Unlocking personalized marketing with customer analytics | Simon AI

Unlocking personalized marketing with customer analytics

Author
Lauren Saalmuller
Content Marketing Lead

Gone are the days when it only took fair pricing and quality service for a business to satisfy customers. Modern customers expect businesses to understand their needs and buying behaviors, providing personalized interactions and a consistent experience across channels.

To understand these needs and behaviors, you need the right data. Customer analytics allows you to take a deeper look into customer behavior, understand why they do what they do, and make better-informed, data-driven business decisions.

Let’s dive into what customer analytics is, how to track your metrics, and the best practices for implementing this process.

What are customer analytics?

Customer analytics refers to collecting, organizing, and analyzing customer data across various channels in order to generate actionable insights into customer behavior. Techniques used in the customer analytics process include:

Predictive modeling
Customer segmentation
Information management
Data visualization

Using customer analytics, businesses capture and analyze customer data. This process helps them create strategies to identify, attract, and keep high-value customers and improve the overall customer experience.

The importance of customer analytics

Analyzing customer data gives businesses a holistic view of their customers, setting the foundation for successful sales, product, and marketing strategies. A good customer analytics platform can help your business in several ways:

Increasing customer engagement, sales, and revenue
Promoting higher customer satisfaction
Improving brand awareness
Increasing customer retention
Lowering lead generation and acquisition costs

Customer analytics helps you understand who each customer is as an individual. By collecting data as customers move through each stage of the customer journey, you can identify how customers discover your products, the features they like best, where they find value, and what might cause them to leave.

Example of customer analytics

Amazon: Product recommendations

Amazon, inarguably the largest ecommerce company in the world, collects and analyzes a wealth of big data to gain customer insights and create better experiences. The more Amazon knows about a customer, the better it can predict what that customer wants to buy.

Whether a customer purchases a product, places it in their cart, or just browses product details, Amazon collects and uses that data to create a complete picture of the customer and figure out what they want. Using this data and information from other sources, such as shipping details and customer feedback, Amazon can fine-tune its product recommendations to persuade customers to buy.

Customer analytics also allows Amazon to use look-alike modeling to make product recommendations based on customers with similar buying habits.

The Farmer’s Dog: Personalized emails

Personalization is essential to The Farmer’s Dog, a company that delivers fresh pet food on a subscription basis. The Farmer’s Dog ran on three lifecycle solutions before unifying its data and delivering personal emails based on the questions users ask when they sign up for service.

Based on these questions and user behavior on the site, The Farmer’s Dog can follow up on abandoned carts with emails:

Unifying customer data for faster analytics 10x’d The Farmer’s Dog’s email experimentation.

Travel + Leisure: RFM analysis

Travel + Leisure tailors their marketing efforts with campaigns based on shopper interest. However, 1:1 personalization isn’t easy to achieve. Travel + Leisure uses a CDP to pull data from everywhere across their company to achieve a score for each customer similar to an RFM:

Propensity: How likely they were to complete that particular action
Value: What’s the action’s value to the business?
Urgency: How urgent is it that the action be completed?

Travel + Leisure aggregates these factors into a score that helps them tailor messaging and timing for customers.

The 4 categories of customer analytics

You can do a lot with customer data! That’s why there are so many disciplines and projects that use it. You can group customer analytics into four main types:

Descriptive analytics

As the name suggests, descriptive analytics help you gather and understand past customer behavior. This type doesn’t give the why, just the how, and it focuses on historical events, not predicting future events.

Diagnostic analytics

Unlike descriptive analytics, diagnostic analytics attributes a reason to past customer actions. This is best paired with descriptive analytics to understand what has happened and why! Diagnostic analytics can be more qualitative — for instance, open-ended review responses or survey forms.

Predictive analytics

This category of analytics is typically powered by ML or AI, and it’s grounded in predicting future customer behavior based on historical data. With predictive analytics, you can identify trends, forecast seasonal changes, and prepare the right messaging for future events.

Prescriptive analytics

Prescriptive analytics is the next step to predictive analytics. Prescriptive analytics can suggest what to do based on historical data, as opposed to simply predicting future or ongoing trends. This can suggest the right times to send a follow-up email, or what type of campaign to run for your product.

How to implement customer analytics

Follow these five steps to implement customer analytics in your business.

