Quirk's Blog

A jolly Black Friday infographic

Editor’s note: Paul Abbate is the senior vice president of Ipsos Public Affairs’ Omnibus Services business in the U.S. This is an edited version of a post that originally appeared here under the title, “What’s the deal with Black Friday shopping?”

It’s near the end of the most wonderful time of the year. Our most recent Thought Starter edition shows that only a third (33 percent) of Americans went to the stores for Black Friday shopping on either Thursday (Thanksgiving) or Friday. Consumers who typically tend to value price over convenience were most likely to shop on these days – 46 percent of Millennials and 47 percent of households with children went to the stores – while older adults (20 percent of those over 55 years of age) were the least likely to go shopping. Makes you wonder if time well spent at Nana and Grampy’s the day after Thanksgiving would ease the burning hole in your wallet.


Posted in Advertising Research, Consumer Research, Customer Satisfaction, Millennials, Retailing, Shopper Insights | Comment

Advancements in geofencing

Editor’s note: Allen Vartazarian is vice president of product at uSamp, a Los Angeles-based global market research firm.

For the past few years, geofencing has allowed researchers to use location-based information as vital data for all types of studies and campaigns. As smartphone technology has evolved, so too have geofencing techniques and applications. Now researchers can collect richer information and target studies with greater precision than ever before, gaining a deeper understanding of consumer behavior.smartphone

In basic terms, geofencing is a virtual fence around a geographic location in the real world. Location-enabled smartphones can detect when someone exits or enters these virtual fences. A geofence is set around a particular latitude and longitude and the radius of the “fenced in” area can be as small as a coffee shop or as wide as a city block. When someone walks through the fence, we have the ability to record the date and time and trigger a notification to that person, linking to exclusive deals from a retail venue, for instance.

For the market research industry, geofencing offers a range of ways to gather information about customers, purchases and real-time surveys. Instead of waiting until a consumer returns home to fill out a long online survey, researchers can alert the user immediately after they leave a store or a movie and send a few short questions to answer.

Retail marketers have also leveraged geofencing to great success. Stores can offer personalized service, for example. Neiman Marcus tested out a geofencing program that notified sales clerks when VIP customers were in the vicinity, and assisted them based on past purchases. Another benefit of geofencing is the ability to deliver specialized coupons. When a consumer approaches a flower shop, hyper-local deals can be delivered immediately, or if a client enters the shop, offers to a nearby jewelry store – to complement a flower purchase – can be sent. Around certain holidays, targeted specials can inform consumers walking by a Halloween costume shop that a 20 percent sale is occurring.

The data geofencing offers is rich and real-time but there is always room to refine. How do you ensure that the right consumers are being notified when they cross a geofence? We’ve implemented two techniques to help us answer this question.

The first is velocity. Velocity allows for setting a minimum and/or maximum speed that someone can be traveling when they cross the geofence in order to qualify for a survey opportunity. For example, if a geofence is set around a grocery store with a maximum velocity of five mph, people who happen to be driving by the location will not be alerted; only those who are walking will be invited to take a survey. On the other hand, if a company targeting motorists wants to measure ad exposure for a billboard alongside a highway, the minimum velocity could be set to 30 mph, thereby ensuring that only those driving by the ad had an opportunity for organic exposure. It almost goes without saying that velocity settings dramatically improve targeting capabilities and data quality.

The second new geofencing advancement is loiter time, which refers to the minimum amount of time that someone must stay within the geofence before they qualify for a survey opportunity. This setting ensures that surveys are only sent to people who are actually at a particular location for an extended period of time, and not just those who may be walking by. For example, loiter time could be set for 90 minutes to survey people who just watched a particular movie at a theater. In this example, panelists who have crossed the geofence would not receive an invitation until they had remained in the geofence for the full 90 minutes. This helps ensure that the right people are taking the survey.

For further improved accuracy, velocity and loiter time requirements can be combined, enabling the ability to set a minimum/maximum speed and required loiter time so that both must be satisfied before the panelist gets invited.

Geofencing has provided researchers intimate access to consumers by providing the ability to capture rich, in-the-moment insights, ensuring the highest possible accuracy in the way insights are collected. With a whole host of creative possibilities to gather data, geofencing may be a market researcher’s best tool for in-context insights.



