Customer Experience Metrics

How can you measure the effectiveness of your customer experience? What impact does it have on profitability, loyalty and purchasing activity?  Companies are increasing relying on their customer experience to differentiate themselves in increasingly competitive markets. In 2008, 64% of companies listed Customer Experience as having a critical role in their strategy (Forrester Research 2008).
Creating a smart customer experience can be hard enough.  Measuring your customers’s experience can be even more challenging.  Customer Experience Metrics have to go beyond traditional call center metrics to capture customer loyalty, satisfaction.  They have to measure intangible concepts, complex interactions, over large amounts of time.  Need help?
Examples of Customer Experience Metrics Include: (more…)

Rethinking Business Intelligence Software

People don’t care about business intelligence software, they care about what it can do for them.  CAN is built on this idea.  Instead of focusing on business intelligence software, we are focused on providing answers directly to our clients.  We are improving this process by launching the CAN Portal.  The Portal is how we work with our clients.  It will allow you to get better answers faster and more securely.
What are your objectives?
(more…)

How CAN Takes a Different Approach

At Contemporary Analysis (CAN), we take a completely new approach to helping companies and organizations get more out of the information they have access to. At our core is the idea that businesses should be working smart and hard.  At CAN, we are different because we always keep the human element, actionable impact, and added value at the forefront of our development process.

HUMAN ELEMENT:
We start the entire process with keeping the human element in mind. Everyone has gone through the frustrating process of being passed off from one person (if you’re lucky enough to reach a real human) to the next throughout a customer service or sales process. In most of these situations, half the time speaking with a new person is catching him or her up on things you have already said to other members of their organization.
At CAN, we understand the importance of having one contact throughout the entire process. This contact, known as a Navigator, takes the time to understand your specific business and helps you distill problems with big impact solutions. Navigators understand the majority of managers and executives don’t have the time to learn about predictive analytics. Navigators take the time and effort to understand the problem or issue from the end user’s point of view and then strategize to reverse engineer an efficient solution. It is our job to couple your expert knowledge and historical data to give you a solution with impact.
ACTIONABLE INSIGHTS:
At CAN, we understand businesses outsource services for added value. The value of using predictive analytics is only as great as the actions and changes made with the information provided. You could have a GPS system in your car, but if you never turn it on, it doesn’t do you any good. From step one in our customer process, we work on finding helpful insights into areas in which you can TAKE ACTION or MAKE CHANGE, not just look at the report and think “Hmm, that’s interesting.” If the information we provide doesn’t induce change on at least some level, we didn’t properly do our job.
EXTREME VALUE:
At CAN, the solutions we provide make sense financially. We use predictive analytics to answer questions in about 30 days. Think about that for a second. In just 30 days you could have an analytical model which, while not being perfect, will allow you to make much more informed decisions. Whether it’s having a better understanding of up-sell, cross-sell, or customer loyalty. It is important to remember, the goal of predictive analytics is to be LESS WRONG, and models continually become less wrong by using current information to test and re-test.
Compare 30 days with CAN to the alternatives – doing nothing or creating an in-house predictive analytics department. In smaller companies, the alternative to CAN is to do nothing. Smaller companies don’t have the resources to create in-house predictive analytics, but have a lot of the same issues as large companies.
The other alternative is in-house analytics. I believe in-house predictive analytics departments are something every large company should invest in. Properly managed and financed in-house departments can change organizations in ways never thought possible, in ways which only the future will show us. No longer would justifications for decisions be based on gut instinct, or worse yet, “because that’s how we’ve always done it.” This business is your passion, and nothing proves a point faster than quantitative justification. However, what if you’re not a large company?
In-house departments require management, direction, and resources. A company looking to develop the smallest possible in-house predictive analytics department will pay for the following:

  • Find and hire a highly sought after Ph.D. or Masters at a cost of 75-150k.
    • Add half the salary again for employee benefits, taxes (FIFA, FICA, etc.), office space, physical equipment, and HR resources.
  • Purchase a Tableau, SAS, or SPSS license costing 10’s of thousands of dollars for just one year

This cost of 175-250k is just the initial investment. You haven’t solved one problem yet. Taking into account the months of required training and corporate acclimation before any useful insights can be made by this new hire, it can be 12 to 18 months before you have a solution to just one of your smallest problems. 30 days and a cost of at least a zero less sounds much better to me.
Like I said before, I would encourage all large companies to take the plunge into predictive analytics. Even with well developed predictive analytics solutions there is a high possibility the department would suffer the same downfall as some IT departments, the disconnect between who designs or provides the technical knowledge and who actually uses it on a day to day basis.
At CAN, we take your expert knowledge and use your data to provide valuable insights you never thought possible. Throughout the entire process, we never lose track of the importance of the human element. Whether you’re a large company with an in-house analytics department or smaller business with no means for self analysis, we care about your business and can give you value in the form of information from which actions can be taken.

