If you think you’ve got your customers packaged up neatly in one place, think again: A recent study from Forbes shows that only 34 percent of executives feel like they have a single view of their customer.

Disparate data sources spread across departments are causing a fragmented view of the customer. When data from customer service, marketing, and CRM software is siloed, no one really gets an accurate or complete picture of who their customers are. Leads turn cold, and customers vanish into thin air.

The only way to get a holistic view of the customer is by creating a centralized customer database that has a strong architecture and pulls data from other sources. As the default home of customer data, your CRM is a great place to start. There are such things as dedicated customer database platforms, but if you’re just starting out, a CRM is your most accessible entry point.

Making your CRM the central hub of your customer data will give you a holistic picture of customers and will increase your customers’ lifetime value by providing more useful information to be able to target customers more accurately.

Here’s how to find your real target customers by creating a customer data hub.

Define your data hub

It’s not that companies don’t see the value of customer data. Forbes found that 78 percent of organizations already have or are developing some type of separate customer data platform to centralize customer data.

It’s that data can be daunting for those that aren’t familiar with data hubs.

It’s easy to get confused. Just looking at this diagram is enough to get a headache.

An example of a centralized customer database
An example of the complexity of multiple data hubs

Gartner outlines at least five different types of data hubs that can be applied in different scenarios and business contexts (full report available to clients):

  1. The Master Data Hub: As you might infer from the name, a master data hub’s got everything but the kitchen sink, from customer data and product data to asset information and location details. This is more common for large organizations with lots of employees, locations, and customers.
  2. The Application Data Hub: This is when one central application or software takes the reins to become the central hub for storing and sharing data between other applications. This can happen within a suite of software, or with different tools being integrated together.
  3. The Integration Data Hub: In this type of hub, data and application sharing is not limited to one distinct tool. Instead, this general style hub is based on the idea of different types of data being shareable across software tools, platforms, and even sometimes different organizations.
  4. The Reference Data Hub: This hub has a more narrow focus on data that can be categorized and classified across sources. Its use is primarily to be able to quickly find and synchronize data across business functions.
  5. The Analytics Data Hub: This hub is where data can be sliced and diced for analysis and discovery.

Since we’re using a CRM to create our centralized customer database, it would fall under the application data hub category. The next step is setting up your CRM architecture to make it fit to become a data hub.

Build a strong CRM architecture

A CRM architecture is the way that you design your CRM to be able to manage data in the most practical and useful way.

As I recently wrote about here, skimping on your CRM architecture is risky business. Without an architecture, data storage and organization can not only become messy, but costly. According to stats from IBM, the yearly cost of poor quality data in the United States sits at a staggering $3.1 trillion.

Among the top reasons for dirty data are human error, lack of communication between departments, and an inadequate data strategy. A CRM architecture can help to alleviate some of these contributors to poor data quality.

A proper CRM architecture is a three step process that starts with an overall CRM strategy, builds up to a CRM structure, and culminates with CRM processes.

An example of a centralized customer database

The three components of a successful CRM architecture

  1. CRM Strategy: At a high level, a CRM strategy outlines your overall goals for using CRM software and how it will contribute to your company’s revenue and profitability. From a data perspective, it’ll help you outline which data you’ll collect and how you want to use it to be able to drive goals.
  2. CRM Structure: The way that you structure your CRM will be defined by your customer journey and sales cycles. It will be the structural representation of how you plan to achieve your CRM goals. From a data perspective, this is where you’ll be able to set the right input fields to ensure that you’re able to collect data that’s essential for decision making.
  3. CRM Processes: This defines how sales reps will carry out tasks. It is the practical implementation of a CRM structure and strategy with formal processes that outline the how and what of customer relationship management. Here is where you can outline data collection practices to ensure that each team member is doing their part to collect the right data and keep it clean.

These components will be the backbone of a strong CRM architecture that stresses the importance of data collection. The final piece of the puzzle is, unsurprisingly, more data.

Integrate, integrate, integrate

You can’t have a centralized customer database without data. The point of creating a hub is to pull data from other sources to create a full view of the customer, not a partial picture based on one source. According to Experian’s Global Data Management Research report, 45 percent of companies say that data integration is their biggest priority data project.

Data integration should be built into your CRM architecture during the structure phase. This will make it easier to pull and consolidate data from other sources, making sure that data fields are normalized for smooth integration.

The two key pieces of software that you’ll need to integrate with are:

  1. Marketing automation: If you’re not already using a suite of software that includes marketing features, this will be essential. Marketing tools have invaluable data about customer reactions and responses to touch points throughout the customer journey, especially in the early stages before they’re full-blown customers. This data will help inform customer demographics and preferences that could otherwise be a mystery.
  2. Customer service: Again, customer service is a key touchpoint along the customer journey that adds crucial data to the customer view. Direct customer interaction is a considerable source of insight into brand sentiment and brand perception that might otherwise be difficult to infer.

Other useful data streams can run the gamut of customer management and marketing tools including survey, customer experience, social media, and email marketing tools.

Analyze your centralized customer database

The foregone conclusion of collecting all of this customer data in one centralized hub is to be able to draw meaningful insights that can help drive revenue and profitability. The key to knowing your customers is to be able to bring them back for more.

To start analyzing all of this data, consider these ways in which you can make use of data:

  • Measurement: This involves measuring overall performance as a whole to devise better strategies for reaching out to or targeting specific customer groups.
  • Optimization: You can improve upon both sales processes and customer experiences by adjusting tactics to those that garner better results.
  • Segmentation: Knowing your customers’ similarities and differences allows for better targeting by segmenting users into distinct groups with more tailored outreach.
  • Storytelling: Use data to see the big picture of your customer journey from start to finish to see bright spots as well as areas of improvement.
  • Predictive modeling: Once you have enough data, you can use machine modeling to make predictions about future sales outcomes or customer behaviors.

Want more on how to use analysis to draw conclusions from your customer data?

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