Tips & Tricks

26 Data Analysis Experts Reveal the #1 Business Problem that Can Be Solved with Predictive Analytics Tools and Software

Many successful businesses today, whether they are online-based, brick and mortar or a combination of the two, utilize some sort of business data management process to support important business functions such as storing customer information, tracking marketing and sales activity, and managing overall finances.

But beyond that, many businesses don’t do anything with all of the data that they collect and store. With predictive analytics, however, a company can meaningfully leverage that business data to diagnose and solve business problems.

Since NGDATA specializes in helping companies harness the power of data analysis to improve business success, we wanted to learn more about specific applications for predictive analytics. We specifically wanted to discover tips from predictive analytics experts on what specific ways predictive analytics tools and software solutions can effectively solve common business issues.

To do this, we asked 26 predictive analytics experts to answer this question:

“What’s the #1 business problem companies can solve with the help of predictive analytics?”

We’ve collected and compiled their expert advice into this comprehensive guide on how to use predictive analytics tools to benefit your business. See what our experts said below:

Meet Our Panel of Data Analytics Experts:



Stephen TimmsStephen Timms

Stephen Timms is President of ClickSoftware, and has been with ClickSoftware for 9 years, rising to President of the Americas region in 2014. In his time at ClickSoftware he has led several teams through significant growth of customers, employees and revenue. He started his career teaching Networking and DataComms to post-grad students, and combines deep technical expertise to his vast sales skills and strong leadership style. He graduated Harvard Business School Executive Education – General Manager Program, in 2013.

Predictive capabilities let business take service to the next level and deliver a superior customer experience, pure and simple. Here’s how:

  • Planners can predict what next week’s (or next months’) schedule will look like, and get ahead of any potential gaps in coverage or capacity.
  • Predictive-powered appointment booking allows call center reps to provide customers with options for appointments that have a higher probability of success.
  • Schedulers and dispatchers can better focus on high risk tasks, consider predicted fluctuation in traffic during the day, and predict task duration based on measured individual efficiency.
  • Field resources can detect a certain physical contexts, analyze situations, and prompt a user to notify a customer when he is 15 minutes away from arriving to the service appointment.

For example, when an HVAC technician begins his week, predictive can compare the jobs for that day, and the next few days, to the parts available on his truck. If he needs three compressors and only has one, the app alerts him to the missing parts. The app will also know that a specific job type requires a specific part, and will prompt the tech to bring that part into the building. If a mobile worker has the right tools in hand every time when leaving the truck—that 10 minutes saved on each job is exponential, and easily equates to an extra job per day.

What we’re talking about is a new generation of empowered field workers. Where data-driven decision making had been reserved to the back office for back office users only, predictive brings it to the user on the front end. When organizations harness predictive analytics as a way that’s both cost effective for the company, and not overly invasive for either the customer or employee, the customer rises to the top like never before.

Dmitri WilliamsDmitri Williams

Dmitri Williams (PhD, University of Michigan) is the CEO, Sensei, and Co-Founder of Ninja Metrics, Inc. Dmitri is a 15-year veteran of games and community research, and a world-recognized leader in the science of online metrics and analysis. The author of more than 40 peer-reviewed articles on gamer psychology and large-scale data analysis, Dmitri‘s work has been featured on CNN, Fox, the Economist, the New York Times, and most major news outlets. He has testified as an expert on video games and gamers before the U.S. Senate, and is a regular speaker at industry and academic conferences.

The #1 business problem companies can solve with the help of predictive analytics tools is…

Predictive analytics can give a developer or marketer unprecedented data to help them understand more about users, including Social Value.

We all know that people influence each other. If your family or friends do something, your chances of doing it go up–whether it’s playing a game, eating tofu, or smoking. This kind of influence can not only be predicted, it can be measured. And if that extra playing, or spending, or eating leads to dollars, that’s a value you can capture and impact. We call this “social value™” and it is responsible for between 20% and 50% of all spending.

If you have social value metrics, you have three ways to take advantage of them. First, you try to acquire more players like them. You can use our funnel analytics to know how valuable players from it really were. Second, you can leverage your influencers with promotions and see which cause more spending among their friends. Third, you know who to focus your retention efforts on. Maybe that player doesn’t seem important because they don’t spend a lot, but if they go, you’ll lose $300 from their friends. Social value analytics will go a step further and tell you which mechanics or experiences are leading to higher or lower values.


