machine learning marketing cloud

How Machine Learning Can Maximize Your Marketing ROI

Marketers can't just go off of their hunches anymore. There’s too much data, it takes far too much time to derive accurate insights and there’s just too much at stake as consumer expectations continue to rise. Customers are constantly offering up new data to brands across an array of devices and channels. However, having the data means nothing if marketing teams cannot act upon that data and recognize insight into customer behavior and preferences. Machine learning applies strategic outcomes to what your customers are telling you, so your marketing team can make better-informed decisions about what content and messages to offer next.  When your customer data strategy is informed by predictive models and real-time insights, you’ll always be able to look forward to the next best action.   

Rather than remain paralyzed by mountains of vanity performance metrics reporting on what actions your customers have already taken, get ahead of the curve through a machine learning-forward approach that allows you to view the entire customer journey in context over time.  Artificial Intelligence (AI) and Machine Learning (ML) can give marketers more insight into valuable customers and the experiences they should deliver, tying marketing efforts directly toward measurable business goals and boosting overall revenue.

Historically, though, it's been difficult for marketing to demonstrate ROI to the C-suite without being able to directly connect a campaign or strategy to results and revenue. Without hard, demonstrable facts to back up creative instincts it can be difficult to tell which efforts are successful and which are a poor use of time and resources. Your gut instincts may have once been good enough, but in today’s digital-first era, just good enough no longer cuts it. Marketing needs to make data-science driven decisions that generate real value and align to business goals.  

By coupling a customer data platform (CDP) with machine learning and predictive marketing capabilities, marketers are able to directly perform multidimensional segmentation that can recommend outcomes based on the entire customer life cycle. This allows marketers to predict average customer lifetime value, improve targeting and personalization campaigns and optimize where they are investing their time and resources.

Intelligent Insight Everywhere

Acquia Machine Learning within the Acquia Customer Data Platform enhances the insights marketers gain from their data, as well as the way marketers identify customer segments and engage with them at every step of the customer journey, bringing data-science driven insights to the fingertips of every business user, enabling agile and decisive decision making that boosts revenue. 

All intelligent insights are calculated and available at the individual profile-level on a fully horizontally scalable architecture, making it simple and seamless to use across all areas of the business, no matter what endpoint the insights are being accessed through.

Transparent and Tailored Intelligence

Your business is unique, and Acquia Machine Learning provides fully customizable, out-of-the-box models, allowing full configurability from data sources leveraged, to tailoring the model itself providing full transparency into how the model is working for your business. Models can be tailored to service different parts of any business, brand, region or other dimension. 

In addition, Acquia’s Machine Learning Framework enables brands to leverage any custom-built models, whether they be built by your data science team, a partner, or our services organization. All models in Acquia’s Machine Learning Framework leverage Acquia’s unified, cleansed, deduped data stemming from any data source. Calculated insights can be shared with external BI tools for external analysis, creating alignment across the business around a unified and trusted data set.

Acquia Machine Learning has enabled our customers to:

  • Boost email conversion rates by 125% 
  • Fuel greater personalization by increasing the relevance of product recommendations 
  • Identify at-risk customers to fuel more targeted retention strategies

Supercharge Return on Ad Spend 

With the marriage of intelligent product recommendations and the Likelihood to Buy ML model, clients have strategically leveraged high-engagement advertising mediums, such as Facebook Dynamic Ads, to drive seamless, impulse conversion directly in the social media feeds of their most engaged prospects.

While effective, these types of ad-placements are expensive, therefore clients needed a way to identify and target customers with high propensities to buy with the products that we’re most likely to make them convert on the fly. By making these intelligent insights directly available to marketers, our customers have seen significant lift in return on ad-spend, acquiring new customers and fostering life-time value throughout their individual journeys.

Individualize Discount Strategy

Blanket discounting is as effective a strategy as batch-and-blasting your customers’ inboxes. Spoiler alert: you're leaving money on the table. Every individual has a different sensitivity to price and for different types of products – some of us are more frugal and always looking for a deal, and others are more than willing to pay full price–but, who are they? 

Leveraging Acquia’s Likelihood to Pay Full Price ML model, our customers have been able to determine which individuals are more or less price sensitive to intelligently target each customer with the optimal price point to incentivize conversion as part of flash sales, seasonal promotions, and individual offers. When coupled with Likelihood to Buy insights, marketers are directly creating and driving programs optimized to target individuals with less than optimal chances of conversion with individually tailored discounts to maximize margins, growing over 25 percent, and grow their recurring customer-base. 

Optimize Offline Reactivation

With consumers opening up to 90 percent of direct mail, it is a fantastic medium to cultivate engagement and reactivate individuals who may have fallen out of their regular buying cycle. Luckily, with Acquia’s Next Best Channel and Second-Best Channel ML models, marketers do not need to waste precious time guessing who those individuals would be given the high cost of any direct mail touchpoint in the customer journey. 

When coupled with the Likeliness to Convert ML model, marketers can leverage intelligence first-hand to create hyper-targeted segments in seconds for individuals with a high, but not optimal, propensity to convert and who will respond to a direct mail touchpoint to optimize ROI and lifting response rates by nearly 20 percent.

Keep Customers Coming Back

Every marketer knows just how difficult and expensive it is to acquire new customers, yet no customer relationship is perfect, nor is it linear. As such, marketers must have a way to identify and mitigate potential customer churn as a central part of every customer journey.  As likelihood to buy changes for each individual based on parameters specific to your business – from changes in engagement, purchases, average order values, and more–marketers are able to intelligently identify and track likeliness to churn.

When coupled with insights such as Predicted Lifetime Value, marketers can take quick and decisive action to understand what might have gone wrong and engage each individual with personalized journeys that foster long-term loyalty, calibrating interaction frequency along the way.

Peer into the Future

Every Machine Learning model, out-of-the box or fully custom, empowers marketers with predictive insight at the customer level, immediately available to be analyzed in Acquia Analytics. These analytics are essential to our customers and help them track, model and plan targeting strategies with a balanced mix of historical and predicted insights across cohorts.

Harnessing predictive insights have been critical for our clients as they rely on Acquia Marketing Cloud throughout the ongoing COVID-19 pandemic. As brands around the world must adapt in real-time, all Acquia Customer Data Platform clients have been able to leverage Acquia’s COVID Analysis Dashboards to help mitigate these risks of uncertainty.

 Want to Learn More? 

Contact us to get a demo of Acquia Machine Learning and start gaining intelligent insights into your business, beginning with consumer behaviors, buying patterns and where you should invest to maximize retention, loyalty, as well as how to turn one-time buyers acquired during COVID-19 into high value customers.

ADC headshot

Alex Dal Canto

Director of Product Marketing  Acquia

Alex Dal Canto is a Director of Product Marketing at Acquia. Alex is responsible for launching and driving business growth for Acquia Marketing Cloud. Prior to Acquia, Alex was a Product Marketer at Adobe, responsible for launching customer journey management solutions to market and part of the Strategic Alliances group at Neolane. Alex studied Business Administration at Babson College.