Customer data analysis is all about getting a better understanding of who your customer is at a 1:1 level through data.
By engaging in customer data analysis, your brand is committing to collect and scrutinize large volumes of data to paint a clear picture of each individual customer (their demographics, behaviors, preferences, inetitions, etc.) so smarter business decisions can be made, especially as it pertains to marketing.
Consumers are under assault from non-personalized, non-relevant marketing that does nothing to grab attention, create resonance, or enhance the customer experience. And while you may not want to admit it, the chances are good your brand is guilty of regularly pushing out this kind of marketing, too. The problem with irrelevant marketing is that it leaves consumers feeling neutral about a brand, and sometimes pushes them toward competitor brands.
The only way to correct this problem is for your brand to obtain a clear view of who each customer is and what each customer wants at a one-to-one level. Getting this view is the key to building a rock-solid customer relationship that maximizes acquisition and retention.
Now, creating a clear picture of each customer isn’t easy. To do it, you’ve got to not only follow customer data analysis best practices, but also connect disparate data sources in a unified way. Moreover, you must ensure the data from those sources is accurate, current, and accessible across business units. In other words, everyone at your organization – salespeople, marketing people, customer service reps, C-suite executives – everyone – needs to be able to access and make sense of your customer data whenever they need it.
In this article, you’ll learn what customer data analysis best practices you need to follow and why you need to take a better approach to customer data management so that you can and your brand can be more effective in connecting with customers across all online and offline channels.
Let’s go through the core customer data analysis best practices to help your brand develop better cross-channel connections with customers and prospects alike.
Performing customer data analysis or engaging in customer data management takes time, energy, and other valuable resources your company can’t afford to squander. So before you can engage in any actual data analysis, you need to get crystal clear on what outcomes your brand is trying to achieve through said analysis.
Getting clear on what outcomes are most important to you and your organization will determine what data is worth paying attention to, that’s why this is number one on our list of customer data analysis best practices.
Your brand has access to more data than it could possibly know what to do with. Rather than trying to incorporate all of it into your data analysis process, cut out what doesn’t matter. In other words, get clear on what data inputs mean the most to your brand. These inputs will be your key performance indicators (KPIs), and the story they tell will shape much of the decision making your company does going forward.
Bear in mind, of course, that what matters to the marketing team is different from what matters to the sales team, which is different from what matters to the customer service department, etc. Because different teams will place different weights on different KPIs, you need to make sure every department within your organization is represented in some way through your KPIs. To ignore any one department’s customer data needs is to invalidate your entire customer data analysis process.
When you know your objective and you know the KPIs that matter most to achieving that objective, it’s time for you to start collecting and collating the data you need. Depending on what customer data your brand is already tracking, this may be as simple as pulling information from your company’s database.
However, if there’s an insight or KPI that you know you need to achieve your objectives, but your business hasn’t been paying attention to it, now is the time to figure out how you can capture and store this critical information.
This is all about customer data management, which we’ll talk about in great detail later on. For now, all you need to understand is that siloed data retained in different systems and platforms is data wasted. It is too difficult and inefficient to mine multiple data sources for actionable insights. What you want for your brand is a true, unified data management platform that makes it easy to understand customers at the individual level, and smart, decisive action
This is all about analysis, pure and simple. Look for both patterns and anomalies across the KPIs you’ve identified as important to your business objectives. Do not try to shape the data into the story you want it to tell. Rather, let the data do the storytelling and accept the conclusions it presents.
Use the conclusions presented by the data you’ve worked so hard to collect and analyze it to make smarter decisions related to your business. These decisions shouldn’t feel like predictions, but cold, calculated moves rooted in evidence.
As long as you haven’t overlooked number four on this best practices list, you should be able to make smarter, customer-related decisions at a 1:1 level. This means you’ll be able to offer a more personalized, relevant customer experience that would otherwise be attainable if you simply settled for “segmenting” customers into large audiences based on collective similarities.
Brands have relied on audience segmentation for years when it comes to creating an optimal customer experience, but it’s no longer enough to build strong-as-steel customer relationships.
No matter how good your data is – no matter how wisely you’ve chosen your KPIs in relation to your stated business objectives – some of the business decisions you make based on your data analysis process will end up being wrong.
Therefore, you and your company have a responsibility to ensure your data analysis is an ever-evolving, self-scrutinizing process. In other words, your customer data analysis needs to be engaging in ongoing optimization based on the real-life outcomes achieved from previous decisions made.
By incorporating artificial intelligence and machine learning into your customer data analysis and customer data management processes, you can revise and optimize your brand’s relationship with individual customers in real-time.
The benefits of developing a data analysis process that uses AI and machine learning to drive a self-correcting customer experience are profound. In terms of both the short and long term, it will allow for personalized, relevant engagements that facilitate higher sales volume, higher per sale value, increased customer satisfaction, improved customer retention, and much, much more.
You should follow these best practices because there’s a BIG problem with customer data at most businesses today: most of it is siloed. When data is siloed, brands lose out on opportunities to sell, upsell, cross-sell, and most importantly, create an ever-improving customer experience that cements loyalty.
If your company is siloing its customer data across multiple departments, it will be impossible for the different teams at your organization to achieve all their customer-related goals.
If your brand uses a combination of systems and solutions to perform customer data analysis or management, you need to make a change. Deploying disparate solutions when it comes to customer data is an old-school way of doing business that’s inefficient.
