To look at the media, white papers, and economic studies, one would think that AI is used primarily for smart shoes, sentient chatbots, and robotic flight attendants. A 2018 NewtonX survey with 400 executives, however, elucidated where AI is actually generating increased revenue and spurring enterprise growth: it’s not through flashy new technologies; rather, through real-time analytics and optimization in Sales and Marketing.
Of the hundreds of executives surveyed, 88% reported that they used an AI-powered tool in either Sales, Marketing, or both. From tools like Salesforce Einstein to Kiite AI, to IBM Watson, executives are investing in AI tools to optimize sales/marketing.
The investment is paying off: Using AI to personalize advertising and promotions can lead to a 7% increase in MRR (Monthly Recurring Revenue) for e-commerce brands alone. B2B companies reported that using AI-powered analytics and sales tools to identify and reach out to prospects (among other things) has significantly increased their prospect pool, while also refining it to make it more targeted. Other studies have also demonstrated that using AI to personalize customer data for promotions can lead to a 2% increase in sales for brick and mortar retailers.
How AI is making enterprises money
Globally, AI can create roughly $1.8 trillion of value in marketing and sales alone. According to the panel of NewtonX experts, this value add has come from four primary areas:
1. Sophisticated prospect targeting using machine learning and real-time analytics
Matching algorithms (such as the proprietary one that my company, NewtonX uses), allow companies to precisely identify prospects with very little manual input. These algorithms become increasingly sophisticated over time through machine learning, giving companies that adopt AI for prospect targeting early a leg up on competition. Additionally, real-time analytics allow sales teams to have a holistic view of campaigns in real-time, giving them improved insight into the funnel.
2. Robust customer data profiles and cohorts updated in real-time for personalization
AI and robust cloud storage have enabled a new era of customer data in which dozens, if not hundreds, of sources feed a single customer profile. Where ten years ago these profiles tended to remain fairly static, today real-time analytics and data processing has enabled real-time profiles and dynamic customer cohorts. This has significantly impacted both marketing and sales: marketing teams can tailor messaging to updated customer cohorts, and create triggers for specific updates in customer profiles, while sales can likewise tailor messaging to real-time profile and cohort changes, as well as target prospects based on changes in specific variables.
3. Dynamic pricing and promotions
I recently wrote about how AI can help sales representatives with deal scoring — Advanced analytics solutions that integrate with CRMs give representatives relevant data about a proposed deal and comparison points to their colleagues who made similar deals. The data can give a snapshot view into the quality of the deal based on the proposed percentage discount, and helps bridge the gap between corporate objectives and on-the-ground sales realities. This helps avoid excessive discounting as a result of representatives trying to meet quarterly goals.
4. Customer service automation
Customer service automation has garnered significant hype over the past two years. The most discussed technology is chatbots, but auto-ticket classification, automated ticket triage, and real-time analytics have also had significant impact on the industry. Traditional customer service is costly, time-consuming, and largely unsatisfactory to customers. Business executives are investing heavily in new AI-powered technologies to combat these three problems.
These key business functions — lead generation, marketing personalization, pricing, and customer service — have generated revenue through automation, efficiency, and new capabilities (such as real-time updates and matching algorithms). 56% of executives surveyed by NewtonX reported that they use AI in more than one of these areas. The first two categories had the highest adoption, but companies reported investing the most money in customer service automation (which, as we’ve previously written, is currently expensive and difficult to adopt effectively).
As these technologies mature, early adopters will be rewarded through efficiency gains and precision improvements. Those who leave the $1.8T opportunity for using AI in sales and marketing behind will either play catch up late in the game, or cede large losses to their competition.