Marketing Data Trends for 2020

1. Data-driven marketing

Today's marketers have an abundance of consumer data. With all of this data, marketing efforts can out-perform competition. A recent McKinsey report suggests that companies making intensive use of customer analytics are 2.6 times more likely to have a significantly higher ROI than competitors: 45 percent versus 18 percent. 

In order to make the most out of your data, using AI can help to personalize campaigns. CIO Review suggests that by anticipating consumer needs, companies can better help customers by suggesting products they may find most interesting. When customers are given content with information that is relevant to them, they feel more valued and understood. 

BizRamp states that personalization isn’t just dynamically inserting a customers’ name or other basic information. It’s about serving them the right message at the right time. It’s essential to set up custom filters and trigger automation when customers and leads meet the specific criteria, such as having viewed your pricing page. 

How do you become data-driven?

Being a data-driven marketer can drastically change the success of your campaigns. Here's how you can use data to create data-driven campaigns: 

1. Use demographic and psychographic data 

2. Use data to make factual decisions, rather than assumptions

3. Enable your multi-channel strategy

4. Create segments for better campaigns 

2. Predictive analytics 

Using predictive analytics is a trend that every company should follow. One way predictive analytics can help businesses is by identifying customers who may churn, based off data from previously churned customers. Klaviyo explains that, for example, the time between purchases indicates how likely a customer is to churn. Using predictive analytics to identify these customers, your company can send targeted advertisements to them to lessen the likelihood of churn. 

Predictive analytics uses statistical modeling, machine learning, and data mining techniques to analyze the past and predict the future. Quantzig suggests that three to five years down the line, predictive analytics tools and solutions are anticipated to be more context specific, accessible, and flexible in terms of customization. Adopting new tools to predict consumer behavior can help businesses better understand their current customers and understand why customers are churning. 

How can you use predictive analytics? 

Here are some common ways to use predictive analytics:

1. Predict and understand churn

2. Estimate customer lifetime value

3. Detailed lead scoring

4. Anticipate consumer needs

3. Ethical data collection and management

Because marketers have an abundance of consumer data, it is important to remember to collect the data ethically and keep the data safe. With the California Consumer Privacy Act going into effect January 1, 2020, it is important to understand what this means for marketers. GoSquared suggests that without the use of tools provided by gigantic corporations, you ensure proper utilization of the data. You’ll know that your customers’ and users’ data is being used as your company intends, and not for the marketing needs of other corporations. 

Deloitte outlines four of the biggest issues in data ethics:

• Privacy

• Lack of transparency

• Bias and discrimination

• Lack of governance and accountability

GDPR in EU aims to tackle all of the issues listed above and protect consumer personal data. Complying with GDPR regulation and the new CCPA regulations will ensure consumer trust.

What can you do to collect and manage data ethically? 

First and foremost, ensure you and your team understand privacy and data collection laws. Once you and your team understand the law, set up policies and trainings for your company. Training your staff on these privacy and data collection laws is key to keeping consumer data safe. Once your entire company understands these laws and how to protect consumer data, simply share with your consumers what their data is being used for. By being transparent about data collection, consumers will feel safer about the data they share and how it’s being used for analysis.