Maria CasuMaria Casu

Maria Casu is the Head of Marketing at Portal, a business and technology consultancy.

Depending on the business model the company has adopted, the chief (and most valuable) use of predictive analytics is to…

Identify individuals who have the highest propensity to buy, or alternatively, to identify those existing customers who have the highest propensity to leave.

This is largely being driven by today’s “empowered customers” who expect to have a consistent experience across the brands they are doing business with.  Instant visibility of customer sentiment via social media coupled with unlimited access to alternative products and suppliers has resulted in a power flip between buyer and seller. Raised expectations about product, price, delivery and service have drastically shrunk the window of time for merchants and service providers to respond to customer interactions and convert to orders.

Traditionally, getting close to customers was seen as a priority for sales and marketing alone, but senior executives are starting to understand that the customer must be front and centre of every employee in the organisation. Understanding the customer is now everybody’s job – from R&D and manufacturing right through to point-of-sale and all post-sale service teams, including accounts, renewals, field service, and so on.

As a result, predictive analytics is being used to uncover hidden insights in customer data to create much more personalised experiences, whilst also enabling the business to operate more effectively by:

  • Identifying hidden patterns and predictive models to target campaigns, promotions and offers;
  • Using churn models to predict which customers are at risk of leaving and identify any clues about why;
  • Measuring the likely lifetime value of customers to improve sales forecasting and profitability; and
  • Ascertaining customer sentiment to spot emerging trends.

In this way, predictive customer intelligence can yield impressive results for businesses which are committed to growing, retaining and satisfying customers by improving their interactions and business strategy across multiple channels.

Will HayesWill Hayes

Will Hayes is the CEO of enterprise search company Lucidworks. In his leadership role, Will Hayes brings search, discovery and data analytics solutions to companies all over the world. Lucidworks is solving the problems presented by ‘Big Data’ with tools for the enterprise built on open source Solr technology. Hayes has 15 years of product, marketing and business development experience, most recently as the head of technical business development for Splunk, an industry leader in enterprise data analytics.

The #1 business problem companies can solve with the help of predictive analytics tools is…

Making sense of large amounts of unused business data.

With the diminishing cost of cloud storage, companies are amassing more data than ever before. While simple data collection is fine, the reality is that most companies only use about 1 percent of their stored information to translate into actual business decisions.

Why? It’s because they can’t find their data. The ability to search and retrieve information is the most important action for scaling your business and realizing the promise of big data. And that search technology needs to be fast, contextual and programmed to read natural language so it’s usable for employees at every level of an organization.

Dean MalmgrenDean Malmgren

Dean Malmgren is a Co-Founder and Data Scientist at Datascope Analytics, a data-driven consulting and design firm in Chicago, where he has helped clients like P&G, Daegis, and Thomson Reuters use data to solve the right problem. Dean received a BS from the University of Michigan and a PhD from Northwestern University, has spoken at conferences like Strata, and published peer-reviewed research in journals like Nature, Science, and PNAS, that have been featured in places like TIME, Wired, and US News & World Report.

The #1 business problem companies can solve with the help of predictive analytics is…

Inaccurate or misleading revenue forecasts and models.

Businesses frequently forecast revenues with extraordinarily rough models. These models are often the boilerplate year-over-year growth from a QuickBooks forecast or sometimes even built in Excel. Most importantly, these models almost always spit out a single point estimate for each month going forward. This is a huge mistake because, as every business owner knows, revenues can fluctuate wildly and we have great uncertainty about next month, let alone the next quarter or year.

Data science offers a unique solution to this challenge. By properly accounting for the particular mechanisms by which companies generate revenue and the uncertainty in sales, businesses can gain much more nuanced revenue forecasts that not only give you the best estimate revenue for the next month, quarter or year, but can also give transparency into the variability of the different revenue outcomes—the good and the bad.

