If global lockdown has taught us anything, it’s that people really cherish their connections to the outside world. We’re being reminded of what is important – and connectivity is high on the list.
The workplace, meetings, tradeshows, festivals – all of these have had to be conducted online, meaning B2B businesses have an opportunity to really cement their standing with prospects, and existing customers, via empathetic, relevant personalised digital experiences.
Because of this, organisations need to be more agile. As lockdowns continue to be turned on and off, they need to respond quicker, with ways to engage that fit the moment. The pandemic has forced marketers to think of new, innovative ways to generate revenue, which has in turn pushed them to evaluate the technologies they are using in order to do so.
For organisations that have already baked Artificial Intelligence (AI) into their technology systems, this is easier than for others. And, in a world where shifting sands might be all we have to base strategy on for the foreseeable future, marketing automation will increasingly become a must.
The popularity of AI is growing, with the global AI software market expected to reach $23 billion by 2025, according to data from Statista. But with it being such a vast ocean of a topic, where do organisations begin?
Here are four key tenets that those in the B2B marketing space should understand before dipping their toes in.
1. Define the problem before seeking the solution
The challenge is not to find the technical solution but rather to define the problem precisely enough to let AI address it. This is a crucial mind shift from thinking ‘AI is the answer’ (how) to considering, ‘what is the problem that we are trying to solve’ (what).
The better the question, the more useful the answer. It may well be that the current situation will catalyse this kind of thinking, while many industries face some new and very real challenges.
Many B2B brands have found that website and Facebook messenger chatbots can help capture leads and provide visitors with more information. Printer manufacturer Epson has also harnessed the power of AI to send out fully automated emails that seem like they’re from a real person.
While these are still fairly practical and simple applications of machine learning, businesses can apply AI in the analysis of hundreds of factors that can speed up decision making when those decisions need to be made and acted upon quickly.
2. From blank slate to cunning machine
Any AI system starts as a tabula rasa, a blank slate. Its efficiency depends on what we teach it to do. AI analyses any data set far more deeply and effectively than we can, but we cannot expect more from it than we give.
If we teach a network to predict marketing trends based on a set of data, it will be extremely useful but only in that particular dimension. AI is only ever as good as its teacher.
AI has come under fire in the past when it comes to issues around, for example, bias in recruitment processes. While the initial system is free from any kind of biases, assumptions or instincts, as data is loaded in, some biases may become learned behaviour. So, it’s incredibly important to test a system periodically (as it learns constantly) against any unwanted outcomes that may result in unintended exclusion or discrimination.
3. Data is everything
Efficient AI learning hinges around human understanding of what constitutes a proper data set. The general perception tends to be that the bigger the data set, the higher performance expectations can be. However, it takes both quantity and quality to determine the efficiency of the machine.
For example, if we want an AI application to predict future clothing trends, we need to make sure we combine various dimensions of the historical reference trends (color, material, pattern, length, etc.). If we want to teach AI to identify trees, but we only present birch trees as learning examples, then we shouldn’t be surprised when it doesn’t recognize an oak tree.
4. Neither man nor machine are infallible
One key thing to remember is that AI systems make mistakes. There is no system free from them. And there is a reason for it. Systems imitate human-like reasoning and the ability to generalize problems is their biggest advantage over classic software programming. However, they also inherit our fallibility (although it is less marked than in humans). So, any application will always require some form of human audit. No action should be left solely to machines (at least for now).
Still, AI is a powerful companion that B2B brands can benefit from in many ways. For example, it can help brands in heavily regulated industries avoid compliance problems when posting to social media. CRM tools are now starting to leverage AI features, like predictive analytics and machine learning, to identify patterns and trends in customer behavior that can help identify marketing leads and alerts the human user so that they can act on them.
We can apply all the best practices to artificial intelligence applications, but it is ultimately humanity that dictates success or failure. B2B customers may be making rational decisions on behalf of their organisations but they still expect engaging, personalized and human experiences. So remember, when baking AI into your B2B marketing toolset, that man and machine must form a perfect team to build success in any AI driven project or application.
This article is written by Jedrzej Osinski and originally published here