The best results happen, however, when you can see and apply all of your customer data collectively in one place. Many businesses are struggling to achieve this “customer 360” view, but technology might finally be ready to make the job easier.
Everything in Different Baskets
Businesses are now in a position to gather customer information relatively easily. A company can conduct surveys to collect direct feedback, for instance, and they can collect data about a customer’s online behavior using tools that monitor clicks, scroll time, browser history and other elements.
The problem, however, is that the data is often siloed or in pieces. The system you have to collect your marketing data might not talk to your system that has your engineering data, for example. You might have to spend a lot of time and money trying to aggregate the data with more manual methods. Failing to aggregate means your concept of what the customers need or prefer can be misguided and that you miss good opportunities to engage with them. The expense and inefficiency can mean competitors gain an edge while your business suffers.
Pulling Data Together
Given that a lack of data aggregation can be so disastrous, finding solutions that break down existing silos is critical.
These tools must be able to integrate with your existing systems, but they can create what is known as a “single source of truth,” also known as a data lake. If every area of data is a stream, then the data lake is where all those streams collect together into a larger, more useful whole. Once the streams are integrated together, they can provide different functionalities that aren’t possible if you look at each stream alone.
Data lakes can serve a broad range of industries well, including retail and financial services. But one of the biggest benefits they have is that they allow businesses to create full customer personas and individual customer profiles. If you’re a bank, for example, you might know such elements as how much money a person has, their capacity to pay back a loan or how often they make deposits. If someone comes into the bank and wants to create a new account, you can compare the new information to your existing data, not only to detect potential cases of attempted fraud but also to determine whether it makes more sense to offer certain banking products over others.
In the same way, let’s say you own a coffee shop. Suddenly, one of your best customers isn’t coming in as much. You can look at the marketing or retention efforts that have worked for customers who have similar profiles and then select a method to immediately deliver something similar, which, statistically, has a good chance of winning the customer back and reducing churn.
Data lakes can also mean you serve customers better in terms of real-time and everyday habits. You could send them a donut ad in the morning and another for garlic bread in the afternoon, for example. If a customer opts in to allow your system to know where they are geographically based on a phone or other mobile device, you could even send them a totally customized coupon when they’re within a certain proximity to your store.
Meeting needs and desires where customers literally and figuratively are in the moment typically provides a significantly improved experience with more opportunities for the customer and company to build a stronger relationship.
Building a Data Lake That Works for You
Some common hurdles still drag even the best businesses into the mire as they try to construct data lakes. To up your odds of success when adopting a data lake:
• Adopt an agile approach.Many leaders talk a lot and tend to be reluctant to sign off until they find the “perfect” solution. A more agile, “never done” approach allows you to keep asking good questions about what you are building, such as how sophisticated your tools are or how quickly your end-users need the data.
• Start small based on your objectives, not technology. The core objectives or mission within your business should not waver. Technologies, by contrast, are fickle. For this reason, design your data lake around your goals rather than specific components, as IBM recommends. Start small and grow over time.
With these tips in mind, creating a data lake breaks down into seven key steps:
Connect all data sources (social media, CRM, cloud storage, etc.).
Integrate all your data. Break data silos.
Ensure data quality. Get rid of repeated, fraudulent or other “bad” information.
Wrangle data. Create a single view of the entire database.
Easily share data. Use a secure, no-code platform to make data accessible to everyone across applications.
Visualize and analyze.Use AI and ML models to get and interpret insights from the data lake in real time.
Build, train and monitor ML models. Streamline how you develop, deploy and manage your machine learning tools.
Are you ready to dive in?
As a leader, you have an enormous amount of data at your fingertips. Getting to a customer 360 view, however, requires you to face the inefficiency of current systems, which are generally siloed.
Once you’re committed to handling and analyzing information in a more collaborative, transparent way, find or create a tool that will pull everything together. Companies are already building data lakes that vastly improve the customer experience while still respecting privacy limitations, so consider which technologies will help you aggregate and dive in as soon as you can.