Why Visualizing Data is Important

Visualizing data can make something easier to understand and perhaps keep you awake.  Most students have learned that the cure for insomnia is take a difficult concept, like the philosophical concept of determinism, and explain it with words alone. In case you don’t know, Determinism is defined as “events within a given paradigm are bound by causality in such a way that any state of an object or event is determined by prior states.” Asleep yet? But what if you explain that concept with a picture?
Data Visualization Determinism
Notice how the explanation become more interesting and relevant with a visual aid? I’m sure you had the same reaction as me. Even though I might not care what determinism means, the picture piqued my curiosity and drew me in to explore the topic than I would have otherwise. The illustration makes a hard concept easy to grasp. Download our eBook, “Dashboards: Take a closer look at your data”.
Visualizing a concept has an amazing affect on the human mind. (more…)

How to become a data scientist

“How to become a data scientist?” is an interesting question, because there’s no real formal training as of yet to become one. Some universities are combining mathematics, computer science, and humanities classes together, but nothing formal has been decided in terms of a major or full concentration of study. Berkeley, Stanford, and the other greats have classes related to data science, but most classes are nestled within existing information technology or math departments. This is perhaps due to the idea that the position still isn’t properly defined, and “data scientist” is usually a catch all term for people with a variety of skills – some that even tend to conflict with each other. Most hard math or science majors are 1+1=2, end of story. Humanities tends to look at the world more abstractly and realize that there is leeway and not everything adds up. Data science requires much from both of these.
The requirements of people with these skills are also somewhat across the board, with specializations reaching from simple large scale data management and storage, to those who can apply analytics and machine learning or artificial intelligence to make predictions of the future or better apply recommendations to consumers ála Netflix, Amazon, Facebook, and Google.
Nonetheless, there are a specific set of skills you can work to develop and fields of study you can dabble in if you’re interested in working with data. While still somewhat vague, the ultimate purpose of today’s data science is to manage, make sense of, and ask questions of data sets.
Statistics
Applied data science is all about measurement, so work on increasing your statistical chops. In addition to being a general good life skill (probability and common statistics can be used in the media to manipulate human behavior or use to fear monger those into believing false or loosely defined relationships. Knowing even elementary statistics helps you spot bad science.)
Computer Science
Depending on the type of data science you’re into (management vs analytics, for example) a good understanding of computers is a strong skill to develop. Even if you’re interested in only mathematical applications, elementary programming classes can familiarize you with a certain logic and problem solving mindset useful in this space. Being familiar with database languages like MySQL, and the statistical language R, and even web technologies like HTML and PHP can help you write applications to gather data and make life much easier.
Economics / Biology / Bio – Informatics / Physics …
I’ve got a soft spot for my own field of study, economics. But any simple or complex science in which you model reality and try to describe it is useful for data science. Economics itself is the study of efficient allocation of limited resources, so many economic models are built to use data to describe processes and how firms and consumers interact, among many other things. Physics and Biology are also concerned with modeling their “ecosystem” and finding relationships between all of its actors. Being fascinated with how changing inputs changes the outputs is a good mindset to have, all while being able to approach it with a scientific method style of hypothesis testing.
Beyond University, there are a multitude of resources out there for learning how to play with data. MIT OpenCourseWare has a lot of free courses, many dealing with computer science, math, and other sciences. LinkedIn has lots of groups devoted to those who work in data. Try connecting with those people.
 

6 Questions Salespeople Need to Ask Themselves

If you are a salesperson, you spend your days asking other people questions. However, there are 6 questions salespeople need to ask themselves. These questions will help you sell to people that are ready to purchase, sell from a position of power, and improve your client relationships. (more…)

Predictive Analytics in Retail

Data is no good if you can’t get it quickly enough and act quickly enough on it. Its all about getting the data fast and acting on it fast
– Andrew Pole
Predictive analytics solves many differnt problems in a many facets of business. The New York Times published an article in February about the retail chain, Target. Target knew that if they could predict buying patterns in a certain group of consumers, they could influence those consumers purchases. Target was smart, they knew exactly who they wanted to go after.
Andrew Pole, a statistician working for Target, created a pregnancy-prediction model that was able to track spending habits and predict when a woman is pregnant. Pole said that, “We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years. As soon as we get them buying diapers from us, they’re going to start buying everything else too. If you’re rushing through the store, looking for bottles, and you pass orange juice, you’ll grab a carton. Oh, and there’s that new DVD I want. Soon, you’ll be buying cereal and paper towels from us, and keep coming back.” (more…)

Missiles, the NBA and Predictive Analytics

Ask any Cubs fan. Sometimes life just isn’t fair. Conventional wisdom in sports says that the teams with money to buy talent win more games. For example, The Yankees, they will always have the best talent money can buy. In basketball it’s no different. The teams stacked with expensive talent always seem to have the advantage. Well, they use to anyway.
A few innovative NBA teams asked themselves a question: How can we win more games against teams that have more money? The answer: predictive analytics and missile tracking technology. Yep, you heard me right, math and missiles. (more…)

What does a CEO do?

I overheard someone the other day telling their friend that there was no way their CEO deserved a million dollars a year.  “What does our CEO even do anyway, ” she said?  “I wish I could come in late, play golf all day, and have no responsibilities.  I would do the job for $500,000 and do it better than him…”
I wish I could say I turned and scolded her about how her CEO probably was at a networking event while she was with her family, works most weekends, including holidays, and never shuts off the pressure of running a business, but truth be known, I didn’t know if that was the answer. (more…)

Why We Outsource

Companies tend to outsource things that they don’t consider important or a competitive advantage.  Contemporary Analysis takes a different approach to outsourcing. We develop close partnerships with great firms, and use outsourcing as a way to develop competitive advantages, streamline their management structure, and create an environment of innovation.  This is important because of how companies are structured at their core.  The book, Innovator’s Solution explains that:
“Rarely does an idea for a new-growth business emerge fullyformed from an innovative employee’s head.  No matter how well articulated a concept or insight might be, it must be shaped and modified, often significantly, as it gets fleshed out into a business plan that can win funding from the corporation…Midlevel managers play a crucial role in every company’s innovation process.  They decide which of the ideas that come bubbling in they will support and carry to upper management for approval, and which ideas they will simply allow to languish.” (more…)

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