The future of behavioral analytics
The web is growing at an incredible rate. Every day the tech we own is getting more and more out-of-date. But in this world on 1’s and 0’s and shiny MacBooks, where are we headed in the world of digital behavioural analytics? We’re making our predictions right here, right now…
Robotic process automation
Robotic Process Automation (RPA) sounds like a mouthful… and it is. What it really means is making everyday business functions automated, giving you more time to sit back and analyse more. This technology applies to a number of industries including behavioural analytics. Info World describes how RPA works with technology to learn every day business processes, such as customer service, data capture and data mining to make your customer experience analytics better.
If you imagine a customer service enquiry for example, there may be a number of steps to the process. You’ll get the initial enquiry, coming from a number of different sources. You’ll then get a customer service assistant who has to interpret the enquiry and then make a decision on the next best action. RPA learns from this process using a number of technologies we’re already aware of such as voice recognition to interpret and understand these inputs. The clever thing about RPA is that it’s designed to keep learning from this process and tries to improve it.
So what does this mean for behavioural analytics? We predict that soon marketers will start realising they can integrate advanced behavioural analytics technologies with RPA. RPA will be triggered based on customers digital behaviours on websites or mobile apps. And thanks to machine learning, RPA software will be able to learn faster and provide tailor made solutions based on digital behavioural analytics. It could even help solve simple problems like identifying bug errors on site that are preventing customers from converting – all in real time without a human hand in sight.
RPA also solves another problem that we discussed in our last blog – the role of human error resulting in data privacy leaks. RPA takes away the risk involved in solving customer problems that involve sensitive personal data such as credit card numbers and password.
Chatbot analytics and data interaction
A chat what? You may have never heard of a chatbot before but they’re already out there, being used by big brands. Essentially a chatbot is a piece of computer programming software, like RPA, that can chat to us online and solve problems using artificial intelligence. H&M, for example, uses chatbot analytics to understand your fashion preferences. Using an algorithm, they ask a few question to get a sense of your likes and dislikes and then it can offer you suggestions.
But what have chatbots got to do with digital behavioural analytics? Well quite a lot actually. With the amount of data analytics providers can offer, with features such as heat mapping and click tracking at the very basic level, it won’t be long until chatbot analytics will be able to act on this data. For example, imagine you’re an online retailer selling watches. Your heat mapping software is telling you where people click and when people decide to exit certain pages. We predict that, in the future chatbots will be able to act on this real time data to pop up on the page and target users who are about to exit the site and ask “have you found what you’re looking for?’. This will provide you with a temporary solution to try and keep users on your site, whilst alerting you to the issue at the same time.
But it could get cleverer than that. As The Guardian discussed in their article, chatbots are able to handle problems 24/7, in large quantities and perhaps in different languages too. These analytics will offer businesses greater flexibility and respite, allowing them to focus on the more strategic elements of the business. This is something that couldn’t be managed easily by humans.
Chatbots and predictive analytics
With the amount of data we have available, there are already those who use data to try to predict what’s going to happen in the future. We already do this on a macro scale, with improved weather forecasting and so on, but what about on the micro level? What about predicting what will happen with individual sessions on a landing page?
We predict that we’ll become even smarter at predicting, to a very high certainty what individuals on a page are most likely to do, all in real time. This will be based on analysing the historic performance of the web page tied into the individual’s expressed need, what they came into the site searching for and their customer analytics record.
Predictive analytics, like chatbots, will be great tools in helping us to solve problems even before they happen. The two together could be the next Batman & Robin, working together to get results for site users.
The marketer will still need to be there
With all the talk of robots taking over the planet, us humans may feel more and more inadequate. But there are somethings that robots simply cannot do. Some bloggers have already criticized some platforms, such as Facebook Messenger’s bots. Writing for Gizmodo technology blog, Darren Orf described how frustrating the bots were. Their writing was very unnatural and they make “dull conversationalists.”
Although there are advancements in the way these chatbots speak to us; Taco Bell’s Taco Bot apparently has “natural language processing” according to The Drum. This bot can apparently make group orders for starving office workers and can even make recommendations in a ‘natural’ way.
However, this type of technology is still way off perfection and we don’t think it ever will be. No matter what, AI will always lack that human element, simply completing a job that it was tasked to do. The marketer will not only have to be there to make sure the ‘human’ element remains within a brand but to make top level decisions and to test things out.
In today’s digital age, the marketer is still the main analyser and always will be, in our opinion. Machines are only as good as you program them to be but marketers are the ones who have years worth of programming to spot trends using real knowledge and insight.
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