In the wide world of information technology, data is like raw, unrefined gold. Out of all the databases, perhaps none is more important these days to the business community than the CRM database.
In this article, we’ll examine in depth the work of data mining in CRM for improved customer relationship management. We’ll demonstrate how this is beneficial for winning new customers, retaining more existing customers, improving customer loyalty, and getting more value out of the total customer lifecycle.
What is customer data mining in CRM?
Before we get to the meaning of customer data mining, let’s quickly remind you of what a CRM is. A CRM is a customer relationship management software solution. It is a platform for keeping track of all your contacts and gives you rich profiles for each as well as full engagement histories.
CRM software is also useful for finding new contacts to grow your network and hopefully create new loyal customers. CRM systems either offer tools for marketing, sales and customer service, or work with those apps.
Data mining in CRM does not mean mining your contact database for data. That is a misconception. There already are a lot of data inputs in your CRM. So it isn’t exactly mining for data, but mining in the data warehouses.
Think of it like saying coal-mining instead of pit-mining, the former refers to what is being mined or extracted, while the latter is where the mining activity is happening. The mining is happening in your CRM data.
So, what’s being mined in your CRM data analysis? The answer is that machine learning algorithms are looking for patterns and trends. Patterns are the coal or the gold that we want to extract from the big data sets inside our CRM.
Elements of data mining
There are three main modeling techniques to data analytics, and they are: descriptive data modeling, predictive data modeling, and data forecasting.
Descriptive data modeling
Descriptive data mining is the simplest form of data mining tools. This is the process where you look at the existing data to answer the questions: What happened? What is going on? For example, what is current customer behavior like?
Being the simplest modeling technique, the answer can be derived from a human looking at the data sets, or by artificial intelligence trained to look for patterns.
Predictive data modeling
For predictive data mining, you move from examining the data for past or current trends to the future. The main question is: What is going to happen based on what is currently happening?
Predictive analysis is at the micro level, looking at your actual activities and predicting what will be the possible results based on specific business decisions.
At first, the difference between predictive modeling and data forecasting is unclear, but you can think of it as the shift from the micro to the macro.
With forecasting, you are looking at the bigger picture for your organization or industry or markets as a whole. The question is: What should we be doing in the future? In other words, it is more for open-ended decision-making and business strategy.
What data is collected in CRM databases?
There is quite a lot of variety when it comes to what types of data set your CRM system will generate.
Individual contact data
For every single individual in your CRM database, there is a lot of standard data. There is demographic data, like age, language, location, marital status, job or salary. Then there is data specific to your company, like their purchase histories, preferred methods of communication or best time to reach out, or other engagement histories like any customer support issues.
Holistic database information
You can take all your contact data and aggregate it for new data insights. These include things like your most and least commonly purchased products. It can also point out regularly recurring customer service issues. Finally, you can see when all contact activity peaks and when there’s no action going on.
General online data
Expanding outwards from just your database to all the data flowing through the world wide web and beyond, you can use analysis to spot wider market trends, new directions for your industry, comparisons of your operations to competing ventures, and for an overall glimpse into the future.
CRM data mining techniques
Now that we’ve looked at data modeling elements, let’s talk about the several data mining CRM techniques. They are:
Association rule learning
Data mining technique 1: Anomaly detection
Anomaly detection is when you search through your data to find instances where the actual information is different than what was planned or expected. This is a useful technique to weed out small problems and issues before they become huge obstacles.
Data mining technique 2: Association rule learning
This next technique is related to big data sets and huge data warehouses. It is less directed and more open-ended to discover patterns in your data, often based on employing hypothetical algorithms to uncover connections to large data sets.
Data mining technique 3: Classification
Data classification starts with data that has been somewhat labeled and where you begin with several categories in mind. Then, this process simply takes the labeled data and places them into these neat classifications. A decision tree is often used with data classification, though the findings of each decision tree do not regularly guide any future actions.
Data mining technique 4: Clustering
Many people need help distinguishing the difference between classification and data clustering. With clustering, you have unlabelled data inputs to begin with, and no idea of how many categories there will be or what they are. Clustering then sorts the data into clusters and from there, you can locate areas that require action.
Data mining technique 5: Regression
Regression analysis is the most complex data mining technique. It is used for finding links and dependencies between data, and to see what data is affected when certain variables are changed in the dataset, how, and to what magnitude. Regression analysis is great for doing marketing trend analysis and customer satisfaction analysis.
The benefits of data mining for improved customer relationship management
We’ve looked at the elements of data modeling and the data mining techniques for CRM. Now it’s time to consider the applications and benefits of data mining in CRM.