1. Decide what data you want to collect

The first step to implementing customer analytics is identifying the data sets you want to collect. To do this you can ask questions such as the following:

Who are our customers?
What is their age range?
What are their demographics?
What touchpoints do they prefer at the various stages of their journey?
How do they like to communicate with the business?

Use customer journey mapping to identify the best channels and touchpoints for more relevant data collection.

2. Capture the data

Collect a lot of data from multiple sources. These can include your website, online and in-store interactions, internet browsing, email marketing, social media interactions, marketing tools, customer relationship management (CRM) tools, and third-party data.

3. Store customer data securely

Choose a secure platform to store data and ensure you frequently back up your information.

Consider merging customer data from all your sources into a central repository such as a customer data platform (CDP). Besides unifying all your customer data into a single trusted location, a CDP helps you structure it in a way that eliminates the occurrence of inaccurate results, which will help us with the next step.

4. Clean and organize the data

Unorganized data makes it more difficult to get accurate, actionable insights. Organize and clean the data you’ve captured to remove irrelevant, outdated, and duplicate data.

Be sure your data is standardized, so it’s consistent formatting, styling, and categorization across all your records.

5. Track metrics and analyze the data

Tracking key customer analytics metrics such as net promoter score (NPS), customer satisfaction (CSAT), and CLV helps you to understand how your marketing campaigns are performing and whether your business is moving toward its strategic goals.

Analytics tools (like a CDP) backed by artificial intelligence (AI) and machine learning (ML) technologies extract useful insights to help you make sense of your data. These tools combine various data types such as demographics, social media data, and customer purchase history to identify trends and patterns.

6. Turn insights into action

Once you understand the actions of your customers and why they take them, you can better predict their future behaviors. This kind of insight drives more customer-centric decision-making, helping you combat issues such as poor customer retention and high churn rates, and allowing you to create marketing strategies around customer segments that offer relevant experiences.

Customer analytics best practices

Here are a few best practices to help you make the most out of your customer analytics.

Organize your data. To gain the most clarity, consolidate your data into a single customer view to create comprehensive unified profiles of customers or segments.
Make use of technology. Use advanced analytics tools with AI and machine learning to identify trends and recommend the next best steps.
Listen to your customers. Their opinions (and complaints) can reveal useful information about their preferences and lifestyles.
Analyze omnichannel customer interactions. Look at data from several relevant sources to understand how your product is catering to different customers in various ways.
Prioritize customer retention and loyalty. Identify your at-risk customers, and take action to reduce churn, increase customer retention, and extend customer lifetime value.
Gather qualitative insights. This is trickier to track and aggregate, but some of your richest insights come from surveys, reviews, and interviews. Don’t ignore this data in favor of quantitative responses — use both to back up your findings.

Customer analytics tools

For every task, there’s a tool that gets the job done quicker. These are tools that can help you collect, organize, and make sense of your customer data.

Google Analytics

Google Analytics is the old reliable. It’s one of the old-school tools for measuring and attributing website traffic. GA largely helps with the collection of data, but not the organization of insights.

Hotjar

When people think of quick and simple tools to analyze user experience, they think of a tool like Hotjar, which records user sessions and heatmaps. This information gives you a better idea of how a user travels across your site.

Tableau

While some tools are known for collecting data, Tableau is famous for visualizing that data. Tableau can pair with your CRM to gather customer data, visualize it, and provide AI-powered predictive analytics.

Kissmetrics

Kissmetrics is a time-honored classic tool trusted by dozens of big logos. Like Hotjar, it’s a tool for understanding customer behavior and identifying UX pain points. You can track behavior across sites and products.

Simon Data

CDPs are heavy lifters when it comes to customer data. Simon is a CDP that helps you aggregate and activate your data. It has AI tools to analyze customer behavior and predictive analytics to suggest how to act.

Get to know your customers with Simon Data’s CDP

Customer analytics plays a big role in understanding your customers, their preferences, and their choices. In-depth insights into a customer’s journey, experience, engagement, and loyalty can help you build closer connections, improve retention, and create marketing campaigns that drive business growth and success.

With Simon’s CDP, marketers can build and personalize cross-channel experiences using customer attributes, demographic, psychographic, and behavioral data – like where your customers are coming from, what they say about your business, the channels they engage with most, and which products they love most.

Our seamless integration with many marketing tools and data sources enables you to create a unified view of your customer, and optimize your marketing efforts to increase revenue.