6 Ways Machine Learning Can Boost Your Marketing Operations

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Everyone is rushing to spread machine learning (ML) in their marketing operations hoping to bring unprecedented strength to outperform the competition. After all, marketing relies heavily on data and communications, and it’s evolving so quickly that many programs are outdated by the time they’re ready for deployment.

ML increases the speed and flexibility of many marketing But it is not a one-size-fits-all solution. Some jobs greatly benefit from a good dose of ML; Others are only marginal. To get the most benefit from any investment in ML, it helps to know which and how different types of analytics are applied to any given situation.

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

  • Descriptive – it is applied to data from past events
  • predictive – used for forecasting and planning;
  • Mandatory – used to determine the optimal courses of action.

Of the three, predictive and descriptive are most commonly used to build machine learning algorithms while descriptive analytics is mostly applied to reports and dashboards. Depending on the size of the data flows and the overall backlog of data, some companies may spend up to two years collecting data to properly analyze consumer behavior and customize customer relationships.

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Until then, ML must be applied strategically in any marketing process, and experience has shown that it provides the greatest benefit for six major functions.

Product Recommendation

When combined with prescription analytics and personalization model, product recommendations are intended to enhance conversion rates, average order value, and other key metrics. Experience has shown that when targeted offers are made using data from past experiences, revenue can increase by 25 percent due to greater relevance of the product or service to consumer needs.

Taking this a 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 many digital properties. ML, along with a unified customer profile, can consider online and offline customers’ preferences, including products purchased and product interactions such as wish lists and views. This can then be used to generate recommendations without having to rely on specific user records. In this way, marketers can make instant recommendations For new users even before they create their profiles. Organizations can also use collaborative filtering to predict user preferences based on socio-demographic variables, such as age, location, and preferences.

ripple rate prediction

While most variable models work very well without ML, a dose of intelligence goes a long way toward mastering the ability to leverage reliable information about customers, which can then be used to enhance customer retention and marketing strategies, such as change rates and offer timing. To do this effectively, however, 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 is able to analyze customers and categorize them according to their tendency to remain 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, and then making predictions about how that spike will happen in the future. Algorithms can be used to simulate consumers’ reactions to specials and other items, which not only helps direct them toward completed sales, but can reduce the cost of these efforts by targeting them more accurately to the right users, or turn off lower performers altogether. .

Repeat purchases

Recurring business is one of the hallmarks of successful marketing, and ML can certainly play a role here, especially with organizations that are experiencing large scale. A properly trained model can help companies identify the exact moment to engage existing customers to maximize purchase opportunities. Not only does it know when a particular product has been repeatedly purchased by other customers, it can identify and recommend complementary items based on past consumer data. 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 often a narrow window in which a follow-up email leads to an additional purchase. Reaching this tag on a consistent basis has been shown to significantly boost click rates.

Customer analysis

Customer analysis is vital to a wide range of marketing functions. By using descriptive analytics, organizations can identify these divisions at a more granular level, even down to the nuances of customer behavior. At the same time, meta-analytics can leverage these insights to speed up and simplify the creation of new models and launch A/B tests to aid in rate of change or even lifetime value (LTV) analytics.

ML provides equally powerful tools for RFM analytics (recency, frequency, monetary value) that drive many marketing strategies these days. In both speed and scope, ML improves the ability to quantitatively rank and aggregate customers to develop targeted marketing campaigns. This is particularly effective for email-based outreach campaigns, where organizations gain the ability to time email messages to generate maximum site traffic and limit offers to those who are most likely to engage in them.

dynamic pricing

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

  • Expenses to maintain the required ROI
  • competitor’s work
  • Fluctuations between supply and demand

The most effective is the forecasting of supply and demand. This is done through aggregation and regression techniques to graph relevant data – such as past sales results for a particular geographic season or season – which can then be used to generate an indicative result. In this way, pricing models are built on data, not intuition, although marketing executives can always set limits as they see fit, including not cutting prices at all.

Not only can ML perform all these vital functions faster and more efficiently, they have already shown that they can be more accurate, provided they are properly designed and trained with high quality data. This will take some investment by the organization, which will vary depending on the business model. In e-commerce environments, for example, the return on investment can range from 1 to 4 years.

Data and ML for Marketing: When and How

The critical question for most organizations is when and how to start implementing ML into a business model. Even so, how can this be done to provide maximum benefit, and certainly to avoid any adverse outcomes?

One thing to keep in mind is that machine learning won’t provide much 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 current conditions that can lead to misleading recommendations.

This is why all businesses, big or small, need to partner with the right service providers to ensure that their ML deployments are appropriately tailored to their business environments. This partnership must continue over the long term to ensure that the platform evolves in beneficial ways.

But one thing is for sure: Machine learning is quickly becoming a popular tool in the pool of futuristic organizations, and it is delivering results. At this rate, it won’t be long before only those who have the skills to master this technology can effectively market their goods and services in the digital economy.

Ivan Borovikov He is the founder and CEO of Mindbox.

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