Artificial intelligence (AI) promises to transform the way every industry operates but it’s one of those technologies that keeps us looking towards the future. Intelligent automation (IA), on the other hand, is very much of the ‘here and now’ and something that cutting edge marketers already have on their radar.
Intelligent automation is the first real AI breakthrough for marketers, making existing tools and strategies that you’re already familiar with more powerful. By applying AI decision-making to automated programmes, businesses of all sizes can tame big data and capitalise on insights that an entire team of analysts would never spot.
Intelligent automation (IA) is where artificial intelligence and automation come together. A standard automated workflow could involve something like automatically running link audits every month or posting new blog articles on your social media accounts as soon as you publish them.
These are linear workflows where a trigger event (e.g. hitting publish in WordPress) results in another action (posting a tweet link to your blog post) without any manual input from you.
This logic can be scaled to create more complex programmes, such as a scripted chatbot that completes automated actions based on user inputs. These programmes can be quite complex and can even interact with other applications, which is often referred to as robotic process automation (RPA).
“Robotic process automation (RPA) refers to software that can be easily programmed to do basic tasks across applications just as human workers do. The software robot can be taught a workflow with multiple steps and applications, such as taking received forms, sending a receipt message, checking the form for completeness, filing the form in a folder and updating a spreadsheet with the name of the form, the date filed, and so on. RPA software is designed to reduce the burden of repetitive, simple tasks on employees.” – Investopedia
An example of this at Vertical Leap is the way that our software, Apollo Insights, collects huge volumes of links about a customer, from all sources, and then identifies low quality links, links that aren’t providing benefit, links that two or more competitors have that the customer doesn’t etc, all using RPA. This quite literally saves hundreds of manual analysis hours each month and is clearly more effective and thorough than if it were carried out by a person.
Another example is how Apollo’s intelligent algorithm, Advisor, analyses colossal amounts of data in search campaigns and performs large scale analysis to identify threats and opportunities, which are then organised in terms of importance and impact for the campaign manager to action. Again, RPA removes the manual drudgery that otherwise would demand so much of a person’s time.
With intelligent automation though, processes are becoming even more advanced.
Thanks to advances in computing power and AI technologies like machine learning, today’s algorithms are capable of making decisions, using complex datasets, and outputting these decisions as triggers for a range of actions.
For example, you could create an automated bidding algorithm for paid advertising that feeds in data for device usage, location, time and audience demographics. This would then determine which combination of factors results in the highest conversion rates and would automatically increase your bids in relation to the expected conversion rates.
To summarise, regular automation completes predetermined actions on behalf of humans, while intelligent automation uses AI to mimic human decision-making and then take the most suitable action.
In May 2019, Deloitte surveyed more than 500 business leaders across 26 countries around the world about their intelligent automation strategies. Respondents were asked about their top three expected benefits from adopting the technology. There were some clear winners from the results.
“Our analysis reveals that three primary benefits are driving uptake of the technology. Organisations expect to achieve increased productivity and cost reduction; greater accuracy, and an improved customer experience.”
According to the same study, business executives estimate that intelligent automation can reduce costs by 22-27% and increase revenue by 11% over the first three years.
A summary published on Harvard Business Review summarises six characteristics of businesses that successfully adopt intelligent automation:
Big data is standard lexicon for marketers in 2020 but managing data at scale is a challenge for any organisation. Intelligent automation doesn’t just replicate human actions but also human decision-making. It’s capable of spotting patterns people would never find the time to unearth from constantly growing datasets.
Talking about productivity and cost-effectiveness is great but what marketing problems can intelligent automation actually solve? Well, data-driven brands are already using technology to scale up their marketing efforts and solve some of the biggest challenges in customer acquisition and retention.
Earlier, we mentioned scripted chatbots as a form of robotic process automation (RPA) and how these programmes can send instructions to other apps, based on user input. However, the most advanced chatbots are using AI to learn from user interactions, make decisions and automate actions with less reliance on scripts.
While AI chatbots still have a lot of progress to make, this is a good example of how AI can combine with RPA to create an intelligent automation system. These chatbots can act as the first line of customer service and technical assistance, allowing businesses to handle a much larger customer base without an ever-growing army of human technicians.
Productivity and cost-effectiveness in action.
With features like Event Measurement in Google Analytics, you can track user behaviour and segment leads based on actions like CTA button clicks. Combined with URL tracking and performance metrics, you can create fairly targeted segmented lists and automate follow-up messages to keep leads moving along the sales funnel.
Even with relatively limited data, this strategy can yield powerful results. The problem is, you’re only ever making decisions based on data from single users or compiling results from your own website visitors.
With AI and machine learning, we’re now seeing a new breed of intelligent segmentation tools that can access data from similar businesses and learn from their customers’ behaviours – as well as your own.
By spotting similarities between your website visitors and millions of other consumers, AI algorithms are capable of detecting subtle behavioural patterns and making accurate predictions about their purchase intent, future behaviours and potential value.
This brings us to the age of behavioural analytics where you can automatically place leads on the most relevant segmented lists, score leads based on their predicted value and focus your efforts on the prospects with the highest calculated value.
As mentioned in the previous section, one of the most powerful applications of AI for marketers is using relevant third-party data to train your own algorithms. Even new businesses with a relatively small customer base can build programmes trained with huge datasets from similar users/sessions.
Some of the industry’s biggest names, including Adobe Experience Cloud, tap into third-party data to make their predictive analytics models more accurate.
Expanding upon behavioural predictions for lead segmentation, you can also use this technology to predict and prevent dropouts along your marketing funnel. You can even calculate what issues are most likely to cause dropouts for specific users in the future – allowing you to take action before problems occur.
You can take this much further too.
By tapping into third-party data, you can accurately predict the lifetime value of new customers (even before they buy) and prioritise your campaigns to nurture customers based on their long-term value. You can predict customer buying cycles and forecast external factors that might affect sales – anything from economic slumps to unfavourable weather patterns.
Prediction is only half of the battle, though. What really drives revenue is turning this data into actions that prevent dropouts, keep your customers buying and increase incentive during difficult times. The more data you have, the more complex and reliable your predictions become, but your automated systems need to scale at the same pace to prevent your marketing and sales teams from drowning in data.
This is the key role of intelligent automation; providing the system that allows you to turn advanced AI insights into action without a team of data scientists. Campaigns can be automated, triggered by specific actions or patterns, and delivered to users when their behaviour signals the timing is right.
Which allows your marketing team to address any consumer interest/concern they identify, knowing that intelligent automation will deliver their message at the crucial moment.
If not, talk to us today about how we can help. Our data science team can help you understand what processes can be handed over to intelligent automation, freeing up significant resources for you to use elsewhere. Call us on 02392 830281 or send us your details here.
Chris is Managing Director at Vertical Leap and has over 25 years' experience in sales and marketing. He is a keynote speaker and frequent blogger, with a particular interest in intelligent automation and data analytics. In his spare time, he enjoys playing the guitar and is a stage manager at the Victorious Festival.
Categories: Data & Analytics
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Categories: Data & Analytics, Data Science
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