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Predictive Intelligence, or How AI Transformed Selling in the Enterprise

Manoj RamnaniIn this special guest feature, Manoj Ramnani of CircleBack higlights the trend of AI in the enterprise and how it’s affecting sales/marketing.​ Manoj is the founder and CEO of CircleBack. With over 15 years of entrepreneurial experience in the information technology space, Manoj’s expertise encompasses everything from developing successful consumer products, enterprise solutions, and mobile applications to starting businesses in the healthcare, e-learning, and federal verticals. Manoj graduated from George Washington University’s business school with a degree in management of information systems.

It’s no secret that artificial intelligence (AI) is forever bound to the future of enterprise sales and marketing. Between the high demand for self-interpreting analytics and the limitless streams of useful customer data pouring in through every crack and seam, AI – and its equally useful counterparts machine learning, natural language processing, etc. – are seen as obvious solutions.

After all, AI tamed Big Data and, with the abilities to process billions of pieces of data and predict future possibilities nearly as fast as information enters their cores, they stand to offer an incredible variety of applications across both B2C and B2B ecosystems.

But, I would argue that we’re just now beginning to see their true values. Yes, they can help us personalize our customer interactions, complete cost-efficient, impactful media buys, and identify trends in our current sales and marketing behaviors, but where they really shine are their abilities to offer predictive intelligence about the enterprise landscape.

On the surface, that proposition – that AI has changed the enterprise landscape forever by offering predictive intelligence about the enterprise landscape – seems circular and self-justifying. It really isn’t; what AI offers the enterprise space is the ability to truly understand the enterprise space, to gain real-time understandings of industry trends, competitor performance data, and even org. charts and decision-maker identification.

And the value in this is twofold: It allows businesses to take hard looks at themselves and their relationships to competitors and other industries, and, more importantly, it’s created an incredible opportunity for inside sales/marketing to truly understand – and sell to – other businesses.

These are the most important advances we’ve seen to date:

Predictive Business Intelligence

Understanding business growth trends is a radical advantage to any particular B2B seller. It allows sales/marketing to be deployed very specifically so that, for example, Hubspot doesn’t waste resources selling their enterprise marketing automation to a seed-stage, bootstrapped startup. Instead, the AIs driving these platforms are able to build models from existing company data and offer everything from advice on which markets will have the best product fit to actually discovering and generating lists of businesses that fit any given criteria.

Tech Stack Discovery

Similar, in many ways, to predictive business intelligence, tech stack discovery allows businesses to develop an understanding of which products another business uses, enabling them to develop highly refined sales pitches. However, rather than looking at industry data and basing decisions on growth, industry, and financial viability, these programs utilize various natural language processing algorithms to parse job descriptions – both titles and employment requirements – and then infer the products being used. This provides inside-sellers with obvious advantages as they can prepare side-by-side breakdowns of their own products against competitors, leverage price gaps, etc.

Decision-maker Identification

Because cutting costs and driving up conversion rates is a priority in any business, decision maker identification tools have become highly important in B2B selling. These tools allow businesses to identify relevant decision-makers so that, instead of delivering marketing and sales messages to, for example, “someone in the IT department” and hoping it makes it way to the top, businesses can target more strategically and deliver their messaging to decision makers and those who actually perform / approve large purchases.

These tools aren’t necessarily AI-driven, but those that aren’t are a dying breed. Rather than having a staff of hundreds calling businesses in an attempt to build an org chart, AIs look at millions of pieces of relevant data – email signatures, CRMs, news and press releases, social media, blog posts, corporate sites, etc. – to construct, refine, and maintain accurate, targetable org-charts.

Highly Personalized Messaging and Deliverability

AI has done a tremendous job in allowing both marketing and sales to personalize messages and deliver them accurately. By looking to all the data we have available about potential customers, AI is able to offer incredible insight into the best language for messaging, the best channels for distribution, the best subject lines for emails… the sky’s the limit. Add to this the profound intelligence AI brings to ad deliverability, and the only thing slowing us down is the negative affect voiced by consumers when targeting is “too accurate.”

When it comes to enterprise selling / marketing, AI offers a true powerhouse of advantage. Armed with insight about a target’s growth stage needs, their current tech stack, the decision-makers, and how those decision-makers like to be talked to / messaged, AI-generated predictive intelligence offers enterprises the ability to truly understand and target the landscapes they operate in.

 

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