Posted in Advertising Research, Behavioral Research, Brand and Image Research, Consumer Research, Customer Satisfaction, Data Collection/Field Services, Online Surveys and Research, Retailing, Shopper Insights | 1 Comment

Transaction volume and customer satisfaction

Editor’s note: Bart Zehren is founder of E-RM, Ill. This is an edited version of a post that originally appeared here under the title, “Moving parts theory of customer service.”

I’m quoting myself here again but this piece is so old by now that I’m giving myself permission to do so. Oh yes, it’s also terribly dated as you’ll see – but it makes a point that still seems relevant

“Try to remember back to the first time you rode in a car with modern features like power steering, brakes, antenna, windows, a stereo tape deck, air conditioning and a sun rogearsof. With all those new and complicated moving parts, didn’t it occur to you that sooner or later something was bound to go wrong?  And didn’t it, usually?

This is the principle behind the moving parts theory of customer satisfaction, which originated from a series of research studies I managed for the personal services side of a full-service bank (Northern Trust Bank, to be specific, my employer at the time) on the topic of customer satisfaction. When the findings showed areas like personal banker services, credit cards and checking services getting relatively lower satisfaction scores (though still quite high), while regular savings accounts and especially safe deposit box services (remember them?) received the highest scores, the moving parts theory was born.

According to this theory, service areas or accounts having greater transaction volume and/or more personal interactions (i.e., more moving parts) are presumed naturally to produce relatively lower satisfaction levels. After all, what can go wrong with delivery of a safe deposit box service? Originally called the moving parts hypothesis of customer service, the concept was elevated to a theory when, upon subsequent studies by the same bank, the effect persisted.

Implications of the moving parts theory

One lesson learned from this experience was that the appropriate frame of reference for analyzing such satisfaction scores is to compare the bank’s overall scores and those of each department, not against each other but each against itself over time. Otherwise, ridiculous conclusions such as this can be drawn: “The safe deposit box area is providing higher quality service than are the personal bankers.”

Management must decide how high it is up for satisfaction scores for the firm as a whole and for each service area. Only then can judgments begin to be made about the quality of service provided and progress toward goals. Of course, those management decisions need to observe the strategic imperatives of the firm. The bank that is committed to growing its upscale business with more sophisticated services and accounts may be well advised to expect increased volume of customer contacts, inquiries and perhaps even lower satisfaction scores as it embarks on that program.

However, in the credit card business it has been shown that while the moving parts theory does seem to apply relative to card member volume, it need not automatically lead to lower overall satisfaction levels. The answer seems to lie in developing a professional and efficient customer service process as a proactive and strategically integrated program, not merely a remedial one.

Could it be – is this old message still on target?

Posted in Consumer Research, Customer Satisfaction, Market Research Techniques, Shopper Insights | Comment

What do Shakespeare and MR have in common?

Editor’s note: Reyn Kinzey is vice president of Kinzey and Day Market Research, Richmond, Va.

It’s pretty audacious to think about re-writing Shakespeare (although thousands of directors have done it), but I’d like to change one scene in Hamlet. When pompous old Polonius advises Laertes, “to thine own self be true…” I’d like to see Laertes interrupt and say, “Ah, but which self?”Hamlet

Characters, like people, have many selves and it’s hard to imagine them all. Laertes seems a relatively straightforward character but that’s partially because he’s a foil (pardon the pun) for Hamlet, the most enigmatic of Shakespeare’s characters. Which is the real Hamlet, the brooding, philosophical stoic or the impulsive young man who offers to fight Laertes at Ophelia’s grave and “eat a crocodile” (yes – that is the line – Shakespeare wrote it, so it must be good)?

Critics have seen Hamlet as the first example of the modern character, divided against himself but for anyone who has experienced the play, Hamlet, although an impossible character to figure out, comes across as being very, very real. We read the play to attempt the impossible, to imagine – in the Elizabethan sense of the word – to figure out what makes this character do what he does.

And beyond all that, the holy grail of criticism, never on this earth to be realized, we read to imagine the character of Shakespeare, who smiles behind his myriad characters like the Cheshire cat.

Well, it’s not all that different from what we try to do in qualitative market research.

