The past year’s hype around artificial intelligence (AI) has been impossible to miss. Companies everywhere are touting AI capabilities – so much so that it’s turned into the punchline of jokes and memes.
Some of the inflated buzz is a laughing matter (AI-generated hands, anyone?). But there’s a lot of substance just under the surface that savvy marketers have been able to use to benefit their campaigns and processes.
AI is benefitting marketing teams in several important ways, including:
- Improving conversions and ROI: Marketers can intelligently identify customers most likely to purchase, churn, or convert to drive toward business goals and increase ROI intelligently.
- Saving time: Marketers can minimize dependencies on IT and engineering, and using AI, they can automate daily tasks – all while executing more optimized campaigns.
- Cutting down time-to-value: Models can be live and deployed in the market within weeks, instantly driving outcomes and automating powerful targeting strategies.
- Better testing and learning: Automated daily tasks mean more time to test segmentation and personalization strategies, so marketers can better understand customer sentiment.
Let’s take a deeper look at exactly how savvy marketers are using AI to drive results.
What’s stopping marketers?
While we may all be hearing more and more about AI, there are still roadblocks that keep a lot of brands from using the technology. Though 44% of companies report completely integrating AI predictive analytics into their marketing strategy, a whopping 90% report difficulty making day-to-day, data-driven decisions.
So where’s the disconnect slowing companies down from fully adopting AI?
- Lack of well-defined use cases: Every business is different, and what works for one brand may not make sense for another. Before diving into AI, work with your team to set clear criteria for what’s a good use of the technology that will help meet your goals.
- Lack of AI strategy: AI is powerful, but without a well-defined strategy, a business won’t be able to capitalize on its benefits.
- Lack of budget for AI-related things: Many companies have tightened budgets this year, leaving less room for AI and the resources it requires.
- Lack of understanding of the risks, regulations, and privacy concerns: According to data from the market research firm Ipsos, four of U.S. adults’ top five AI concerns all involve impersonation, manipulation, and misinformation.
- Lack of access to the data that are required to fuel successful AI models: Above all, some brands are finding their AI implementation to fall flat because they simply don’t have the right data feeding their models, so they can’t scale or get their desired results.
“AI modeling is only as good as the data that’s used to model against it. We firmly believe that having access to all customer data is going to help marketers drive more revenue with AI – and MessageGears is the only customer engagement platform uniquely designed to do that.”
–MessageGears CEO Roger Barnette
Understanding the different types of AI
While AI has been around since the 1950s, the launch of ChatGPT and DALL-E 2 in late 2022 made AI a lot more accessible. But before diving more deeply into how you can use AI to benefit your brand, we need to take a moment to understand the different types of AI at play.
There are several AI applications we hear most about in the business world:
- Generative AI
- Conversational AI
- Predictive AI
When most people think about AI these days, they’re thinking of generative AI. This technology is used to create content, graphics, code, and music and can translate data into different formats. Generative AI can create marketing content, including blog posts, social media posts, product descriptions, and email newsletters. Marketers can use generative AI to automatically generate A/B testing variations for web pages, email subject lines, ad creative, and more.
Though not all chatbots are equipped with artificial intelligence (AI), chatbots increasingly use conversational AI techniques like natural language processing (NLP) to understand questions and automate responses. Chatbots and virtual assistants can have natural, dynamic conversations with customers. They answer inquiries, offer product recommendations, and guide users through the sales funnel, enhancing the customer experience.
Predictive AI leverages historical and real-time data to help marketers anticipate customer needs, improve targeting, and ultimately achieve better campaign results. Predictive AI can analyze historical customer data – such as purchase history, website interactions, and social media activity – to forecast future behavior. Marketers can use these predictions to tailor their marketing strategies, including product recommendations, content personalization, and the timing of marketing campaigns.
For this blog, we’ll specifically focus on the application of predictive AI – because, in our humble opinion, it has the biggest opportunity to impact marketing revenue.
Top 10 predictive AI models for sophisticated marketers
What if you could predict key customer lifecycle events, and then time and customize engagement with your customers according to those events? With predictive AI, you can.
