"Data mining techniques and tools allow you to predict future movements and act accordingly to take advantage of them."

Key data mining techniques for better sales insights 320

Key data mining techniques for better sales insights

Whether it’s shaping the decision-making process, reducing customer churn, or providing seamless customer service and experience, data drives modern business in a number of ways. With the ever-growing volumes of data available, the question on everybody’s mind is:

How to put raw data to actionable use and derive real business value through sales insights?

Enter data mining, a huge contributor to the cause. It’s the automated process of sorting out gigantic data sets to uncover trends and patterns, solve business problems, generate new opportunities and establish relationships through data analysis. Data mining techniques and tools go beyond looking at historical data to take intelligent actions: they allow you to predict future movements and act accordingly to take advantage of them. If used properly, data mining can give you a big advantage over your competition.

The term “data mining”, as well as the process itself, is applied rather broadly in the tech industry. For this post, I’ll present you with key data mining techniques that leverage data for better sales performance. Disclaimer: these techniques can seem complex and technical, so bear with me if not for one reason only: the more you understand about the data mining process, the better you can use it to your advantage and leverage data to close deals.

Data ChartsImage credit: healthitanalytics.com

1. Association rule learning

First up is association rule learning, also known as market basket analysis. This technique can uncover interesting relationships between variables in large data sets, and helps recognize hidden patterns that can be used to identify point-of-sales data. These patterns can be used for product or service recommendations based on what others have purchased before, as well as based on similar products/services bought together.

As such, associative learning is critical in forecasting behavioral characteristics of customers. Apart from analyzing sales transactions, this data mining technique can be used to determine interests of potential customers. SaaS companies can figure out what overlapping technologies their prospects are using. By using these sales insights, they can benefit by uncovering new potential verticals to tap into, as well as other technologies and geographies to focus on next.

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2. Classification analysis

A systematic process of obtaining valuable and relevant information about data, as well as metadata, classification analysis plays a key role in identifying the proper categories data belongs to. It’s closely linked to clustering analysis (I’ll get to it in a minute) as the classification itself can be used to cluster data. Here, the data is classified into various sets in order to make an accurate analysis and/or forecast. In other words, generalize familiar structures to apply to new data points.

A common example would be your email provider classifying incoming messages as either legit or spam. From the sales point of view, classification is used for grouping and categorizing, where proper classification allows for a more precise description and qualification for companies that seek to leverage sales intelligence. For instance, if your SaaS solution is focused on analytics-based services, you’d want to know exactly what kind of analytics best fits your offering: is it conversion optimization, lead generation, application performance, A/B testing, etc? Then, if a certain criteria meets your requirements (say, their web traffic is over 5 million monthly visits or their tech usage is among specific geographies), they qualify as a prospect.

3. Clustering analysis

Through clustering analysis, data is grouped in clusters based on its similarity to other data objects in some way. This technique allows companies to understand both the similarities and differences within the data. Clusters have common traits that can be utilized to improve targeting, such as customers with almost identical buying behavior can be targeted with similar products and services. The general idea is to group data objects so that the level of association is at its maximum within each cluster and minimal outside it.

Sales-wise, the result of a clustering analysis is the buyer persona, a fictional representation of a targeted customer based on specific demographics, behavior, and other data. A practical example would be the report feature found in sales insights platforms, whereas a detailed overview of an account, a group, or an entire category can help in reducing customer attrition, as well as significantly improve customer retention. Clustering offers valuable input to sales leaders in terms of decision-making, customer focus, and account prioritization. Clustering Analysis

Image credit: Quora

4. Regression analysis

Also called value estimation because it revolves around estimating a value for a given variable, regression is used for predicting sales, prices, revenues, and other numerical values for a specific data set. There is a response variable (dependent) and one or more predictor variables (independent). Changing the value of predictor can alter the value of responsive variables (but not vice-versa, mind you). As such, it is often used as a forecasting technique. For instance, a sales forecast for sales per customer per month, based on this type of data mining can help set achievable, realistic goals and motivate salespeople to be more effective. By putting regression to work in order to estimate sales goals, sales leaders can provide their sales team with attainable targets that can drive their performance.

Mining for sales insights

Data mining can help businesses identify and select the most relevant and important information. This information can then be leveraged to develop different models that predict how the market will behave so you can anticipate and jump on it. The more data there is, more business value there is for your company. Each of these four discussed data analysis techniques can be employed to answer a different sales question. It all depends on the type of data and the ultimate goal of the analysis.

Dozens of companies provide data mining tools and services, either through proprietary software or open-source efforts. Rarely does a single data mining technique solve the entire problem for a business. Instead, all the techniques should go hand in hand to get to the bottom of an issue. While it’s important to know what data mining techniques are more relevant to your business, at the end of the day, you want the finished product: raw data converted into sales insights, or in other words: a format that can be easily digested by your sales team. After you have the relevant information in the format you need, you can easily apply it to widen your knowledge and in-depth understanding of customers’ interests and needs to make smarter and more informed decisions. Without it, it’s going to be a rough ride.

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