Archive for the ‘marketing’ Category

Former Customers Can Be Goldmine – Both in Marketing Research and Winback Sales

August 24, 2010

The other day, I stumbled across this May 28, 2010 blog post from, which discussed how to re-activate former customers. While you should definitely reach out to former customers and try to get them to buy again, your former customers can also provide a wealth of information from a marketing research and process improvement standpoint.

If a customer has lapsed for, say a 90- or 180-day period, or a customer who used to buy once a month is now only buying every other month, reach out to that customer and mention that you noticed he/she isn’t frequenting your business as much, and ask if there’s anything with your company that they aren’t getting, or would like to see. It could be that they’re not happy with the product, or they found a similar, less expensive product from a competitor. Maybe they’ve “outgrown” your company’s products; or maybe they lost their job and can no longer afford it, whatever. You won’t know unless you ask.

For the purposes of marketing research, a lapsed customer can be more valuable than a loyal customer, especially when you consider that acquiring a new customer is six times more costly than retaining an existing customer. Taking the time to hear out a former customer can help you take corrective action to prevent other customer defections, improve your practices and product benefits, and even win back your lost customers.


Help us Reach 200 Fans on Facebook!

Thanks to all of you, Analysights now has more than 160 Facebook fans! We had hoped to get up to 200 fans by this past Friday, but weren’t so lucky. Can you help us out? If you like Forecast Friday – and our other posts – then we want you to “Like” us on Facebook! And if you like us that much, please also pass these posts on to your friends who like Insight Central and invite them to “Like” Analysights! By “Like-ing” us on Facebook, you’ll be informed every time a new blog post has been published, or when new information comes out. Check out our Facebook page! You can also follow us on Twitter. Thanks for your help!

Do-it Yourself Focus Groups

August 23, 2010

The unstructured nature of focus groups enables marketers and businesses to draw out ideas, perceptions, feelings, and experiences from prospective customers that might not be possible to extract through structured quantitative approaches like surveys. By using focus groups, businesses can come up with ideas for new products and services; lay the groundwork for surveys and advertising campaigns by understanding the vocabulary customers use when describing products and services; understand why customers feel the way they do and their needs; and understand the findings from quantitative research.

Focus groups can be very expensive, yet doing them without careful organization can be disastrous to your marketing efforts. Yesterday’s episode of Your Business, on MSNBC, had a segment on “Do-It Yourself Focus Groups.” The segment covered the following 10 topics/tips when doing your own focus groups:

  1. Why have a focus group?
  2. How do you get started?
  3. Who do you choose (to participate)?
  4. Choose current customers
  5. Choose former customers
  6. Choose employees
  7. Start with a “Trend Question”
  8. Go around the room
  9. Ask for a rating
  10. Follow-up is key.

Although Analysights doesn’t presently do focus groups, we thought we’d share this information with those of you who are interested. Here’s a link to the 3 ½ minute segment. Enjoy!


Help us Reach 200 Fans on Facebook!

Thanks to all of you, Analysights now has more than 160 Facebook fans! We had hoped to get up to 200 fans by this past Friday, but weren’t so lucky. Can you help us out? If you like Forecast Friday – and our other posts – then we want you to “Like” us on Facebook! And if you like us that much, please also pass these posts on to your friends who like Insight Central and invite them to “Like” Analysights! By “Like-ing” us on Facebook, you’ll be informed every time a new blog post has been published, or when new information comes out. Check out our Facebook page! You can also follow us on Twitter. Thanks for your help!

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.


Help us Reach 200 Fans on Facebook!

Thanks to all of you, Analysights now has more than 150 Facebook fans! We had hoped to get up to 200 fans by this past Friday, but weren’t so lucky. Can you help us out? If you like Forecast Friday – and our other posts – then we want you to “Like” us on Facebook! And if you like us that much, please also pass these posts on to your friends who like Insight Central and invite them to “Like” Analysights! By “Like-ing” us on Facebook, you’ll be informed every time a new blog post has been published, or when new information comes out. Check out our Facebook page! You can also follow us on Twitter. Thanks for your help!

Analyzing Subgroups of Data

July 21, 2010

The data available to us has never been more voluminous. Thanks to technology, data about us and our environment are collected almost continuously. When we use a cell phone to call someone else’s cell phone, several pieces of information are collected: the two phone numbers involved in the call; the time the call started and ended; the cell phone towers closest to the two parties; the cell phone carriers; the distance of the call; the date; and many more. Cell phone companies use this information to determine where to increase capacity; refine, price, and promote their plans more effectively; and identify regions with inadequate coverage.

