Top 3 ways digital marketers can harness the power of AI
2019-08-02 16:10 Friday
These days, digital marketers have access to a lot more data about their customers and users. These treasure troves of numbers and figures allow them to deep dive into the mindset of clients, and figure out exactly what drives their desires and motivations.
For example, ad platforms such as Google and Facebook now offer their customers a wealth of data about who is seeing their adverts and how they are interacting with them on a step-by-step by basis. The backend interfaces allow marketers to create targeted funnels and actions to help guide customers along the buying path as never before.
What's more, code embedded into the company's own website and social media pages allow marketers unprecedented opportunities to create a whole ecosystem of ways to attract would-be purchasers and take their digital efforts to another level.
However, there's no denying all this data is a bit complicated to handle. This becomes especially apparent as the number of sources increases and the number of different devices, applications, social media accounts and other technologies come into play.
This situation has created a growing need for artificial intelligence (AI) and machine learning (ML) to lend a helping hand.
Thankfully, an ecosystem of services are already available, and marketers who want to get more from their outreach and attract the right audience at the right time using the best platforms would be well advised to explore the capabilities that AI provides.
Let's take a look at the top 3 ways machine learning can help digital marketers get more from their budgets and deliver better results to companies taking on the challenges of the Internet era.
1. Smart content derived from customer analytics
As anyone will tell you, good content is one of the most crucial foundations of marketing. Sometimes, even if marketers hire the best copywriters and designers, the content they produce might not always resonate well with audiences.
Furthermore, content that's published by the company may appeal to some audiences for a good proportion of the time, but not all audiences all of the time. Of course, that is only natural.
But, with the help of data, marketers can at least find out what content appeals to the right customers, and make sure it reaches them at the time and place that delivers the best reaction. It can also help marketers understand which types of content work best, and inform writers and other creatives appropriately.
2. Faster real-time editing of online offers
These days, marketers have access to all kinds of different data. Whether it comes from previous campaigns or from backend analytics, commercial agencies can track exactly who's visiting their page, what elements they engage with, how long they stay and what makes them leave. The can also discern which products or services are getting more eyeball time from visitors, among other things.
Of course, marketers should use such data to change how campaigns and landing pages will look like going forward.
However, if they build even smarter platforms, companies can use this collected data to not only help them make important decisions about future changes, but also form a better picture of their target audience and make micro-changes in real-time to help ease them along the sales funnel.
Obviously, such tactics are most useful to e-commerce retailers, but all kinds of companies can benefit from similar strategies and capabilities.
3. Better decision-making in ad campaigns
When organizations understand what their customers are really looking for, along with what they like and how they engage with the company, they are more likely to adjust their ad spending to match those preferences.
However, in many cases, there is a gap in the time between collecting data and optimizing the reach of the new advertizing campaigns. Using machine learning, marketers can speed up their variation tests, and better allocate budgets to campaigns that customers actually respond to solidly, say experts.
In addition, machine learning can also help to simplify predictions for marketers by using data from various past campaigns and factoring in data from other up-to-date sources to create better, smarter, and more watertight schedules, insights and budgets.
In the end, the new technologies can be harnessed in an almost limitless number of ways. The potential is only restricted to the imagination of the innovative marketer.