Posts Tagged ‘Wall Street Journal’

Big Data, Big Bucks

May 6, 2014

(This post appeared last week on our successor blog, the Analysights Data Mine)

In their 1996 bestselling book, The Millionaire Next Door, Thomas J. Stanley and William D. Danko constructed profiles of the typical American millionaire.  One common characteristic the authors observed was that these millionaires “chose the right occupation.”  When Stanley and Danko wrote Millionaire, I doubt many of their research subjects were data analysts, predictive modelers, data scientists, or other “Big Data” professionals; but if they were to write a new edition today, I’ll bet there would be a lot more on the list.  “Big Data” jobs seem to be “the right occupation” today.

In a recent interview with the Wall Street Journal, veteran analytics recruiter Linda Burtch of Burtch Works predicted that job candidates with little familiarity with “Big Data” will face a “permanent pink slip,” while observing that analytics professionals earn a median base salary of $90,000 per year. Ms. Burtch distinguishes between “analytics” professionals (who typically deal with structured data sets) and “data scientists” (who typically work with large, unstructured data sets), when classifying income levels.  Data scientists, Burtch Works found, make a median base salary of $120,000.

Even more impressive is the median base salaries of entry level professionals, those with three years’ experience or less: $65,000 for analytics professionals and $80,000 for data scientists.  At nine or more years’ experience, the median base salaries rise to $115,000 and $150,000, respectively.

Much of the reason for the hefty salaries is that companies don’t often understand what skill sets they need.  Ms. Burtch mentions this in her comments to the Wall Street Journal, and I indicated as much in a previous blog post.  Add to that the fact that needed skill sets are also highly specialized and relatively few professionals have such skills, or a large pool of them.  Because of the scarcity, candidates can command such high salaries.

For companies, this suggests that in order to get the most value out of a “Big Data” hire, it must first decide the typical projects it will expect the candidate to perform, and then base the required skill set and years of experience accordingly.  Then the company can budget the salary it is willing to pay.  This will ensure that the company isn’t hiring someone with 10 years’ experience in data analytics and paying that person $120,000 per year just to pull data for mailing lists, when it should have hired someone out of college for about one-third of that.

For candidates, the breadth of skill sets employers seek in “Big Data” professionals suggests they can maximize their salaries by continuing to broaden their skills and experience within the data realm.  For example, someone with years of SAS programming and SQL experience may branch out to other programming tools, such as R and Python. Or, such a professional may expand his or her skill set by developing proficiency in data visualization tools such as Tableau of QLIKVIEW.

Working in “Big Data” may not make someone “the millionaire next door,” but it may bring him or her pretty close.

 

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Read All About It: Why Newspapers Need Marketing Analytics

October 26, 2010

After nearly 20 years, I decided to let my subscription to the Wall Street Journal lapse. A few months ago, I did likewise with my longtime subscription to the Chicago Tribune. I didn’t want to end my subscriptions, but as a customer, I felt my voice wasn’t being heard.

Some marketing research and predictive modeling might have enabled the Journal and the Tribune to keep me from defecting. From these efforts, both publications could have spotted my increasing frustration and dissatisfaction and intervened before I chose to vote with my feet.

Long story short, I let both subscriptions lapse for the same reason: chronic unreliable delivery, which was allowed to fester for many years despite numerous calls by me to their customer service numbers about missing and late deliveries.

Marketing Research

Both newspapers could have used marketing research to alert them to the likelihood that I would not renew my subscriptions. They each had lots of primary research readily available to them, without needing to do any surveys: my frequent calls to their customer service department, with the same complaint.

Imagine the wealth of insights both papers could have reaped from this data: they could determine the most common breaches of customer service; by looking at the number of times customers complained about the same issue, they could determine where problems were left unresolved; by breaking down the most frequent complaints by geography, they could determine whether additional delivery persons needed to be hired, or if more training was necessary; and most of all, both newspapers could have also found their most frequent complainers, and reached out to them to see what could be improved.

Both newspapers could have also conducted regular customer satisfaction surveys of their subscribers, asking about overall satisfaction and likelihood of renewing, followed by questions about subscribers’ perceptions about delivery service, quality of reporting, etc. The surveys could have helped the Journal and the Tribune grab the low-hanging fruit by identifying the key elements of service delivery that have the strongest impact on subscriber satisfaction and likelihood of renewal, and then coming up with a strategy to secure satisfaction with those elements.