Like many market researchers I came to the game as a second career. In my previous life, I had been a college English teacher. When I started conducting focus groups, I found the analysis of the findings to be fairly similar to what I had done with literature: you start with what the characters (respondents) say and do in your groups. Sometimes what they say and think seems at odds with logic and reality but just as all works of art have their own coherence, so we as market researchers (or anthropologists) need to believe that all people have thoughts, values and beliefs that are internally consistent and therefore true for them. The trick is to figure out the consistency so that we can make business decisions.

I’ll admit, I’ve had some trouble convincing other moderators of the analogy between reading literature and reading consumers in focus groups but I’m now beginning to get some empirical support.

Last year NPR published an article Want to read other’s thoughts? Try reading literary fiction (Nell Greenfieldboyce, October 4, 2013). Greenfieldboyce summarizes a study in the journal Science that suggests our abilities to read the thoughts and feelings of others can be enhanced by reading serious literature:

“On average, people who read more literary books… did better on tests (to measure social perception) than people who read either nothing, read nonfiction or read best sellers.”

The article points out that it’s hard to precisely define what distinguishes literary fiction from best sellers but it points out that literary fiction focuses on the psychological and inner lives of characters more than best sellers do (not to say there isn’t plenty of action in literary fiction: count the bodies on stage at the end of Hamlet!).”

The article continues, “Readers have to watch what (characters) do and infer what they are thinking and feeling. This is the same process that we engage in when we try to guess other people’s thoughts and feeling and emotions, and to read their minds in everyday life.”

And, I would argue, this is what we engage in our everyday job as market researchers.

The moral of the story is “read a good book: it’ll make you a better researcher.”

Posted in Consumer Research, Market Research Best Practices, Qualitative Research | 1 Comment

Data mining 101

Editor’s note: Isaiah Adams is the manager of social media development at marketing research and analytics firm Optimization Group, Mich. This is an edited version of a post that originally appeared here under the title, “Explaining data mining in useful language.”

For those not familiar with data mining, simply the mention of the term often leads them to mentally check out. The term is carelessly thrown around, leaving the definition unclear. The truth is, the subject is full of jargon, tedious detail and complicated math but if you can understand some of the basic concepts it can be extremely valuable.

To provide insight into this important topic I enlisted the help of Optimization Group’s Director of IT, Jim Kenyon. We tend to look at this from a marketing perspective. In other words, what does the data tell us about our client’s marketing?Data mining

Before we look at some of the fundamentals of data mining, it’s important to understand one principle: data mining is most effective when you ask specific questions. We like to use the illustration of peeling an onion. By peeling the onion one layer at a time, you can see more of what’s really going on with your marketing and ask more specific questions.

To help you peel back the onion and better understand data mining, specifically as it relates to marketing, we’ve asked Kenyon to answer important data mining questions in language that’s easy to understand. Each question is designed to help you get the most out of data mining and understand what it takes to get started.

How much historical data do you recommend a client has before they can do any meaningful data mining?

We want as many observations as we can get, though we’ve had reasonable success with three years of monthly observations (36 observations). The quality of the model goes up with more data (to a point).

Is there a recommended time period classification (observation frequency)? In other words, does it make a difference if the data is recorded daily, weekly, monthly or annually?

Models that use macro-economic data tend to use monthly sampling as most econometric data is reported monthly. It’s a least common denominator.

How do I know if my data is in good or bad shape? What are the indicators?

Do you have monthly observations across a continuous time range? Are there valid values for all observations? Valid values are values that are within the expected range for a field. For example, if the field is “age of person,” negative numbers would not be valid. Missing values are another case – for example, if TV spending is missing is it because there was no spending (in this case it would be a zero, not missing) – or is it because accounting lost the data for that month?

Next, is the data recorded on the same scale/unit of measure for each observation? Does the format (file layout) of the data vary from observation to observation (or year to year)? Is the data recorded in a common file format (CSV, Excel, fixed column width)?

If someone answers no to more than one of these questions, chances are the data is in bad shape and will need significant work. It doesn’t necessarily mean that their data is unusable – on the contrary, most of the engagements we see have data that’s in pretty rough shape.

If my data is in bad shape, how is it cleaned and prepared for data mining?

Data scientists can restructure the data and load it into a relational database. Once in the database, it is transformed into monthly observations of features. Descriptive statistics are calculated for each feature. Descriptive statistics are things like mean, mode, standard deviation, median, frequency distributions, etc.