Multiply these different pieces of data by the number of calls in a year, a month, a day – even an hour – and you can easily see that we are dealing with enormous amounts of records and observations. While it’s good for decision makers to see what sales, school enrollment, cell phone usage, or any other pattern looks like in total, quite often they are even more interested in breaking down data into groups to see if certain groups behave differently. Quite often we hear decision makers asking questions like these:

  • How do depositors under age 35 compare with those between 35-54 and 55 & over in their choice of banking products?
  • How will voter support for Candidate A differ by race or ethnicity?
  • How does cell phone usage differ between men and women?
  • Does the length or severity of a prison sentence differ by race?

When we break data down into subgroups, we are trying to see whether knowing about these groups adds any additional meaningful information. This helps us customize marketing messages, product packages, pricing structures, and sales channels for different segments of our customers. There are many different ways we can break data down: by region, age, race, gender, income, spending levels; the list is limitless.

To give you an example of how data can be analyzed by groups, let’s revisit Jenny Kaplan, owner of K-Jen, the New Orleans-style restaurant. If you recall from the May 25 post, Jenny tested two coupon offers for her $10 jambalaya entrée: one offering 10% off and another offering $1 off. Even though the savings was the same, Jenny thought customers would respond differently. As Jenny found, neither offer was better than the other at increasing the average size of the table check. Now, Jenny wants to see if there is a preference for one offer over the other, based on customer age.

Jenny knows that of her 1,000-patron database, about 50% are the ages of 18 to 35; the rest are older than 35. So Jenny decides to send out 1,000 coupons via email as follows:


$1 off

10% off

Total Coupons





Over 35




Total Coupons




Half of Jenny’s customers received one coupon offer and half received the other. Looking carefully at the table above, half the people in each age group got one offer and the other half got the other offer. At the end of the promotion period, Jenny received back 200 coupons. She tracks the coupon codes back to her database and finds the following pattern:

Coupons Redeemed (Actual)


$1 off

10% off

Coupons Redeemed





Over 35




Coupons Redeemed





Exactly 200 coupons were redeemed, 100 from each age group. But notice something else: of the 200 people redeeming the coupon, 110 redeemed the coupon offering 10% off; just 90 redeemed the $1 off coupon. Does this mean the 10% off coupon was the better offer? Not so fast!

What Else is the Table Telling Us?

Look at each age group. Of the 100 customers aged 18-35, 65 redeemed the 10% off coupon; but of the 100 customers age 35 and up, just 45 did. Is that a meaningful difference or just a fluke? Do persons over 35 prefer an offer of $1 off to one of 10% off? There’s one way to tell: a chi-squared test for statistical significance.

The Chi-Squared Test

Generally, a chi-squared test is useful in determining associations between categories and observed results. The chi-squared – χ2 – statistic is value needed to determine statistical significance. In order to compute χ2, Jenny needs to know two things: the actual frequency distribution of the coupons redeemed (which is shown in the last table above), and the expected frequencies.

Expected frequencies are the types of frequencies you would expect the distribution of data to fall, based on probability. In this case, we have two equal sized groups: customers age 18-35 and customers over 35. Knowing nothing else besides the fact that the same number of people in these groups redeemed coupons, and that 110 of them redeemed the 10% off coupon, and 90 redeemed the $1 off coupon, we would expect that 55 customers in each group would redeem the 10% off coupon and 45 in each group would redeem the $1 off coupon. Hence, in our expected frequencies, we still expect 55% of the total customers to redeem the 10% off offer. Jenny’s expected frequencies are:

Coupons Redeemed (Expected)


$1 off

10% off

Coupons Redeemed

18-35 45 55 100
Over 35 45 55 100
Coupons Redeemed 90 110 200


As you can see, the totals for each row and column match those in the actual frequency table above. The mathematical way to compute the expected frequencies for each cell would be to multiply its corresponding column total by its corresponding row total and then divide it by the total number of observations. So, we would compute as follows:

Frequency of:



18-35 redeeming $1 off: =(100*90)/200


18-35 redeeming 10% off: =(100*110)/200


Over 35 redeeming $1 off: =(100*90)/200


Over 35 redeeming 10% off: =(100*110)/200



Now that Jenny knows the expected frequencies, she must determine the critical χ2 statistic to determine significance, then she must compute the χ2 statistic for her data. If the latter χ2 is greater than the critical χ2 statistic, then Jenny knows that the customer’s age group is associated the coupon offer redeemed.

