Nissan’s Layered Approach to Data Science: Cutting Costs While Maximizing Sales

Layered-Approach

The most convenient thing about data intelligence is that the same resources gathered in one part of enterprise can also be used by another.

Data on sales patterns can be applied to supply chain optimization or marketing efforts, and nearly everything informs intelligent customer profiles. To get the most out of their data, companies need to maximize its usage across departments.

Consider auto manufacturer Nissan. They’ve created an intuitive, futuristic experience for drivers while lowering their operational costs.

How? By implementing a layered approach to data science that spreads data utilization across the operational structure from sales to manufacturing and maintenance.

Pulling Sources Together

Nissan is emerging as a leader in turning data into actionable business insight. They use a large percentage of their available data, which comes from sources like:

  • Regional sales data (sorted by vehicle model, color, and type)
  • Website activity
  • Consumer interactions with online “vehicle design” features
  • Marketing campaigns
  • Social media
  • Dealer feedback
  • Warranty information
  • Vehicle status reports from GPS/system monitoring functions
  • Driving data

To avoid privacy issues and protect drivers, Nissan anonymizes most vehicle-generated data. For example, instead of noting “this specific vehicle had a computer fault” they track the percentage of vehicles which throw the same fault.

Putting Data to Work

Data is at the heart of Nissan’s growth strategy. Asako Hoshino, Senior Vice President of their Japan Marketing and Sales Division, put it best at a speech at a 2016 Ad Week conference:

“You can’t just be bold, because your success rate will not increase. You have to couple boldness with science. It has to be grounded in science, and it has to be a data set that will underline and support the big decisions you make.”

Nissan uses their data to increase sales in carefully targeted ways. They run the usual sales tracking by region and vehicle, but they also seek out additional details.

Potential customers looking for a test drive fill out an online request form that gives Nissan location-specific data about popular colors, models, and features. This feeds into a tailored inventory for the region and guides dealership placement. It also helps to create highly targeted advertising.

Advertising is another area where Nissan excels. They use advanced visualization tools to make real-time performance metrics on their marketing campaigns accessible to senior leadership.

The data builds a dynamic profile of customers, suggesting which incentives might work best in certain markets and which tend to .

Like much of Nissan’s data structure, marketing data has wider applications. It’s used to create research and design initiatives that deliver features customers actually want.

Some features matter more to consumers than others, but there’s room to show off new technology while still keeping the features that drive sales. Data highlights these opportunities for technological distinction.

Technology is a big pull for today’s drivers, especially when it saves them time and money. Nissan pushes data-centric “connected car” features like predictive maintenance, advanced navigation software, remote monitoring of features, and over-the-air updates that take a lot of the guesswork out of vehicle ownership.

Increasing sales is only half the benefit of data science. Nissan has reduced their operational costs as well. Predictive maintenance- using data to service equipment before it breaks down- keeps their manufacturing process working smoothly.

That’s essential in a market where cars need to be more customized but still built to high standards on a short timeline.

Drivers have busy lives as well, which is why Nissan has a customer-facing application of their predictive maintenance data. They track aggregated vehicle data to detect potential flaws and plan repairs before they become expensive recalls (or worse, cause accidents).

When a vehicle does come in to a dealership for repairs, technicians can use the onboard data to quickly and easily verify warranty claims. This saves the driver time while lowering investigation costs and preventing unwarranted repairs.

Measurable Results

In 2011 Nissan set a goal to achieve 10% market share in North America. Nissan North America reached 10.2% market share in February of 2017.

They relied heavily on data science for guidance, specifically in providing targeted inventory and marketing to smaller regions while giving local leaders the right analytics tools to plan their own sales campaigns.

What Nissan Does Right (And What Others Can Learn)

Breaking down data silos

Data silos had been a major hindrance to Nissan’s data science efforts. In late 2016 to early 2017 the company began to address this by employing Apache Hadoop to create a “data lake”. The data lake holds 500TB of data, all potentially accessible for analytics.

Using data in multiple ways

Data is usable by key leaders throughout the company and can be referenced wherever needed. This leads to data-driven decision making at every level. It has the side effect of lowering the individual “cost” of data since it’s reused multiple times.