Plots are created for each feature. Typical plots are frequency distribution and time series (value of a feature by time period, from the start of the range through the end, in chronological order). These statistics and plots are reviewed and cross-checked with original (raw) data to make sure the transformation did not alter the data. The data review is then conducted with client data stakeholders/providers to check for and explain anomalies.

How does the quality of my data affect any potential data mining project?

Poor data quality can reduce the predictive accuracy of a model. It can, in the extreme, prevent model development entirely.

I’m not sure where all my data is. Where is data most commonly stored?

In the sock drawer. Seriously, in an ideal world all data is stored in a data warehouse. More commonly, it comes from spreadmarts – an Excel spreadsheet from Bob in finance; another one from Jill in media planning; a CSV from some legacy mainframe application; and three external SaaS applications that two different guys in sales bought because they saw them in an airline magazine.

What are the most overlooked and under-appreciated aspects of data mining?

Data mining doesn’t require big data. That is, you don’t need millions of customer records to take advantage of the power of machine learning techniques. Monthly marketing spending and sales data over at least three years can produce very useful models to improve the effectiveness of your marketing dollars.

How are outside variables (weather, MCSI, etc.) incorporated into a data mining project? How is the impact of outside variables measured alongside of internal marketing variables?

External variables are included at the same observation frequency (typically monthly) as data is provided. The machine learning tools include these features while constructing the candidate models and determine if any of them are contributing the predictive accuracy of the model. If they are contributing, they are included in the model. If not, they aren’t. They are not treated differently.

Does it matter how many variables are in a data mining project? How does this affect time, cost, etc.?

There is a limit based on the number of features mining tools can handle (varies by tool). The number of features is reduced through “feature selection” – an iterative process that looks for features that tell the same story (for example, temperature reported in Celsius and Fahrenheit – the second copy doesn’t add information to the model they tell the same story but in different units) or are highly correlated. Only one copy of such a group of features is carried into the modeling phase.

Additional features that are not in analytic-ready (one record per observation period, with a variable being an additional column in said record) add to extract, transform and load time. This can be expensive if the data requires significant work to get it into an analytic-ready format.

10. What’s the first step in every data mining project?

The first step is to understand the business problem being solved. If this step is ignored or given short shrift, one ends up with a very good answer to the wrong question.

11. What is typically the client’s role in a data mining project? What things fall on the client?

Defining the business problem; providing data only the client can provide; (to the extent possible and/or desired) delivering client data in an analytic-ready format; reviewing data during ETL to help make sure the process didn’t introduce errors; and reviewing candidate model(s) to see if they make sense.

12. What steps are taken to make sure the data model delivered is the most accurate model?

Interestingly, we tend to ignore “the most accurate model” as these tend to be precisely wrong rather than generally accurate. That is, they can suffer from “over fitting.” Rather, we look for candidate models that: make sense; are explainable, simple and have good accuracy; and are biased in the way that best suits a client’s needs. For example, it’s better to have a model that includes some false positives when sending direct mail advertising pieces than to have false negatives. In this case, you spend a few extra cents per piece to people who won’t respond rather than not send to people who would respond and generate revenue.

Posted in Big Data, Data Collection/Field Services, Data Processing, Market Research Best Practices, The Business of Research | Comment

Could the impulse buy soon be a thing of the past?

Editor’s note: Rachael Genson is the North American public relations manager at Bazaarvoice, Texas. This is an edited version of a post that originally appeared here under the title, “Are we waving goodbye to the impulse buy?”

How many times have you gone to the grocery store with a list, only to throw a handful of random items in your cart because they looked appetizing? Or gone shopping for a new pair of shoes, only to come back with two? You can always use another pair of sensible work flats, right?Man in supermarket

For me, these scenarios are nothing out of the ordinary. In fact, I’d venture to say that at least half of my purchases were unplanned, impulse buys. It seems, however, that my shopping habits may soon become an exception to the norm since a recent Wall Street Journal article professed the impending end of the impulse buy.

But why?

We’re all well aware of the impact that the Internet has made on the way consumers shop – the ease and convenience of buying online drove many consumers to choose e-commerce over the traditional model of shopping in-store. But more than that, it created a major shift in consumers’ in-store buying practices.With the Internet came unlimited access to product information, both from the company and from the very consumers that use those products. This unparalleled access created a more informed consumer, which in turn slowly results in someone who lacks the urge to buy on impulse.

