The value of your data is only as good as the actions you’re able to glean from it. As the amount of data collected grows in both variety and volume, companies need assistance to help them gain insights into their customers to decipher connections, identify new behavior patterns, predict spending likelihood and assess lifetime customer value. Companies are looking for some edge — any edge — as customer spending habits can shift almost overnight. To give them that edge, many marketing departments are exploring machine learning tools for marketing, according to the latest Gartner research.(1)
One way to leverage machine learning (ML) is through customer data platforms with ML capabilities built in. Having the ability to process data through powerful machine learning algorithms can produce a wealth of advanced customer insights, according to Huiyan Wong, senior product marketing manager for Treasure Data. “Without machine learning, you can get basic customer knowledge from your CDP, such as attributes or demographics. Taking the next step into machine learning helps you to discover behaviors that might not be obvious at first glance. Understanding customer behavior allows you to predict what they’ll do next and market to that action.”
How can ML-equipped CDPs help marketing teams? Wong suggests several use cases and offers advice on best practices for setting up a CDP to gain insights, even if your marketing team isn’t deeply technical.
You Have Use Cases, Machine Learning Has Answers
Once marketers start listing all the use cases they could attempt with machine learning, the list gets pretty long. Some may need industry-specific metrics, churn prediction and the trends that could reduce churn, or predictive modeling. Machine learning models can then take that data and make recommendations for the next step in customer interactions. Machine learning can also produce insights that show propensity and affinity for certain customer characteristics.
All these insights allow marketers to build robust audience profiles with well-defined characteristics, which in turn enables targeting the right people with the right message on the right channel at the right time. “Why send your customers emails in the morning when the data says they’re most likely to read their emails in the evening?” asks Wong. “Meeting the customer where they are makes it more likely to convert a sale, retain a customer and prevent churn.”
Meeting customers where they are when they’re the most receptive to communication has the added effect of growing customer loyalty and improving brand reputation. Customers appreciate it when they aren’t being bombarded with marketing pitches all the time, and they appreciate companies that respect their time and space. This increase in customer loyalty can also translate to increasing the lifetime customer value; especially if the ML insights drive customers to your company’s other product lines that customers might not have considered.
The other thing ML insights save is time. By gathering insights from an ML-equipped CDP, marketers can be more efficient with their time and focus on crafting strategy rather than getting deep in the weeds with the data.
Non-Technical? Machine Learning Can Still Deliver Insights
Ease of use is a huge factor in any technological adoption. However, marketers that feel like they don’t have strong technical expertise don’t need to shy away from an ML-enabled CDP. Treasure Data’s machine learning capabilities have plenty of pre-built options for non-technical marketers who are nonetheless looking to get insights from the technology. Insights and actions for non-technical marketers include automating tasks, understanding the likelihood of customer purchase, unsubscribe certainty, predictions of which customers will visit a store within a given time frame and more.
For the Technical Marketer, the Possibilities With ML Are Endless
One of the advantages to the Treasure Data CDP is the flexibility around the machine learning capabilities within the platform, unlike the black-box approach commonly seen in the CDP space, Treasure Data’s CDP not only gives the technical users clear visibility to the algorithms but also allows extensive customization. This transparency is especially useful if marketers need custom algorithms and want to gather unique insights about their customers.
“Treasure Data’s CDP excels at handling unique business use cases because of this flexibility,” Wong says. “The technology allows data scientists to tweak the algorithm as needed, which can potentially lead them to the answers of whatever they want to know.”
The pre-packaged machine learning options built into the CDP allow for immediate out-of-the-box capabilities. But the ability to design their own algorithms empowers marketers to build their own queries and gain industry- or segment-specific insights beyond the traditional set of questions and answers. In this way, Treasure Data’s CDP truly integrates with a company’s business strategy.
Making a CDP Work for You
Like all technology, machine learning capabilities aren’t without challenges. Machine learning for marketing presents a long, potentially steep learning curve, according to Gartner research.(2) You need a lot of good, detailed data for machine learning to work.
Gaining insights from machine learning is by nature, a virtuous cycle.. “Using machine learning within your CDP should be an iterative process,” says Wong. “The insights you get from an ML-backed marketing campaign can be used to refine the model and make better predictions for the next campaign. The more data you run through the model, the more accurate the model gets and can better deliver accurate predictions.”
Ideally you should have a use case or two in mind when exploring CDP options. If your company just needs a way to sort your data, then you don’t really need the extra capabilities that machine learning provides. If, however, you need to gain new insights into your customers, then you need a CDP equipped with the ability to deliver insights via machine learning.
Marketers can leverage machine learning capabilities within a CDP for a host of business use cases. At its core, machine learning models help marketers target customers better, ensuring that the right product is targeted to the right customer on the right channel at the right time.
The sheer amount of data coming at marketers every day makes a CDP with machine learning capabilities a necessary marketing component. The insights and reports provided by algorithms can free marketers to focus on more strategic tasks, rather than spend their time on manual data collection and analysis.
Learn more about how Treasure Data’s machine learning capabilities can give your business the insights it needs at treasuredata.com.
- 1, 2 Gartner (2020). Hype Cycle for Digital Marketing