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Everybody is rushing to deploy machine learning (ML) into their marketing processes in the hopes that it will bring unprecedented power to outperform the competition. Marketing, after all, relies heavily on data and communications, and it evolves so quickly that many programs are stale by the time they are ready for deployment.
ML increases both the speed and flexibility of many marketing processes, but it is not a one-size-fits-all solution. Some functions benefit mightily from a good dose of ML; others only marginally. To derive the greatest benefit from any investment in ML, it helps to know which is which and how different types of analytics apply to any given situation.
For most marketing applications, data analysts typically utilize 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 applies mostly to reports and dashboards. Depending on the size of data flows and the overall accumulation of data, some businesses could spend up to two years accumulating data to properly analyze consumer behavior and personalize customer relationships.
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Even then, ML should be applied strategically within any marketing process, and experience has shown it to provide the greatest benefit to six key functions.
When incorporated into a prescription analytics and personalization model, product recommendations are intended to boost conversion rates, average order value and other key metrics. Experience has shown that when targeted offers are made using data from previous experiences, revenues can increase by 25 percent due to the increased relevance of the product or service to the consumer’s needs.
Taking this a step further, organizations can employ collaborative filtering and other tools to identify similarities between users, and this data can be used to deliver relevant product recommendations across multiple digital properties. ML, in combination with a unified customer profile, can account for customer preferences both online and offline, including purchased products and product interactions like wish lists and views. This can then be used to create recommendations without having to rely on specific user histories. In this way, marketers can make instant recommendations to new users even before their profiles are established. Organizations can also employ collaborative filtering to predict user preferences based on socio-demographic variables, such as age, location and preferences.
Churn rate prediction
Although most churn models work very well without ML, a dose of intelligence goes a long way toward perfecting the ability to leverage reliable information about customers, which can then be used to strengthen customer retention and marketing strategies, such as churn rates and offer timing. To do this effectively, however, the ML model requires access to some highly specific predictive data, such as recent purchase history or average order value. With this in hand, the model is able to analyze and classify clients according to their propensity to remain engaged.
ML is also highly adept at gauging the incremental effect of a marketing campaign at the user level, as well as revenues, sales and other data, and then making predictions as to how this uplift will play out into the future. Algorithms can be used to simulate consumer reactions to special offers and other elements, which not only helps to guide them toward completed sales, but can lessen the cost of these efforts by more accurately targeting them to the right users, or discontinue the lowest performers altogether.
Repeat business is one of the hallmarks of successful marketing, and ML can certainly play a role here, particularly with organizations that are experiencing dramatic scale. A properly trained model can help businesses determine the exact moment to engage existing customers to maximize the chances of a purchase. Not only does it know when a given product has been repeatedly purchased by other customers, it can identify and recommend supplemental items based on previous consumer data. This requires careful analysis of multiple data points, however, such as the number of orders made in the past, the 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. Hitting this mark on a consistent basis has been shown to considerably boost click rates.
Customer analysis is vital to a wide range of marketing functions. Using descriptive analytics, organizations can define these segmentations on a more granular level, even down to the nuances of customer behavior. At the same time, prescriptive analytics can leverage these insights to speed up and simplify the creation of new models and launch A/B tests to assist in churn rate or even lifetime value (LTV) analyses.
ML brings equally powerful tools to the popular RFM (Recency, Frequency, Monetary Value) analyses that drive many marketing strategies these days. At both speed and scale, ML vastly improves the ability to quantitatively rank and group customers to develop targeted marketing campaigns. This is particularly effective for email-based outreach campaigns, with organizations gaining the ability to time emails to generate maximum site traffic and limiting offers to those most likely to engage them.
Consumers are becoming increasingly price-sensitive in the post-pandemic era. Dynamic pricing allows businesses to optimize special promotions like sales and discounts to provide balance across their financial structure. In general, there are three approaches to identifying pricing opportunities:
- The expense to maintain a desired ROI
- Competitor action
- Fluctuations between supply and demand
Of these, the most effective is predicting supply and demand. This is done through clustering and regression techniques to graph out the relevant data — such as prior sales results for a given geography or season — which can then be used to generate a prescriptive outcome. In this way, pricing models are built on data, not hunches, although marketing executives can always establish limits as they see fit, including not reducing prices at all.
ML can not only perform all of these critical functions faster and more efficiently, but they have already shown that they can be more accurate, provided they are modeled correctly and trained with quality data. This will take some investment by the enterprise, which will vary depending on the business model. In e-commerce environments, for example, 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 begin implementing ML in the business model. And even then, how can it be done to provide the maximum benefit and, most certainly, to avoid any harmful outcomes?
One thing to keep in mind is that ML won’t provide significant benefits if it only has limited data to learn from. This can be a problem for small businesses that tend to lack the resources to work with high-volume data, leaving ML models with incomplete views of existing conditions that can result in misguided recommendations.
This is why all businesses, large or small, need to partner with the right providers to ensure that their ML deployments are tailored properly to their business environments. And this partnership should continue over the long term to ensure that the platform is evolving in ways that are beneficial.
But one thing is certain: ML is quickly becoming a common tool in the kit of forward-leaning enterprises, and it is producing results. At this rate, it won’t be long before only those with the skills to master this technology will be able to effectively market their goods and services in the digital economy.
Ivan Borovikov is founder and CEO at Mindbox.