Examples of AI marketing are all around us.
In fact, just about every company’s website, personalized email, social share, and targeted ad today is developed with some help from artificial intelligence and machine learning:
- Social listening tools use natural language processing (NLP). This helps marketers gauge brand sentiment by analyzing mentions of their business in search and on social media.
- Search engine optimization (SEO) tools provide marketers recommendations to rank higher in Google. (Which also uses machine learning in its search algorithms.)
- AI-powered marketing tools optimize many companies’ email marketing campaigns. Notably, they help them determine when to deliver personalized emails and what personalized content or product recommendations to send to different segments.
These are all instances of AI marketing that help businesses carry out vital (if basic) tasks. Marketers’ jobs are undoubtedly easier with these artificial intelligence resources on hand.
However, there are more advanced AI use cases that can help you scale and streamline your engagement efforts, improve customer experience, and gain a competitive advantage.
Ones you can implement with ease using a pure-play customer data platform (CDP).
Why an AI marketing strategy is essential for your business in 2021 (and beyond)
Roughly 1,500 executives in the U.S. were recently asked about artificial intelligence and the ways in which it can be leveraged to accelerate growth by the Brookings Institute.
In short, AI is now a critical component of companies’ customer engagement strategies. And many companies recognize they can benefit from onboarding AI-driven technologies:
- 70% of the martech solutions used by marketing professionals today have artificial intelligence features. — 2018 Brightedge “Future of Marketing and AI Survey”
- 40% of marketers said AI and machine learning tools are critical to success. — 2019 Dresner Advisory Services “Data Science and Machine Learning Market Study”
- 82% of executives deemed early adopters of AI marketing tech reported a positive return on investment from the solutions. — 2018 Deloitte survey
Of course, AI has other uses too. According to the August 2019 edition of the CMO Survey:
- 56% of companies use AI for predictive analytics regarding customer journeys
- 38% of organizations leverage AI for programmatic ads and media buying
- 41% of brands turn to artificial intelligence for customer segmentation
- 25% of businesses implement conversational AI for customer service
Many companies are clearly taking advantage of of all AI has to offer them today.
But some organizations are lacking in three key areas that prevent marketing (and other growth-focused teams like analytics and ecommerce) from making the most of AI tools.
Where AI marketing has failed for many companies today: 3 common issues
Here’s how many digital marketers have failed to take advantage of AI today. (And what they can do to better leverage it in their engagement efforts to their target audience.)
Issue #1: Skills
You don’t need to know everything about AI to build an AI marketing strategy. That is, you don’t need an advanced degree or technical skills to employ it in your engagement efforts.
However, a lack of hard-coding skills in your marketing team can lead to longer project timelines. Especially if your company is solely reliant on limited data science resources.
Solution: Read up on the basics of AI and machine learning in our special guide.
This can help your marketing org better grasp the concept and its applications. (The Marketing AI Institute run by PR 20/20’s Paul Roetzer is a great AI resource as well.)
Also, brush up on core coding principles to get going with AI on the ground level. This will help you become more confident when communicating needs to data scientists.
Just as importantly, though, you’ll then know how to identify the exact AI-enabling tech you need for your use cases (e.g., dynamic segment creation based on customer scoring, lookalike models, and segment analysis for more relevant targeting).
For many organizations today, this ideal business technology is BlueConic.
AI Workbench has built-in features and out-of-the-box predictive models — customer lifetime value (CLV) scoring, forecasting churn propensity, etc. — that help marketers and other teams better understand and engage prospects and customers.
Issue #2: Process
Data scientists spend most of their time sorting, scrubbing, and organizing customer data.
This leaves them little time to help marketers with their needs. For example, with building, training, and deploying machine learning models to get insights about target segments.
The most common barrier to developing streamlined AI marketing processes? Tech tools that don’t communicate (or at least communicate easily) with one another.
Data scientists are stuck manually pulling customer lists from various marketing and business technology sources to get a complete data set before they even start to build models.
Let’s say they want to create smarter segments with CLV model outcomes. By the time those segments are uploaded to activation tools, it’s already too old to be useful for marketers.
This creates data latency and hinders marketing from realizing better ROI from their efforts. (Particularly with their cross-channel customer lifecycle orchestration activities.)
Solution: Get a profile database (see: BlueConic) that connects with all your tools.
With our CDP, you get real-time data access thanks to our persistent profiles. (That is, profiles that dynamically update and feature all first-party data for all contacts.)
That means no more data latency — and, in turn, no more inefficient processes.
What’s more, the outcomes of machine learning models can enrich these profiles. This can help you build richer segments that you can then activate whenever and wherever you need.
Issue #3: Technology
There are two main factors that can hinder marketers from fully realizing AI’s potential:
- 1) The number of places customer data exists (and marketers’ inability to access it)
- 2) Black-boxed tech that uses machine learning, but operates within a single system
As we noted, customer data often exists in so many disparate systems for companies.
Without a single customer view (i.e., all behavioral, interest, purchase, and engagement data in one UI), your models won’t be effective. You also won’t be able to adjust your models or understand what’s driving predictive scores and outcomes.
Solution: Make a pure-play CDP the center of your tech stack and data ecosystem.
BlueConic was built to unify data from across channels, systems, and touchpoints. Why? So marketers and other tech users didn’t have to manually un-silo their data.
Our CDP offers open-source machine learning models that directly connect to your database. That means they are free to test and learn from models and modify them as needed to the analyze whether or not they are driving the desired business outcomes.
How to combine artificial intelligence and marketing with the ideal CDP
Securing a CDP that unifies all first-party data for efficient cross-channel marketing activation is crucial. But you need a CDP that helps you analyze and act on that data in real time.
As mentioned, you don’t need to be an AI expert to leverage AI in marketing.
You just need some moderate knowhow, clear processes — and the right tech.
Bottom line: Artificial intelligence is here to stay. (As it should be.) Now it’s time to onboard a pure-play CDP like BlueConic to extract even more value from your marketing program.
Want more advice on how you can get started with an advanced AI marketing strategy? Watch our webinar, which features in-depth examples, insights, and ideas.