The Best Data Science Methods for Predictive Analytics

best data science methods

Predictive Analytics is among the most useful applications of data science.

Using it allows executives to predict upcoming challenges, identify opportunities for growth, and optimize their internal operations.

There isn’t a single way to do predictive analytics, though; depending on the goal, different methods provide the best results.

What is Predictive Analytics?

Predictive analytics is the area of data science focused on interpreting existing data in order to make informed predictions about future events.

It includes a variety of statistics techniques.

  • Data mining: looking for patterns and relationships in large stores of data
  • Text analytics: deriving analysis-friendly structured data from unstructured text
  • Predictive modeling: creating and adjusting a statistical model to predict future outcomes

In short: predictive analytics puts data into action as actionable insights.

It’s useful in every area of business:

  • Marketing: Predictive analytics predicts campaign opportunities and helps find new markets for products and services.
  • Operations: Analytics power smart inventory management systems, forecasting supply and demand levels based on a variety of factors. They’re also used to optimize repair schedules to minimize equipment downtime.
  • Sales: Identifying a company’s best clients and predicting customer churn are two strengths of predictive analytics.

Choosing The Right Model for the Job

Predictive analytics has a wide spectrum of potential applications.

It follows logically that there’s an equally wide variety of models in use.

These can be roughly grouped into some main types:

Regression

Regression models determine the relationship between a dependent or target variable and an independent variable or predictor.

That relationship used to predict unknown target variables of the same type based on known predictors.

It’s the most widely used predictive analytics model, with several common methods:

  • Linear regression/ multivariate linear regression
  • Polynomial regression
  • Logistic regression

Regression is used in price optimization, specifically choosing the best target price for an offering based on how other products have sold.

Stock market analysts apply it to determine how factors like the interest rate will affect stock prices.

It’s also a good tool for predicting demand will look like in various seasons and how the supply chain can be fine-tuned to meet demand.

Classification

This form of predictive analytics works to establish the shared characteristics of a dataset and determines the category of a new piece of data based on its characteristics.

It predicts future classes of data, so it does involve defining those classes.

Some classification techniques include:

  • Decision trees
  • Random Forests
  • Naive Bayes

While it sounds like classification would be primarily useful in descriptive rather than predictive analytics, it’s productively applied when forecasting values.

Classification answers questions about a customer’s potential lifetime value, or how much a particular employee is worth.

Executives consider this information when prioritizing clients or deciding which employees they should invest training in and promote.

Clustering

Clustering involves grouping data by similarities into “clusters”, or groups of closely related data.

During clustering, the most relevant factors within a dataset are isolated.

The process maps the relationships between data that can then be applied to predict the status of future data.

K-means clustering is arguably the best known form of clustering, though other techniques are in place.

Clustering has the advantage of letting data determine the clusters- and therefore the defining characteristics of the class- rather than using preset classes.

It’s extremely helpful when little is known about the data in advance.

Analysts frequently use cluster models during customer segmentation.

Here, it finds the traits that actually separate classes of customer from each other rather than relying on human-generated classes like demographics.

Those classes can be taken a step further to inform targeted marketing strategies.

Combining Models

Few problems are so simple that they can solved with a single predictive analytics method.

In practice several techniques are usually applied together or in succession in order to produce the most accurate representation of the data.

The Future of Predictive Analytics

Machine learning has made predictive analytics more efficient than ever by enabling the analysis of vast amounts of data.

It’s likely, then, that predictive analytics will continue to be a popular and well-known application of data science.

Are you having trouble finding useful predictions within your company’s data? Concepta has the data visualization tools to put your data into perspective. Contact us for a free consultation!

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Automatic​ ​Customer​ ​Classification:​ ​The​ ​First​ ​Step​ ​to Segmentation​

automatic customer classification

Customer segmentation is a critical part of identifying your best customers, but you can’t do it until you know more about them.

That’s where automatic customer classification comes into play.

This article will explain the distinction between classification and segmentation, outline the core concepts of classification, and highlight the actual business benefits of automatic customer classification.

Classification Vs. Segmentation

In simple terms, segmentation is applied to the results of classification.

Segmentation can’t happen without having some characteristics to use, and classification is pointless if the information is not put to use.

Customer classification is the act of seeking out and identifying common traits in a group of customers.

It answers a broad question: what is similar about these people and their purchasing habits?

Segmentation takes that a step further by subdividing customers according to those similarities.

It answers a more focused questions: what is the most useful way to group these people based on the commonalities found during classification?

Unbiased results

Automatic classification involves using an algorithm to sort customers as data about them becomes available.

When done right, it incorporates all data resources regardless of whether a person thinks the information may be relevant.

Customer data is thus drawn from the silos where it tends to collect and put to work.

There are undeniable benefits to using automatic classification methods.

When a person does classification – even when setting up a series of filters – they can only filter by what they think might be relevant.

People typically end up using demographics as differentiating factors (age, family structure, income, residence).

Using diagnostic analytics, an algorithm can find unexpected points of similarity that better predicts customer behavior and potential lifetime value.

Algorithms have no preset bias about how people of various socioeconomic brackets or regions behave.

The labels they generate are based solely on patterns found within the given dataset.

They might reveal unexpected commonalities in high-value customers such as path to purchase, lifestyle factors, similarities gleaned from touchpoint analysis (how recently the customer interacted with the brand before purchase), or other factors that are hard for human analysts to detect.

Cluster analysis

A common method of customer classification is cluster analysis, also known as cluster modeling or cluster-weighted modeling.

Cluster analysis gathers data points into clusters based on both their similarity to each other and their difference from other clusters.

Some of the more popular clustering algorithms are:

  • K-Means clustering: Clusters data points together based on Euclidean distance. The amount of clusters is determined organically in response to the data.
  • Hierarchical clustering: Creates a ranked group of clusters. It either begins with all data points in their own clusters and moves up to pull together similar points or starts with all data in one cluster and moves down, breaking out data points that are no longer similar to each other until each is in its own cluster.
  • DBSCAN: This is a very common clustering algorithm based on distinguishing coherent clusters from outliers. It also doesn’t need to be fed the number of clusters before sorting.

Most of the time, several techniques will be combined to realistically represent the data.

Benefits of automatic customer classification

Automatic classification can handle more data than human analyst.

It’s faster and more accurate, making the best possible use of a company’s customer data.

Letting machine learning determine what characteristics actually impact value uncovers useful information about the customer base.

These insights suggest ideas for future products and services or areas where the company can improve to widen their appeal.

Finally, automatic customer classification informs a highly precise customer profile.

Better profiles lead to a more personalized buying experience, where customers are treated as individuals with different needs instead of being presented with generic offerings.

Looking forward

With buying experience fast becoming the leading differentiating factor among brands, understanding who customers are is of paramount importance.

Automatic customer classification is the first step on the path to a closer, more profitable connection with customers.

 Are you having trouble managing your customer relationships? Let Concepta show you how our advanced CRM systems can provide the support you need!


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