Today, data is the equivalent of gold in the 1800s. Everyone wants it and it’s extremely valuable — but it’s also buried deep and hard to extract. One of the most prized stores of data your company has is in your CRM software. That’s why CRM data mining is an essential element that your business can’t afford to ignore.
Mining your customer data is critical to enhance marketing initiatives, improve the customer experience, encourage customer loyalty and more.
With so much data being generated every day, the companies who use big data tools and techniques to make sense of their data will have a competitive advantage.
Data mining may sound like a complex, confusing concept. And to some extent, it is. But you don’t have to become a pro data scientist to unlock the nuggets of info hidden in your CRM.
This article will equip you with the tools to understand how mining your CRM data is an effective, practical way to upgrade your marketing efforts. Let’s jump in!
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What Is Data Mining?
Techopedia defines data mining as “the process of analyzing hidden patterns of data … which is collected and assembled in common areas, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision making and other information requirements.”
In layman’s terms, that simply means data mining is a method for analyzing a set of data, such as a CRM database, to extract valuable insights. Data mining takes your company’s sea of information and gives it meaning. The goal is to make it useful in some way.
The ability to pinpoint key patterns will inform your decisions and build your strategies on the solid bedrock of data rather than the shifting sands of guesswork. But how exactly do you accomplish this? Let’s run over a few of the main data mining techniques.
We won’t go super deep, but it’s helpful to have an understanding of the main functions in play when you use data mining. If that’s not your cup of tea (or you’re already in the know), feel free to skip ahead to the next section on data mining benefits.
Association
This is one of the simpler (and most familiar) techniques. It involves making a logical connection between items in order to uncover patterns. A real-world example of this is Amazon’s “Customers who bought this item also bought” feature. The algorithm draws an association based on previous buyer behavior to add extra insight for shoppers.
Classification
Classification is the act of determining a specific class of items based on their various attributes. In marketing, an age demographic of 35-50 would be a classification, as would the leads who entered your system by downloading a CRM best practices guide from your website.
Clustering
You can use clustering to build off your classifications to gain a better understanding of your data. For example, it could reveal job title trends for those leads I just mentioned. One cluster could be sales managers, while another might be marketing managers.
Sequential Analysis
You could consider sequential analysis an “in progress” technique. It takes a long-term approach by looking at data over time, which enables you to spot trends or other regular events. To illustrate, a sequential pattern could reveal which times of the year are most lucrative for your company or which products are best combined for cross-sell based on past purchases.
Anomaly Detection
What happens when there’s a break in the data’s pattern? Detecting these outliers is useful for gaining a better understanding of your data beyond looking at the big picture. If, for example, you always see lower sales in the spring except for the first week in April, you’ll want to investigate to identify what’s causing the anomaly.
Regression
Regression is a more advanced technique and helps pinpoint the correlation between different variables. It’s typically used for planning various scenarios. For example, you could project annual revenue based on the market landscape, your current customer base, product demand and other factors.
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How CRM Data Mining Creates Smart Marketing
Business intelligence tools and big data analytics have opened up nearly unlimited ways to understand and use data more intelligently. From a marketing perspective, data mining is the means to understand your customer base more thoroughly, locate patterns such as why certain pieces of content are more engaging and more.
Let’s look at four practical ways that CRM data mining will help your company level up its marketing game:
1. Build Better Target Audiences
The first contribution of data mining is to your customer identification.The most engaging marketing campaign is useless without first defining who you want to reach with your messaging.
Your target audience is the group of consumers that in some way need or desire your product or service. How can you determine who fits those criteria? Data mining.
You can begin by classifying the broad population based on a range of attributes. If, for example, your company sells onsite security systems, you could break down the market based on demographics such as location, type of building owner and so forth. A rural farmer isn’t as likely to need in-home security as a high-rise apartment in the middle of the city.
Your CRM contains a wealth of historical data that’s helpful in refining your audience. Using target market analysis, you can explore who your potential customers are. And a customer map provides a visual breakdown so you can more easily digest the data. Analyzing your target audience informs your marketing strategy as you plan how to most effectively reach them.
Once you have a target market mapped out, clustering enables customer segmentation. Not everyone who’s a candidate for your product will need the same version. For example, two of your segments could be landlords and residential homeowners.
Landlords need to comply with local laws and show they take resident security seriously. Homeowners care about keeping their family and valuables safe. Those are two vastly different customer journeys and each would need a different level of security system.
Data mining allows you to break the target market into smaller groups so you’re able to build messaging strategies appropriate for each based on their individual attributes.
2. Market to the Right People
Let’s face it: we’re becoming jaded to all the marketing shoved down our throats. We’re exposed to hundreds, maybe thousands, of ads every day — the number is hard to pin down. But the point remains: people are tired of the constant bombardment.
Adding to the difficulty, consumers are demanding relevant, personalized experiences when interacting with brands. Six in 10 customers say that a central part of earning their business is providing tailored engagement using past interactions, according to a Salesforce survey.
