AI and Machine Learning Can Fast Track B2B Lead Gen and Sales Productivity

Last Updated: December 16, 2021

By deploying artificial intelligence and machine learning solutions strategically, B2B marketers can meaningfully improve their lead generation efficiency and sales productivity — while also unleashing new capabilities that spark customer engagement-transforming innovations, writes, Neil Michel, Associate Director, Accenture Interactive.

The ever-smarter machine learning algorithms fueling AI are empoweringOpens a new window (some) B2B marketers with new and very usable insights into their enterprise buyers’ increasingly complex – and increasingly digital – buying processes. AI technology is maturing at a particularly interesting moment: across industries, sales productivity has actually fallen by 12 percentOpens a new window over the past five years and the leading cause is, ironically, investment in sales and marketing productivity tools.

Unfortunately, many B2B sellers’ rich appetite for these solutions and what they promise is soured by the reality that they often come with steep learning curves and require extensive efforts to adopt – after which the improvements gained are merely incremental. Given this environment, I believe machine learning-based marketing solutions offer nothing short of a revolutionary leap forward in productivity. I believe we are quickly approaching a futureOpens a new window in which our every interaction with technology — in marketing and beyond — will be infused with AI.

Learn More: Improved Sales Forecasting Through AI and MLOpens a new window

Artificial Intelligence-Driven Insights 

AI-driven insightsOpens a new window are arriving in a post-cold-call world, one in which B2B marketers must be informed about each customer’s challenges and needs if they’re going to have much success in B2B sales. Getting this knowledge right enables marketers to better vet and target the potential customers they ought to approach, optimize the timing of those approaches, and engage with them meaningfully and informed.

While a human marketer can compile all pertinent information detailing a potential customer’s market position – industry news, the business’s key prospects, competition, etc. – substantial time and effort is required to collect, understand, and interpret this array of data points into a usable (let alone well-optimized) marketing strategy. AI, enhanced by decades of training models that have culminated in human-like capabilities to sense, comprehend, and act on data-based marketing opportunities, can gather this same data and perform analysis with machine speed and precision, positioning B2B marketers to offer solutions based on new ways of processing customer data.

Role of Conversational AI-powered Bots

The development of conversational AI-powered bots is an especially interesting addition to the B2B marketing and sales toolset that, in my opinion, ought to be employed more than it is. By aggregating an ever-expanding collection of industry, marketing, sales, and customer service data into machine learning platforms, conversational AIs have advanced to the point that they are capable of engaging with marketers in a human-like fashion, much as chatbots now converse with customers via text. In this way, marketers can communicate with bots to understand how best to approach sales prospects, and maximize the effectiveness and appeal of the value offered or delivered. This can mean determining content or product recommendations more quickly and with greater relevance, or directing ad buying and serving with newfound efficiency.

Going a step further, it can even mean informing product design to include new features and data that users desire through better understanding their behavior, preferences, and pain points; AI-based analysis may be able to surface issues and solutions in minutes that wouldn’t come to human developers’ attention for months. At the same time, conversational AI enables marketers and sales team without any data science background to very easily query data and build reports, simply by speaking out loud in natural language. This means that many, many more individuals within a B2B seller organization can now stand at the helm of vast data sets, and harness the insights needed to create successful new customer experiences for B2B clients – simply by asking the right questions.

Learn More: Using AI to Pull Actionable Insights From Sales CallsOpens a new window

The capabilities of these AIs to sense opportunities, comprehend insights, and take decisive action will only accelerate into the future, as incremental cycles in the machine learning process are driven not just by humans but also by the machines training themselves. This long investment in careful training has now brought the technology to maturity as a strong option for addressing B2B marketing and sales needs on several new fronts. For example, smart CRM capabilities equip businesses to target customers by using new insights, tailoring offerings (and special offers), and qualifying leads in order to reduce risk and maximize sales efficiency. AI-driven commerce and content platforms are also at work optimizing pricing and making cross-sell and upsell efforts as appealing as possible. And these efforts are succeeding: a Harvard Business Review studyOpens a new window finds that enterprises using AI-based sales technology are able to increase leads by more than 50%, while reducing costs 40-60%.

By embracing machine learning and AI technologies now (and now is certainly the time), B2B marketers can realize improved efficiency and new capabilities, sparking innovations that can transform customer engagement; and we can use our own natural language to get there.

Neil Michel
Neil Michel

Associate Director, Accenture Interactive

Neil Michel is an Associate Director at Accenture Interactive, where he delivers business and market insights that drive impactful creative and technical projects. Neil is a thought leader in the field of communications with two decades of marketing experience in B2B and B2C projects across a wide range of roles. He received an MA from the University of California Davis, and has published several award-winning academic journal articles. With a background that traverses creative arts and data analytics, Neil delivers holistic thinking to some of the world’s most successful companies, including Boeing, Nike, Microsoft, GitHub, Hortonworks, Motorola, Carhartt, Carbonite, Northwestern Medicine, and Hewlett Packard Enterprise.    
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