The Basics of Predictive Analytics: From Data to Insights

What is predictive analytics?

Predictive analytics involves looking at historical data to predict future outcomes. The information generated by predictive modeling and data analytics is generally accurate in predicting a future event, but it's not perfect. However, it can be applied to a variety of scenarios that a business faces on a daily basis.

Predictive analytics uses data mining techniques, machine learning, and statistical modeling to find patterns in data and identify risks and opportunities.

This area of data science is much like using a search engine, but the queries are more involved, and the resulting data is expected to be highly accurate in predicting the future.

How does predictive analytics work?

The predictive analytics process starts with a problem and goes through a series of steps to achieve the desired result.

The problem can be anything from detecting fraud to ensuring shelves are stocked for the holiday season. Relevant data sets or databases are collected for examination and then processed for analysis. The data scientist then applies the relevant tool to find the desired data and then validates the results for deployment to stakeholders via a report.

Types of predictive models

Predictive analytics software uses different predictive modeling techniques to generate coherent data that is applied to various situations.

There are several types of predictive models that can be used in different scenarios, depending on the business. The most common types of predictive models include:

Classification model

Classification models are the simplest predictive models in terms of the questions it answers.

This model draws on historical data to generate categories and answer the queries. It's best used for questions that require a yes or no answer, and helps the user make a decision based on the results.

Forecast model

The forecast model is used to forecast future outcomes regarding consumer behavior, sales, supply and demand, and more. This model will generate answers based on numeric values drawn from historical data.

For example, a retailer can get an idea of how much stock to have on hand during a certain time period to satisfy customer demand and avoid running short on inventory before the end of the period of demand. The model can also be used to take into account increased footfall or demand from a nearby event that's expected to draw more people to the area for a period of time.

Outliers model

The outliers model is more reactive than proactive, but it can accomplish both actions by predicting the potential of an adverse event.

For example, a new product is released but generates a volume of complaints that's larger than expected. This can indicate an issue with the product that may result in a large number of product returns not long after it was released to the market.

Clustering model

The clustering model combines groups of people with similar attributes into clusters. This makes it easier to create a targeted marketing campaign and group together people in high- or low-risk categories.

With this information, you can reach a large group of people with similar tastes at the same time and identify groups by their potential risk factors. Clustering models can also help law enforcement identify locations where crime frequently occurs.

Time series model

The time series model uses time as the input that data is being sought after. It draws upon data from a selected time period from the previous year that's used to predict demand for the same time period in the current year.

The model is run a few weeks before the beginning of the period of the historical data to have enough time to respond to the information that's been generated.

For example, a seasonal food operation needs to have enough supplies before the start of the busy season. The time series uses the prior year's sales to estimate how many supplies are needed to satisfy a similar amount of sales for the coming year.

What is predictive analytics used for?

Predictive data analytics is used across many industries to anticipate future outcomes of a given scenario. All industries need to anticipate future demands, which predictive analytics models can assist with.

Forecasting

Some of the most common uses of predictive modeling techniques is using past data to create accurate forecasts about future inventories, demand, and need for services or supplies.

With forecasting, historical data is pulled from a database to look at past performance and anticipate what is most likely to happen soon.

For example, a hotel had a busy season the previous year, and wants to prepare for the same busy season in the coming year. Forecasting helps the hotel plan for staffing, supplies, laundry, food, and other needs to avoid running short when the season arrives.

Marketing

Marketers use predictive analytics to answer questions about marketing campaigns, such as how a consumer might react to a new type of visual branding.

A good predictive analytics example for marketing is the toy industry. A toy maker wants to get an idea of how consumers will react to the announcement of a new toy that's being added to an existing line. Predictive analytics techniques can help the toy maker gain deeper insight into the most likely reaction from consumers.

Fraud detection

Predictive analytics techniques can be used to identify fraud patterns in an organization. A predictive model is selected, and historical data is used for the modeling.

One predictive analytics example is looking for customer fraud and determining if there's a pattern of bad orders or a fluke in purchasing patterns. Sifting through the data helps uncover issues and prevents them from reoccurring in the future.

Supply chain

Supply chains are under constant pressure to anticipate various issues, such as a spike in fuel costs, fewer drivers available to deliver goods, and a shortage of goods to satisfy an order.

Using predictive models enables the players in the supply chain to get ahead of potential problems and minimize their impact on the steady flow of goods.

Human resources

Predictive analytics helps anticipate human behavior in the workplace.

For example, predictive analytics can detect behavioral patterns that indicate the likelihood that someone will quit in a given timeframe, determine if a candidate will be a good addition to the company, and decide who's likely to be among the top performers.

Benefits of predictive analytics

From fraud detection to improved business operations, there are numerous benefits of using predictive analytics in the workplace, including:

Detects fraud

Fraud is typically detected by looking for patterns of activity over a period of time. That is, someone engaging in fraud has figured out the best time or window of opportunity to engage in fraudulent activities.

It's difficult to cover up the evidence of fraud as electronic tracking leaves traces behind that are uncoverable by using search queries in databases and machine learning. These tools find unusual patterns and report them to an end user, who then analyzes the data for evidence of fraud.

Improves operations

Operations, whether it's the way in which a cashier rings up a customer or how a quality control monitor looks for imperfections, consist of a set of processes that can jam up over time.

Predictive analytics takes the data from these processes to show different outcomes based on different parameters. It can be applied to improving the efficiency of an operation in all divisions and levels.

