Using customer data toinform your marketing strategyand increase sales may sound like a daunting task–but with today’s tools it’s very doable. And worth it: once you break down what customer data is and how it works, you open up a world of sales possibilities.
Here’s a walkthrough of what sales and marketing teams need to know about customer data so they can successfully integrate it into their growth strategy.
Table of Contents
What is customer data?
Customer data is information collected on a company’s customers; it’s used to personalize marketing campaigns, identify target audiences, and increase sales. In addition to demographic information like age, gender, and occupation, it includes purchase history, browsing data, and preferences.
Customer data enables marketing and sales teams to identify the needs of their customers and tailor their messages and offerings to meet those needs with precision and effectiveness.
While this applies to all sales-based businesses, it’s especially true for retailers.
“Retailers should collect information about who their customers are and what their shopping patterns are, so as to develop demographic and psychographic segment profiles,’ says Akshay R. Rao, General Mills Chair in Marketing at theUniversity of Minnesota’s Carlson School of Management.
Collecting data is always useful to develop insights about consumer behavior and segmentation. If certain types of consumers shop on certain days of the week and certain times of the day, then, for example, prices could be adjusted to cater to those consumers.
Types of customer data
With so much information you could be collecting about your customers, where should you begin? Personal, preferential, and behavioral data are relevant to a wide range of B2C companies, and particularly to retailers.
Personal data
Personal data, also known as demographic data, includes things like a customer’s age, ethnicity, income, socioeconomic status, education, marital status, and where they live. These data points provide a basic profile of a customer, and often influence buying behaviors.
Preferential data
这种类型的数据与客户的参照erences. What do they like? What do they value? How loyal are they to brands? Do they choose where to shop based on brand loyalty, or do they price shop?
Behavioral data
Behavioral data is all about how a customer interacts with your company. This includes information likepurchase historyand what, when, where, and how a customer shops with you.
If you’re a retailer, how often do they frequent your store? Do they shop online or in-store? Do they only buy full-price items, or do they wait for sales and use coupons? How much time do they spend browsing your app? What kinds of items do they purchase?
Identity data
Identity data is linked to a particular customer’s identity, and includes details such as name, email address, phone number, and purchasing frequency. This data also indicates when customers are most active and are, therefore, more likely to make a purchase.
Engagement data
Engagement data tracks the interactions of customers with your product or products. It may show, for example, the time spent on each page of your website or app, which emails have been opened, and which products have been viewed or added to carts. This data provides valuable insights into customer behavior and preferences and can be used by sales and marketing teams to create more personalized, targeted campaigns.
Attitudinal data
Attitudinal data explores how customers feel and think about a product or service. It helps sales and marketing teams understand their customer's motivations, dreams and desires, so as to create campaigns that connect with their target audience.
How to collect customer data
- Sign-up forms:Gather data from customers such as name and email through sign-up forms.
- Website or app tracking:Track customer actions and behaviors on your website or app.
- Social media monitoring:Monitor customers’ conversations and interactions on social media.
- Loyalty program data:Analyze customer data from loyalty programs to better understand customer behaviors.
- Point-of-sale (POS) data:Analyze data from transactions, such as the type of product purchased, frequency, and location.
- Third-party data:Leverage third-party data such as market research, demographics, and customer insights.
- Email marketing:Analyze customers’ interactions with email and use this data to improve campaigns.
- Search engine data:Monitor customers’ search queries to gain insights into their interests and preferences.
- Surveys:Ask customers questions to gain insights into their needs and preferences.
How to analyze customer data
Once you’ve gathered customer data, you need to make sense of it. This involves validating and maintaining the data (as well as establishing a data solution to make the process easier), and then drawing insights from that data.
Here’s a short overview of each step in the data analysis process.
Validate the data
To ensure that all customer data is accurate and up to date, you use data validation tools. Keeping up with data hygiene is a big part of data validation; data changes constantly, so it isn’t a one-and-done process. Sales and marketing teams often have to enrich their first-party data with information from multiple sources, to make sure their data is updated, usable, and helpful for drawing insights.
Use a customer data platform
Customer data collection and integration is an integral part of digital marketing today. Customer feedback is more important than ever for actionable insights that inform the sales process.