Consumers now are more intentional – they know exactly what they need to buy and have a specific reason for every item that makes its way into their shopping basket. As the Wall Street Journal article informs, this decline in impulse purchases can have major repercussions on the way companies market products in-store. A savvy consumer is less tempted by the in-store marketing tactics brands have become accustomed to utilizing, choosing instead to rely more heavily on the word-of-mouth opinions of friends, family and like-minded consumers. Before ever heading to the store, today’s consumers spend ample time researching their desired products in order to gain a deep understanding of its strengths and pitfalls, and how it could fit into their life.

This behavioral shift makes a company’s online presence more important than ever before. With that comes an increased need for user generated content like consumer reviews. According to Google, 70 percent of Americans say they look at reviews before taking the next step to conversion – and that was in 2013. Imagine how important user-generated content has become in the years since.

It’s the content that consumers see in their initial research that can turn a product from “nice to have” to “can’t live without,” and in today’s shopper mindset, brands must create that level of need before the consumer ever enters a store. The best way to do that is to take advantage of the user generated content your audience is already creating every day by:

  • Integrating reviews directly on the product pages – User-generated reviews are a proven sales driver since they often provide the hard facts and information consumers seek before committing to a product purchase. They help consumers determine the benefits of choosing one product over another and answer the question “Why do I need this product.” Also, these reviews are often trusted more than company content as it comes from other like-minded consumers.
  • Curating user images and videos from social channels – Consumers, especially younger generations, love to be seen as brand influencers and tastemakers, so they take a lot of pride in companies’ including their product photos and videos in marketing materials or on site. Use this to your advantage! Not only does it create an outlet for engagement and add excitement to marketing materials, but it answers many questions that reviews don’t necessarily fulfill. Where reviews help answer “Why do I need this product,” visuals will take it a step further by helping consumers understand how a particular product makes them feel, and how it will fit into their lives.

So while the Wall Street Journal’s article may speak truth about the end of impulse purchases, it doesn’t spell disaster for companies; it simply ups the ante. Those that can alter their strategy to appeal to the more prepared, savvy and thoughtful consumer by giving them all the information needed to make a purchase decision are the ones who will see the most success in this new retail environment.

Posted in Advertising Research, Behavioral Research, Consumer Psychology, Consumer Research, Retailing, Social Media and Marketing Research | Comment

Will MR adapt to survive in the age of big data?

Editor’s note: Editor’s note: David Day is president and CEO of global research company Lightspeed GMI, London.

With the continued rise of online and mobile research, the lines between market researchBig data on blackboard and marketing are blurring. Consumer surveys are becoming part of the communications and marketing process. As tech providers enter the market place with online survey tools and brands dip into big data, is there a danger that traditional market research could be side-lined if it fails to adapt to the new status quo?

My view is that in this time of rapid change, market research companies need to decide who they are and what they stand for.

It’s never been easier for brands to survey consumers and then send them targeted media. Many media owners provide an opportunity for brands to gather their own research, passive or otherwise. Online survey software companies offer low cost options to companies who have never conducted traditional market research. So it’s no wonder that some brands now see surveys as an increasingly important part of the communication process, key to their image and marketing. The distance between the points where research is taken and where the consumer is marketed to is getting shorter. Research has of course always supported advertising but the new technology is closing that gap.

Some brands are moving from using traditional market research programs to relying increasingly on good enough information from a snapshot of social media or off-the-shelf surveys to inform their marketing decisions. While additional data sources are undoubtedly valuable and should be embraced, it needs to be remembered that they are usually skewed to existing or past customers or customers of a particular channel and unlikely to give a complete picture of the market.

Then there are the self-styled market research companies who are, to all intents and purposes, marketing companies. They follow up their research with targeted marketing. This could be damaging to the market research industry, deterring people from engaging in legitimate market research in the future.

Such trends could have enormous implications for the market research industry and the future health of market research itself. We need to remember that more than ever that consumers call the shots, engaging with brands on multiple devices in ways completely impossible only a few years ago. If the market research industry does not rise to meet the new challenges of online and mobile, if it does not adapt its approach, then poorer substitutes will fill the gap and everybody will lose.

So how do we keep up and stay relevant?

Improve mobile research

We must never forget the fundamental objective of market research – to identify consumers’ needs, find out how consumers feel about a product or service in that context and decide how best to market those new products and services to them.

