Here’s why digital marketers need to leverage the power of AI
DIGITAL marketing has come a long way since businesses first started buying ads on search and display platforms such as Google and Facebook.
Today, not only do ad platforms offer customers a lot of data about who is seeing their ads and how they’re interacting with them but also allow customers to create funnels and actions to help guide customers based on their reactions.
Further, through the company’s own website and social media pages, digital marketers have access to a lot more data about their customers and users — allowing them to create synergies and transform their digital marketing efforts.
However, all this data is a bit complicated to handle — especially as the number of sources increase (with IoT, apps, etc) — which creates a need for artificial intelligence (AI) and machine learning (ML) in this space.
The technology is available ready, and marketers who want to get more out of their ads and reach the right audience at the right time and on the right platforms must explore the capabilities that machine learning provides.
Here are the top three ways machine learning might help digital marketers get more out of their budgets and deliver better results to the company:
# 1 | Faster alterations to online offerings in real-time
Marketers today have access to all sorts of data. Be it from past campaigns or from webpage analytics, companies can track exactly who is visiting their page, how long they stay, what elements they engage with, and what causes them to leave — and also products/services are most interesting, among other things.
Right now, marketers use this data to change how future campaigns and landing pages will look like.
However, if they build smarter platforms, data that companies collect can not only help make big decisions about future changes but also paint a better picture of the customer and make micro-changes in real-time to help nudge them along the sales funnel.
Obviously, this is most useful to e-commerce sellers, but all kinds of companies can benefit from such capabilities.
# 2 | Better decision making for campaigns
Elaborating on the previous example, when organizations understand what their customers really want and like and how they engage with the company, they’re likely to adjust their ad spends to those preferences.
However, in many cases, there’s a lag between collecting data and optimizing ad budgets (or even creatives). Using machine learning, marketers could speed up their A/B tests, and allocate budgets to campaigns that customers actually respond to in a positive way — all in real-time.
Further, machine learning could also help simplify forecasting for marketers by using data from various historical campaigns and factoring in data from new sources to create smarter, better, and more robust schedules, budgets, and insights.
# 3 | Smarter content based on user/customer analytics
To be honest, content is an important part of marketing. And although marketers might hire the best copy-writers and designers, the content they create might not always resonate with the audience.
Further, content that is put out by the company might appeal to some audiences some of the time but not all audiences all of the times — and that’s only natural.
However, with the help of data, marketers can find out not only what content appeals to the right customers and make sure it reaches them at the time and on the platform that is most convenient to them but also help marketers understand what content works best (and therefore guide writers and designers appropriately).
- The role of business process re-engineering in digital transformation
- Singapore HDB uses technology to make construction sites safer
- Can an RPA bot-army support an organization in the long-term?
- Gartner sees blockchain technology picking up in 2020
- Why bankers aren’t concerned about digital-only banks gaining momentum