Encouraging internal adoption throughout the business

Data can be transformative – but only if it’s used. Nissan North America invited key data users from a variety of business areas to an educational internal event on data. They held workshops on their data platform and visualization tools, encouraged networking between IT and end users, and provided resources for further training.

As a result, active users of the analytics platform went from 250 to 1500 by end of its first year. IT saw fewer data requests, most of which were asking IT to add verified sources instead of looking up information.

Creating a layered approach to data science looks intimidating, but it can be as simple as uniting reporting streams in a single place. Concepta’s developers can design a dashboard solution tailored to your company’s unique needs, presenting real-time streaming data through dynamic visualizations. Set up a free consultation to find out more!

Request a Consultation

 

How Data Science Can Help Your Enterprise Generate More Revenue

data-science-revenue

Data science is a dry term for a surprisingly cool field. When used right it acts like a team of digital detectives, sifting through a company’s data to ferret out inefficiencies and spot opportunities in time to act.

“Used right” is the key phrase here. Data science is a complex field, and finding a path to revenue presents a challenge for companies trying to modernize their digital strategy. Sometimes it’s hard to see past the hype to the actual business value of investing in data science.

To help cut through the noise, here’s a clear, results-focused look at exactly how data science generates revenue for enterprise.

Laser-focused marketing campaigns

When it comes to marketing campaigns, there’s no such thing as too much data. Over 80% of senior executives want detailed analysis of every campaign, but they often lack the time or data to gain real insight into campaign performance.

Source: Concepta, Inc

Data science addresses both those concerns. Artificial intelligence and machine learning methods cut down on the time necessary to process data while better data management provides the fuel to feel analytics.

The right combination of data science techniques can help track how campaigns are doing by market and by demographic within that market. This includes information as general as click-through rates to sorting the time spent on a company’s page by the originating site.

Armed with this information, marketers can refine the ads they push to each market based on what works, not what should work based on broad demographics. They can even identify customers who failed to convert late in the process. About 70% of these customers will convert after being retargeted.

The results are impressive. Using data to guide marketing campaigns leads to a 6% increase in profitability over companies that are reluctant to adopt data science.

Better e-mail follow-through

E-mail optimization is probably the most direct example of data science driving revenue.

E-mail is a major source of revenue for enterprise, especially for B2B companies and those that focus on e-commerce. A full 86% of professionals prefer to use e-mail for business correspondence.

The same percentage are happy to receive e-mail from their favorite businesses (providing it doesn’t get excessive).

More than half of CMOs say increasing engagement is their main concern about e-mail marketing this year, but three quarters of them don’t track what happens after e-mails are sent.

Only 23% use data science tools to track e-mail activity. A mere 4% use layered targeting, and 42% use no targeting at all. (Four out of five do perform at least some customer segmentation, though.)

This oversight has a serious effect on the bottom line. 51% of marketers say a lack of quality data is holding their e-mail campaigns back. Without data to guide them, they struggle to evaluate customer satisfaction with the frequency and quality of the company’s e-mails.

Increasing e-mail quality using data science has measurable benefits. When customers make a purchase through links in an e-mail they spend about 38% more than other customers.

80% of retail leaders list e-mail newsletters as the most effective tool in keeping customer retention rates high.

On a smaller scale, personalizing e-mail subject lines increases the open rate by 5%. Triggered messages such as abandoned cart e-mails have an astounding 41% open rate (and remember that 70% retargeting conversion rate from earlier).

Lead management

Sales staff only have so much time, and analog lead assessment methods yield questionable results. Artificial intelligence-powered data science tools can analyze a company’s past sales and customer data to effectively score leads, letting sales staff make the most of their business days. These tools consider factors like:

  • Actual interest in product as demonstrated by events like site visits and social media discussion
  • Position in purchase cycle based on time spent on specific areas of a website
  • Demonstrated potential purchasing power and authority to enter contracts

Using AI in lead management results in 50% more appointments than other methods. Those appointment are shorter and more productive, too, since businesses can target customers who are ready to buy.

The overall reduction in call time averages around 60% without damaging customer satisfaction rates. That’s why 79% of the top sales teams use data science to power their lead management.

