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Leveraging CRM Analytics for Data-Driven Decision Making

Presented By: Amanda Winstead

 leveraging crm

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Customer relationship management (CRM) analytics are invaluable to businesses. You can collect, process, and analyze data on customer behaviors, preferences, and challenges to help you gain insight into your customers and their wants. This helps you create products and content that inspire repeat purchases and long-term relationships with your brand. In addition, your business evolves into one that relies on data for confident, results-oriented decision-making.

Becoming a data-driven business is advantageous because your decisions will be based on hard facts and proven patterns, increasing your profitability and year-over-year growth. 

What is CRM Analytics?

Before we dive in, let’s discuss CRM analytics. CRM analytics is an umbrella term for all the ways you analyze customer data intending to make better business decisions. They provide a 360-view of your customer to help you elevate your sales, marketing, and customer service processes.

Data mining, reporting, and natural language processing are some of the techniques used to support CRM analytics.

Four Tips for Leveraging CRM Analytics to Drive Data-Driven Decision making

A core goal of CRM analytics is to drive better decision-making in your business. Making more informed decisions about how to market to your audience, what sales channels to use, and how to provide the level of service your customers deserve, only results in a more profitable, sustainable business.

These four tips will help you use CRM analytics to support your business decisions.

Identify the Right Key Performance Metrics

The effective use of data starts with collecting data on the right things. Collecting every kind of data is the fastest way to overwhelm yourself and your team. Massive datasets are incredibly time-consuming and challenging to analyze.

It’s much wiser to figure out what kind of information you need and the key performance metrics to track that provide that data. You’re using your time and analysis energy much better this way. And you will get the data you actually want and need to achieve your business goals.

Identifying the right key performance metrics starts with defining your goals for CRM analytics.

For example, do you want to know where your website traffic is coming from? Are you looking for insights into purchasing process challenges? Do you want to find out what kind of content your customers favor on each of your marketing channels?

Once you define your CRM analytics goals, you can choose key performance metrics that align with those goals.

Keeping with the sample goals above, you could track abandoned cart rate for insights into purchasing process challenges. Organic and paid traffic metrics will help you analyze where website traffic is coming from. And you can track reach, views, comments, and shares for content engagement.

After attaching key performance metrics to your goals, set a schedule for when you’ll look at the associated data and analyze it. Allow time for data to accumulate so that you have information to dissect.

Use Quality Data Collection Methods

Your goals and key performance metrics mean nothing if you don’t have quality data collection methods. You must ensure that the methods you’re using to gather data are reliable, accurate, and consistent.

For example, you can trust data straight from your customers through surveys, polls, and market research groups because it’s coming directly from them. Website analytics tools like Google Analytics offer consistent reporting on user behavior on your site. Install voice analytics software to track customer insights through customer service phone calls.

In addition, you can gather data from business events. Attendee demographic, contact, purchase intent, and interest data are some of the information you can gather about current and potential customers at business events. Collect this data through online surveys, registration forms, and one-on-one conversations.

Be strategic about your data collection methods because you need the most accurate, trustworthy data to use to drive business and customer decisions.

Learn How To Arrive at Meaningful Insights

Analyzing data is probably the most difficult part of the analytics process. You have to dig into all of the numbers, statistics, and patterns to figure out what it all means and how it impacts your customer relationships, marketing, and sales processes.

Going beyond beginner-level analytics concepts to more advanced ones will help you arrive at fresh customer insights from your data more often.

For example, you could use heatmaps to get a visual for how often areas of your website pages are clicked to determine conversion rate optimization. Pinpointing customer effort scores will help you see how much effort a customer is putting in to engage with your business. You could also use AI technology to conduct social sentiment analysis to flesh out the emotions and tone behind customer feedback.

It’s also essential to refer back to the goals you defined to extract meaningful insights from CRM analytics data. Revisit the key performance metrics for each goal. Organize the numbers, statistics, and patterns revealed for each metric.

Then, weigh the data you collected against the original goal. Did you learn what you wanted to? If not, how can you revise the goal or question to get the information you need?

Properly Use Data To Inform Decisions

You have your list of meaningful insights. Now, how do you use them to make decisions? Making decisions with the attitude that everything goes when the data backs it up might not be the best approach.

Instead, it should be a balance of what the data says and what your team’s experience and skillsets tell them. Your team offers the human perspective while your analytics tools offer the numbers and statistics you need to confirm what your team is feeling.

Data-driven decision-making must be a collective effort for it to be successful.

Final Thoughts

CRM analytics isn’t something you can master in a day. Collecting and analyzing customer data and pulling meaningful insights from it is a layered skill to learn. But if you do, customer relationships and business decision-making will improve.