Data and analytics have always played a major role in business, but with more information now available to companies than ever before, how data is being used has shifted dramatically.
Think back to the business of yesteryear, when relatively simple data like demographics were a key component of business analytics. Now, with a steady diet of social media, user-generated content, and an overwhelming amount of detailed purchasing habits available, simply knowing the age and ethnicity of your core customers isn’t enough.
Consumers have more choices than ever, and they make their purchasing decisions quicker than ever, which means that in order to stay competitive, companies need to pick up the pace in how they make decisions. The key to achieving that is knowing not just what your customers want, but why they do—or don’t—want it.
Beyond the analytical surface
On the surface, product recommendation features on sites like Amazon and Walmart seem relatively simple. Since you bought this one item, you might be interested in this other one, etc.
Once you dig deeper into how these recommendation engines work, however, you find a complex webbing of unstructured data, machine learning, and cloud-based infrastructure working in tandem.
Companies today are able to mine an ocean of data to surface not just items of interest based on past behavior but products a customer didn’t even know existed or would have a need for.
These recommendations are fueled by data captured in billions of previous purchases and product searches, and it’s all happening behind the scenes instantaneously.
It’s not just online retailers that are leveraging more advanced analytics technologies. Traditional brick and mortar retailers can now leverage edge computing for analytics at the point of sale, then use the information they’ve gathered to quickly inform everything from pricing to display locations.
This not only allows the companies to make rapid decisions that improve their bottom line, it often leads to better shopping experiences for customers—the key component to successfully earning trust from a customer and managing their lifecycle.
A catalog in every pocket
In the late 1800s, the Sears, Roebuck and Co. catalog was billed as the “cheapest supply house on earth.” The catalog, which began as a way to sell mail-order watches, had ballooned to a 300-page listing of nearly every product a consumer could imagine.
Fast forward two hundred years, and smartphones now offer the same experience on a much grander scale. In addition, each search a customer makes and each purchase they complete creates valuable information for companies to utilize.
Smartphones and other connected devices also produce data that better informs marketing. If a person buys a product they discovered on TikTok, for example, that information can be used to drive more advertisements on the platform.
The challenge with the ever-growing number of devices and platforms is cutting through all the noise to unlock true insights. The more data you have available from various outlets, the greater the chance something key will be missed.
I’ve previously written about the steps to building an analytical organization, but to quickly sum up, one of the first areas to focus your attention on is “democratizing your data.” That means:
- Knowing where you data is coming from
- Understanding what data you have
- Identifying any potential privacy or regulatory issues in your data
- Properly storing data with governance in place to ensure only those that need specific data can access it
- Filling in gaps in your data
These steps can be filed under “best practices for analytics in general,” but when it comes to the consumer lifecycle, an important area to focus on is creating a customer data strategy that encapsulates the entire journey a customer makes when purchasing a product or service.
That begins with knowing just what your organization is trying to achieve with its analytics, then charting out the key stages a customer goes through to better understand where limitations—or outright roadblocks—exist in relation to your overall goals.
Digging deeper, you will need to pinpoint the metrics that are successful throughout the lifecycle stages. These metrics can be high level or granular, depending on which element of your organization is utilizing them.
On a foundational level, building a data strategy that makes capturing data in realtime is absolutely critical. Twitter, Facebook, the aforementioned TikTok—your ability to examine data produced on these platforms as quickly as possible is increasingly the dividing line between success and failure with your marketing and sales.
By systematically observing how your customers interact with you and your products throughout their lifecycle, you can leverage data to make much smarter decisions faster. It takes work—and a lot of planning and collaboration between your teams—but if you can make data available for everyone in your organization to play with, you’re more likely to keep your customers happy, engaged, and returning.
Want to learn more about building out a successful customer lifecycle analytics foundation in your organization? Reach out to one of our experts.
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