Some examples of how predictive analytics tools can be used include determining the cost of fuel prices in the future to improve a customer's checkout experience.

Reduces risks

The ability to predict future events via predictive analytics means it's easier to anticipate potential problems and avoid them as much as possible. This is also known as risk reduction because the models can uncover and define an event you may not have foreseen.

The data doesn't define when and where an adverse event will happen so much as it shows the odds and possibilities of the occurrence of a defined event, enabling you to plan and react appropriately.

Optimizes marketing campaigns

One of the predictive analytics capabilities is the ability to make a marketing campaign more effective. A predictive model can be set up to use data points such as demographics and apply them to key aspects of the marketing campaign to determine how likely a certain type of shopper will respond.

Improves customer satisfaction

Predictive analytics can be used to gain actionable insights into customer behavior, their pain points when buying something, and what makes them feel good about their purchases.

This data can be used to remove the obstacles a customer experiences during their shopping efforts, resulting in less stress for the customer while improving their level of satisfaction.

Increases revenue growth

Making it easier for a customer to buy a product, making sure there's enough of a product available, and removing obstacles for the delivery of a product results in fewer work hours spent dealing with adverse situations.

It also means customers get what they want when they want it, and in sufficient quantities. Smooth delivery of goods and services increases revenue because less money is diverted to solving problems.

Challenges of predictive analytics

The field of data science is only as good as those who use predictive analytics software. It's not unusual for the resulting data to be skewed by bias, generate poor quality data because the source data is poor, and for the model to turn out bad data due to a lack of updates.

Just as there as benefits of predictive analytics, there are also some drawbacks, such as:

Data quality

Using poor quality data is always going to deliver poor quality results due to the fact that most predictive analytics models are literal in terms of how they process data. Ultimately, the quality of the data delivered from predictive analytics depends on the quality of the data source.

Data bias

People are prone to putting their inherent biases into their queries no matter how hard they try to keep them out. These biases then become part of the modeling process and result in data that isn't as accurate as it could be.

For example, the person setting up the model may prefer the color blue, but the person requesting the report wants data from a variety of colors. The inherent bias towards the color blue results in a report that leans towards a solid color instead of a rainbow.

Privacy and security

Privacy and security are two major issues businesses face when using predictive analytics. The data analytics process sometimes involves going through user accounts for data, something the user agrees to when they sign up.

However, the business is responsible for preserving account security and must act responsibly with the resulting data. Personal information is easily abused and used for fraudulent activities, and the business can be held liable for their failure to maintain privacy and security.

Interpretability

The effectiveness of the data that's delivered by predictive analytics is dependent on the person who's reading the data.

The data derived from the model may be excellent, but if the person reading the results doesn't have a good idea of what they're looking for, the data is essentially useless.

Whoever prepares the data needs to make it accessible for readers who can understand the results, but may not be able to parse the syntax, so the data makes sense.

Model maintenance

Predictive models need regular updating and tuning to return high-quality results.

The results worsen when the person responsible for maintaining the models doesn't do their job or if someone in charge doesn't invest in the models. The parameters used for the modeling get out of date, creating faulty reports that don't do a good job of determining future outcomes.

Tools used in predictive analytics

The tools used for predictive analytics enable the user to refine their queries to guide the analysis.

For example, someone who wants to use machine learning to create multiple neural networks that anticipate human behavior can use a machine learning predictive analytics model to achieve their goal. Machine learning isn't typically used in predictive analytics, but it can be used in conjunction to help refine the resulting data and save time.

Other examples of predictive analytics tools include statistical tools to determine the average in a data set, machine learning tools for developing neural networks, and visualization tools that produce graphs, charts, and other types of imagery that show potential outcomes for a specific query.

The wide variety of predictive analytics tools means that just about every business can find an answer to their questions about potential problems in the future.

How Mailchimp uses predictive analytics to help you

Predictive analytics can help you make better business decisions and improve your business processes. Fortunately, Mailchimp is equipped with several predictive analytics tools that you can use. Mailchimp uses predictive analytics to improve business operations in many ways, including:

Optimize your email campaigns

Mailchimp uses predictive analytics tools to help you optimize your email advertising efforts with email campaign ideas that resonate with your target audience and boost open rates.

Know what message to send your customer

Our predictive analytic tools can help you create a solid message that your customers will appreciate and react to positively. You can be confident that the campaign you create with the help of data analytics is one that grabs the attention of a larger customer pool and resonates with the target audience.

Plan your campaign strategy

With Mailchimp, you can plan your campaign strategy around the data delivered by predictive modeling to reach your ideal customer and convert their response and interest into a purchase.

Identify high-value prospects

Predictive analytics helps identify high-value prospects and the kind of message they'll respond to. By identifying your high-value prospects, you can create a positive customer experience with your business and build a new business-customer relationship that results in customer loyalty over time.

Improve your decision-making process with predictive analytics

Predictive analytics helps your business make better decisions based on historical and transactional data that's otherwise difficult to uncover and organize. It saves time, helps uncover inefficiencies, and makes your customers happy because you can anticipate their needs.

At Mailchimp, we want to help you get the most out of your marketing efforts, whether by using predictive analytics to uncover patterns in data or creating dynamic email content that wows your customers.

Our comprehensive resource library features everything from email campaign ideas to tips for writing a privacy policy for your website. Come check out what we offer and see how our services can boost your business to the next level.