Pratibha VuppuluriPratibha Vuppuluri

Pratibha Vuppuluri is the Founder & CEO at KeyInsite Inc., a data analytics company. She is former Investment Banker with over 10 years experience and has authored an academic publication on “The Impact of Negative Economic News Coverage on Consumer Confidence”. Pratibha hold a Masters degree from Columbia and has completed her undergraduate from Cornell University.

The #1 business advantage of predictive analytics is…

Lowering marketing promotion campaign costs and increasing the ROI from these campaigns.

The need for increased online brand awareness through an optimized digital marketing spend is a crucial factor to most companies’ success. To deliver on promotion $, all digital promotional campaigns should have pre-event targets and predictions to project and measure against. The 3 vital components to estimate promotion dollars are a) the projected exposure, b) the average value of a response, and c) the predicted response rate:

*Promotion $ = Exposure x Average Redemption Value x Redemption Rate*


Exposure is a factor of how many people, a campaign would reach via the channels (mobile, online, social…) on which the message is delivered. Based on the objective of the marketing effort, it is vital to predict the type of exposure a campaign would receive in channels selected.

Average Redemption Value (ARV)

Average Redemption Value is the net margin a campaign would deliver when a customer acts on it. The ARV is quite often a factor of the product category and the average ticket size of the customer transacting in the promotion. Patterns such as while a digital customer’s average propensity to spend is higher, he is more likely to ‘price shop’ and hence the promotions should be meaningful and value worthy is of essence.

Redemption Rate

Perhaps the most challenging of them all is to predict the redemption rates. Partly because channel open rates are highly volatile, susceptible to erratic consumer online behavior and subject to macro-factors. Using ‘like-event’ modeling with statistical confidence interval approach provides a directional approach to estimate redemption rates of segments of the target audience.

To deliver on the promise of promotional ROI, all these factors can be predicted using historical data to give marketers an advantage in lowering promotion campaign costs and increasing the return from these campaigns.

David CohenDavid Cohen

David Cohen is the Director of Information at Shutterstock, a leading provider of digital imagery licensing, operating in more than 150 countries and 20 languages.

In general, when looking for problems to apply predictive analytics, I encourage my analysts to…

Focus on micro-decisions.

Too often, analysts get caught up in trying to solve relatively large problems with predictive analytics. Focusing on the small decisions that are made thousands of times tends to yield better results, in my experience. These include things like what checkbox to pre-check, what item they are most likely to buy, or what parts of the site a user is most likely use. While improving these can yield relatively small percentage gains, they happen so frequently that the total improvement can be high.

Chuck RussellChuck Russell

Chuck Russell is the CEO and Founder of BestWork DATA.

The number one business problem that can be solved with the right data is…

To reduce turnover by half easily. This is done by using the essential data of a person’s cognitive abilities and hard wired personality traits matching their strengths with the critical criteria of the job.

Jeff CatlinJeff Catlin

Jeff Catlin serves as the CEO & Managing Director at Lexalytics Inc. with over 15 years of experience in the field of search, classification and text analytics products and services. His roots go back to the earliest days of search on the internet, where he worked on the development and scaling of the Infoseek search platform.

The #1 business problem companies can solve with the help of predictive analytics tools is…

Understanding what their customer actually wants.

A lot of companies don’t know what their customers want. A great example is Kodak failing to see that their customers were interested in digital cameras.

A good predictive analytics system can analyze unstructured data (such as customers voicing their opinions & experiences through social media) and match the results with other sources of structured data (such as geographic and demographic information) to accurately forecast a customer’s needs & behaviour.

If Kodak had used a predictive analytics tool that could analyze customer feedback, they would have known what their customers actually wanted instead of guessing incorrectly and eventually declaring bankruptcy.

Nathan GnanasambandamNathan Gnanasambandam

Nathan Gnanasambandam, Ph.D is a Xerox Senior Research Scientist at PARC’s Big Data Analytics lab. His expertise includes experience in quantitative modeling including behavioral and contextual profiling, risk modeling, text and graph mining, and big data analytics. His current interests are in social and healthcare analytics.

Customer service is a top priority as well as a tough business challenge for every business. Data analytics is a key tool that can be used to not only provide better customer service – but also deepen customer relationships.  Predictive analytics can pay a major role in the customer experience by…

Decreasing – and in many cases – eliminating customer issues before they even occur.

