When it seems that in ten years time we will all be talking to our toasters, Alan Turing’s famous question is more important than ever before: Can machines think?
Artificial intelligence (AI) and machine learning (ML) are terms that imply they can.
Lead generation is the start of a marketing process that seeks to identify potential customers and cultivate their interest. B2B lead generation differs from that of B2C because it often has to appeal to teams of people at companies, rather than individual consumers. This typically means lengthier sales cycles and a narrower audience scope, making B2B lead generation a challenge for marketers.
The in-depth analytics that AI can provide a B2B marketing team, can champion them toward stronger lead generation strategies which will drive company growth and ultimately increase revenue.
What is AI?
AI in popular media is known as a computer’s ability to imitate the intelligence of a human. But many academic definitions avoid drawing similarities between human intelligence. Instead, they define AI as a computer’s ability to act rationally, and give an ideal performance upon receiving information.
In the current tech world, AI can be heard through the voice of your Alexa, or seen in the smile of the emoji you send to a friend. From showing you a hilarious dog meme, to driving airplanes and cars, AI makes the technology you use seem like a genie at your fingertips.
What is machine learning?
As a subset of AI, machine learning can dive deeper into given information by identifying patterns in data and using them to determine the ideal way to act. These autonomous decisions occur without algorithms being explicitly programmed by a human.
So when Netflix suggests a reality dating show you pretend you don’t love, or when Google knows you want a greasy shawarma if you search ‘nerest kebva shp’, machine learning is working behind the scenes to provide you with a personalised internet experience. It uses the information it already knows about you, and makes a calculated guess as to what you want to see from what it has to offer.
AI and machine learning promise us great strides in the way of working with data; strides that B2B marketers would be foolish to ignore…
And foolish they are not! In 2016, a study by Demandbase revealed that 80% of all marketing executives believe AI will revolutionise marketing by 2020. Let’s see if they were right!
How can AI improve data collection for lead generation?
A B2B buyer’s journey should begin with a strong value proposition. This means that visitors should gain something valuable from landing on the site. Potential clients may want to be educated, either about a topic that interests them, or about a product/service that entices them. AI can provide analytics that identify user’s needs, challenges, and behaviours. This makes it much easier to post content that draws the right audience of people who would be interested in your product or service.
Data can be acquired by a human, but it requires a huge amount of time and resources to collect and interpret, even before developing a marketing strategy!
AI takes all the work out of information collection, organisation, and analysis.
With a wide scope of website visitor data, marketing teams can see which customers are high quality vs low quality leads. This allows them to spend more time on developing strong strategies that will create awareness through an inbound method. In creating awareness, you can draw the right people to your brand.
AI might have even drawn you here.
What kinds of data can be analysed to improve B2B lead generation?
- Text data – it can trawl through emails, social media posts, news articles, and conversations. It uses natural language processing to understand themes, key words, and even emotion displayed in online language! Aylien is one of many text analytics tools that can understand human language across multiple platforms at a large scale.
- Journey data – this measures a variety of visual elements to determine which images, colours, and buttons gain the most clicks and the most time spent on a page. Could the colour of your landing page be scaring people off? Alterian’s Real Time CX Platform is a journey analytics tool with inbuilt AI to make decisions for you to optimise the performance of particular touchpoints.
- Trends data – this gives insights into specific industries to provide a measured scope of competition. This can give marketers a competitive advantage, as they know how to position themselves within the market. Black Swan Data is a tool that trawls through social media conversations to track keywords and themes over time to predict if trends will incline or decline. This can help in identifying growth opportunities and pushing companies towards the helm of an industry or niche.
How can machine learning impact lead generation?
In B2B marketing, where AI has revolutionised what you know about potential clients, machine learning is revolutionising the way you interact with them.
By relying on autonomous technology to form relationships, every approach can be personalised, meaningful, and engaging, without the hard work of a sales team. This will maximise the amount of trust built as a client forms their own personal user experience.
Machine learning algorithms can optimise an inbound strategy by identifying a user’s intent, tracking their behaviour on a site, and then adjusting the content to present customised information.
For example, machine learning can distinguish between prospective clients looking to be educated, and those who are looking to get in contact with a representative. This distinction can tailor a recommendation or an email pop up. It can even offer various website designs or colours. Some people might really prefer burnt orange over cerulean blue.
Based on a users past behaviour, not only can machine learning suggest where a user might want to go next, or what they might want to see, it can also stop users from exiting your website altogether.
For a B2B marketer, content is key to a successful inbound strategy. For the past few years machine learning has been slowly brought into top media companies to automatically generate stories that seem like they were written by humans.
In 2017, The Washington Post’s robot reporter Heliograf, wrote 850 articles. Robot reporters might soon be writing most of the articles you read!
Can humans still take part?
Although AI and machine learning can offer a great advantage to B2B marketing teams, it is important to leave strategic decision making and emotional insights to humans. We may not be able to collect, retain, and analyse millions of data points, but we can provide plenty of valuable insight with our personal experiences, intuition, and unique perspectives.
Human reasoning and emotional intelligence give us empathy incomparable to any other animal or computer. In order to connect with the people that run the businesses we are targeting, we must use our own interpersonal skills to strengthen the relationships with leads AI may have helped us attract.
It is important to enforce this idea with a marketing and sales team, who might be resistant to AI in fear of being replaced.
It is predicted that by 2022, AI will replace more than a third of analysts in marketing organisations.
But don’t worry!
Marketing teams will now have more time to focus on other priorities. Managers should let their teams know that human communication with clients is always valuable throughout the marketing strategy.
Hold on, something doesn’t quite feel right…
There is an undoubted ethical concern around machine learning in marketing, which makes discriminatory decisions that fuel systematic bias. There is a danger in knowing a person’s age, gender, and ethnicity from one profile picture. This puts internet scrollers in a vulnerable position. We only have so much control over what we see online, and the rest is left up to advertisers.
Even if a marketing team denies discriminating against their potential clients, they might unknowingly be using questionable machine learning applications that are built into popular tools such as Facebook Ads.
The Facebook Ads algorithm has previously come under fire for being racially discriminatory. For example, one study in 2019 found that ads for new houses were shown to majority white Facebook users, whereas ads for rentals were shown to minorities. These machine-lead decisions make AI and big data seem like a serious threat to equality.
AI has a long way to go before it can be considered unquestionably ethical. But the good news is, studies are being done to unveil machine learning’s moral downfalls, and AI researchers are already working on practical solutions to these challenges.
AI and machine learning should be embraced by the marketing industry. It presents us with an unprecedented amount of knowledge, and puts the power in our hands to steer businesses in any direction we see fit.
Maybe in the future we will be befriending or falling in love with computers, but for now, AI hasn’t caught up to our thousands of years of evolution. Let’s hold human values at the core of our marketing efforts. Let’s keep speaking human to human.
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