Companies gather a plethora of data on their customers, and nowadays, they can find multiple ways to analyze data from your customers. Still, one of the most substantial challenges is to make data-driven decisions and correctly interpret the findings.
Technology has become a pivotal part of analyzing customer data since it provides valuable unbiased insights.
In this blog post, we’ll define the term “customer data analytics,” see how it works, check the main data types, and go through some of the most common tools available on the market that can help you avoid mistakes and boost sales.
What is Customer Data Analytics?
Customer analytics means you collect data from different sources, such as consumer surveys and ad networks, and combine it to create a complete picture of your customers. This data can include customer traits such as buying preferences, site behavior, and demographics.
This type of analytics aims to provide a more specific, tailor-made experience for your customers. It follows the customer journey from A-Z and lets you create more accurate and personalized marketing campaigns.
Customer Revenue: some customer analytics metrics, like AOV (average order value) and CLV (customer lifetime value), can help you predict the revenue stream. By apprehending and tracking these key metrics, you can better understand how your business is performing and make more informed decisions about the areas worthwhile your resources.
Customer Acquisition: many customer data analysis metrics can help you gauge your success in acquiring new customers, such as order frequency and customer conversion rate. You can amend your marketing and sales strategy by tracking these metrics to improve your results.
Customer Retention: a bunch of techniques out there, can help you reduce your churn rates, increase the number of existing customers, improve the customer loyalty rate, and assist you with the predictive analytics.
How Does It Work?
Customer analytics is a vital part of any business owner’s day-to-day work. After all, it gives them a good idea of how their customers interact with the company’s products and services. Well, it can be tricky to do perfectly — to make sense of the data, you’ll need to link up multiple systems and go through lots of spreadsheets to interpret the rules beneath them.
But to provide a smooth customer experience, you would need to combine the efforts of multiple teams — your marketing teams, your sales team, your customer service team leaders, and everyone who is in touch with clients and has access to crucial data.
Why is It Important?
Customer analytics, and the insights one can glean from them, are invaluable tools that businesses can use to build and optimize their audience. Customer analytics work in a way that lets businesses find solutions to problems that have bothered them for a long time.
Furthermore, engaged customers who fit into specific profiles are more likely to spend more on products they enjoy while decreasing visitor bounce rates through optimized websites.
Let’s have a look at some of the most critical reasons people use customer data analytics for their teams:
Customer profiling: implementing customer analytics allows a more detailed reading of customer behavior and better marketing campaigns. We have already discussed Verfacto’s real-time customer profiler and how it helps in making business decisions.
Drive engagement: because you become good at profiling, you can tailor better marketing initiatives to drive more engagement rates, as you will get closer to your customers’ “hearts” (and wallets).
Increase sales: without a doubt, sales growth is an important topic when talking about your business. Data analytics can help you find the right ways to upsell and cross-sell, thus finding more profitable revenue channels.
What are the Four Main Customer Analytics Types?
Customer analytics is an excellent solution for many companies because there are four different types of goals that can fit your needs in many situations.
Descriptive analytics lets you pull user history, tracking your customers’ movements, habits, and interests — not just a small detail at the end of the purchase funnel but valuable data that can shed light on where traffic is coming from.
Descriptive analytics lets you answer basic questions, like “What happened?” It enables you to analyze past events to note what worked well and what did not, and effectively focus on the campaigns that delivered results. Of course, with the help of this type of data analytics, you can also get rid of bad practices.
As sales productivity drops, business owners need to look for customer insights to pinpoint the root of the issues holding their businesses back. It answers questions like “Why it happened?”
Instead of relying on broad customer surveys and market analysis, diagnostic customer analytics takes customer insights from the past (explored through data discovery and data mining approaches) to diagnose root causes and solve problems.
Predictive analytics is more than just data collection, and it’s about taking the insights from collecting and analyzing big data and coming up with probable estimates for future outcomes.
Predictive data analytics can identify key trends that help you plan ahead — helping you figure out which resources are required to meet future sales targets or ensuring you won’t have issues with high CAR (customer attrition rate). While these predictive customer analytics are not always 100% accurate, most of the time, they could be a helping hand in finding the right path.
Prescriptive customer analytics is the “twin” brother of predictive customer analytics. They are just as important to the customer journey, as they show the measure that ecommerce businesses need to take to achieve the milestone.
Imagine you have to fulfill your target to reach $100,000 in sales by the end of Q4, but the global recession means you will have 10% fewer paying customers from the same traffic. A 10% first-order discount can help you achieve your goal.
How to Collect Info About Your Customers – and Store It
Customer shopping data allows brands to make sound business decisions quicker. Analytics can identify a barrier preventing their customers from completing specific tasks, thus helping them reach their purchase goals. For this reason alone, companies should keep an eye on customers abandoning carts and nailing the launches for products and categories in demand.
However, laws like GDPR prohibit ecommerce companies from collecting data involuntarily. That’s why there should be some ways to understand customer behavior.