Imagine a customer getting a call from a company with a fix for a product recall before they even experienced and issue. By synthesizing data from a number of sources predictive analytics can be used to be proactive, helping see what is around the corner and addressing it before a customer is even aware.

For example, Xerox has been applying predictive analytics in the healthcare industry to predict possible reasons for future doctor or hospital visits potentially leading to early diagnosis, treatment and potentially saving lives.  Early pilots show that we can use predictive analytics to predict issues or questions patients will most likely have and proactively provide the information in a personalized fashion before the patient even has the issue.

We are also seeing promising results using predictive analytics in customer care centers.  Early indications from pilot studies show that customer care agents can provide better service because we applied predictive analytics to build an evidence backed recommender system that helps the agent make proactive suggestions during a customer service call.

Predictive analytics can help any company transform mountains of data into valuable insights that deepen customer relationships.  I don’t need analytics to predict that when a customer is happy – your business will succeed.

Tesla MartinezTesla Martinez

Tesla Martinez is the Director of International New Business Development for FOCUS Brands. FOCUS is the franchisor and operator of over 4,500 ice cream shops, bakeries, restaurants and cafes in the US and 63 foreign countries under the brand names Carvel®, Cinnabon®, Schlotzsky’s®, Moe’s Southwest Grill®, Auntie Anne’s®Pretzels, and McAlister’s Deli.®  Here insights are featured in, The New York Post, The Today Show’s Life, Inc among others. Tesla’s global strategy relies on predictive analytics to effectively monitor global challenges and opportunities.

New regulations and channels of distribution are changing the way companies do business in the global economy. Companies need to maintain awareness; be proactive and not reactive to these changes. Predictive Analytics can help organizations:

  1. Stay relevant to their consumer
  2. Provide insight into trends impacting the industry
  3. Warn of macroeconomic impacts which may effecting local operations

Charles SettlesCharles “Drew” Settles

Charles “Drew” Settles is a Product Analyst at TechnologyAdvice, a firm dedicated to educating, advising, and connecting buyers and sellers of business technology.

As I’m sure you well know, predictive analytics tools can provide guidance for a myriad of business uses, but the number one area of opportunity (in my humble opinion) surrounds….

Healthcare – specifically population health modeling.

One great example is Google’s ability to predict the spread of the influenza virus using only search terms. They’ve been able to more quickly and accurately predict the spread of the virus than either the CDC or WHO’s antiquated testing and modeling methods. As more and more data is collected, (particularly with personal fitness trackers like FitBit, Jawbone, etc.) better, more accurate models of individual and population health will be developed. Imagine a cardiologist using predictive analytics to determine the individual risk and likelihood of a heart attack!

David ScarolaDavid Scarola

David Scarola is the Vice President of The Alternative Board, where he is responsible for executive oversight of Information Technology, Member Management, and Marketing Operations. In his position, Dave seeks ways to fuse technology with process to build TAB’s brand awareness and community of small business owners.

The #1 business problem companies can solve with the help of predictive analytics is…

Tyranny of the urgent.

Too many business owners and executives spend virtually all of their time focusing on things that are urgent. They do not consider Covey’s advice of spending time on activities that are both urgent and important. The root cause is that most businesses, especially smaller ones, are not operating off of a strategic plan. While urgent tasks will also exist in any business, operating from a strategic plan will ensure that at least some time is allocated to the critical success factors for a business.

Jenna PuckettJenna Puckett

Jenna Puckett is a Junior Technology Analyst at TechnologyAdvice, a Nashville based company that connects buyers and sellers of business technology through meaningful relationships.

The biggest business problem that predictive analytics tools could help solve is…

Eliminating the unknown.

Uncertainty is a huge problem facing business leaders. Whether it’s over their workforce, customer retention, or products– uncertainty has a way of causing decision paralysis. Predictive analytics is not prophecy nor panacea, but it does encourage agility by enabling companies to make decisions on insight, not hindsight. Swift decisions based on data will solve a lot of business uncertainty. The faster you can gain insight and take action, the faster you will learn, innovate, and pull ahead of the competition. Companies that move to the top won’t be the ones discovering they lost a customer after he left. It’ll be the ones building customer profiles, identifying exit triggers, and offering the kind of personalized marketing that gets him to stay.