The key to attracting attention rather than driving it off is to give the right people the right content, and — here’s the kicker — at the right time. That’s only possible (at least consistently) by first analyzing your data.
When it comes to marketing, efforts fall into two main categories: direct marketing and inbound marketing. Direct (or outbound) marketing includes things like TV commercials, Facebook ads, billboards and so on. Inbound marketing, on the other hand, lets people come to the content, in the form of blog posts, videos and the like. Inbound methods are much more effective and therefore more popular than outbound tactics.
That’s nice, but how does this relate to CRM data mining?
You need to understand your data in the context of your marketing efforts. Is the data you’re looking at based on inbound tactics or direct marketing? That will influence how effectively you can serve up content to the appropriate audience and the results that follow.
Here are a couple inbound examples:
- You can use CRM data to optimize your blog strategy. If Tuesday and Thursday mornings historically receive more page views, you know those are the best times to publish new content.
- If data mining uncovers an unexpected downturn in social media engagement, you can use that insight to identify the cause and tweak your strategy.
On the direct marketing front, forecasting and regression techniques allow you to model various activities. Going back to the security system example, if you hold an annual security seminar, you can build an ad campaign strategy based on how campaigns from previous years performed.
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3. Boost Customer Retention
Attracting customers is only half the battle. Customer retention offers vast opportunity for companies that want to thrive. Building loyalty programs, executing one-to-one marketing and handling customer issues are a few ways to encourage customer loyalty.
First up, loyalty programs. Once someone becomes a customer, it’s important to make them feel valued. Putting a loyalty program in place is an excellent way to accomplish that. Using classification and clustering allows you to identify the best candidates for a loyalty program based on factors like their purchase history and engagement with your brand.
One-to-one marketing is another helpful retention method. Marketing is about interacting with people, not a set of database information. Data mining techniques including classification and association help you personalize each interaction, such as sending customers the most relevant offers based on data patterns. That brings a human element so customers know you care about them as individuals.
Email marketing tools are a great way to send personalized messages.
Finally, customer service quality is a marker for customer satisfaction. If people have a poor experience, it’s much harder to keep them from leaving. Sequential analysis is particularly helpful in this regard. By allowing you to spot trends, it can uncover potential weak areas in your customer service processes.
For example, looking at the data could reveal a pattern that most complaints relate to a lack of timely responses from your help desk. With this insight, you can take measures to cut down on the wait time so customers receive prompt help.
What do these examples all have in common? They offer personal, meaningful interactions to customers. Given that consumers expect no less, it’s no surprise that companies who place a high priority on sending such messages are much more likely to succeed.
Salesforce performed a study to weigh in on the topic. It found that high-performing marketers are over eight times more likely than underperformers to be fully satisfied with their ability to use data to send appropriate messages using the best channel at the best time for their audience.
An Evergage survey of marketing professionals found a similar value in personalization. According to the data, three out of every four respondents believe personalization has a strong to extreme impact on customer relationships. The survey also revealed that 88% of respondents think customers expect personalized experiences.
Clearly, it’s in your best interest to include personalization in your strategy. And gathering insights from your CRM data goes a long way in supporting that goal.
4. Gain Long-Term Value from Customers
Which customers have the highest lifetime value (LTV)? Where are the best cross-sell and upsell opportunities? Questions like these are important to answer if you want to sustain long-term success. They’re also much easier to answer when rooted in data.
When determining LTV for each customer, classification and clustering provide a way to make sense of your customer base by dividing it into smaller segments. Forecasting and regression also play a critical role. After all, LTV is something you can only predict, so using historical data can help inform which current customers will be most valuable.
Market basket analysis, which is common in the retail space, is another technique for uncovering associations so you can gain the most value from customer purchases.
At a basic level, it looks for the relationship between different items that someone buys, using an if/then rule. For example, if someone goes on Amazon and orders dog food and sunglasses, market basket analysis seeks to explain the correlation.
It’s a complex concept, but the underlying goal is straightforward: gain a better understanding of buyer behavior to increase sales. Physical stores put this into practice by placing products that customers often buy together in the same vicinity.
What about cross-selling and upselling? Sequential analysis and association let you drill into the data to uncover the best opportunities. Say a cable company wanted to encourage upgrades from their basic product to an extended package that included extra channels but cost more. They could use sequential analysis to determine that, historically, men in their 40s upgrade most often, making them the best type of customer to upsell.
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Conclusion
CRM systems contain a wealth of customer data. But that data is only valuable if you can use it to inform your marketing decisions. Data mining helps make sense of your information so you can market more effectively at every stage of the customer journey.
The first step on that road is to invest in the right CRM software. With so many solutions available, it can be hard. To help make your search less complicated, we created a free comparison report. You can use it to get an overview of vendors and speed up your selection process.
With the right CRM system implemented and a strong data mining strategy in place, you’ll be on your way to striking it rich in the gold rush of the 21st-century.
How would CRM data mining add value to your marketing efforts? Let us know in the comments!