Cookies are a perfect way to collect data from your customers, giving you access to many things. For example, cookies can help you track what pages customers visit on your website, what products they are interested in, and what ads they click on. However, cookies will have diminishing returns, especially after 2023, when third-party data analytics becomes almost impossible to retrieve.
Surveys are an amazing way to collect data, and they allow you to gather specific information from customers that can be extremely helpful in improving your business. By conducting surveys regularly, you can stay on top of customer needs and wants and make changes to your business accordingly. But what makes surveys truly remarkable is that you can set your own criteria and ask specific questions.
Social media is a fantastic way to collect customer data. Customers can reach you directly through social media, providing you with valuable insight into their needs and wants. By using social media to collect customer information, you can be sure that you are getting accurate and up-to-date information that can help you improve your products or services. On top of that, social media plays a significant role in giving your brand more value.
How to Analyze These Statistics?
Analyzing data from your customers is a long process, and you should follow all steps as they are.
Set your target objectives
While it may sound tempting to focus on everything, our experience has shown that the more areas you focus on, the slimmer the chances to succeed. Instead of spreading wide, you need to set a specific objective and create a plan. When achieving good results, only then move on to other targets.
Create a framework
When analyzing customer data, it is crucial to create a framework. This will help you to focus on the most important aspects of the data and make more informed decisions. At this stage, you should consider how you can fit your concept and find data that backs it up, rather than checking at the data first.
Get your data analytics. It is where a lot of raw data will appear. But apart from collecting every bit of data about your customers, you will have to get those chaotic lines of code into a well-organized data warehouse. Only then will you be able to find what you’re looking for.
Sort out your data
Not everything will be of good use, and some customer analytics metrics might not be necessary. In this case, it’ll be best to eliminate them and create an organized structure. Otherwise, you risk missing out on crucial insights.
Present it in front of key stakeholders
Regardless of how good your data is, stakeholders ask a simple question: “How much revenue is there for taking?”. Often, the high management doesn’t care about CLV, CAC, AOL, and other metrics that we use in marketing campaigns. What matters is how skillfully you can present opportunities and back them up with numbers (customer analytics).
Best Practices When Analyzing Data from Your Customers
There are good practices that can help your brand achieve better results. Let’s see them.
Identify your goals before your report
One of the most common problems with master data management platforms is they can bring many results. However, if you don’t know your goals, you’ll often “get lost” in your customer logs. Try to set a couple of objectives and work on them.
Do not misinterpret data
Even if you use the most advanced marketing tools, you can’t get far if you don’t understand the numbers. If stats show that customer engagement has dropped significantly, don’t blame it on the economy. Instead, try to find the problem inside — as your sales funnel.
Identify better revenue streams
Using customer interactions as a foundation, you might find your business’s most profitable revenue channels. For that reason, you should leverage metrics such as customer lifetime value (CLV), net promoter score (NPS), conversion rate (CR), and customer acquisition costs (CAC).
Best Customer Data Analytics Tools
Let’s see some of the best customer analytics tools on the market.
What makes Verfacto great is that it doesn’t rely on a single technology to be successful. Instead, you can use good practices from many customer analytics tools out there and have them in one powerful tool, which Verfacto is. The visitor profiler feature lets you segment customers by using different criteria. Another good thing is that the platform lets you track Meta customers’ preferences even after iOS 14.
Adobe Analytics is a platform that can handle everything from website analytics to in-store data, including audio and video. It’s built for flexibility with various APIs and integration options so you can seamlessly integrate other systems into your digital marketing strategy.
Google Analytics is a comprehensive and at the same time free tool that gives businesses in-depth information about their website’s traffic and user actions. Without requiring additional steps to set up custom data pipelines for different systems or technical expertise, analytics programs like Google Analytics directly take customer behavior from the website to you.
Power BI by Microsoft
There are many customer analytics platforms on the market, but Power BI is one of the leaders. Not only does it do an brilliant job of collecting data, but it also integrates well with Teams, MS Office 365, and Azure. Power BI is a valuable tool for businesses that want to make the most of their data and integrate their customer analytics tools with other programs.
Hubspot lets you create specific customer segments. It can gather priceless data, find multiple customer journeys, and allow multiple teams to develop informed business decisions. We must admit, it’s a customer relationship management software with plenty of capabilities to analyze data from other sources, too.
Third-Party Cookies and the Future
By the end of 2023, we will see the “death” of third-party cookies as we know it. This creates a significant gap in targeting customers, and the companies that adapt first will get an advantage. Without the ability to collect data from customers across the web, businesses will need to find new ways to target their audience.
Those who are able to adjust quickly and efficiently will have a significant advantage over their competitors. First-party data will become increasingly crucial in the coming years, so companies must be prepared to make the most of it.
Customer data analytics can help your ecommerce store gather helpful insights and strategies. However, it doesn’t replace the basics. Good old-fashioned customer service still holds the key to success in ecommerce. You need to ensure your customers are happy, that you’re providing them with what they need, and that they keep coming back for more.