Andrew ThomasAndrew Thomas

Andrew Thomas is Director of Business Development at AiR Healthcare Solutions, a behavioral healthcare solutions provider.

At AiR Healthcare Solutions, we’ve built a suite of cloud-based predictive analytics tools that allows our clinicians to provide dynamic management and supervision of patient care as well as utilization of resources. Some of the primary benefits of these tools are…

  • Our care coordination technology streamlines the reimbursement and utilization review process while assuring payers that patients will receive the best level of care at the best possible cost.
  • Our technology helps clinicians make determinations about patient status and appropriate care.
  • Our algorithms are based on years of experience in behavioral health care and provide situational awareness for each patient, so each data point that is entered points the clinician to the optimal next step for the patient.

Ultimately, this helps take human error out of the process and drives continuous success in outcomes. That enables a lower cost initial treatment assignment but doesn’t subject patients to sub-optimal care for the sake of cost management.

Jared SiegelJared Siegel

Jared Siegel is a Partner at Delap LLP, one of Portland’s largest local accounting firms. Delap LLP provides tax, audit and financial consulting services to companies ranging widely in size and industry.

We’ve leveraged predictive analytics to…

Democratize innovation.

Businesses in every industry are facing some level of “disruption”. Consequently, business leaders are actively exploring innovative initiatives that end up being guesses and bits financial bets.

With predictive analytics, businesses can now iterate in a virtual environment father and more efficiently than actually building the business and learning from real world experiences.

April_WilsonApril Wilson

April Wilson is the President and CEO of Digital Analytics 101, a full service digital marketing agency. Her focus is empowering businesses of all sizes to implement best practices in digital marketing, social strategy, and analytics. She served on the Board of Directors for the Digital Analytics Association as VP 2006 to 2009. She teaches several seminars a year at institutions like SMU, Stanford, and local community colleges.

Considering how hard it is to win back lost customers, and how much cheaper it can be to keep customers instead of acquiring new ones, the best use of predictive analytics for businesses is…

To model out what causes attrition.

Why are customers leaving? If you can spot a customer at risk of leaving in 3 months, 6 months, 12 months – you have time to put strategies in place to show them how valuable they are to you. The first step in strong relationship-building with your customer base is to recognize them. Predictive analytics gives you clear visibility into how to recognize them.

Ted ZollingerTed Zollinger

Ted Zollinger is Senior Vice President of Marketing and Product Management at AccuData Integrated Marketing. Ted specializes in using modeling and predictive analytics to drive campaigns for both AccuData and its clients. He provides long term strategic leadership and direction for marketing efforts as well as the development and definition of product offerings.

The #1 business problem companies can solve with the help of predictive analytics tools is…

Understanding your business’ ideal customer.

Predictive analytics are really a blend of the art and science of understanding who your ideal customer is, and why they interact with your brand in the ways that they do.

Most companies have a somewhat arbitrary image of who their best customers are, and predictive analytics is the key to either substantiate or rebuke this idea. Once you have clarity on who you’re trying to connect with, analytics can be used to craft a marketing plan around exactly who to market to, how to speak with them, and how to time your messages.

Sandip DevarkondaSandip Devarkonda

Sandip Devarkonda is Product Manager at Vizury, a firm that helps marketers secure customers for life through personalized marketing solutions.

Predictive analytics is an important part of an organization’s arsenal that enables them to…

Quell uncertainty.

It is significantly more important to those organizations that have difficulty in adapting to inconsistency in business factors be it quality or quantity and would welcome any additional agility.

Simply put, in many cases the cost of unpreparedness or sub-optimal decisions is too high (cost of unused inventory, unavoidable lead times etc.). It is imperative for such companies to be as nimble-footed as possible and it is in this challenge of maintaining operational efficiency where predictive analytics has the most crucial role to play.

Ryan NaudéRyan Naudé

Ryan Naudé is the Manager of Data Solutions at Entelect Software, a South African software engineering firm.

Below is one of the main problems I see business can solve with predictive analytics…

For the past 20 odd years businesses have been extremely focused on getting a single view of the customer, and then predicting how to upsell/cross sell to these customers. They have been predictive sales trends, click through rates and conversion rates.

