7 ways marketers can (and should) leverage data science

Marketers today have more intel available to them than ever before. Data provides insights on customers, markets, competitors and financial conditions.

Too often, however, marketers fail to leverage the power in data science. Doing so puts their brands at a decided disadvantage. Using information gleaned from various sources allows marketers to build clearer perspective. With better data science comes better profiles, campaigns and results.

Here are 7 ways marketers can (and should) leverage data science.

1. Develop customer profiles

Data collected on past and current customers helps you build detailed profiles of who your customers are. These profiles can provide insights into who your audience is and what they want. Data-based profiles also demonstrate what motivates customers to make a decision to buy your products or services.

Developing comprehensive customer profiles means collecting data at every touchpoint. What emails do they click on? What parts of your website do they visit and how long do they stay there? What campaigns drive more new customers?

2. Segment your existing customer base

In addition to comprehensive customer profiles, you’ll want to segment your customers. Information is available to build multiple profiles, developing customer personas that align demographics, purchasing preferences and other characteristics.

Data science can provide algorithms and machine learning tools to enhance segmentation. These tools can be used to scour massive data sets to identify the traits, patterns and similarities you need for more accurate segmentation.

Of course, once you’ve segmented your data, it’s time to determine what marketing best suits each profile. By fine-tuning your messaging, you can be more resonant in what you provide to each customer, enhancing their journey and experience.

3. Track engagement across channels

A sophisticated marketing strategy today likely includes multiple channels. The challenge can come from how to interpret and compare data across those channels.

Your customers are likely to engage with you across multiple channels and platforms. They may interact with your emails, your website, your social media and in-store. As a marketer, you must be ready to engage with your customers wherever they are.

The challenge is that customers expect you to know them, and their history with your brand, across those channels. Marketers need to be ready to engage with customers in any platform at any time. Good customer data helps marketers understand how and where customers are engaging and how to respond to these interactions to maximize the experience.

4. Personalize the experience

Customers want their relationships to be personal with brands they buy. Data science can help you build personalized relationships with your customers. Doing so results in more sales as marketing offers them products and services they’re most likely to want.

Artificial intelligence software helps with chatbots and virtual assistants that can hone in on what they need. These tools can guide customers to the right segment of their website or the right customer service representative. They can quickly answer common questions with relevant, accurate results.

Other data science-driven tools can personalize the buying journey through recommendations, automated ads on social platforms and dynamic content when they visit your site. These customized experiences, based on customer history and personas, will create better interactions, more engagement, more clicks and increased conversions.

It’s important to note that these tips are interconnected. The better the customer profiles, for example, the better the personalized experiences you can provide. The more data collected and analyzed means better recommendations and accuracy in an engagement.

5. Use predictive analytics

Predictive analytics tools can provide you with meaningful, real-time insights. These tools allow you to predict customer behavior and see patterns as they emerge.

The data can be used to build predictive models that provide accurate projections about future outcomes. Used in real time, these models can help marketers make course corrections and faster decisions. Data can be used to refine customer profiles, change strategies or confirm assumptions. Predictive models allow to you set projections, KPIs and goals and see progress towards those goals quickly.

6. Analyze social media trends

Tracking social media behavior is not only actionable, but necessary. Marketers need to understand what consumers are saying about them and what posts are getting responses. Data science allows for better social media monitoring, tracking likes, shares, clicks, comments and views. Sentiment analysis lets you measure social media conversations for positive and negative feedback related to your brand.

Leveraging social media lets you determine which platforms are best for your brand, when to engage and how, audience types, and the optimal times or posts. It’s also an effective way to monitor your competition.

7. Acknowledge privacy concerns

Privacy today is top of mind for customers. That’s why your marketing strategy needs to recognize, acknowledge and provide transparency about privacy. Increasingly, regional, national and state agencies are passing more stringent regulations about the collection and use of data. Your use for marketing must be mindful of both the regulations and customer sentiment about data issues.