What is customer data analysis?
Customer data analysis is the process of collecting and interpreting information about your customers to inform all aspects of your business strategy, including your B2C marketing campaigns.
This practice can help you unearth insights about the customer journey—from points of friction to areas of opportunity—so that you can invest your resources strategically to create a better customer experience and generate more revenue.
Why customer data analysis is crucial for brands
Customer data analysis translates information into actionable insights, which can drive meaningful business outcomes, including:
1. Create more personalized experiences that customers love
In today’s marketing world, B2C businesses have to know their customers. In fact, 74% of consumers expect personalized experiences from the brands they shop with, according to Klaviyo’s future of consumer marketing report.
Importantly, personalization is also the path to making more sales. According to Deloitte, brands that excel at personalization are 48% more likely than their peers to exceed their revenue goals.
Having the right data, and knowing how to analyze it to extract insights, is key to uncovering the intel you need to deliver the kinds of tailored experiences that not only make consumers feel seen, but also boost your bottom line.
2. Increase customer retention and lifetime value
Customer data analysis doesn’t just drive the first sale—it’s also critical for keeping people coming back. Deloitte’s report found that the companies that were leaders in personalization were 67% more likely to drive repeat purchases and 71% more likely to report improved customer loyalty.
Whether it’s troubleshooting high drop-off points in the customer journey with personalized marketing campaigns or using insights to send well-timed offers to your VIPs, data analysis works double duty by increasing your customer retention rate and bolstering customer lifetime value (CLV).
3. Improve business operations and marketing effectiveness
Customer data analysis also reveals precisely which channels, messages, and campaigns drive purchases.
You might discover your audience responds strongly to SMS messages and push notifications while showing minimal engagement with email. Or that sending recommendations for complementary products after major purchases significantly boosts your retention rates.
Regular data analysis helps you continuously refine your marketing strategy, improving profit margins by redirecting resources away from underperforming campaigns and toward what truly resonates with your customers.
Understanding the different types of customer data
Customer data is an umbrella term that captures a wide array of information, including:
- Personal: basic information about a customer, such as their name, address, phone number, or email address
- Demographic: information about a customer’s personal characteristics, like their age, gender, or location
- Behavioral: information about a customer’s past actions, such as their website browsing history, “add to cart” events, customer support interactions, or email opens and clicks
- Transactional: information about a customer’s past transactions, such as their order history or return history
- Survey and review: information you collect directly from customers, like CSAT score, net promoter score (NPS), product reviews and ratings, or qualitative feedback data
- Psychographic: information about a customer’s preferences, mindsets, or beliefs, such as the activities they enjoy, personal interests, and their political leanings
4 types of customer data analytics
While customer data provides the raw information about your audience, customer analytics transforms this data into meaningful insights. Here are 4 common approaches:
1. Descriptive
Descriptive analytics is the most common approach. It gives you a clear picture of historical events. Descriptive analytics includes observing data points like CSAT scores, NPS, and past sales metrics.
2. Diagnostic
Diagnostic analytics goes deeper than descriptive analytics by revealing why historical trends happened. This insight typically comes from customer feedback sources like surveys, reviews, and focus groups.
For example, if descriptive analytics shows you have a low NPS, diagnostic analytics helps identify the root cause. By analyzing open-ended survey responses, diagnostic analytics could show, for example, that customers love your product quality, but shipping delays are driving dissatisfaction.
3. Predictive
Predictive analytics uses historical data to forecast future behaviors, such as purchase frequency and timing. Then, you can proactively build campaigns to address this, such as implementing win-back strategies for at-risk customers or timely reminders for those who need a gentle nudge to repurchase a product.
Men’s personal care brand Every Man Jack, for example, used to send a replenishment flow encouraging customers to re-order after 45 days without a purchase. The timeline wasn’t customizable to each customer’s unique behaviors and preferences.
Then, the team adjusted their replenishment flow to send on—or slightly before, or after—each customer’s unique predicted next order date. Now, 12.4% of the brand’s Klaviyo-attributed value comes from predictive analytics segments.
4. Prescriptive
Seeing what’s on the horizon is helpful, but knowing exactly what actions to take to engage customers is most important for brands. Prescriptive analytics builds on predictive analytics by recommending specific actions to optimize future outcomes.
For example, Klaviyo’s email deliverability hub reviews your data and makes specific recommendations to improve your performance.
Different techniques for analyzing data
While there are countless ways to slice and dice your data, here are 6 techniques we recommend starting with:
1. RFM analysis
RFM analysis looks at each customer’s recency, frequency, and monetary value to help businesses understand and categorize their customers.
You can use this data to segment customers and target each group with offers that are likely to be appealing. For example, you could target less active subscribers with retention campaigns, while rewarding your most loyal customers with exclusive offers.
2. Funnel analysis
Funnel analysis is the process of identifying where and why people drop off before moving to the next stage of the customer lifecycle. B2C marketers use funnel analysis to address points of friction in their marketing funnel to ultimately increase conversion rates and time to purchase.
If you notice a high cart abandonment rate, for instance, you could improve the check-out experience or send emails offering discounts.
3. Cohort analysis
Cohort analysis is the process of grouping customers by shared behaviors or traits to learn more about their purchase patterns at scale.
A brand that’s looking to reduce customer acquisition cost (CAC), for example, might look at a cohort of customers who made their first purchase during the holiday season and offer them special discounts for subsequent holiday seasons to encourage more purchases.
4. Repeat purchase trends
Analyzing repeat purchase trends lets you look at all of your historic conversion data to understand the factors that lead to purchase, including the influence of specific marketing campaigns on sales. You can monitor repeat purchases to find trends that help you increase them over time.
5. Product purchase trends and purchase journeys
Analyzing product purchase trends and purchase journeys lets you see, for each product purchased, which products customers tend to buy in the same cart or shortly thereafter. You can use this to promote the right products at the right time to drive repeat purchases—and ultimately grow CLV.
6. Historic and predicted customer lifetime value
Use CLV to identify your most loyal customers and engage with them accordingly. By analyzing total CLV, the sum of a customer’s historical CLV and predicted CLV, you can compare the relative value of RFM segments, or reward customers with CLVs above a certain threshold with a special perk that encourages brand advocacy.
A better CRM = better customer data analysis
High-quality customer data analysis requires a powerful CRM that can help your brand collect, analyze, and activate your customer data.
Klaviyo is the only CRM built for B2C brands. It brings together your marketing, service, and analytics in a single platform.
With Klaviyo B2C CRM, you can get the most out of your customer data analysis by personalizing experiences at scale, measuring every touchpoint of the customer journey, and turning insights into action.
If you’re looking to make better use of your customer data to give your customers a better experience while driving more sales, sign up today.