Whilst these measure warrant predictive analytics I think one of the biggest issues businesses face at the moment is retaining top talent. I think predictive analytics is in the process of changing the way businesses manage their human capital. We have now got a whole lot of historical data on employees that we’re in a position to start predicting an employees’ sentiment, predicting sick leave, and most importantly predicting if an employee is going to resign.

With these predictions we can now put measures in place to pro-actively manage our staff turnover, giving business an opportunity to combat loosing top talent and key skills.

Ian AronovichIan Aronovich

Ian Aronovich is the President and Co-Founder of, a site that compiles and provides information about government auctions of seized and surplus merchandise from all over the country.

When it comes to valuable business uses for predictive analytics tools…

We use predictive analytics to effectively market to an audience who has already shown an interest in our service and is likely to convert into paying customers.

One area where predictive analytics is especially useful is in Pay-Per-Click marketing. Because PPC marketing has become increasingly expensive in recent years, it’s important to maximize your advertising dollars. By utilizing data about a users activity that has already visited our website, we are able to market to that user on a wider variety of keywords, that may not have been profitable, had we not had access to this data.

Elissa FinkElissa Fink

Elissa Fink is Chief Marketing Officer of Tableau Software and has over 20 years’ experience helping companies improve their marketing operations through applied data analysis.

Businesses have the opportunity to grow by aligning their operating model around the customer to drive an effective marketing strategy. A key step in designing such a model is to analyse and predict a customer’s future buying behaviour. Therefore, one of the key drivers for using predictive analytics in business is….

To better understand consumer behaviour, to predict future trends and demand with greater accuracy.

Whilst most marketers already know that data analytics can enable brands to understand customers and target campaigns more effectively, today businesses have access to more data than they can comfortably handle. What is less known is how best to go from the masses of data to actionable insights. One way to help any business in their marketing efforts, with or without a statistician, is to use data analytics tools to enable marketing departments to see and understand their data – no matter how big it is.

With data analytics tools, marketers can visualize trends and outliers, inform key insights and enable better decision making about the direction they want to take their brands. Marketers can actually see trends, even microtrends, over time, leading to greater ability to use analytics to predict what’s going to happen next – allowing marketers to stay ahead of the curve.

Indeed, using past data is a great basis for predicting what might happen in the future. For instance, if you are marketing ice cream and you know that you generally sell 50% more ice cream when the temperatures rise, then you know to watch the weather forecast and stock your shelves accordingly. Where predictive analytics don’t do so well are when there are unexpected events – say an especially cold summer – that you just can’t forecast.

Majid ShaalanMajid Shaalan

Majid Shaalan, Ph.D. is Associate Professor of Computer and Information Sciences & Analytics at Harrisburg University of Science and Technology in Pennsylvania.

According to statistics, the #1 business problem companies are investing in, between researching and adopting of predictive analytics software tools, is…

Marketing (specifically decision analysis and optimization in customer service, and product development). With cross-selling, campaign management, customer acquisition, attrition, and loyalty models come first, followed by budgeting and forecasting applications.

The main reason behind this trend is that predictive analytics is inductive, while all business intelligence (BI) techniques (query and reporting tools, online analytical processing (OLAP), dashboards, and etc) are deductive in nature.

This means that, BI tech users must initially have some sense of the patterns and relationships that exist within the data based on their personal experience derived from the business model they adopted. It is a de jure process; a process that is supervised by some desired outcomes (supervised inference)..

On the other hand, predictive analytics (as the core data mining computational component) works in the opposite way; it doesn’t presume anything about the data. Rather, it lets data lead the way. It presents users with a de facto set of hypotheses in the form of metrics and let them compare, contrast, and decide which hypothesis works better for them among all possible actionable choices, without any supervision of any initial assumptions (unsupervised inference).

Predictive analytics employs machine learning techniques to explore all the data, (instead of a narrow subset of it) to extract meaningful relationships and patterns. Furthermore, it enable making assumptions and suggesting some scenarios about future events to help making smart decisions about such events. This would fall in the category of risk modeling and management (forecasting), the biggest part of decision analysis and optimization.
Machine learning technologies use statistics, probabilistic inference, artificial neural network models, robotics, and computational mathematics, under the big umbrella of artificial intelligence (AI).

