Everyone is rushing to implement machine learning (ML) into their Marketing processes in the hopes that it will bring unprecedented power to outshine the competition. After all, marketing relies heavily on data and communication, and it evolves so quickly that many programs are old by the time they are ready for implementation.

ML increases both the speed and flexibility of many marketing processes, but it is not a one-size-fits-all solution. Some functions benefit greatly from a good dose of ML; others only marginally. To get the most benefit from any investment in ML, it helps to know which and how different types of analysis apply to a given situation.

For most marketing applications, data analysts typically use three basic approaches:

• Descriptive — applied to data from past events
• Predictive — used for forecasting and planning;
• Prescriptive – used to determine optimal courses of action.

Of the three, predictive and prescriptive are most commonly used to build ML algorithms, while descriptive analytics are mostly applicable to reports and dashboards. Depending on the size of the data streams and overall data accumulation, some companies could spend up to two years collecting data to properly analyze consumer behavior and personalize customer relationships.

Even then, ML must be applied strategically within any marketing process, and experience has shown that it delivers the greatest benefit across six key functions.

Product recommendation:

When incorporated into a prescription analytics and personalization Model, product recommendations aim to increase conversion rates, average order value, and other key metrics. Experience has shown that when targeted offers are made based on data from past experience, sales can increase by 25 percent due to the greater relevance of the product or service to consumer needs.

Taking it one step further, organizations can use collaborative filtering and other tools to identify similarities between users, and this data can be used to provide relevant product recommendations across multiple digital properties. ML, in combination with a unified customer profile, can take into account customer preferences both online and offline, including purchased products and product interactions such as wish lists and views. This can then be used to make recommendations without relying on specific user histories. In this way, marketers can immediately make recommendations to new users, even before their profile is established. Organizations can also use collaborative filtering to predict user preferences based on socio-demographic variables, such as age, location, and preferences.

Prediction of the course

While most churn models work very well without ML, a dose of intelligence goes a long way toward perfecting the ability to use reliable information about customers, which can then be used to strengthen customer retention and marketing strategies, such as churn percentages and offer timing. However, to do this effectively, the ML model requires access to some very specific predictive data, such as recent purchase history or average order value. With this in hand, the model can analyze and classify customers based on their tendency to stay engaged.

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ML is also very adept at measuring the incremental impact of a marketing campaign at the user level, as well as revenue, sales, and other data, then making predictions about how this increase will play out in the future. Algorithms can be used to simulate consumer reactions to special offers and other elements, which not only help guide them to a completed sale, but can also reduce the cost of these efforts by targeting them more accurately to the right users, or to stop the lowest achievers altogether.

Recurring Purchases

Repetitive assignments are one of the hallmarks of successful marketing and ML can certainly play a role here, especially in organizations that have dramatic scale. A well-trained model can help companies determine the exact moment to engage existing customers to maximize the chances of a purchase. Not only does it know when a particular product has been repeatedly purchased by other customers, it can also identify and recommend additional items based on past consumer data. However, this requires careful analysis of multiple data points, such as the number of orders placed in the past, average order value, frequency of purchases, or other factors.

There is also often a narrow window in which a follow-up email will result in an additional purchase. Achieving this figure consistently has been shown to significantly increase click-through rates.

Customer analysis

Customer analytics is essential for a wide variety of marketing functions. Using descriptive analytics, organizations can define these segmentations at a more granular level, even down to the nuances of customer behavior. At the same time, prescriptive analytics can leverage these insights to accelerate and simplify the creation of new models and launch A/B testing to aid in churn rate or even lifetime value (LTV) analysis.

ML provides equally powerful tools for the popular Recency, Frequency, Monetary Value (RFM) analytics that power many marketing strategies today. With both speed and scale, ML vastly improves the ability to quantitatively rank and group customers to develop targeted marketing campaigns. This is especially effective for email-based outreach campaigns, giving organizations the ability to time emails to maximize site traffic and limit offers to those most likely to use them.

Dynamic pricing

Consumers are becoming increasingly price sensitive in the post-pandemic era. Dynamic pricing allows companies to optimize special promotions such as sales and discounts to provide balance in their financial structure. In general, there are three ways to identify pricing opportunities:

• The cost of maintaining a desired ROI
• competitor action
• Fluctuations between supply and demand

Of these, forecasting supply and demand is the most effective. This is done through clustering and regression techniques to plot the relevant data, such as past sales results for a particular region or season, which can then be used to generate a prescriptive result. In this way, pricing models are based on data, not hunches, although marketing managers can always set limits if they see fit, including not lowering prices at all.

Not only can ML perform all these critical functions faster and more efficiently, but they have already shown that they can be more accurate, provided they are correctly modeled and trained with quality data. This will require some investment from the company, which will vary depending on the business model. For example, in e-commerce environments, the ROI can range from 1 to 4 years.

Data and ML for marketing: when and how?

A critical question for most organizations is when and how to start implementing ML into the business model. And even then, how can it be done to provide the maximum benefit and, certainly, to avoid harmful consequences?

One thing to keep in mind is that ML offers no significant benefits if it only has limited data to learn from. This can be a problem for small businesses that often lack the resources to work with large amounts of data, leaving ML models with an incomplete picture of existing conditions, which can lead to ill-considered recommendations.

Therefore, all businesses, large or small, need to partner with the right providers to ensure their ML implementations are well aligned with their business environments. And this partnership should be continued in the long term to ensure that the platform develops in a way that is beneficial.

But one thing is certain: ML is quickly becoming a widely used tool in the suite of forward-thinking enterprises, and it is delivering results. At this rate, it won’t be long before only those with the skills to master this technology can effectively market their goods and services in the digital economy.

Ivan Borovikovy is founder and CEO at mind box.