Intelligent customer profiling

Knowing who the customer is and what they want is key to both marketing and customer service. Data science removes the potential for human biases about customers. Specifically, it looks for what customers have in common and groups them by that instead of imposing arbitrary demographic boundaries.

Profiling software analyzes all available data on a company and its customers to find previously unnoticed similarities. These hidden connections can be then used to drive revenue in different ways.

They’re particularly good at identifying customers with the highest potential lifetime value or highlighting potential extra services current customers might enjoy.

A great intelligent customer profiling success story in this arena comes from video distributor Giant Media. After using data science to build data-driven customer profiles they found 10,000 new leads across the United States.

500 brands were in their desired New York City market. The software even isolated 118 businesses that matched Giant Media’s idea profile and provided contact information fast enough to enable effective sales calls.

Improving customer experience

One theme keeps popping up in sales and marketing discussions: customer experience is king. It’s predicted to be the primary brand differentiator by 2020. 86% of customers value a good buying experience over cost and will pay more for better service. Once they’ve had that positive experience they’re 15 times more likely to purchase from the same vendor again.

Source: Concepta, Inc

What is considered “good” customer service? Besides obvious factors like reliable customer service and solid quality, personalized service seems to be the key to winning over customers.

Data science provides insights that allow for that personalized service on a large scale. It can offer tailored interactions such as:

  • Suggesting products based on past purchases
  • Retargeting customers at appropriate intervals (for instance, reorder reminders for pet food or garage coupons as a customer’s vehicle hits certain milestone)
  • Reminders around holidays (like Mother’s Day or family birthdays)

In short, creating an outstanding customer experience requires knowing what the customer values and being able to offer it on demand. Data science is invaluable here. Chatbots in particular are useful for providing assistance that customers need, when they need it, and in an accessible format.

Timely sales forecasting

Sales forecasting without modern data science methods takes far too much time. Reports are huge, hard to get through, and don’t arrive in time to help sales staff. As a practical compromise sales staff often rely on wider-scope numbers which are more readily available instead of targeted data on local customers.

Data science – specifically predictive analytics – can provide near-real time information on what’s selling, where it’s selling, and who’s buying it. This prepares companies on a structure level to spot opportunities and make the most of them.

It increases overall enterprise flexibility. Plus, sales staff can use the information to build better pitches, improve relationships with their customers, and generally make better use of their time.

Supply chain management

Managing the supply chain feeds directly into revenue. After all, companies can’t sell what they don’t have. Data science provides insights that enable more efficient internal operations, which leads to better margins. To get specific, insights gained from data science can be used to:

  • Keep enough inventory on hand to meet demand, regardless of season
  • Make deliveries on time despite potential delays
  • Schedule services more accurately so customers can plan their day

Pitt Ohio Freight Company saw a major boost in sales after applying data science to their supply chain problems. They trained algorithms to consider factors like freight weight, driving distance, and historical traffic to estimate the time a driver will arrive at their delivery destination with a 99 percent accuracy rate.

Their customers were highly impressed. Pitt Ohio now enjoys $50,000 more in repeat orders annually, and they’ve reduced the risk of lost customers as well.

Price optimization

Pricing is tricky. The goal is to find a profitable price that the customer is happy to pay so as to ensure repeat business. An enormous number of factors affect pricing, and it’s hard for humans to tell what’s important and what isn’t.

Data science has no such handicap. It can be applied to customer, sales, inventory, and other market data to uncover what actually influences a customer’s willingness to buy at a specific price. Based on that, companies can find the ideal price to make everyone feel satisfied with the purchase.

Airbnb uses a dynamic system based on this concept. The company tracks local events, hotel trends, and other factors to suggest the best price to its hosts.

This is a major part of their business strategy since hosts aren’t usually professional hoteliers; that guidance is necessary to keep hosts happy and listing with Airbnb.

Some hotels have a more complex system for setting prices. Rates used to be uniform across the board. Changes were only triggered by season or maybe rewards club status. Data science opened the doors to a more egalitarian pricing strategy.

Now each user can be shown a customized price based on a number of objective factors.