AI and predictive modeling can intelligently identify and score customers according to their likelihood to take – or not take – specific actions. Here are a few ways sophisticated marketers are using predictive AI to enhance campaigns:
Time of Day
Predict the best time of day to engage with a customer, raising the likelihood that you’ll meet them when they’re open to making a purchase or taking the action you want them to take. You might use this form of predictive AI to decide when to trigger a push notification or send an email with a coupon or special offer. This makes it easy to schedule communication when your target audience is most active and thus more likely to open, click, and convert. Retailers can schedule time-sensitive promotions during peak shopping hours, for example.
Day of Week
Predict the best day of the week to engage with a customer. Similar to the time-of-day model, picking the best day of the week for engagement raises the likelihood that you’ll engage customers when they’re open to making a purchase or taking the action you want them to take. This can vary widely by industry, so it’s hugely beneficial to use your brand’s own data to model these predictions.
Next Best Channel
Determine the channel that’s most likely to convert your customers. You might use this form of predictive AI to increase your retention rate by targeting high-priority customers with a win-back campaign. You can deliver your re-engagement campaign on customers’ preferred channels, making it more likely that they’ll respond positively.
For example, online retailers can identify the most appropriate channel for promotions and cart abandonment reminders, and financial institutions can leverage the optimal channel for customer communications regarding account updates, transactions, etc.
Purchase Propensity
Use this model to predict which customers are most likely to make a purchase. You can use this form of predictive AI to deliver targeted product recommendations to customers who show a high likelihood of making a purchase based on their previous activity.
Customer Lifetime Value (LTV)
Predict the long-term value that a customer is likely to generate for your business over the entire relationship. You can use this form of predictive AI to trigger loyalty and rewards-based email sequences for customers with high predicted LTV.
Companies can use this form of predictive AI to encourage additional purchases from high LTV customers by offering complementary products or upgrades. LTV data can also help guide how you design your loyalty programs and rewards, and it can inform pricing strategies that align with your segments.
Churn Propensity
Use this model for targeted interventions and win-backs. You can use this form of predictive AI to re-engage customers with high churn risk across owned and paid channels. Send them a coupon or special offer to make sure they won’t take their business elsewhere.
2nd Purchase
Drive repeat purchases and establish customer loyalty using this model. You can use this form of predictive AI to deliver a tailored welcome series based on your customer’s likelihood to buy again after the first point of purchase.
Product Recommendations
Use customer purchase history to fuel product recommendations using this model. If a customer previously purchased furniture in a certain style, you might consider offering a complementary piece. Shoe purchasers might consider a coordinating coat or pair of jeans.
Engagement Index
Predict prospects’ likelihood to engage with your brand using this model. This can help you identify customers at risk of churning, so you can enable targeted retention efforts. You can also assess user engagement to refine and optimize product features. Ultimately, when you know a prospect is likely to take an action, you can put more energy into cultivating that relationship with perfect-time, perfect-place communications.
Contact Frequency Optimization
You don’t want to ghost customers, but you also don’t want to come on too strong and scare them away. Using this model, you can score recipients’ preferences and tolerance for how frequently they get communications from your brand – helping you to make sure your outreach is always just right!
Get started using AI for your next marketing campaign
From customer loyalty to improved personalization to optimizing your investment in paid media, predictive AI can help your brand take customer data to heights that your marketing team likely only dreamed of before.
Here’s a quick look at how our clients can leverage AI right where audiences are already being built and campaigns are being sent in MessageGears.
By creating a segment using four different models, you’re able to target high-value customers who are at risk of churning with a cross-channel campaign.
You can automatically send messages to customers on their preferred channel at their preferred time. Within a single campaign, an SMS text promoting Super Bowl apparel is sent to one customer, while another receives an email for toddler and baby clothes, and yet another gets a tailgating promo highlighting collegiate socks.
Leveraging models in this way makes testing and optimizing actually possible for marketers – with way less effort and more return on the investment in your customer data.
MessageGears is passionate about connecting people in meaningful ways. We’re here to help you take your marketing campaigns to the next level – from emails and texts to mobile notifications and even TV screens.
Learn more about how to implement highly sophisticated AI models, so you can deploy the marketing campaigns of your dreams.
Elsbeth Russell
Elsbeth has nearly two decades of experience helping brands attract and engage audiences through content. For the past six years, she’s been dedicated to helping B2B companies in the email marketing space connect with audiences through community building and social media marketing.
