How Machine Learning Can Maximize Your Marketing ROI
Customers constantly offer up data to brands across an array of devices and channels, so marketers don't have to rely on their hunches anymore when planning communications, campaigns, ads — really any touchpoint along the customer journey. There's enough data and tech prowess for marketers to take actions based on concrete insights around customer behavior and preferences.
Machine learning (ML) powers that newfound ability. By applying strategic outcomes to what customers tell you, 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 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 to 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 link 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 data-first era, just good enough no longer cuts it. Marketing needs to make data-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 can 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 invest their time and resources.
Intelligent insight everywhere
Machine learning within the Acquia Customer Data Platform enhances the insights marketers gain from their data. It helps them identify customer segments and engage with buyers at every step of the customer journey, bringing data-driven insights to every business user, enabling agile and decisive decision-making that boosts revenue.
All insights are calculated and available at the individual profile-level on a fully horizontally scalable architecture, so it's easy and seamless to use across all areas of the business, no matter what endpoint accessed the insights.
Transparent and tailored intelligence
Your business is unique, and Acquia Machine Learning provides fully customizable, out-of-the-box models, allowing full configurability from leveraged data sources to tailoring the model itself. This capability means you have full transparency into how the model is working for your business. Models can be customized 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're built by your data science team, a partner, or our services organization. All models in our machine learning framework leverage the company's unified, cleansed, de-duped data from any source. Calculated insights can be shared with external business intelligence (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%
- Improve personalization by serving relevant product recommendations
- Identify at-risk customers so retention strategies are more targeted
Supercharge return on ad spend (ROAS)
With the marriage of intelligent product recommendations and the Likelihood to Buy ML model, clients have strategically leveraged high-engagement advertising media, such as Facebook Dynamic Ads, to drive seamless, impulse buys from the social media feeds of their most engaged prospects.
While effective, these types of ad placements are expensive. Companies need a way to identify and target customers with high propensities to buy with the products most likely to make them convert on the fly. By making these insights available to marketers, our clients have seen significant lift in return on ad-spend, acquiring new customers, and fostering lifetime value throughout customers' individual journeys.
Individualize discount strategy
Blanket discounting is as effective a strategy as batch-and-blasting your customers’ inboxes. Spoiler alert: You leave money on the table that way. Every individual has a different sensitivity to price and for different types of products. Some of us are more frugal and seek out deals; others are more than willing to pay full price. But who are these groups?
With Acquia’s Likelihood to Pay Full Price ML model, our customers can determine who is more or less price sensitive. Armed with this information, they can target each customer with the price point that speaks to them and thus incentivize conversion through flash sales, seasonal promotions, and individual offers. When coupled with Likelihood to Buy insights, marketers can create and drive programs that target individuals who are less likely to convert. This targeting allows them to serve individually tailored discounts, which maximizes company margins, growing them over 25% while also nurturing a recurring customer-base.
Optimize offline reactivation
With consumers opening up to 90 percent of direct mail, it's a fantastic medium for cultivating engagement and reactivating individuals who may have fallen out of their regular buying cycle. Luckily, with our Next Best Channel and Second-Best Channel ML models, marketers don't need to waste precious time guessing who those individuals are, 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 firsthand to create hyper-targeted segments of individuals with a high (but not optimal) propensity to convert and who will respond to direct mail. This capability optimizes ROI and lifts response rates by nearly 20%.
Keep customers coming back
Every marketer knows just how difficult and expensive it is to acquire new customers. And customer relationships aren't linear. As a result, marketers must be able to identify and mitigate potential churn as a key element of every customer journey. As likelihood to buy fluctuates with each individual based on parameters specific to your business – such as changes in engagement, purchases, or average order values – marketers can intelligently identify and track likeliness to churn.
When paired with insights such as predicted lifetime value, marketers can quickly understand what might have gone wrong and take action. They can engage each individual with personalized journeys that foster long-term loyalty, for instance, and calibrate interaction frequency along the way.
Peer into the future
Every machine learning model, be it out-of-the box or fully customized, empowers marketers with predictive insight at the customer level, immediately available for analysis 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 has been critical for our clients who relied on Acquia Marketing Cloud during the COVID-19 pandemic. As brands around the world adapted in real-time, all Acquia Customer Data Platform (CDP) clients leaned on Acquia’s COVID Analysis Dashboards to help mitigate risks and uncertainties.
Want to learn more?
Contact us for 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 and loyalty, as well as how to turn one-time buyers into high-value customers.