We should take advantage of the huge amount of information now available from every step of the consumer journey, filtering it, analyzing it and using it to inform intelligent and targeted market research that’s sensitive to the needs of consumers.

Even today, too many researchers want to exclude mobile respondents from surveys as if they are different when in actuality including them is more representative of today’s consumers. Mobile is not just the future. In the developed world well over half of the economically active population owns a smartphone, with half of Internet access taking place on mobile. In the next few years the number of people taking surveys on smartphones and other portable devices will rise. Market researchers need to ensure that all surveys work on the full range of screen sizes available. It is the respondent that chooses the method of completing a survey, so designing it to be easy and fun to complete – as well mobile optimized – is moving from a nice to have to being a critical research ingredient. We need to ensure the experience of giving feedback is as fast, painless and rewarding for consumers as possible, as we compete for their attention online.

We need to remember respondents are just like us! They have little time and lots to do. So the goal is to make surveys short, engaging and fun without asking redundant questions.

Use social media

Conducting market research on social media has to be approached with care. The new social media site Ello offers a free service with the ability to pay for features that users might want and promises that the site will never make money by selling ads or user data. The fact that people are signing up to such sites shows they have serious concerns about privacy. As researchers we must be at the vanguard: aware of and sensitive to people’s privacy issues and up to date with privacy laws, which very considerably around the world.

So what does the future hold for market research? As brands become more responsive to consumers in real time, is traditional market research in danger of being side-lined?

I don’t believe so. As vast quantities of rich and detailed information is collected, brands will turn to the expertise of market research companies to help them to make sense of it, to filter it and to follow up with the right questions in order to provide high quality, actionable insights. We could even find ourselves occupying a more central role, pitching research that will engage consumers, working more closely with the CEOs and marketing directors of companies.

As long as we can adapt to engage with consumers online and mobile, the market research industry should be more relevant than ever.

Posted in Big Data, Business and Product Development, Consumer Research, Data Collection/Field Services, Data Privacy, Data Processing, Innovation in Market Research, Market Research Best Practices, The Business of Research | 1 Comment

2014 holiday shopping habits

Editor’s note: Phil Ahad is vice president at Toluna QuickSurveys, Washington, D.C.

Regardless of the holiday they celebrate, it’s no surprise that consumers will be shopping this month. Of the 1,000 consumers we polled on how they plan to spend their hard Shoppingearned money this holiday season, 90 percent of respondents will be purchasing gifts and their average spend will be $700. For retailers, this means that there is money to be made this month, so demystifying consumer habits is essential.

Using the who, where, what, when and how approach, we created a cheat sheet to consumer shopping habits this holiday season.

Who: 90 percent of consumers.

Where will you be doing the majority of your holiday shopping?

  • Seventeen percent say online (52 percent male; 48 percent);
  • thirty-seven percent say in store (52 percent male; 48 percent female); and
  • forty-six percent say an even mix of both online and in store (43 percent male; 57 percent female);


What consumers will do to get a deal:

  • nineteen percent are likely to open a store credit card to receive additional discounts;
  • fifty-one percent say they’ll purchase items for themselves while shopping for others (59 percent of Millennials will do this);
  • fifty-five percent of Millennials will purchase items far in advance of the holidays;
  • fifty-two percent say they’ll likely join a retailer’s email list to receive discounts (59 percent of Millennials will do this);
  • sixty-three percent will wait for items to go on sale before purchasing them; and
  • thirty-four percent will not purchase items at full price just to ensure they’re able to get them.


What retailers consumers will shop online:

  • ninety percent on Amazon.com;
  • forty percent on Walmart.com;
  • twenty-nine percent on eBay.com;
  • twenty-eight percent on Target.com;
  • twenty-five percent on BestBuy.com;
  • eighteen percent on Macys.com;
  • seventeen percent on JCPenney.com;
  • seventeen percent on Kohls.com;
  • fourteen percent on Overstock.com;
  • three percent on SaksFifthAvenue.com;
  • four percent on Bloomingdales.com;
  • seven percent on Nordstrom.com; and
  • three percent on NeimanMarcus.com.