A customer data platform (CDP) helps with both data collection and customer data integration. With the CDP, you gather, store, and analyze customer data from different sources as part of your greater digital marketing strategy. This allows for easy access to all customer data in one place from which you can draw actionable insights. Centralizing your data collection process makes it easier to create go-to-market campaigns.
Segment the data
Segmentation enables marketers to break down their customer base into smaller, more distinct segments. These segments are based on specifics like customer demographic and psychographic information, as well as on purchase behavior and preferences.
Draw data insights
Once you have your customer data segmented, it’s time to draw insights from that data to inform your marketing campaigns. This is done through sophisticatedanalytics toolsdepending on your end goal. Drawing insights from your customer data also helps withpersonalizationand allows you to tailor your marketing messages and offers to your customers.
The value of customer data
The ultimate goal of collecting customer data is helping you reach the right business decisions faster, resulting in more sales. Akshay names “optimizing pricing and product assortments, as well as shelf displays” as some of the ways retailers use shopper data.
Other benefits of gathering and managing customer data include:
- Having a better understanding of the customer journey
- Being able topersonalize marketing efforts
- Improvingcustomer loyalty
- Increasing sales
Improved understanding of the customer journey
Data gives you a clearer vision of the path a shopper takes, from browsing to making a purchase. This understanding can drive business decisions that make it easier to turn a window shopper into a customer.
If you’re a retailer, for example, you may discover that customers tend to find out about new products via your newsletter and ultimately make purchases in-store. If so, you can display the products featured in your newsletter at the front of your shop toincrease foot traffic.
或者,如果你注意到,客户放弃他们的汽车ts online after learning shipping costs, you might decide to introduce free in-store pickup or free shipping (while adjusting prices to accommodate your new costs).
More effective personalization
Cookies and other data tracking systems help you better understand your customers’ habits and preferences. With these insights, you create more effective marketing campaigns bypersonalizing your effortsand sending the right offers to the right customers at the right time.
Research shows that shoppers are more likely to take action on promotions or offers that are personalized to their likes, needs, and interests. According toresearch by McKinsey & Company, personalization can increaseconversion ratesby up to 15%, boost sales by 2%, and reduce marketing costs by up to 20%.
When you spoon-feed shoppers the products they’re looking for, you’ll be able to drive sales. Let’s say a customer has looked at a specific bed frame on your website dozens of times, has put it in their cart, but hasn’t pulled the trigger because they’re price-sensitive. You could send them an exclusive offer for 10% off bed frames to help them make the purchase.
Improved customer loyalty
Returning customers are worth more than one-off customers because they’ll spend more with you over a lifetime. The more you know about your customers, the better you can curate their shopping experience, which boosts loyalty and repeat business. For example, with a customer loyalty program in place, you can send customized flash deals toincentivize salesand increase purchase frequency.
Increased revenue
Ultimately, all of these benefits lead to one thing: generating more money for your business. Knowing your customers empowers you to bring them what they want (and things they didn’t know they wanted), when they want it.
Plus, customer data helps you present upselling and cross-selling offers that shoppers will be more likely to accept. This helps you increase the value of each purchase andimprove store performance.
Say you find that customers typically purchase a specific toolkit and TV mounting set from you. That’s a great opportunity to cross-sell these products online and in-store. You couldbundle these products, feature them in the “Others also bought” section of your online product pages, and/or place them next to each other in store displays.
Customer data and beyond
Customer data is essential for sales and marketing teams to understand and target their customers. By leveraging different sources of customer data such as personal, preferential, and behavioral data, businesses stand to create more effective marketing and sales campaigns.
Customer data FAQ
What are the 4 types of customer data?
The four types of customer data are personal data, preferential data, behavioral data, and identity data.
What role does customer data play in CRM?
CRM stands for customer relationship management. It is the practice of managing customer information, i.e. data, and using it to create relationships with customers, understand their needs and preferences, and optimize the customer experience.
Where can I find customer data?
Customer data can be gathered from surveys, sign-up forms, website or app tracking, loyalty programs, third-party data sources, email marketing, and search engine data.