Only by means of machine learning, predictive analytics can be that intelligent agent or a robot that sift through all your data, finds something interesting, and show it you. Visualization is an embedded component of this scenario to present these findings in a very representative way that is easily understood by the decision makers even if they don’t have all these computational expertise.

Sean HigginsSean Higgins

Sean Higgins is the Co-Founder of ilos Videos, the video platform for sharing knowledge.

Predictive analytics can help me as a business leader understand…

How I can bring the most value to my customer.

Just mapping how my current customers use my product with a multiple linear regression model can help me segment my market and better understand my customer types. It can take companies years to figure out changing consumer behaviors or recognize a need to pivot.

Imagine if you worked at SnapCar, the European Uber competitor that just raised 2.5M, and you knew that there was a statistically significant difference between the growth rates in business transportation and consumer transportation. Looking at this and the competitive landscape you can make a big move like pivoting your entire business to the business transportation segment. That’s just a glimpse into the power of predictive analytics when it comes to understanding your customer.

Allyson KuperAllyson Kuper

Allyson Kuper is a Consultant at Bug Insights, a marketing and human capital analytics company that provides prescriptive analytics to help organizations make better business decisions. Allyson is an experienced consultant who has spent a number of years supporting large-scale organizational effectiveness and employee research projects for Fortune 500 companies.  She has significant experience in preference measurement, total rewards strategy, organizational effectives, and project management.

The #1 business problem companies can solve with the help of predictive analytics tools is…

Addressing turnover risk.

A company’s employees are one of its most important assets but are also one of the most expensive. Typically employee rewards are the second largest expense employers face behind cost of goods sold, and when the cost of turnover is factored into the equation, especially turnover of high potentials and high level individuals, these financial implications grow exponentially. Studies show that the impact of turnover for a single employee can cost organizations as much as 2x his or her annual salary – a figure that adds up quickly when considering the average turnover rate is expected to increase in the coming years.

Predictive analytics provides an opportunity for organizations to address this turnover risk in their employee populations. By understanding the characteristics of individuals who are high risk for turnover and measuring employee preferences around total rewards, organizations can leverage predictive analytics to not only identify flight risks, but also identify intervention strategies at the individual level to potentially keep flight risks from exiting – likely increasing employee engagement and ultimately reducing the cost of turnover to an organization.

Michael WeissMichael Weiss

Michael Weiss is the Managing Partner of C-4 Analytics, LLC, a digital marketing agency. An expert in current industry trends, Michael oversees the strategic evolution of C-4 Analytics by creating a comprehensive team environment. Michael brings over 12 years of experience in interactive online marketing, public relations and advertising to the company. This experience allows him to implement strategies across all market sectors and be accountable for the return on investment (ROI) that C-4 Analytics clients are looking for. He holds a bachelor’s degree in computer science from Lehigh University, and his technology-neutral vision has enabled Michael to lead successful innovation at enterprise-level companies.

The #1 business problem companies can solve with the help of predictive analytics is simple and that is…

Having the right inventory.

I run a digital marketing and analytics agency based in the Boston area. We’ve been in business since 2009, and we are recipients of the Inc 500 and the Deloitte Fast 500 awards (among many others). We attribute a lot of our success to predictive analytics. Let me explain with an example.

A number of our clients are auto dealers. We know December is going to be a good month for commercial truck purchases based on the data we have collected from hundreds of dealers over a number of years. There are tax incentives for purchasing a commercial vehicle before the end of the year. We see the commercial vehicle sales spike in every November and December. So, we look at a number of things – from search trends of previous years and the current year to specific activity on a dealer website. We analyze
all of the data together.

We are not only able to predict the demand for these commercial vehicles well in advance, but we are able to zero in on the specific models that the consumers will be interested in with some deep analysis of the data.

So, a few months in advance, based on the previous year’s data tied in with search demand on this year’s models, we are able to tell our clients what vehicles to order, because we know, based on predictive analytics, what consumers will be purchasing. And when our clients have the right inventory to sell, they are able to increase their sales substantially based on data we all already know and have.

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