Part 4: Why Legacy Analytics Tools Are Failing Marketers

Building systems of action with AI at the core

(Ed note: This is the final installment of a four-part series discussing how AI marketing technology disrupts the traditional analytics continuum with Amplero’s head of product Garrett Tenold. In “Part 1: Moving marketers out of neutral,” we covered the strengths and limitations of legacy analytics and business intelligence tools, as well as the current interplay between analytics and human input.

"Part 2: Exploring the relationship between analytics and action" discussed how marketers can influence preferred action and understanding the difference between correlation and causation.

“Part 3: The pain of running testing programs at scale” covered the complexities involved in building personalization at the modern B2C enterprise.)

In our previous posts, I may have come across as a bit doom and gloom, I admit.

While the marketer’s job is to achieve outcomes by acting, there is little help from the big data and analytics world on actually implementing that action.

Testing can deliver a path to learning the right actions to take, but it’s slow, manual, and includes considerable resource and technology bottlenecks. Even when tests shed light on the what works best, marketers must manually adjust each time they learn something new.

So, how does adding core AI to your existing systems of engagement provide better outcomes than your legacy analytics?

Rather than spending time writing rules for static A/B or multivariate tests, marketers utilizing a core AI orchestration layer are able to run thousands of ongoing customer experiments simultaneously to ensure the optimal message is delivered to the optimal user at the optimal time.

Sure, it sounds good, but what does this look like in the real world?

First, and critically, the system must be able to both learn and act on those learnings. These AI systems will gather knowledge of how to most effectively influence through repeated testing and discovery, then seamlessly deploy those learnings back into the stream of action without requiring manual intervention. This addresses the fundamental bottlenecks that we explored in the earlier part of the series.

Secondly, the application of machine intelligence will accelerate the transformation from narrow channel-centric marketing to more holistic user-centric marketing. When we examined the pain for of executing manual rules-based tests in part 3 of our series, we barely scratched the surface on the explosion of pain in trying to test the multi-contact, multi-channel influence that represent reality. Today’s manual, rules-based testing tools are often directly tied to a single channel-specific system of engagement like a website, and focus on clicks, opens and in-session conversions. Many of these vendors are beginning to add some machine learning to their offerings, including some auto-target type capabilities that combine continuous learning and action. Still, marketers are all too familiar with the pain of passing segments back upstream to attempt to manually orchestrate experiences across other channels. And more importantly, they are missing the opportunity for something much larger when the overall decisioning process is reimagined from the ground up with AI at the core.

Instead, emerging core artificial intelligence marketing (AIM) platforms, like Amplero, act as channel-agnostic orchestration layers or customer engagement hubs that live between existing data environments and systems of engagement (website, mobile app, email, social, SMS, call center, POS, etc.). Based on the full scope of customer behavioral and profile data, these platforms leverage productized machine learning to simultaneously run thousands of experiences to learn to influence across any channel—continuously optimizing based on changing customer behavior and market conditions. Learnings automatically deployed between channels, with no complicated audience passing required.

The advantages of these types of systems include the ability to optimize for long-term customer value metrics like retention or average revenue per user. It also centralizes experimentation into one engine—allowing for easier onboarding of new data sources and engagement channels or systems of delivery. In addition, it limits cannibalization of revenue KPIs in favor of a channel-specific metrics and provides a level of centralized governance for both data and machine learning models.

As these new capabilities come to market, what does the integration of AI-fueled systems of action mean for the marketer?

1. The marketer is no longer tasked with writing a rule of what experience to deliver in every scenario.

Instead, the marketer provides the KPI to which to optimize, sets the outer boundaries for testing, and uploads available assets. The system then begins recursively testing and continually adapting based on what is learned.

2. Insight practice moves from report generation to answering deep-dive business questions and identifying/evangelizing transformative findings.

Rather than identifying and applying findings from each test into the next round of campaigns weeks or months later, insights are automatically and continually applied to each customer experience. This allows digital analysts and optimization strategists to shift their time and focus to pursuing answers to high-impact business questions, as well as understanding and evangelizing key insights from the machine that can be applied in future marketing efforts.

3. Creative, strategy, and customer experience become prioritized.

With core AIM platforms shouldering the load of segmentation, targeting rules, and performance reporting, marketers have more time to dedicate to improving the customer experience and driving innovation strategy.

At Amplero’s AI at the Core Conference this past May, one panelist from a Fortune 1000 company explained how their workload shifted after implementing an AIM platform.

“We spent a lot more time building new creative and thinking about our KPIs,” she said. “With AI, the platforms will optimize toward whichever KPI you tell it to. If you’re not aligned organizationally on your KPIs, you’re going to run into challenges.”

For one EMEA-based major telecommunications provider, the Amplero AIM Platform actually found that significantly reducing the number of customer touchpoints resulted in revenue lift and adapted accordingly. This wouldn’t have been identified within a pre-defined customer journey or an isolated A/B test experience. You can imagine that customers certainly appreciated more fluid, less intrusive interactions—and the provider was rewarded with an increase in revenue.

For marketers looking to get started implementing enterprise orchestration with AI at the core, contact us today for an initial demo and AIM roadmap consultation.

To learn more about building systems of action through AIM technologies, download the case study on how Amplero helped a leading mobile provider gain 650% lift in ROI, or schedule an AIM Platform demo for your team today.

About Garrett

As head of product for Amplero, Garrett dwells at the forefront of the machine learning-powered marketing revolution. Building the Amplero Digital Intelligence Platform since its inception, Garrett is a SaaS product leader focused on marketing transformation with deep experience across both MarTech and AdTech industries.

With more than a decade focused on product innovation, he’s built forward-focused technology and analytical solutions for brands such as XBox, Sprint, American Express, Citibank, and Ganett. Prior to Amplero, he held product leadership roles at Microsoft Advertising, AdReady, and aQuantive.

Contact him directly at gtenold@amplero.com.

About Amplero

Headquartered in Seattle, Amplero is an Artificial Intelligence Marketing (AIM) company that enables business-to-consumer (B2C) marketers at global brands to optimize customer lifetime value at a scale that is not humanly possible.

Unlike traditional rules-based marketing automation systems, Amplero’s Artificial Intelligence Marketing Platform leverages machine learning and multi-armed bandit experimentation to dynamically test thousands of permutations to adaptively optimize every customer interaction and maximize customer lifetime value and loyalty.

With Amplero, marketers in competitive, customer-obsessed industries like telecom, banking, gaming and consumer tech are currently seeing measurable lift across key performance indicators—including 1-3% incremental growth in customer topline revenue and 3-5x lift in retention rates.

For more information, contact us today or follow us on Twitter.