Moreover, if your brand is using multiple systems and solutions, you’re going to end up relying on an equally diverse array of “solutions experts” to help you merge different data sources and extract actionable takeaways that your teams can actually use with customers. This is both inefficient and expensive.
To perform proper data analysis, your brand needs a data management process and platform that’s capable of creating a seamless, 1:1 customer view. Unfortunately, most brands’ approach to customer data management is nothing short of chaotic, incorporating a patchwork of nonuniform solutions from a customer relationship management (CRM) solution to a customer data platform (CDP) that deliver on different needs.
Let’s go through some of the most commonly deployed disparate solutions below.
Master data management platforms are limited by two factors: the information they contain and data maintenance they require. Generally speaking, an MDM will only include limited amounts of known customer data such as name, an email address, and phone number.
To be of value, this kind of data must be regularly scrubbed to ensure accuracy and enriched with other, supplemental data. The good news with an MDM is that it’s a solution that can typically be used across your business’s various departments with relative ease.
The issue you’ll encounter with any customer relationship management platform as it pertains to data analysis is that a CRM is built to focus on data from the standpoint of communication. In other words, a CRM is about the preservation of customer data for the benefit of sales and marketing outreach exclusively. It does not make it easy to create a true, 1:1 customer view.
The anonymized customer data collected and kept within a data management platform can be combined with your brand’s owned first-party data to create a more complete customer picture during the analysis process. However, that picture will only help certain parts of your organization. Specifically, it will help your digital advertising and paid media teams – everyone else at your company is cut off from that enriched data.
In most circumstances, customer data platforms work to provide data integration from various sources – a key component of good customer data management. However, how your business and its various departments can make use of that merged data is limited by the quality and depth of the original source data. Put in plain English, the audiences, segments, and 1:1 customer insights created within a CDP are very difficult to activate in a meaningful way.
There are many reasons your brand might invest in the wrong solutions for customer data management, but the most likely reason is FOMO. Brands big and small can fall victim to technology hype cycles, and yours is no different. A fear of missing out on the newest, most impactful solution for customer data management can lead to your brand investing in an expensive product that neither fits your business needs nor brings you closer to achieving your business’s goals.
Do not let this happen to you. Don’t chase a technology trend just because your competitors are doing so. Chasing trends only leads to more data-management complexity, increased operational costs, and a whole lot of disappointment. What kind of disappointment? Well, if your brand isn’t careful in picking a solution for its customer data management needs, it can end up dealing with a number of unpleasant consequences.
From CRMs to MDMs, all solutions used for customer data management come with a price tag – and in most cases, the price is steep. The cost of implementing a solution isn’t limited to money itself, it’s also tied to a number of non-monetary costs your brand needs to be aware of.
In other words, picking the wrong solution can leave your company paying a major price for years and years.
Depending on the industry your business operates in, what your company goals are, what your individual department goals are, and several other variables, one customer data management solution will meet your needs better than another.
Because there is rarely a one-size-fits-all option, your brand will be forced to bolt multiple customer data management solutions together in a futile attempt to achieve the functionality it needs for proper customer data analysis. As a result, your company will end up with an incomplete approach to customer data management and customer data analysis that leaves many members of your organization frustrated.
The odds are better than good that your brand would favor a flexible solution when it comes to dealing with customer data – something that’s nimble enough to move between data standardization, normalization, hygiene, and enrichment in real time.
The trouble is most solutions offering customer data management don’t feature that kind of flexibility. If your business isn’t careful in making a selection, you won’t end up a flexible solution that can adapt to your evolving customer data needs.
The key to a winning solution lies in its ability to provide a true, 1:1 customer view that’s accessible by all the stakeholders in your organization. However, very few solutions on the market are actually capable of offering this because it requires the blending of all known and anonymous customer data which is difficult.
For one thing, known data, which is data tied to an identifier that links directly to a known customer record – requires ongoing hygiene and enrichment to be useful.
For another, anonymous data, which data tied to a unique identifier for a particular product, channel, or device, and acts as a proxy for a real customer – only becomes actionable if it’s graphed and matched due to the high-volume of identifiers and data points involved. In the case of anonymous data, this graphing and matching is usually done through trusted, third-party data partners who can provide both depth and scale.
If known and anonymous data can be connected, creating a singular view of each customer that can be used across all departments of your organization is possible.
When seeking a solution for customer data management, remember the following:
As mentioned earlier, you must avoid the fear of missing out at all costs. Caving to FOMO is the biggest mistake your brand can make when it comes to evaluating solutions for customer data management. You cannot chase a solution just because competitors are chasing it.
To pick the right solution for your customer data management needs, you must focus on your business’ primary objective for both the short and the long term (e.g. maximizing customer retention).
What you’re looking for here is a solution capable of offering a 1:1 view across all channels, campaigns, and devices. Something that can build robust profiles of individual customers by leveraging any structured or unstructured data source.
Ideally, any solution you and your company decide to use for customer data management will come with real-time profile enrichment. Using real-time profile enrichment, your brand will be better able to recognize individual customers in real time and, therefore, have an easier time elevating the customer experience both online and offline.
Following the provided customer data analysis best practices, and the suggested approach to choosing a customer data management solution will transform your business. However, it won’t transform your business overnight. It takes sustained effort to build a better understanding of each individual customer, and a period of time to start hitting your large-scale customer-centric goals.
But if you’re willing to adhere to the best practices listed in this article, this transformation will happen, and your customers will thank you for it down the road.
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