What retailers consumers will shop in-store:

  • sixty-three percent at Wal-Mart;
  • fifty-six percent at Target;
  • thirty-six percent at Best Buy;
  • thirty-four percent at Kohl’s;
  • thirty-one percent at Macy’s;
  • twenty-four percent at Old Navy;
  • twenty percent at Sears;
  • four percent at Bloomingdales;
  • five percent at Neiman Marcus;
  • eight percent at Nordstrom; and
  • four percent at Saks.


What types of gifts consumers will purchase:

  • seventy percent gift cards;
  • seventy percent clothing;
  • fifty-two percent electronics;
  • fifty percent toys;
  • thirty-eight percent books;
  • thirty-six percent jewelry; and
  • fifteen percent tickets.


When consumers will shop:

  • only 3 percent plan one to two months ahead of time;
  • twenty-four percent plan three to six months ahead of time;
  • nineteen percent less than one month ahead of time;
  • eight percent plan more than six months ahead of time;
  • six percent don’t plan ahead; and
  • forty-three percent of primary household shoppers will plan one to two months ahead.


How consumers will shop:

  • fifty-three percent say they plan ahead so they can budget for their purchases;
  • forty-one percent don’t really plan but will purchase items as they see them;
  • six percent aren’t much into shopping and will wait until the last minute;
  • sixty-two percent prefer to do their holiday shopping alone;
  • sixty-four percent will ask for lists from people they plan to purchase gifts for; and
  • the majority of females (59 percent) say they’ve picked out their own gifts for someone else.


Holiday shopping

Posted in Advertising Research, Behavioral Research, Consumer Research, Retailing | Comment

Why B2B MR is essential to positioning offers on value vs. price

Editor’s note: Julia Cupman is vice president of B2B International, a business-to-business market research agency, New York.

The past decade has been challenging for business-to-business marketers in North America. The worldwide financial crisis, low-cost Asian competition and the rise of e-commerce have made markets more competitive and put huge pressures on margins. The response to this by many businesses has been one of resignation and an acceptance that prices have to be reduced and business less profitable.B2B

However, this needn’t be the case as the requirements of decision makers are now more sophisticated than they have ever been. We have seen a shift in many B2B markets – those price buyers who are so price focused are now in the minority, with value buyers making up the majority. The latter group is becoming increasingly more discerning, with elevated needs based around service, brand and consultancy. This provides a great opportunity for sophisticated businesses to differentiate themselves through value marketing and value selling.

Remember, it’s NOT all about price

Our research has found that up to half of companies in B2B sectors believe that price and product quality are all that matter. The myth of the price-focused market is often perpetuated by individuals in B2B sales who are driven by short-term targets – these people in B2B companies often misunderstand, miscommunicate and simplify customer needs. Our data clearly shows that the average proportion of any market that ranks price over all other factors is 20 percent. That means that 80 percent of business-to-business buyers do not prioritize price, which in turn means that price-focused sales people are leaving value on the table in 80 percent of cases. Even in highly undifferentiated markets such as utilities, fewer than half of buyers give precedence to price. Independent market research and a more long-term, marketing-oriented mind-set are good ways of improving the business’s understanding of customer requirements.

Recognize what customers really want

Many companies fail to recognize that customer needs may change over time and a surprising number conduct no systematic research into customer requirement. Our recent survey of 67 B2B businesses in the U.S. revealed that only 42 percent use sophisticated customer segmentation. This, not falling prices, could be the reason why many companies struggle. Market segmentation facilitates customer choice by aligning different propositions against groups of customers with diverse needs, as well as arming suppliers with the knowledge of who not to do business with. As marketing is the profitable satisfaction of customers’ needs, customers or segments that will not provide businesses with profitable revenues should be deselected. Detailed definition and analysis (both market analysis and financial analysis) of a firm’s segments built around rigorous market research is the first, crucial step in deciding which customers they wish to serve.

Protect and build the brand

A strong B2B brand is one of a company’s biggest assets, providing credibility and differentiation as well as supporting price positioning. The value marketer and seller must recognize the power of brand in communicating and delivering value to the customer, and in extracting value from the market. On average around 5 percent of a company’s stock value derives directly from a company’s brand image, yet of the 67 U.S. B2B businesses that we surveyed, 56 percent said they struggle to build a strong brand and only 39 percent have a program in place for measuring the value of it. Brand management is crucial. In recognizing the strength of brand, companies can attain a high return on investment for relatively low cost. Market research can be used to demonstrate how a brand is positioned against competitors to allow for necessary alignment and strengthening. Only when a business understands its strengths and weaknesses – and those of competitors – are intelligent brand strategy possible.