Determining the Critical χ2 Statistic

To find out what her critical χ2 statistic is, Jenny must first determine the degrees of freedom in her data. For cross-tabulation tables, the number of degrees of freedom is a straightforward calculation:

Degrees of freedom = (# of rows – 1) * (# of columns -1)

So, Jenny has two rows of data and two columns, so she has (2-1)*(2-1) = 1 degree of freedom. With this information, Jenny grabs her old college statistics book and looks at the χ2 distribution table in the appendix. For a 95% confidence interval with one degree of freedom, her critical χ2 statistic is 3.84. When Jenny calculates the χ2 statistic from her frequencies, she will compare it with the critical χ2 statistic. If Jenny’s χ2 statistic is greater than the critical, she will conclude that the difference is statistically significant and that age does relate to which coupon offer is redeemed.

Calculating the χ2 Value From Observed Frequencies

Now, Jenny needs to compare the actual number of coupons redeemed for each group to their expected number. Essentially, to compute her χ2 value, Jenny follows a particular formula. For each cell, she subtracts the expected frequency of that cell from the actual frequency, squares the difference, and then divides it by the expected frequency. She does this for each cell. Then she sums up her results to get her χ2 value:


$1 off

10% off

18-35 =(35-45)^2/45 = 2.22 =(65-55)^2/55=1.82
Over 35 =(55-45)^2/45 = 2.22 =(45-55)^2/55=1.82






Jenny’s χ2 value is 8.08, much higher than the critical 3.84, indicating that there is indeed an association between age and coupon redemption.

Interpreting the Results

Jenny concludes that patrons over the age of 35 are more inclined than patrons age 18-35 to take advantage of a coupon stating $1 off; patrons age 18-35 are more inclined to prefer the 10% off coupon. The way Jenny uses this information depends on the objectives of her business. If Jenny feels that K-Jen needs to attract more middle-aged and senior citizens, she should use the $1 off coupon when targeting them. If Jenny feels K-Jen isn’t selling enough Jambalaya, then she might try to stimulate demand by couponing, sending the $1 off coupon to patrons over the age of 35 and the 10% off coupon to those 18-35.

Jenny might even have a counterintuitive use for the information. If most of K-Jen’s regular patrons are over age 35, they may already be loyal customers. Jenny might still send them coupons, but give the 10% off coupon instead. Why? These customers are likely to buy the jambalaya anyway, so why not give them the coupon they are not as likely to redeem? After all, why give someone a discount if they’re going to buy anyway! Giving the 10% off coupon to these customers does two things: first, it shows them that K-Jen still cares about their business and keeps them aware of K-Jen as a dining option. Second, by using the lower redeeming coupon, Jenny can reduce her exposure to subsidizing loyal customers. In this instance, Jenny uses the coupons for advertising and promoting awareness, rather than moving orders of jambalaya.

There are several more ways to analyze data by subgroup, some of which will be discussed in future posts. It is important to remember that your research objectives dictate the information you collect, which dictate the appropriate analysis to conduct.


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Are We Cramming Surveys Down Peoples’ Throats?

June 16, 2010

Yesterday, I wrote about the problems free online survey tools can cause if a survey is not well-constructed and its purpose isn’t properly thought out. Today, I want to spend time talking about another issue, one that is directly influenced by the availability of several online survey tools, both free and full-price: the overabundance of surveys. Back in the day, I used to look forward to those mail surveys that used to come in the mail once and a while, so that I could open them up and take the dollar that came with them and throw the surveys into the trash (I was in high school then)! Surveys were so few and far between that they were practically a welcome interruption in our lives then.

Fast-forward twenty years. Email, social media, and the Web have transformed everything. Communication is a lot faster. Competition for customers in almost every industry is cutthroat. Angry customers will not hesitate to tweet or blog their dissatisfaction to anyone who will read, retweet, or forward their rants. People have so many choices for entertainment, where and on what to spend their money, and who to buy from.

Businesses need to stay relevant to their customers and need constant feedback. Quite often, the best way to do it is the survey. When you buy a new car, the dealer sends you a survey. Have lunch at Panera? Your receipt will have a website you can visit to take a survey. Stay at a hotel? Survey. Attend a seminar? There’s an evaluation form at the end of the session. Welcome to survey Hell.

