Behavior Tracking, Customer Data

  |   12 Min Read

Retailers: Are You Tracking the Right Data for Your Brand?

Retailers have been tracking customer data ever since the first dry goods store owner jotted down customers’ purchases in a leather-bound ledger with a fountain pen. The Internet revolution added another layer and the advent of analytics another layer onto that, but it’s the explosion in social media engagement that has added an exponential amount of detail for retailers to use. The amount of data is nearing the point where retailers could target customers nearly one-to-one.

The amount of digital content created by brands and consumers today would fill 18 libraries of content in just one year. It amounts to an unfathomable 7.9 zettabytes, (there are one billion gigabytes in a zettabyte) created just in 2015, a nearly three-fold increase since 2012.

This amazing graphic from data research company DOMO reveals just how often consumers add data to the Internet every minute.

Even more amazing is the fact that each individual action results in several new bits of consumer data. It tells data collectors the device the individual used, the time at which they went online, how long they stayed online, even how much money they spent, and where. Big data must be used to help retailers improve revenues and profits and/or reduce costs.

If you’re a marketer, you’ve most likely heard that of Big Data. The biggest complaint marketers have is a lack of access to this data, and even a lack of resources to make the best use of it. This post helps retailers determine what data to start with and where to look to find the insights that increase profits and decrease costs.


Use a Loyalty Program to Split Your Audience Into Narrow Segments

According to recent research, 88 percent of consumers over the age of 16 belong to a loyalty program. Eleven percent of those consumers belong to 10 or more!

Experts agree that the loyalty program’s purpose is to collect data for the business and provide value to the customer. Learning as much about a customer as possible allows businesses to tailor products and services to their unique needs. However, when the business has masses of data about large groups and only take averages, they miss the mark. It’s specific data that allows more accurate segmentation of the market and brings benefits to both customers and businesses.

Specific data helps bricks and mortar retailers boost revenues. In 2014, Jersey Mike’s Subs redesigned their loyalty program to collect specific information on each customer. They stored their customer’s behavior, such as how many visits they made to the store and which sandwiches or meal combinations they ordered most frequently. They also determined whether the ideal way to reach that customer was through text, email, or social messaging. Jersey Mike’s CMO Rich Hope explains the shops will better be able to,


“…serve offers that are more in line with what our customers may want or what we may think they might want to try,. . .  data really allows you to not just give a blanket offer to lots of people, but rather specific offers within specific groups based on their purchasing activity.”


Clearly, when presented an offer of their favorite meal, delivered via the method they’re most likely to check, consumers are happy and conversions are increased. Professional data analysts are careful to divide an audience by the most critical metrics. If you’re collecting data on your consumers, make sure the individual examining it knows they can create more than two or three categories. Going from two to 10 can mean a significant boost in sales.


Use Social Data to Enhance Customer Personas

For decades, marketers have created personas based on spreadsheet numbers. With only this two-dimensional information to go on, they depend on their own unique opinions and experiences to fabricate significant customer characteristics.

Enter: social media.

With Facebook and Twitter, marketers and researchers overhear the exact words current and potential customers use to discuss their needs, preferences, and buying habits. If your company hasn’t started this aspect of marketing yet, rest assured that very few have. Even giant automaker Nissan just began their “social listening” program only three years ago. Mark Deep, managing partner of the marketing agency that got them on this track explains, “social media creates a pretty nice picture and a good source to work from once you reveal that enhanced view of the consumer.”

Customers get the greatest benefit when businesses use data to create new offers that benefit them. Data collected from social media has brought new depth to data and analytics.


Use Data to Service Current Customers

Most retailers find it less expensive to retain a good customer than fight with competitors over new ones. The good news? Consumers like to stay with their favorite brands for years on end. It’s easier for them. In fact, only drastic mistakes or egregiously poor service prompts current customers to seek out new vendors.

When companies collect purchase dates, amounts, demographics, and more, they can use this information to predict when their customers need upgrades, service, or, in the case of Jersey Mike’s above, a sandwich fix. Many customers appreciate that their favorite company has anticipated their needs enough to send a reminder email or text.


Use Data to Create Predictive Analytics

You’ve probably already learned that data should be put into four categories:

  • Descriptive, which describes exactly what happened
  • Diagnostic, which answers why a purchase or cost occurred
  • Predictive, which estimates what will happen in the future
  • Prescriptive, which helps explain how a team can make it happen

Predictive analytics applies statistical algorithms and machine-learning tools to data to determine future events. The most commonly known example of predictive analytics is a credit bureau’s assessment of a consumer’s likelihood to pay back a loan. The credit score affecting future events is based on numbers collected. It’s in the past used daily to reduce risk and gauge future behavior.

Given reliable data and algorithms, this process undergirds effective decision-making. Predictive analytics can identify the trends businesses need to know to supply the right products or services in the right amounts. Marketers in retail can use predictive analytics to determine which promotions will be most appropriate for customers, determine the amount of product to stock, and identify what to offer to build brand loyalty.



Make Sense of Your Data to Boost Your Success

Research from Monetate reveals that 62 percent of retailers plan to focus their big data efforts on merchandising and 60 percent on marketing. Both retail strategies determine how much product to stock and which campaign will return the most profit. The amount of data available now can reduce the number of failed marketing campaigns and overstocked warehouses.

Luth Research’s state-of-the-art software brings customer trends, habits, and preferences to light so that companies can use that data to offer more relevant products and services, serving their audiences more efficiently. If you’re interested in how we can help bring you closer to your customers, get in touch. We look forward to diving into your market!


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