Transcending the term buzzword, AI has proven it is here to stay and is manifesting itself in many different ways; from futuristic ‘robots are taking over our jobs’ visions up until basic voice search solutions. It’s easy to feel lost in the search of how AI can be of any help for your business right now. The technology has been around for decades, but practical solutions for marketing are fairly new. So, is it necessary to invest in heavy machinery to be able to use AI? Luckily, the answer is ‘no’. Because by bringing it back to machine learning there are plenty of possibilities available right now.
Machine learning is a technique to make predictions, based on historical data. Which is a broad definition, so before you start using this technology ask yourself what sort of predictions are valuable for your business. If you can answer that, dig deeper to specify what sort of data is necessary, what you already have and what third-party or open sources you could use. Having that set-out makes the right usage easier. Or the other way around: what data do you have available, and what kind of predictions could you make out of it?
The future looks bright, start now
As Kai-Fu Lee describes in his book ‘AI Superpowers’, the technology comes in four so-called ‘waves’:
- Internet AI: mainly used by big internet players, the FAANG (Facebook, Amazon, Apple, Netflix, Google), to make their services smarter.
- Business AI: this is very applicable for many businesses like banks, telco’s and utilities. It’s about using business data in predictions with algorithms.
- Perception AI: usable for consumers. Think Google Glass, which was a first (although not very successful) step into consumer AI. We will have more and more applications reaching consumers.
- Autonomous AI: this is very much in the future, like self-driving cars and autonomous warehouses. We’re working on it, but we’re not there yet.
Although it is tempting to start dreaming about how AR glasses will unlock the full potential of perception AI, or if drivers licenses are part of the past any time soon, for now, the second wave is the most relevant for businesses. That’s where the biggest opportunities are at the moment. Why? Because most companies are not Amazon or Google, their data, knowledge and tools can be used in a smart way to enhance businesses now.
Four layers for business AI
Within business AI there are many ways to embed the technology. Breaking it up into four layers gives a clear overview of how to make business easier. An important thing to note is that the focus is not on building self-driving cars, but creating smart enhancements on your existing service.
Cognitive services
By using existing cognitive services like a Vision API, customers get to insert data on request much easier. Take speech (to text) & text to speech API. You can use this to monitor your call centre, to record and transcribe all conversations taking place. This data can become very valuable in the coming years when you want to train your robocall centre agents with appropriate dialogues. To automatically subtitle instruction videos. To convert text to speech can be valuable to switch over from a human agent to a robot-agent at the end of dialogue to read out specific terms and conditions and end the conversation politely without wasting human agents time. The next step in this would be natural language and dialogue API, with which full robocall centre agents are a futuristic possibility.
Use your own datasets
Leverage the power of transfer learning by feeding algorithms with existing knowledge. There’s always a starting point, but the convenient part of algorithms is that the first level of knowledge is only to expand and improve. For example, when you have a large catalogue with images of products, you could use transfer learning to get a model which specialises in accurately identifying your products. This technique uses the baseline of the general image recognition models, but then you train it a bit further with your own limited labelled image sets. So, if a consumer wants to place a repeat order of a pair of jeans, he or she just has to take a few photos of his old ones to get a personalised suggestion of a new pair in the current collection of your brand. This is applicable to all types of products.
Yield management
When you have inventory that you need to sell, you want to make sure that the remaining inventory is connected to pricing and advertising models. Because knowing beforehand how much you have left and how much extra budget you need to spend on selling the remaining part really boosts efficiency. Connect the data you collect per season to optimise pricing and advertising strategy, therefore saving budget, or spending it more efficiently.
Segmentation & targeting
Basically, this is the classic advertising game, but on steroids. Customer segmentation for personalisation is one of the best practices by now. However, you can take it to the next level by integrating back-office data in order to get a better view of real customer lifetime value. It will give you detailed information on real-time conversion behaviour in certain consumer groups. Use these insights to choose customised targeting. On top of that, historical behaviour also shows you the worth of each type of consumer. Take the die-hard shoppers for example. They may sound like the perfect customer to target to buy more, but when it’s the group that also returns 80% of every order, they need a different approach as their intention differs from other groups. Targeting to order more, just ends up in losses for your business. Connect your internal first-party data sources (CRM and ERP) to your advertising and online data to pick out the best customers. With segment-based buying behaviour, you’ll be saving on your marketing spend and end with increasing revenue.
Just do it
For all these examples there is a pretty common denominator: the better the quality of your internal data – and the easier the availability of this data to your marketers and CX builders – the easier it becomes to start working on these enhancements. It’s actually pretty simple, you just have to put your back in it and use the enormous amount of opportunities and services available to incorporate AI into your marketing. So what is the road to AI? To just do it.