We are bombarded with surveys everywhere. I used to be on a consumer panel to take surveys and earn reward points. After two months, I stopped answering because I was getting three of them a week! When you have a business to run, a life to live, and other responsibilities, you just can’t take every survey. After a while, these surveys get complicated and involved. At 9:00 pm, as I struggle to stay awake, I don’t want to take a survey that makes me think!

Let’s consider the various problems involved with survey overuse. Among them:

Reduced response rates

Too many surveys reduce their value. If you get one survey every two weeks, you might complete most, if not all of them. If you get a survey every two days, you’re probably not going to complete even half. There’s just not time. Also, because free online tools have enabled many amateurs to launch a survey, many of these amateur survey “professionals” construct questionnaires with vague, misleading, loaded, or double-barreled questions. Some questions have too many choices. These surveys tend to frustrate respondents, who may choose not to participate. Furthermore, amateurs may pay no attention to the relevant population, and send the survey to anybody, and only those with an interest will respond (and their responses will be biased).

Biased or bogus responses

Imagine getting a survey that wasn’t relevant to you. You might either not respond to it, or you may jokingly fill it out and send it back. Either way, the result is useless to the one conducting the survey. Or, imagine getting a survey whose questions are described as above. If you do respond, your responses won’t be truthful. Or, if the survey is complicated, requiring you to rank several items, or choose from a long list, you may be tempted to answer just the top choices, or pick your choices randomly or haphazardly. You might be compelled to do the same if you just get tons of surveys, or surveys that pay you for taking them.

Another way bias rears its head is in customer satisfaction. When I bought my car four years ago, the dealer told me I would be receiving a survey. He asked me to give him 100%, because his performance evaluation depended on the number of buyers who gave him 100% satisfaction. Another time, I was eating in a Corner Bakery Café in downtown Chicago, when an employee came up to me and said that if I could fill out a customer satisfaction form favorable to the store, there would be a free pastry in it for me. Seeing any problems here?

Reduced Brand Image

Survey abuse can even hurt your company’s brand image. Imagine if different departments send out their own surveys. What if marketing sends out a customer satisfaction survey, while the product development department sends out a survey of its own? Without coordination between the two departments, they could be surveying many of the same people with many of the same questions. As the respondent, you see only the company sending you the surveys, not the individual departments. Hence, you view the company as inefficient and “clumsy,” so you begin to question its brand, service, and quality.

What to do?

There are several ways we can remedy the abuse of surveys. The most immediate thing to remember is that surveys are not the “be-all and end-all.” There are many different opportunities to collect feedback from customers. Businesses need to be nimble, but not be superfast. Remember, haste makes waste. Here are some suggestions:

Save surveys for major projects and initiatives; use other immediate forms of feedback

It’s OK to have a very brief survey to give to customers at the point of service to understand their satisfaction. But nine times out of ten, you should save your surveys for obtaining really important information: identifying the optimal price to charge, determining the size of a market, understanding public opinion, identifying which marketing messages work best, or conducting surveys if and only if there is no other good way to get key information. Instead, try to generate feedback from less formal channels. A hotel might train its service desk employees and concierges to ask guests at various touchpoints about how their stay is going; ask what services or amenities they could use; and ask what can be done to make the remainder of the stay even more enjoyable. The employees can note the responses privately, and feed them into a client database, enhancing marketing messages and service level treatment for future stays.

Don’t tie customer satisfaction to employee incentives

Customer satisfaction is important, but if you tie employee compensation to increasing satisfaction, you’re likely to get scenarios like those I faced with the car dealer and the café. Customers can say in their survey that they were satisfied and that they would return, but then never do so. Instead, base employee compensation on other customer service factors that will truly increase satisfaction.

Try other ways to engage customers

Instead of having seminar attendees fill out an evaluation form, instead, a moderator could take the last 10 minutes to solicit open, honest feedback from the audience to see what they liked, didn’t like, and what could be done better. It’s one thing for people to write things down privately, but another to give thoughts publicly. There is strength in numbers, and people may be inclined to give more honest feedback, for better or worse, collectively. Other businesses might encourage their customers to talk about their experiences on the company blog, Twitter, or Facebook.

In summary, surveys are not the only means of obtaining insights. A good combination of open-customer communication, social media, secondary research, and customer service delivery, along with carefully thought out study objectives can prove highly invaluable. If you see response rates dropping steadily from survey to survey, it probably means you are surveying too much!