The key elements of an analytical CRM system are data mining and business intelligence application for things like marketing campaigns, sales forecasting, and the search for new potential customers. To get more specific, here is a list of data mining applications:
Market Basket analysis
Predictive life-cycle management
Customer loyalty and retention
CRM data mining helps with market segmentation
Data mining for CRM can look at wider markets and create silos or segments, which can then be useful for planning marketing campaigns with more on-point messaging for the right target market.
Customer segmentation can be based on customer identification demographics, purchasing patterns, general interests, or deeper psychological consumer patterns. All marketing strategies should start with some customer segmentation to best serve your target market.
CRM data mining is used for sales forecasting
If marketing is meant to find potential leads, then sales has the job of converting those leads into opportunities and paying customers.
Data mining in CRM can assist with sales forecasting, showing you the best times or days to reach out to your customer base or via which communication channels like email, social media or text messaging. This is greatly beneficial for retail and e-commerce operations aiming for cross-selling or upselling if you want to know what items will be trendiest.
CRM data mining is used for market basket analysis
What is market basket analysis? This is a form of data analysis that looks at customers' shopping baskets or shopping carts in order to compare the items inside them with other baskets.
For example, if your lead has running shoes in their basket, you can see what other customers who had similar shoes in their baskets also have, like gym shorts, which you can then advertise directly to your running shoe customer.
Data mining in CRM is good for predictive life-cycle management
Selling one item to one customer one time is fine, but true value comes in turning one-time customers into loyal lifetime customers. You can use data mining with your CRM datasets to get an idea about the total lifetime value of each customer, that is, how much you believe you can sell to them from the very first sale until the very last.
It is about the total customer’s lifetime value. Ensuring long-term loyalty over the customer life cycle requires decent decision support based on data for how to treat and reward existing customers you aim to keep for the long run.
Data mining is useful for database marketing
What is database marketing? It is sort of like the opposite side of the same coin of segmentation marketing. Instead of segmenting and categorizing your existing contacts, you take all your data to create ideal customer personas or types, and then to plan around these types. Database marketing is useful for planning the allocation of money and resources and for better project planning.
CRM data mining helps with customer loyalty and retention
Similar to predictive lifecycle management, individual customer retention and loyalty is one of the ways that CRM data mining gives you an advantage. Based on things like historical data and current behavior, it can offer predictions about the likelihood a customer will complete a sale, the possibility that they will drop out of a sales funnel, otherwise known as churn, or to give you an idea of overall customer satisfaction.
Additionally, as an element of business intelligence, you can also get smart suggestions for reducing churn and increasing customer retention, as well for cross-selling and new customer referrals.
CRM data mining is useful for product customization
When brainstorming, planning and designing new products, whether they be goods or services, you can take advantage of the data in your CRM to help with decision-making regarding things like size, scale, pricing, functionality, and target market. This is achieved by looking at past product sales, reviews, issues, and other product data which is lurking inside your CRM data warehouse.
Use CRM data mining to predict warranty claims
When planning product sales, especially with manufactured goods, most companies offer warranties to their customers to give them extra assurance that the goods will deliver as promised.
Since there might always be a percentage of customers who are not satisfied and will file a warranty claim, it’s a good idea to have knowledge beforehand regarding the likelihood and volume of these warranties. CRM data mining analyzes historical data about warranties and can give you solid predictions going forward.
Use data mining in CRM for fraud detection
Not all business runs smoothly, and not all customers are one hundred percent honest. By applying data mining to your CRM historical data, you can spot instances of fraud, fraud patterns, and other red flags to help you catch fraud early and deal with it, or to prevent fraud in the future by refining your anti-fraud practices.
There is little need to spell out the advantages of data-based fraud detection for your CRM and sales endeavors.
Key takeaways on how the use of data mining in CRM improves your business
To sum up again, in this article, we’ve gone over the definition of CRM and then looked at the elements of data modeling that are applied to CRM software. Afterward, we discussed exactly what kinds of data our CRM systems generate. Then, we went over the 5 CRM data mining techniques, after which we looked at the various applications and benefits of doing data mining with your CRM information.
Of course, there are many types of CRM platforms and all of which produce slightly different data. It is analytical CRMs which are most poised to collect and aggregate the largest data sets of them all, as these systems have the right tools to break down, segment and analyze data, and from there, make descriptions, predictions, and forecasts.
All in all, the main takeaways regarding data mining as a means to improve customer relations are as follows. Leveraging data and business intelligence means you have a better idea of who your existing customers are and what they like, as well as what your potential market for new customers is all about. You boost customer loyalty and customer satisfaction, reduce waste and avoid errors, and increase ROI and profitability for your business.
Can you complain about any of that? We don’t think so. In the end, we hope you are using a CRM, and you aren’t letting all that priceless CRM data go to waste. Mine that stuff for gold!