Marketing media has evolved from static print and television ads to include more dynamic online advertising such as search engine optimization, content marketing, and native advertising. The torrent of user data captured online enables personalization and targeting previously unavailable in prior mediums.
While tools like Google Analytics provide increased oversight into digital ad performance, the ability to act on that information can still be a difficult process. Luckily, where there’s data, there are opportunities to leverage that data and automate processes using artificial intelligence (AI). The spend on AI in marketing was estimated to be $12B in 2020 and is expected to grow at a CAGR of 32% and reach $108B by 2028.
AI can be utilized throughout the marketing campaign process from research to implementation and control of the marketing campaign. Marketers can also leverage AI outside of the process of defining, creating, and managing their market campaigns in order to manage brand health and improve brand messaging and even improve and manage public relations.
Lessons from industries that have implemented AI-powered Adtech and Martech tools
As the capabilities of AI have grown over the past years, so have the ways that companies leverage AI in their marketing process. Let’s look at a few examples:
MasterCard uses natural language generation (NLG) to automatically generate content at scale
NLG is a branch of AI that automates the process of creating narratives from structured datasets. One NLG provider is Narrative Science (recently acquired by Salesforce). Narrative Science’s solution, Quill, is used by companies like USAA, American Century Investments, Franklin Templeton, and MasterCard to translate data into written narratives including personalized campaign copy and product descriptions to actionable insights and reports from Google Analytics data.
Harley-Davidson leverages AI to increase ad performance online
Given the quantity of data and analytics collected on ad performance, leveraging AI to better manage and monitor ad performance seems obvious. After six months of using Albert (an AI/ML platform that offers insights into the effectiveness of paid advertising), Harley-Davidson announced that it credited 40% of its motorcycle sales in NYC to Albert. Albert not only helps manage campaign structures and the “aggressiveness” of optimization but also helps manage the channel mix, adjusting based on performance for the optimal mix and cost.
Another company helping companies manage, optimize, and better understand their ad spend is Trajektory. Trajektory helps customers understand the value of ad placements (like Superbowl ads) so that both the seller and buyer understand the value and return of the ad better.
Gusto uses AI to predict which marketing efforts will be the most successful
Leveraging customer data, including behavioral data, can help companies better predict which marketing efforts and copy will resonate the most with their customers. Companies like CaliberMind provide insight into a customer’s psychographic and behavioral profiles, revealing how best to appeal to those customers. Gusto, Citrix, and other companies have leveraged CaliberMind to generate higher conversions and stronger leads.
Lloyds Banking Group leverages AI to identify risks to brand health in real-time
Lloyds Bank uses NetBase for a host of use cases, one of which is tracking the health of the brand by using NetBase to sort, measure, track and escalate potential risks to the brand’s health and reducing the amount of manual time to process the data and the number of false alarms.
In addition to tracking and maintaining brand health, NetBase helps Lloyds and its other customers understand, benchmark, and grow their brands better.
The use cases for AI in marketing aren’t limited to just direct-to-consumer. Companies like Acrolinx uses natural language processing to create content to better target major brands like IBM, Microsoft, and Boeing and better predict content success with their target customers. As mentioned above, startups offer AI-powered tools to help with everything from hyper-personalization of content and targeting to automation and management of clicks. There are even solutions that can improve customer engagement and convert data and analytics into actionable and digestible content for business leaders within the organization.
While there are many benefits, it’s worth remembering the challenges and limitations of AI as discussed in our last insights report. Inherent data set bias, and data privacy are non-trivial concerns when leveraging AI to target customers and personalize content. While the challenges in implementing AI shouldn’t deter usage, education, maintaining awareness, and monitoring the appropriateness of usage are necessary.
What does this mean for the future?
Leveraging AI for marketing purposes is shifting from the pilot stage to the implementation stage across numerous industries. Leveraging AI-powered insight to optimize marketing effectiveness and efficiency seems attractive to many companies despite implementation risks.
When discussing AI within the insurance value chain, the use cases most often discussed are claims or underwriting. However, it’s worthwhile to take a closer look at the opportunities within marketing as well. By improving the quantity and quality of the top-of-the-funnel leads through better targeting, more personalization, improved management of content, and/or better management of marketing spend, insurers have the ability to not only grow their business but impact their bottom line.
