Data-Based Marketing: Top Use Cases and Tips for Adoption

With customers’ ever-shrinking attention spans and the fast-paced nature of our digital environment, marketing needs to efficiently use data and advanced analytic techniques as never before. 

Customers go through an avalanche of often impersonal and generic offers daily. To stay competitive, companies need to provide customers with increasingly relevant and personalized interactions that consider a multitude of parameters that form a customer’s profile.

With unceasing streams of data, marketing departments can now not only analyze those but also predict customers’ future needs and wants. 

In this article, we explore how marketers can leverage the power of data to make the most of their campaigns. 

1. Customer Segmentation

Customer segmentation is one of the pillars of a successful marketing strategy. Grouping customers into segments based on demographics, purchasing patterns, interactions with promotions, and other characteristics have been proven extremely useful for setting up effective outreach campaigns. 

However, the effectiveness of these campaigns directly correlates with how well you can process your customer data. With accurate behavioral profiles and machine learning, you can set the best time for cross-selling by predicting customers’ important events for your micro-moment marketing.

To make this happen, you would need to continuously monitor customers’ behavioral cues and train your ML algorithms accordingly. 

Current data processing capacities allow companies to dig deeper into their data and cluster customers into hundreds of granular segments, which is commonly referred to as customer micro-segmentation. 

2. Conversion Optimization

Undoubtedly, the bigger the audience of your marketing campaign, the bigger the probability of a sale. However, when companies have a limited budget and for that reason try to maximize the performance of their existing marketing assets like landing pages, newsletters, etc., it comes down to identifying the most valuable assets and directing the budget towards them.

While there are a plethora of marketing studies trying to figure out which exact channels improve conversion and which metrics are the most important ones, the majority of businesses still need to develop their individual approach. 

Shopping patterns can vary dramatically depending on customers’ location, age, interests, etc. With custom machine learning models, companies can accurately identify their highest-performing assets, gauge response to them through sentiment analysis based on natural language processing, and focus on making the most of them. This is especially relevant for small businesses, which are often hesitant to spend big budgets on outreach campaigns. 

3. Lead Scoring

Remember the Pareto principle? It also applies to marketing. More often than not, 80% of sales are coming from 20% of your customers. In practice, it all comes down to identifying that 20% of customers and allocating more resources to reach them.

Data science enables companies to identify customers who are most likely to have the highest lifetime value (LTV). Then, a custom ML model can rank customers based on LTV or any other metric that is relevant for your business. After that, the logic is quite straightforward: you should put more effort into reaching and engaging the highest-value prospects. 

Your First Steps in Data-Driven Marketing

The aforementioned use cases of data science in marketing are just a fraction of its full potential. While certain small data-driven initiatives can provide short-term success, we are all aware that business is more like a marathon than a sprint. To make data-based decision-making a routine, you’ll need solutions that are scalable and adaptive. 

A Customer Data Platform

As CRM systems are now standard business tools used across all industries, customer data platforms (CDPs) are poised to follow the same path. 

Essentially, these are partly automated decision-making platforms that consolidate all the data a company has to create comprehensive customer profiles. Regardless of the data maturity level, most companies have information about their customers that isn’t utilized to its full potential. 

A CDP can unify all transaction, behavioral, web, mobile and demographic data in one place, which enables real-time analytics of customer behavior.

By implementing an ML model into a CDP, the system will continuously study profiles, enabling more granular insights into customers’ needs, wants and shopping habits. For example, with more sub-segments in place, it becomes easier to understand customers who buy occasionally. 

CDP-based Decision-making

In many cases, marketing comes down to offering the right product at the right time. This task can be largely automated with custom-built ML models, which can analyze customers’ important behavioral cues.

These models can be linked to social media activity (liking photos with hashtags that indicate interest in a particular product), actions on e-commerce websites (time spent on a product page), and practically any footprint left on the internet. 

However, just like humans, ML models need to learn first. Figuring out what type of offers are effective for particular consumer groups can take a sufficient part of your budget and time.

Testing your marketing hypotheses should always be done in collaboration with all relevant departments. With data and consent-related regulations now taking center stage, data scientists, technologists, and legal professionals become integral to marketing as well. 

Closing Thoughts

Big data has already become the driving force of today’s marketing efforts. While marketers have always been making decisions based on data, customers are increasingly expecting hyper-personalized offers, which can’t be created using conventional techniques. We like to call data the new gold, but it’s insights that are truly precious. 

Generating those insights comes down to having solid data governance principles, seamless data access across all parts of the organization, CDPs with meticulously trained ML algorithms, and an environment where data-based initiatives are encouraged.

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