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How Machine Learning Takes the Guesswork out of 5 Common Marketing Challenges

How Machine Learning Takes the Guesswork out of 5 Common Marketing ChallengesThe amount of digital data organizations have the capacity to collect from their customers and prospects is astounding. On top of that, marketing strategists are racing to meet rising expectations for more personalized, more engaging business experiences, 24/7, on every platform their audience can access.

Until now, on premise and point solutions have been helping companies take clues from this data to make better decisions about product rollouts, customer retention strategies, and who to market to and when, but there’s only so much intelligence that this segment of predictive analytics can provide.

In fact, according to a recent study by Forrester Research, most customers think they are taking advantage of only 12% of the data they have to make better marketing decisions. So marketers are now turning to Machine Learning—a more advanced form of predictive analytics—to turn even the most subtle statistic into a tool for increasing engagement and ROI, at every stage of a customer’s lifecycle.

By finding patterns in historic data and using that data to predict future actions at a more finite level, Machine Learning is taking the guesswork out of five of marketing’s biggest challenges:

Maximizing Prospect Conversion

Companies have databases full of potential prospects and likely have their sights set on many more. Machine Learning allows marketers to allocate resources more effectively by identifying which leads (both those already in the pipeline and those still waiting to be found) are most likely to convert.

US Bank is a great example of a smart marketer who turned to increased conversion rates by over 100% by using predictive analytics to integrate and interpret data across channels.

Making the Most out of Customer Contact

Machine Learning can make each contact with your customer more valuable in terms of enabling a more timely and relevant customer experience. By recognizing patterns in past engagement and customer response activity, Machine Learning can improve performance by recommending when to contact them, through what channel, with content that is most relevant to their lifecycle stage.

Machine learning can also analyze data from a company’s contact center history to improve workflow and ROI in other parts of the organization.

Case in point: find out how Sears used predictive analytics to streamline inventory and reduce service time in its appliance repair unit.

Increasing Revenue Per Customer

Machine Learning can find patterns in past customer interactions, channel preference, market segmentation and lifecycle status to maximize revenue per customer.  For example, software applications that track the physical movements of customers through a store provide data that can help retailers optimize the selection and placement of both products and services.  Likewise, data derived from past customer interactions can help predict what time of day your first-time millennial customer is most likely to purchase a product.

Using data analytics, Starbucks realized that customers did not engage with touchpoints the way they thought previously.  They leaned on this new data to better inform the store design process.

Refining Customer Segmentation

Machine Learning can use information from both external sources and customer interactions to provide more variant-rich customer views and enable personalization at micro-levels not previously possible with other segmentation approaches.

This McKinsey study reveals how pharmaceutical companies are using analytics-driven segmentation to fine-tune and individualize launch tactics.

Forecasting Customer Lifetime Value

Machine Learning finds patterns in past customer behavior to predict a customer’s lifetime value at the beginning of their lifecycle—improving efficiency in resource allocation, campaign management, and ROI forecasting.

This CMO article features how one online hotel booking site ditched traditional, short-term customer lifetime value (CLV) metrics for data analytics that allowed them to better strategize channel investments among other things.

Businesses applying data driven marketing are quickly outpacing those who may be gathering good data, but have yet to include readily accessible, predictive analytics platforms in their marketing strategies.

In an age where even the slightest nuance of data can secure a company’s ability to make marketing decisions with increasingly scientific accuracy over time, machine learning is quickly becoming the core intelligence tool for monetizing data down to the smallest exchange. It’s math meets marketing and it is turning what used to be an intangible art into a very usable, accessible science.

Download our Case Study to learn more about machine learning in action.

Thredup Case Study

Topics: Machine Learning