  • Is the trip for business or pleasure?
  • What rates did the customer receive for past stays?
  • How valuable is the customer as a client?
  • Does the customer have a booking at a competitor which they might be willing to change?
  • Will the customer be using cash or points?
  • Does the customer have past incidents of bad behavior at the family of hotels?

Interestingly, customers who caused expensive trouble during previous stays may be shown higher rates to discourage a booking.

Marriott, who were early adopters of data science in the hospitality industry, is an interesting case. The hotel chain was generating $150-200 million per year in the 1990s by intelligently managing their Revenue Per Available Room, or RevPAR. It’s still growing at a rate of 3% a year.

As a general trend, applying data science to price optimization increases revenue by 5-10%. The most benefit is seen in season-dependent industries such as hospitality.

Looking to the future

Industry leaders are taking note of these benefits. As a result, data science is fast becoming the preferred way to fuel digital transformation efforts. Global revenues for big data and business analytics were up 12.4 percent last year, and commercial purchases of hardware, software and services to support data science and analytics exceeded $210 billion.

Source: Concepta, Inc

Companies who hesitate to adopt data science will soon be left in the dust by their better-prepared competitors. Now is the time to make the business case for integrating data science- before it’s too late.

Not sure where to start? Concepta can advise on and customize powerful data science systems to meet your specific needs. Schedule a free, no hassle consultation to find out how!

Request a Consultation

 

Python Vs R: What Language Should You Use For Data Science?

python-r-data-science

R and Python are the two most popular programming languages for data scientists, and choosing which to focus on is one of the most formative, career-shaping decisions young analysts make.

R and Python have a lot in common: both are free, open source languages developed around the same time (in the 90s) and favored by the data science crowd.

Read on for a quick look at both languages, an overview of the debate, and when each would be the better choice for a specific data science project.

What is Python?

Python is an interpreted programming language used mainly for web applications. It’s high level, robust, and object-oriented.

Python features integrated dynamic semantics and dynamic typing and binding.

Applications written with Python have lower maintenance costs because of the focus on readable syntax.

The language has a fast edit-test-debug cycle which makes it useful for Rapid Application Development.

It supports module and packages, allowing for modular design and reusability of code across projects.

Debugging Python is simple, too. Instead of causing segmentation faults, bad input and bugs raise exceptions.

Data scientists have several good reasons to like Python, including:

  • Simplicity: Python is easy to learn and use, letting data scientists focus on their work rather than wrestling with arcane code.
  • Productivity: A Swiss study of programming languages found Python to be among the most productive languages around.
  • Readability: Python was explicitly designed to be both terse and readable.
  • Support: There are huge support libraries and third-party packages available.

What is R?

R is an open source programming language and software environment with a focus on statistical computing and numerical analysis. It is procedural as opposed to object-oriented.

What sets R apart is its wide variety of statistical and graphic techniques, including clustering, time-series analysis, linear and nonlinear modeling, classical statistical tests, and classification.

It supports matrix arithmetic, with packages that collect R functions into one place.

In addition, users find creating polished plots with scientific symbols and formulae easy with R.

R has a lot to offer data scientists, specifically:

  • Granularity: R offers deep insights into large data sets.
  • Flexibility: There are numerous ways to accomplish a specific goal with R.
  • Visualization: R features superior data visualization tools that help make data approachable by scientists and non-scientists alike.

Common Criticisms on Both Sides

There are drawbacks to each language. Python isn’t the best tool for mobile applications, for example. That doesn’t impact its use for data science much, but there are other considerations.

Python suffers from the slower nature of interpreted languages, which must be executed through an interpreter instead of a compiler.

Also, like all dynamically-typed languages it requires more testing to avoid runtime errors.

Some data scientists have criticized Python’s weak database layer for making it hard to interact with complex legacy data.

The language can be weak with multiprocessor or multicore workings. There’s also the fact that data analysis functions have to be added through packages.

R has its own critics. Many of them relate to complexity. R is harder to learn, and its syntax isn’t as clean as Python’s.

It can’t be embedded in a web browser. R does have more statistical analysis tools than Python, but otherwise there are many fewer libraries.

