How consumer companies leverage data for better marketing decisions

If you have a company with an online presence, then it’s gathering data right at this moment. Will you use that data, or will you let it sit in a data warehouse, gathering digital dust? 

It’s time to learn the different types of data and how to leverage it so you can make educated marketing decisions.

Why data matters

More consumer companies are adopting a customer-centric culture—this is the concept of revolving your business model around customers’ behavioral patterns. 

Data is as valuable as gold in a customer-centric model. You don’t need to know your customers like you do your children, but you do need basic information about their demographics, their behavior, and general attitudes toward your brand. When you have this data, you can develop every phase of the customer journey around this information.

Data types for consumer companies

The following are useful data sets to collect and analyze for your customer-centric marketing plan. We also present some data analytics in marketing examples to demonstrate how a customer-centric approach is taken.

Demographic data 

Demographic data is also known as descriptive data and includes basic information about the consumer, such as gender, age, income level, and educational background.

Examples

• Marketing your higher-end product line to a high-income-earning audience
• Using social media platforms like TikTok, or using social media language (e.g., “U” in place of “you,” LOL, emojis) when marketing to young adults ages 18–25

Behavioral data 

Behavioral data encompasses how your customers interact with your site or how they respond to your marketing material. Data in this category includes how often customers log in to your site, the email open rate, the browsing history, etc.

Examples

• Showing recommended products based on a customer’s purchasing or browsing history
• Sending email reminders to customers who placed items in their cart but did not finalize the purchase

Financial data

This is a subset of behavioral data and relates to a customer’s purchase patterns. What is the customer’s lifetime value to date? How often does the customer make a purchase? What’s the general price range of the purchase?

Example 

• Recommending a product related to the customer’s latest purchase; for example, if a customer normally purchases vitamin C supplements, then you can recommend a vitamin D product, which studies show can aid in the bioavailability of vitamin C

Product data

You don’t just gather data about your customers in a customer-centric approach. You also analyze data from your products. This goes beyond product sales numbers. It also includes how often a product is browsed, its reviews, etc.

Examples

• Pitching a product that may be lagging in sales—you can pitch to an audience segment that’s more likely to make a purchase based on their behavioral or financial data
• Increasing sales of a lagging product by bundling it with a top-selling product

Best data analytics practices

Even if you have ample data, it isn’t going to be enough to benefit your company’s bottom line if it’s not analyzed properly or put to use in an optimal way. Here is how to leverage data for a more enriched customer experience and net revenue increase.

Make the data accessible across the board

All data should be accessible across all departments. Traditionally, the sales team would only access the sales data, the marketing team would only get the marketing data, and so on. For an effective customer-centric approach, bring the data silos together so all teams are operating out of the same data sets.

Focus on data quality over quantity

Not all data is created equal. Depending on your industry, some data sets will be immensely valuable; others less so. Determine which data forms will yield the information you need to create the optimal customer experience. Focus on acquiring the relevant data sets from first-party data. Third-party data may not provide the same quality or accurate information.

Prioritize customers with a high lifetime value

Not all customers are equally valuable. Identify customers with the highest lifetime value, preferably within the last two quarters or 12 months, and nurture this audience segment. Prioritize retention efforts on this demographic. Analyzing financial data will identify high-value customers. Retaining high-value customers is seven times more cost-effective than acquiring new customers.

Invest in the right marketing analytics tool

The extent to which you can leverage your data depends heavily on your marketing analytics tool. There’s simply too much data volume to review manually, much less compile into a report for human eyes. 

While there’s a wide range of data analytics software on the market, they’re not all created equal. You need a system that can seamlessly integrate your data silos and make them accessible across a remote team—anywhere, anytime. Get started with a 30-minute demo of Lytics’ Cloud Connect and Decision Engine. Our systems enable consumer companies to leverage customer-centric data and develop meaningful campaigns that convert.