Is Your Business Ready for Big Data?

Big Data could mean Big Bucks for the companies that know how to gather it and use it. However, since the discovery that big data can help drive up revenue the number of organizations that actually use it are few and far between.

What is Big Data?

As our world becomes more technology driven, large companies have been at the forefront of implementing new technologies and seen data management become an important part of business. High definition video, Tweets, Facebook, tags, SMS and intelligent chips do more than process information and accelerate communication, they also leave a digital trail. This trail, when analyzed properly, can be used to track what consumers consider important.

What Do You Do With Big Data?

This is the key question that many businesses are struggling with. But fear not, the business world has given us the Data Scientist. This new specialist position is not merely an IT guru or analyst, but leaders who forge a collaboration of sorts that combines the creativity of marketing aspects with the science of the numbers to turn out actionable data to drive new marketing strategies.

Why Aren’t More Businesses Using Data Scientists?

Indicative of technology; the idea of Data Scientists has spread fast and created a demand. The number of Data Science experts is not nearly great enough to meet the demands. Mashable reported in the largest-ever global survey of the data science community that:

  • 63% of data science professionals believe demand for data science will outpace the supply of talent over the next five years.
  • Lack of training (32%) and resources (32%) were identified as the two biggest obstacles to Data Science adoption.
  • Only 1/3 of respondents report they are confident in their ability to make business decisions based on new data.
  • 38% of business intelligence analysts and data scientists strongly agree that their company uses data to learn more about customers.

Results suggest that Data Science, as a field is still in its infancy and elusive to many companies. Some of which are oblivious to the growing necessity for Data Scientists.

No Matter the size of your organization, the utilization of Data Scientists could drive a huge boost in your bottom line. Being able to turn your tracking information into actionable data that results in profits is quickly becoming the new frontier of business. Implementing the Data Scientist concept to help streamline your marketing plan will not only generate mere income but it will also change the structure of today’s businesses.

Customer Experience & Data Analytics Take Center Stage

Improving customer experience has been reported as one of the top priorities for businesses in 2012. Some companies are even adding a new C-level position to their rosters, Chief Customer Officer (CCO).

Data Analytics

A big driver of this focus on customer experience can be attributed to the gold mine of information gleaned from data analytics. There is so much information available about consumers’ likes and dislikes that companies can analyze the data to build a better experience.

While all this information is great, companies must be cautious in how they extract and use the data. If companies extract only the information which validates their decision making, the customer takes a backseat and all the progress from this data analytics will be lost.

Customer Needs

The bottom line is that the customer should always be the top priority for the business. Yes, businesses should be making solid, innovative products. However, if the customer isn’t interested in buying the product, then the product will have no audience. Before you consider launching a new product, you should listen to what your customers are saying and identify their needs.

Brand Experience

In addition to offering solutions to customer needs, it’s important to ensure the customer has a great experience with the brand. If the customer, again and again, has a bad experience in your store, or with your product, or with your sales force, you can bet that your product will have limited reach. The customer experience should be top-of-mind from production to manufacturing, and from distribution to sales & support-it is paramount in cementing a good relationship between the consumer and your brand.

Determining Demand with Statistical Demand Analysis

Today we’re going to do some thinking and learn about statistical demand analysis.  Why is statistical demand analysis important? Because estimating product demand is essential to reduce the risks inherent with pricing, production and inventory decisions. A failure to accurately estimate demand can lead to over or under production, and if priced incorrectly can negatively affect profits.

How is demand determined?

We’ll talk about two types of demand: Consumer demand and Market or Industry demand.

  • Data on consumer demand is usually obtained through consumer surveys by asking how much they would purchase at a given price point (predictive) or how much they have purchased in the past (retrospective).
  • Market Demand is looked at through a series of price changes over time and analyzing the subsequent change in purchasing activity.  Finally, and the point we will focus on, using naturally observed data on price and quantity and subjecting them to statistical demand estimation.

