Iterable optimizes AI to hyper-personalize marketing and predict future purchases

With people living on their phones and constantly deluged by messages, emails and notifications, so-called “batch-and-blast” emails, targeted web ads and word-of-mouth are no longer adequate when it comes to customer outreach.

“We’re online more than ever before, acutely aware and concerned with how our personal data is used and, importantly, we demand to be valued and recognized for what makes us unique,” said Andrew Boni, CEO of customer engagement platform company Iterable. 

Marketers must then achieve the “extraordinary task” of reaching as many customers — and in as many hyper-personalized ways — as possible. And, matching each consumer with “a personal marketer and targeted message at every touchpoint” is impossible without artificial intelligence (AI) and machine learning (ML), said Boni. 

To this end, Iterable today announced its new AI Optimization Suite. The tool is equipped with new predictive goals with explainable AI capabilities to help drive individualized brand-consumer communication, said Boni. 

The company will officially unveil its new platform to the public at its Activate Summit North America in September. 

“Understanding the inner workings [the ‘why’ and ‘how’] behind the system gives marketers the clarity and confidence to improve and refine the predictions they create, design goals that fit their unique business needs, and implement fresh ideas about how to approach customer-first campaigns,” said Bela Stepanova, senior vice president of product at Iterable. 

Iterable treats customers as its most valuable asset

Most brands have access to customer data and a goal in mind for connecting with their customers. But the magic formula is often missing: Building true customer knowledge — and acting on that. 

This has provided a significant opportunity and rapid growth in AI-powered customer engagement platforms. According to Markets and Markets, the global customer engagement solutions market will grow to $32.2 billion by 2027, representing a compound annual growth rate (CAGR) of nearly 11%. 

The global market for retail omnichannel commerce platforms will reach $16.9 billion by 2027, as forecasted by ReportLinker.com. This represents a CAGR of 16.4% from 2020 — growth largely propelled by the COVID-19 crisis, according to the firm. 

Companies competing for that market share include Adobe Marketo Engage, HubSpot Marketing Hub, Pega, Blueshift, Braze and MoEngage. 

And Iterable is rapidly increasing its presence: The 9-year-old series E “centaur” company that now serves more than 1,000 customers — including DoorDash, Calm, Jersey Mike’s Subs, Zillow and SeatGeek — has surpassed $100 million in annual recurring revenue (ARR), and has recently expanded into Latin America and Asia Pacific territories. 

Ingest, centralize, activate

Iterable’s AI Optimization Suite helps brands “ingest, centralize and activate” customer data and use real-time information to craft individualized campaigns, said Boni. 

Its predictive goals functionality allows brands to establish goals unique to their business needs and predict how their customer base might respond. They can then use those predictions to establish customer segments and tailor messaging to maximize conversion. 

The explainable AI feature within the predictive goals functionality allows marketers to pinpoint the data points that contributed to forecasts. This provides an “under the hood” look at AI and further insights that can inform future campaigns, said Boni.

Furthermore, marketers can understand behaviors that drive predictions, ranked in order of importance, according to Boni. They can also assess the quality and reliability of a goal using a predictive strength tool and receive insight into correlated variables that make outcomes more or less likely. 

Building a ‘What to Do’ journey

As an example: A travel company is looking to drive incremental purchases and increase rental car revenue. 

Predictive goals creates a list of users most likely to book a rental car within the next 30 days. Brands can then score that list and select and target the highest propensity users with cross-channel messages triggered after an airline ticket purchase, explained Boni. 

Using Explainable AI, the travel company can then go a step further by identifying the attributes that target those high-propensity customers — browsing a “Tours and Activities” section of their website after booking a flight, for instance. These gleaned insights can be used to create a “What to Do” journey for airfare purchasers. This might highlight tours and other activities in their travel destinations, wrapped together with rental car promotions, said Boni. 

The company plans to expand the platform to help marketers understand how goals perform over time, he said, as well as allowing them to more deeply analyze cohorts and optimize individual user experiences with journeys and experimentation.

“By setting goals and measuring outcomes, marketers can lean into strategies that are working and adjust any that aren’t,” said Boni. 

He marveled that, “we can make predictions about the future. This is where the world is heading. It is less about looking into the past and making a guess, and more about (establishing) future behavior.”