Predictive Modeling

Another way both newspapers might have been able to intervene and retain my business would have been to predict my likelihood of lapse. This so-called attrition or “churn” modeling is common in industries whose customers are continuity-focused: newspapers and magazines, credit cards, membership associations, health clubs, banks, wireless communications, and broadband cable to name a few.

Attrition modeling (which, incidentally, will be discussed in the next two upcoming Forecast Friday posts) involves developing statistical models comparing attributes and characteristics of current customers with those of former, or churned, customers. The dependent variable being measured is whether a customer churned, so it would be a 1 if “yes” and a 0 if “no.”

Essentially, in building the model, the newspapers would look at several independent, or predictor, variables: customer demographics (e.g., age, income, gender, etc.), frequency of complaints, geography, to name a few. The model would then identify the variables that are the strongest predictors of whether a subscriber will not renew. The model will generate a score between 0 and 1, indicating each subscriber’s probability of not renewing. For example, a probability score of .72 indicates that there is a 72% chance a subscriber will let his/her subscription lapse, and that the newspaper may want to intervene.

In my case, both newspapers might have run such an attrition model to see if number of complaints in the last 12 months was a strong predictor of whether a subscriber would lapse. If that were the case, I would have a high probability of churn, and they could then call me; or, if they found that subscribers who churned were clustered in a particular area, they might be able to look for systemic breakdowns in customer service in that area. Either way, both papers could have found a way to salvage the subscriber relationship.


Was Marketing Research Absent from Liz Claiborne’s Strategy to Target Younger Consumers?

August 17, 2010

Yesterday’s Wall Street Journal reported that Liz Claiborne’s efforts to appeal to younger female consumers may have been the company’s downfall. This month, J. C. Penney will launch an exclusive line of Liz Claiborne clothing, home and accessories. As part of the agreement, Claiborne cedes control of production and marketing and converts the label into a mass market line in exchange for royalties, the article reported. In five years, Penney also has the option to buy U.S. rights to the Liz Claiborne name.

This may well be the concluding chapter in what appears to have been a failed attempt by Liz Claiborne to broaden its appeal to younger women. Apparently, Claiborne realized correctly it would need to move to a younger consumer, as most of its customers had been working age Baby Boomers, who have begun to retire. However, the Wall Street Journal indicates that its efforts to target the younger female consumer actually did more to harm the brand.

In trying to appeal to the younger crowd, Liz Claiborne nixed, sold off, or licensed out tried and true lines; it changed designs so much that it confused its existing customer base; it introduced lower priced items, eroding its appeal as a high-end brand; and it alienated its long-term relationship with Macy’s.

As I read the article, I couldn’t help asking myself whether Liz Claiborne did its homework. I don’t know whether Claiborne did or didn’t do marketing research, but deciding to pursue a new target market requires extensive marketing research, because so many mistakes can be made because of unaided judgment. Among other things, it is important to have surveyed the younger female shoppers to understand what they needed for workplace casual attire; and to have looked for common ground between existing product lines and the new, emerging fashions that the younger crowd was embracing. Most likely, the research would have led Claiborne to develop lines that were new enough to appeal to the younger working woman, but traditional enough to maintain loyalty with its existing boomer customer. If the research showed that the younger women wanted something drastically different in the way of style, then Claiborne could have used that information to develop a completely different line (likely by launching a whole different brand) aimed at those preferences.

When appealing to a new target market, it is also important to do pricing research. Surely younger consumers don’t have the discretionary income that older ones do. But that doesn’t necessarily mean a company should introduce lower-priced apparel. As Van Westendorp pricing theory suggests, a price can communicate one of four things to consumers: a good buy, a luxury item, an overpriced item, or a cheap, low-quality item. I could only wonder whether the introduction of lower priced merchandise might have led consumers to believe the newer lines of Liz Claiborne were of lower quality.

Companies that don’t conduct marketing research – or conduct it inadequately – increase their risk of failure, declining sales, customer defections, and increased competition.

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