Statistical Demand Analysis using Liner Regression Model

Recall that regression analysis is a statistical technique for estimating the relationship between variables.  In this case, Demand as a result of price vs quantity sold, city, production, consumer demographics, etc.  Demand, being the dependent variable, is shown in relation to and linked to various parameters (aka assumptions) explaining the relationship.  This is shown by the following linear regression model:

Q = a + bP + cM + dPR +u
Q = quantity demanded (observed) and is the dependent variable
P,PR,M = price, price of related goods, income (observed) = independent variables.
u = random error (not observed)
a,b,c,d, = parameters – unknown and estimated.

Using regression analysis the analyst first obtains a set of survey data (regression data) for Q, P, Pr and M.  Next an estimation of a, b, c, and d is used.  These estimations are difficult and rely on experience, industry knowledge and internal data. When done, you test for significance of the parameter values and using the estimated values of the parameters for analysis, are able to predict the future value of the dependent variable, e.g. demand.

There are three types of regression data: Time series, Cross section and Panel data.  Time series is an observation made of different time periods while Cross section is for different observations at the same point in time.  Panel data is a combination of both.  These are the Data for Q, P, Pr and M.

Specify the Demand Model

Here we’re talking about the unknown or estimated parameters. It is up to the analyst to determine if she will rely on consumer behavior, price, income, price of related goods, size of market, tastes, price expectations etc.  Then, choosing a functional form, usually a choice between liner form and power function.

Linear form looks like this:

Qx – a + bPx + cPr + dM + eN + u

The parameters in this linear function tell the change in Qx for 1-unit change in the associated variable.  It looks like this”

b = ?Qx/?Pr ; c = ?Qx/?Pr; d = ?Qx/?M; e = ?Qx/?N

Determining demand is not an exact science and the accuracy of any prediction is based, in large part, on the experience, knowledge and industry expertise of the analyst.  This is true because of the assumptions the analyst must make in determining the unobserved and unknown variables present in the linear regression analysis.

Linear regression analysis for statical demand gives a more accurate picture of future demand rather than relying on crude projections based on past sales.

We did not cover the power function which is used frequently if the analyst believes demand is non linear.  If anyone can tell me how the power function would look, feel free to comment.  See you next time in Making Molehills out of Mountains University, Market Analysis 101.

Useful Facts about Factor Analysis

While some may have heard of the technique “factor analysis” many remain unclear about exactly what it is. How does factor analysis figure into decision making? How is it applied? What is it used for?  What are “latent factors”? While these terms may sound complicated or otherwise cumbersome the fact is that the ideas behind factor analysis are rather straightforward.

What Does Factor Analysis Do?

We are all living, breathing statistics. That may sound cold, but it’s a fact. We are all consumers of media, news, information, products, and services. Because all people really are to statisticians is statistics, it’s helpful to be able to strip away unnecessary information. Take people; as complex and individual and amazing though they are; and try to reduce them down to the lowest common denominator. In doing this the statistical “dimensionality” is reduced. Rather than complex octagons we are now simple boxes.

Latent Factors

Another important thing which this technique addresses are the “latent factors.” What does that mean? Well anything latent; a feeling, an impression, a hunch; is something that can’t be measured. So when you’re doing market research there are things you can know; name, age, gender, ethnicity, income level, occupation; and things you can’t know; passions, intelligence, motivating factors, upbringing. Still with factor analysis we’re able to group these individuals accordingly by the responses they give. As some have keenly observed “the observable data doesn’t create the underlying factor; the underlying factor creates the observable data.”

How Does it Help

One thing which many people conducting surveys do is ask questions which have no real merit unless observed repeatedly. If you asked 100 people in a restaurant with 35 tables and 100 chairs and 1 waitress how their experience was; likely all of them would say terrible. However if the one waitress was covering for 30 others, then the data you got for this one particular day about this one particular waitress would not stick in any real way. Diners experience on the next day when the place was fully staffed would be far different.

As the body giving the survey you want to be dealing with facts. Cold, hard, facts; things which can be correctly transposed to different settings with the same result. A plane takes off from tremendous speed; a helicopter takes off from a stationary position. These are things which cannot be debated. If you’re trying to group results together, factor analysis can really help. You are dealing with measureable items, your latent observation has been borne out and you can group these respondents accordingly.