At scale R’s complexity only grows. Maintenance becomes difficult, and poor memory management causes it to slow down when too many variables are stored.

It’s sometimes slower than Python, though neither is known for speed.

Finally, R is considered less secure than Python.

The risk can be mitigated using container options on Amazon Web Services (AWS) and similar, but developers need to pay special attention to this potential weakness to avoid costly breaches.

Which to Choose and When

Using both languages will give the best results, but that isn’t always practical or even sensible.

To choose the right programming language, data scientists should consider their primary interests and purpose.

R has superior data visualization. It was specifically built with statistics and data analysis in mind. Users have created packages that cover an impressive amount of specialized statistical work.

There are more packages available for tasks like machine learning and analysis. In contrast, Python has limited package options.

Python is a general purposes programming language, so it’s more robust than R. It excels at building analytics tools and services: automating data mining, data munging, and scraping websites.

R has packages for machine learning, but in general Python better supports machine learning and deep neural networks.

So which language should data scientists use? When the task ahead is mainly mathematical and leans heavily towards statistics, use R.

When the task is engineering-heavy or involves experimenting with new methods, use Python.

There is a generous overlap between languages, but following this guideline will steer data scientists in the right direction nine times out of ten.

Are you having trouble interpreting results from your data science software? Does your company have trouble reconciling data from one program with another? Concepta’s developers can build a custom dashboard to put your data where it can do the most good. Schedule a free consultation today!

Request a Consultation

What’s the Difference Between a Data Scientist and a Data Engineer?

data-scients-data-engineer

As technology advances, areas that were once covered by the same position have become more specialized.

Nowhere is that more distinct than the field of Data Science.

With so many evolving disciplines covered by that one umbrella term, it can be hard for executives to distinguish the exact type of specialist to use for a particular project.

To confuse the matter, job titles in Data Science are often very close to each other while having nearly opposite areas of interest.

Take data scientists and data engineers, for example. The disciplines are commonly confused for each other.

While each could probably do some part of the other’s job, however, their primary functions address different segments of the Data Science process.

What is a Data Scientist

Data scientists focus on analysis. They collect and clean data.

Once it’s ready for use they interpret it, drawing meaning from the data to address practical business problems.

While data scientists need to have a solid grounding in statistics and computer programming, they should be familiar with business science, too.

It’s their job to find real-world value within data. To do that they that need to identify business challenges and decide which specific data-analytics solution is best suited to provide answers.

Data scientists are also responsible for visualization methods that bring data to the average team member.

Not everyone is versed in technical jargon, but visual representations let anyone with understanding of the business interpret data through dynamic models.

Some typical responsibilities a data scientist might have include:

There are a number of tools they might use to accomplish these tasks. Statistics programs like SPSS, MatLab, and SaS are common.

As far as programming languages they might prefer R, C++, or Python (Python is popular).

Data scientists with a focus on predictive analytics and machine learning are likely to be familiar with RapidMiner.

What is a Data Engineer

While data scientists are concerned with preparing and interpreting data, data engineers have a material focus: architecture.

They’re in charge of the “data pipeline” that feeds other disciplines.

Data scientists design and build systems that accept, store, share, manipulate, and maintain data.

What exactly does that entail? Data engineers are generally responsible for:

  • Databases
  • Data warehousing
  • ETL (Extract, Transform and Load)
  • Collecting and managing data
  • Large scale processing systems

Some data engineering software, like Hadoop, overlap with the typical data scientist toolkit.

Data engineers use MySQL and NoSQL database tools. Warehousing software such as Hive and database management systems (DBMS) like Oracle are fairly well-established tools as well.

The programming languages used in data engineering are usually Java, Javascript, Unix, Linux, and SQL.

Finding Common Ground

Data engineers build, optimize, and maintain the tools data scientists use to explore and interpret data.

In other words, engineers supply the scientists with data and keep it under control while scientists turn the data into business solutions. The two fields work in tandem.

There is a skill overlap, but since nearly everyone specializes it would be unreasonable to expect them to do each other’s jobs.

Finding one person who can oversee the data architecture while simultaneously doing regular data science duties is a Herculean task.

The combination is so rare that HR managers jokingly call data scientists who also do data engineering “unicorns”.

