B2B applications of AI in marketing: Two use cases that matter
Columnist Daniel Faggella predicts the ways artificial intelligence will shape the future of B2B and takes a look at two current examples of how AI is being used in marketing to improve processes and services.
Artificial intelligence and machine learning are proving to be very useful in just about every business function in the enterprise, and marketing is no exception. AI is already impacting marketing, and it’s going to further shape the future of how business is done and how relationships are forged between companies and their clients.
As I wrote recently in MarTechToday, most AI in marketing applications are focused on B2C use cases, many of which we’re very familiar with as consumers ourselves. Most of us know that the ads that show up on Facebook, on banners or on Google are targeting individual users directly based on past behavior, demographic data, location information and more — a process that couldn’t be done at scale without the aid of AI.
How B2B marketing can benefit from machine learning
For companies that sell to businesses, communication between salespeople and marketing teams is critical. A day in the life of a salesperson is often chock-full of tasks that could be seen as marketing-related. Educating and training customers, following up via email and scoring leads are all possible gray areas at the intersection of marketing and sales — and, as it turns out, AI applications in marketing are aiming to tackle a lot of these critical functions.
I decided to analyze two particularly important examples of how AI is being used in marketing today to improve processes, make suggestions and find solutions in a business-to-business context.
Use Case 1: Boosting lead generation
For decades, B2B lead generation has been a process of hours of human research into different companies and categorization of people with purchasing influence within each company. In this instance, the value of AI lies in the machine’s ability to identify and generate B2B leads that grow your company’s database.
LeadGenius does so by picking out the top decision-makers with buyer roles in each company, giving you a direct contact to several potential new clients. Once the potential clients have been targeted, the tool generates a target list and highlights key data points to help you segment your audience and develop personalized messages.
LeadGenius can save your sales team hours of manual searching for target clients, giving them a solid pipeline to work with — one which they can add custom details to any time they wish. This leaves them plenty of time to focus on discussing sales and closing deals.
In talking with AI marketing executives, it’s common to hear the notion of “the audience of one” — an ideal state of marketing where campaigns and messages are dialed into an individual level (rather than a demographic group or identified market segment).
Publicis.Sapient is a global multinational advertising company that manages around $90 billion of advertising spending for its clients, both B2C and B2B firms all over the world. In an interview with the company last fall, we discovered that even traditional advertising firms are making the shift to this same dialed-in AI approach. Josh Sutton, lead of Publicis.Sapient’s artificial intelligence practice, told us in a podcast:
It used to be that we’d determine who would be our customer by generating personas and avatars… and now it’s about collecting data and finding what makes a good prospect at an individual level. So instead of making assumptions — we’re increasingly looking at the individual data-points and patterns … and we’re often surprised by that.
I imagine that once hyper-targeted lead gen becomes a norm for the clients of Publicis.Sapient, it won’t be long until the SMBs (small and medium-sized businesses) of the world need the same kind of technology to keep up in the marketplace.
Use Case 2: Analyzing sales calls
No matter how large or small a B2B company is, the phone is going to be an important part of the sales and marketing process. The problem is, it’s very difficult to track, analyze and improve these calls, for obvious reasons. A number of companies, such as Qualtrics and Marketo, have latched onto San Francisco- and Tel Aviv-based startup Chorous.ai’s “conversation intelligence” solution.
What Chorous does is record, transcribe and analyze all sales calls using natural language processing. The tool can join in on conference calls in just the same way a salesperson does, but while recording and transcribing the conversation in real time, the tool will also highlight important topics that crop up during the course of the call.
For example, Chorous may flag moments when a potential customer mentions pricing, the name of a competitor or pain points. These indicators can be used to help your sales team develop deeper customer insights and improve on how they close a deal. This kind of “labeling” of instances within a sales call would normally be a laborious (and annoying) task for humans.
Chorous aims to eliminate this problem. Companies can understand their sales calls more easily, find the key issues of the conversations, and decide how teams could respond differently to achieve better outcomes. The platform is easily integrated with the most popular online meeting platforms, such as ClearSide, GoToMeeting, Join.Me and WebEx. I imagine that other sales and marketing enablement technologies will follow suit and allow for interoperability with existing tools.
And indeed, there are plenty of other well-funded companies chasing the same market — many of which do connect directly to established CRM (customer relationship management) and call recording technologies. Qurious.io has predicated its value proposition on the automated analysis of sales calls, and finding patterns and scripts that work (read: close deals), allowing sales and marketing teams to use the same language and patterns in their own efforts, replicating success at a very nuanced level. And there are a number of companies in the sales enablement machine learning landscape with a similar focus.
A look ahead at B2B marketing AI
How business owners view machine learning technology has changed rapidly over the past five years, with many companies becoming more enthusiastic about adopting the new technology.
According to Meabh Quoirin, co-owner and CEO of the Foresight Factory & Future Foundation, marketing departments are eager to incorporate AI into their businesses, mainly because it saves so much time, which means there is more money to be made. As Quoirin told the American Marketing Association:
Broadly speaking, we tend to find that as soon as people are using [technology] like this in a context where it helps them get things done faster, they adjust to that convenience very quickly. What we see is that it is a question of “when” rather than “if” with AI. But it will happen bit by bit.
AI in marketing will undoubtedly continue its rapid expansion, though it’s likely that large companies with big budgets (AI integrations can be costly and require specialized skills) and huge amounts of historical and real-time sales and marketing data will be the first to benefit. Vendor companies will be quick to downplay the time investment in setting up their technologies
But as with other marketing tech developments (like CRM or modern marketing automation software), the marketplace will naturally bring down the integration cost and learning curves, and SMBs will be able to access AI’s abilities almost as well as their larger competitors (in both data and bank accounts) in the Fortune 1000.
We are already seeing how technology is changing the concepts of who consumers are, how they interact with marketers via technology and how this tech is becoming interwoven with the marketer/client relationship. As a result, companies are becoming more savvy about how consumers represent themselves online and how marketers can use this information to increase their revenue and improve their services while enhancing the customer experience.
Opinions expressed in this article are those of the guest author and not necessarily MarTech Today. Staff authors are listed here.