Return Shoppers

You can also get measurable results if you’re asking the right questions. Using this technique you should be able to accurately peg a customer’s likelihood that they will purchase a product or service after using it once. If this technique was found to be accurate over an extended period of time it would be immeasurably valuable to all businesses who are trying to discover a products survivability in the marketplace. Using a tool like factor analysis to determine which products to push on full force and which to abandon would vastly change the landscape of the marketplace.

Of course whether or not this remains true over the longer haul; whether this techniques implementation has any lasting chance of thinking before and ahead of consumers, is still up for debate. Still with so much potentially riding on factor analysis’ success or failure, it’s something we all should try and get our heads around sooner rather than later. Nobody wants to be behind the 8-ball on the next big, sure thing!

3 Ways to Measure Customer Loyalty

Customer loyalty is a tricky sentiment to track.  It is difficult to measure customer loyalty because proof of loyalty, the state of being loyal, is most often shown after an action occurred that indicates a person’s loyalty.  However, past action often indicates but doesn’t guarantee future loyalty.

So how do you measure something that hasn’t happened yet?  You can look for patterns when analyzing responses to survey questions designed to measure specific indicators that, when taken in context by the analyst, have varying degrees of certainty as to future action.  Bob Hayes, author of Measuring Customer Satisfaction and Loyalty, breaks it down into 3 measurements: Retention, Advocacy and Purchasing.

Retention as an Indication of Loyalty

Retention is a reflection of a customer’s willingness to remain with a particular company’s service or products and is useful to measure customer loyalty.  Questions designed to determine loyalty are often based on the “How likely are you…” model to predicate future behavior.  Among wireless or other service provider companies, Retention is most often asked by the question, “How likely are you to switch?”  This question is an indication of the relationship the customer has with the company and may be an indicator of overall satisfaction. Although, the smart analyst should be aware that the question alone, without corroborating evidence, may be an indication of a deeper dissatisfaction with the competition rather than satisfaction with their current company.

This least of all evils attitude is often found in service industries such as cable/internet providers, wireless companies and banking.  To be helpful, retention questions should be supported by an investigation of the second measure, Advocacy.

Measure Customer Loyalty by Measuring  Advocacy

“How likely are you to recommend…?”  or How likely are you to purchase other products from us?” and ” How satisfied are you with…?” are typical advocacy questions.  They are related to retention because the assumption is that a customer that is a cheerleader for or satisfied with your organization is likely to remain with you.  They relate to the customer’s perception of the company’s image that they are doing something right.  Determining what that “right” something is requires additional investigation.  It may be related to a single experience or simply to an overall – but general – impression.

There is overlap between Advocacy and Retention but they are distinctly different.  Advocacy requires less action on the part of the customer, because to advocate does not mean purchasing.   Whereas Retention requires the costumer to engage with your company through the basic transaction of making an additional purchase (or renewing a service) which in itself is a strong indication of customer satisfaction.

However, the strongest indication of customer satisfaction is related to Purchasing.

(RE)Purchasing is a strong Customer Satisfaction Indicator

Purchasing questions like, “How likely are you to (continue)(increase)(purchase different) products from X Company?” are the best indicators of growth through customer loyalty.  They seek to determine if the amount spent per existing customer will increase or decrease based on additional purchases within or across product lines.  It is distinguished from the retention question of how likely are you to switch because a switching question may me a repeat of the same revenue (0 growth) rather than an increase in spending (positive growth)

Use All 3 To Measure Customer Loyalty

All three customer satisfaction indicators are closely related in that they measure costumer intent.  negative responses to these types of questions usually indicate a loss of that customer.  Either they will re-up, purchase additional products or feel good about your company/product/services — or they won’t or don’t.  It is fairly straight forward to develop relevant survey questions to receive the data.

What becomes difficult is providing the context for analyzing the survey data into a meaningful construct that can be used by decision makers.  That is the job of the analyst to rely on his or her experience, knowledge and expertise to put the data into perspective.