Our most recent survey of North American businesses revealed that branding research is the top type of market research that firms think would be most useful over the next two years, with 61 percent of respondents citing it as a priority.

Embrace a cultural change

In order for value marketing and selling to succeed, it needs to be deeply embedded in the company culture. However, in most B2B markets, the limited size of the target audience results in a labor-intensive marketing and sales process, with a heavy emphasis on salespeople, volume and short-term results. Value marketing and value selling require a reversal of this approach, with marketers joining salespeople as the heroes of the B2B business, with profit supplanting volume as the number one KPI and a longer-term outlook communicated from the top down.

Posted in Business-To-Business Research, Consumer Research, Market Research Best Practices, Market Research Techniques, Marketing Best Practices, New Product Research | Comment

The phone is ringing, are you ready?

Editor’s note: Mark Sullivan is director of analytics at CallRail, an Atlanta based call tracking service.

Call tracking, or technologies that track inbound phone calls in much the same way that Google Analytics tracks Web site visits, is an old technology undergoing a major renaissance. The rise of the smartphone and the new mobile customer, are making click-to-call far more appealing. This is part of what’s driving the 24 percent year-over-year increase in inbound calls to businesses in 2014. Despite this increase in calls and more money than ever going into mobile Woman calling on smartphoneadvertising, many marketers are still missing the boat on call tracking.


For reference, the call tracking technologies available today provide in-depth information in as much detail as a marketer may need – right down to which Google AdWords ads, Google search terms or even Google Maps clicks are generating phone calls. For advertisers that are familiar with the technology, tracking has become an essential tool for the measurement of mobile advertising ROI. Looking at the not-so-distant past tells us why call tracking is finding new life.

What do you get from tracking?

Not so long ago, in a world dominated by the likes of the Yellow Pages, prospective customers would either discover local businesses from a directory, memorize ones they had seen on a billboard advertisement or use a business recommended by a friend. The high success of phone books, specifically the Yellow Pages, had everything to do with having the right information, at the right time in the customer’s buying cycle. After all, when someone is looking for a local transmission repair shop in a large directory, that person is transaction-ready. Getting in front of those customers fetched high dollar from advertisers.

Smartphones are the new way for businesses to get in front of these transaction-ready customers. All of us are using smartphones to view business directories (Google Maps, Yelp, etc.), recommendations from friends (Facebook reviews, Google Plus reviews, etc.) and the modern equivalents of billboards (banner ads, display ads, retargeting ads). With this discovery happening on a smartphone, it’s no wonder that businesses are seeing an increase in incoming phone calls. Click-to-call calls to action (CTAs) remind us that the device we’re holding in our hands is still a phone, capable of connecting us with someone at the business we’re viewing.

This shift from landline to smartphone has fundamentally changed the frequency, nature and origin of over-the-phone customer interactions. For the average business owner, there has been confusion for years around how ad dollars spent correlated with the leads they valued most: inbound phone calls. Companies providing services ranging from transportation to health care to automotive have always converted over the phone but how were customers finding them? Large companies had access to call tracking technology but without an enterprise sized budget, many business owners were left wondering what made the phone ring.

In the same vein, marketers who spend money on mobile ads are challenged to prove the ROI of a particular ad beyond clicks or other proxies for revenue. Clicks are no longer the holy grail of conversations that they once were for desktop ads. Many of us in the industry are trying to solve this challenge by providing more insights into which ads are paying off and which are failing to convert. None of the other solutions currently available come close to the affordability, accessibility, and ease-of-use that modern call tracking platforms provide.

The future of call tracking

As more advertisers realize the value of accessing transaction ready customers directly on the device customers are shopping on, the more dollars will go into measuring the effectiveness of those ads. We’re set to see a correlative increase in click-to-call CTAs as well – for the simple reason that call tracking optimized numbers can bring phone calls into the data driven world of today’s marketers. Luckily for most business owners and their marketers, call tracking technologies close the ROI loop without breaking the bank. That’s a good thing for all of us spending money on mobile ads.

Posted in Advertising Research, Consumer Research, Promotion Research, Shopper Insights, Social Media and Marketing Research | Comment