estimated that 61% of businesses implemented AI in 2017, up from just 38% in 2016 (source: Narrative Science, 2018).
Business leaders have been facing the cave wall watching the shadows of an Information Revolution that has shaped our decision-making process in the last couple of decades. We’ve reached the limits of spreadsheets and macros to help with our decision-making.
Today, the ability to see the “angles” in a given market requires a higher order of intelligence just to navigate a highly commoditized business environment. Artificial intelligence, in the form of a new Predictive Modeling paradigm, is breaking the chains that bind, and only those leaders who choose to leave the cave and see the new reality that will succeed in the decades to come.
Unfortunately, predicting with certainty what a human being will do at a given time when presented with a given option at a some given price, is impossible. Prediction is all about probability or something close akin (e.g., “likelihood” or “confidence” that some given thing will happen). Machine Learning is about taking Big Data as input and building a Predictive Model that might be used to “score” the likelihood of something happening.
Last week while presenting at an AI (Artificial Intelligence) Summit, I was struck by how many companies were still struggling with the question of how to implement AI in their business. After some reflection, I understood that the reasons might simply be that AI appears ‘too big’ and there are simply ‘too many’ unknowns.
Therefore, I created this 9-Step framework for companies to use as a starting point for the, ‘Should we implement AI?” discussion.
Step 1: The first step is choosing a department or area of concentration you feel AI might be useful. Is there a department that is lacking in data, efficiency and/or results?
Summary: In this week’s AI in Sales Podcast I speak with Basil Fateen, CEO of HireHunt.com, an interactive recruitment platform that allows applicants to connect with jobs by showing who you are and what you can do,...far beyond what a simple resume/CV can transmit. In this podcast Basil talks a bit about what's wrong with today's traditional recruitment process and the inherent biases involved in hiring an applicant that can hinder a company's decision making process. He also describes how HireHunt is using Machine Learning to create a different type of job match-making experience that fits today's real-world demands. We discuss how their platform uses text messages or email interactions to qualify a candidate and what the system looks for. All this and a gamification assessment example that quite literally was something I would never have thought of.
Summary: In this episode of AI in Sales, I interview Bruce LeWolt of www.JoyAI.com, an AI platform for you can use AI to connect with the right people in an organization, understand how they buy and use vocalization to resonate with clients to increase your close rate. We also touch on how to use the latter to improve your cold calling.
Summary: Salespeople can sell more by incorporating data science and technology into the way they actually sell. Data science offers reliable answers to the questions salespeople ask most often, such as: Where should I focus my time? Who should I sell to? What actions should I take? Data science can do this because it is able to weigh thousands of attributes that make up an ideal prospect or next best action in a way that the human brain simply cannot. Reps need this superpower incorporated into the way they work both in and out of the CRM. Host: Victor Antonio with Guest: Gabe Larsen, VP at InsideSales.com
Last night I was watching the movie, AlphaGo on Netflix. If you’re not familiar with AlphaGo, that’s the deep-learning artificial intelligence company that developed the machine ‘DeepMind’ that beat Lee Sedol (seated on the right); a 9 Dan level Go master. The game of Go is far more complex than chess and the defeat of Sedol harkens back to DeepBlue beating the Russian chess master Garry Kasparov in 1997.
In the best of five matches of man (Sedol) versus machine (DeepMind), DeepMind won four out of five.
What really caught my attention, and the world for that matter, was Game Two when the DeepMind machine made a move that (1) no one expected, and (2) that no human player would ever consider making! The move was ‘intuitively’ obvious…only to the machine!