Taking a Practical View

Instead of trying to navigate the subtle nuances of data science titles, many companies sidestep the issue by outsourcing their data science needs.

There’s also a growing trend towards self-service analytics, where analytics tools built into enterprise apps or other internal software let executives handle their own data.

 

What data science skills does your company lack? Concepta’s developers can help fill the gap with the latest data science and business intelligence tools. Schedule your complimentary consultation to find out more!

Request a Consultation
Download FREE AI White Paper

How to Use Predictive Analytics to Forecast Sales Staff Commissions

forecast sales staff commissions

Predictive analytics is increasingly accepted as a way of improving the customer experience or optimizing supply lines, but it’s underutilized in one area: forecasting labor costs.

That goes double for sales staff that work on commission.

Managers need to be able to predict their commission expenses, but the qualities that make sales staff good at selling make them bad at predicting which deals will close.

Their optimism is a problem for CFOs trying to forecast expenses.

Enter predictive analytics, the voice of reason that brings hazy forecasts back in line with reality.

Executives can use tools already at work in other areas of the company to better prepare for the future. How?

To understand, start with the specific difficulties of predicting commissions and then see what a predictive analysis does differently.

Commissions as an accounting problem

Accounting for commissions is one of a CFO’s biggest headaches.

Although commissions aren’t paid until a sale is made, best practices require that they be included when the cost is incurred to track profitability.

There’s a surprising amount of detail involved in forecasting commission expenses.

It involves predicting not only sales but also which agents will close which sales. Most companies have a variety of pay structures to account for based on who made a sale and when; a small mistake could have a large impact on the overall budget.

Choosing an estimation model isn’t easy, though. There are a few common approaches:

  • Use the first months of a year to create a fixed monthly estimate for rest of the year. This method is easy to use but not very accurate. Fixed monthly estimates don’t account for season, labor fluctuations, product changes, and other factors.
  • Use the previous year’s monthly commissions as monthly estimates. Previous-year totals are as easy to manage as fixed monthly estimates. They’re also more accurate since they reflect seasonal influences and company-specific trends. What they miss are allowances for outside influences (market fluctuations, new competitors, supply problems) or internal change (new staff, commission structure changes, mergers).
  • Rely on sales staff predictions to project expenses. Good salespeople are often bad forecasters. 54% of deals predicted by sales staff never close because agents tend to be unwilling to admit defeat on a sale. In addition, staff paid through commissions have little incentive to accurately forecast since doing so takes up time they could be selling.

Getting answers with Predictive Analytics

Predictive analytics offer greater accuracy than traditional models.

The process begins with feeding a machine learning algorithm reams of data on customers, market fluctuations, sales staff activity, and more.

The algorithm looks for patterns and relationships between factors that may impact performance.

It then uses those conclusions to produce a tailored month-by-month prediction of commission expenses.

These predictive estimates are a game-changer.

Companies who implement data-driven forecasting have a 82% accuracy rate on a deal by deal basis versus a 46% rate for those using other methods.

In aggregate their accuracy rises to 95%, nearly 20% higher than the industry average.

Using predictive in this manner is highly efficient.

Much of the needed data is also beneficial elsewhere in the organization. For example:

  • Sales numbers help project revenue.
  • Staff performance data informs human resources processes.
  • Market factors are useful in optimizing the supply chain and spotting opportunities.

Bring the whole team on board

Predictive models don’t replace the sales staff in forecasting, but they do provide incentives for participation.

When the data they submit is accurate salespeople are rewarded with results that identify which clients are most useful, where their time can be spent most profitably, and what commissions they can expect throughout the year.

That promotes large-scale support of predictive methods within the company.

Consistent internal adoption increases the ROI on technology investments.

In short, extending predictive analytics into the accounting realm can positively affect overall profitability and performance.

Savvy CFOs should investigate how their processes might be improved by embracing predictive analytics.

An intuitive, easy-to-navigate interface makes predictive analytics accessible to everyone, not just the CIO and IT. Contact Concepta to learn about our custom analytics dashboards!

Request a Consultation

Download FREE AI White Paper

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!

Request a Consultation

Download FREE AI White Paper