How to learn machine learning Unlock digital marketing success for banks

By Andrew Miller

Bank marketers are used to change and perhaps no change is more effective than the impact of machine learning on attracting new customers online. Marketers are often asked to “do more with less” and access our data to the human capacity to consume, analyze and react in real time. Luckily, computers are great at processing lots of data and don’t need naps.

It’s safe to assume that most bank marketers don’t like moonlight programmers and many of us don’t have access to teams of developers. So how does one get the benefits of machine learning without having to write any code?

First, let’s establish a few basic terms on the same page. Then we will explore the current “no code” machine learning tools that help us reach more prospective customers, more efficiently.

How is machine learning different from artificial intelligence?

The two terms are often used together but actually have very different meanings.

Machine learning is a process for training computers without learning large data sets in advance of every possible outcome. Artificial intelligence describes technologies, often built on machine learning models, that enable a computer to simulate human behavior.

In most marketing contexts, ML tools analyze data to predict future outcomes. Behind all the “black box” metaphors and buzzwords are simply a method of making more educated guesses about what’s most likely to happen. Not what will happen.

Bank marketers for machine learning tools

One does not have to look far to see machine learning at work. Many ad platforms, content creation tools, and analytics platforms already incorporate advanced machine learning algorithms to improve performance and efficiency.

Here are some of the advantages you can make with machine learning today:

Set optimal cost-per-click bids based on campaign goals. Google Ads, Facebook Ads, Microsoft Advertising and many other pay-per-click ad platforms use machine learning algorithms to optimize bids in each auction. Their algorithms take into account many signals, including many that are unavailable for manual bid adjustments.

Advertisers can select campaign goals such as “maximize conversions,” “maximize conversion value” and “target cost per action”. Achieving your marketing goals.

Online display ads for Target the best audiences and placements. Advertisers no longer need to appear on their display ads for specific sites. Google Learning, Microsoft Advertising and Facebook Ads automatically combine the best site placements, ad units and bids based on individual web users’ likelihood to convert your site.

As an example, the ad platforms are getting better at distinguishing between a user looking for a consumer banking product versus a commercial banking product. This is more about showing their likelihood of being an appropriate ad based on their intent.

Marketers can experiment with new campaign types such as Performance Max (Google Ads) and Automated Ads (Facebook Ads) to extend their campaigns’ reach and optimize performance across multiple devices and channels. For example, a single performance Max campaign can show ads on Google Search, the Google Display Network, Google Maps, Gmail, YouTube and Android devices. Previously, each channel would have separate budgets and audience targets with separate campaigns of management.

Create the perfect ad for every prospective customer in real time. Google Ads offers machine learning tools to search for copy and display ad creative. Creative Search Simultaneously Instead of creating complete ads, advertisers upload several variations of each element — headlines, descriptions, images and calls to action. Google automatically rotates the ads and optimizes the combinations most likely for each visitor.

Regulatory messaging is required when bank marketers face a challenge. Advertisers can “pin” certain elements of an ad to ensure they are up to the users, but Google will be able to do so because of the lower quality of the products and fewer testing opportunities.

Determine the value of Determine with each marketing channel. We are all concerned with less data as a result of privacy regulations and limits on personal identifiable information. This can limit our ability to understand the full value of each marketing channel and the use of website analytics to measure results such as account openings and financing applications.

Marketers that use Google Analytics 360 or Google Analytics 4 can use a Data-Driven Attribution Model that helps in tracking data collection gaps by modeling predictive behaviors based on predictive behaviors. DDA takes advantage of machine learning to look at 50+ recent touchpoints in a channel that reports on each customer’s journey to value.

Unlock insights buried in mountains of data. Analytics tools and ad platforms create more data than a human can possibly analyze and act on. Machine learning tools can detect trends and anomalies in large data sets faster and with greater precision.

For example, if your bank’s website gets 300 percent more traffic from a neighboring town then your minor league baseball sponsorship is activated. Google Analytics Insights will surface the +300 percent anomaly and allow you to act on it, perhaps by creating a new webpage and promoting a new account offer by fans.

Incorporating machine learning tools for practical considerations

Machine learning is a buzzworthy topic. But it’s not all your marketing woes for a panacea. Marketers should consider these scenarios when augmenting their work with ML tools:

• Machine learning requires a lot of data to train the algorithms. Few banks have enough structured marketing data to train and test their own ML tools, but advertising and analytics platforms can aggregate data to generate recommendations and insights across industries.
• Machine learning makes educated guesses based on historical data and cannot be expected to navigate unfamiliar situations such as local regulations, offline events or strategic shifts.
• Humans are still required to provide oversight and monitor the computer-generated insights and recommendations on how to react — or whether to react.
• There is probably one ML tool that does everything well. Marketers will have a suite of specialized tools that will enable myriad combinations of platforms, data structures and strategies.

How to prepare for the future of marketing, augmented by machine learning

Fewer humans will need to be trained to handle repetitive or data-intensive tasks. But there are many areas where human marketers have a distinct advantage. We are still better at articulating objectives, developing strategies and brainstorming new creative ideas. We will also provide qualified human beings with the tools and know-how to put on machine learning tools for oversight and guardrails.

We can anticipate further consolidation of machine learning tools to achieve broader tasks, but humans and machines can accomplish more than either.

Andrew Miller is co-founder and VP for Strategy at Workshop Digital, a digital marketing agency in Richmond, Virginia.