Do you know which of your marketing programs are working and which ones aren’t? If you are like most marketers, you continue to struggle with marketing measurement—despite the abundance of tools to help track success.

Part of the problem, as explained in the infographic below, is that difficult-to-measure channels, such as television and newspaper advertising, will continue to dominate marketing budgets. And problems persist even in the easy-to-measure online world. The emerging field of voice-based marketing automation is working to address these measurement challenges because it is becoming more important to determine the right metrics for campaign measurement.

Ifbyphone, a leader in voice-based marketing automation, assembled statistics on how marketers currently measure ROI. What tools are they using? What key metrics are they tracking?

Use this knowledge to start planning your own measurement initiatives for 2012.

There’s no secret that many new business ideas never get off the drawing board. The risks and initial investments are just too great for the majority of new business ventures. However, I will reveal a few secrets that most people don’t realize they can do at home to determine the viability and potential market of a new business idea.

When researching demand for a new business venture or idea I have, I always start online. There are a number of procedures and tools that you can use to determine demand for a particular product or service—even if no one else is offering it yet and it’s completely unique to the marketplace.

With five simple (and free) steps you can easily determine whether your new business idea is viable.

1.) Use keyword tools to estimate the search volume of the keywords relevant to your business idea. To determine how many people are interested in products or services relating to your new business idea, use search tools to get an estimate on how many people are actually looking for them. For example, at my site, I found out the number of monthly searches for things like: “customer appreciation program,” “customer retention,” “customer thank-you gifts,” etc. That gave me a good idea of how many people were actively interested in a customer-retention program or gifts for customers.

There are a number of free and paid tools you can use that are fairly accurate. Google Keyword Tool is the best free tool. SEO Book Keyword Suggestion Tool, Wordtracker, and Wordstream are also great applications to estimate keyword search volume. By using a few different tools and comparing the results, you can get a very accurate picture of the demand for your product.

2.) Search the terms on Google and analyzed the results found. Find out who else is  offering comparable services and ranking for those keywords that you just analyzed. After filtering out the results for relevance, you can use a number of other free tools to identify the estimated traffic for those domains and the top referring keywords for each site. This will help you identify how successful the competition has been and helps give you a clearer picture of what you can hope to accomplish. You can also analyze the backlink profiles of those domains to see what kind of press their comparable products have been receiving—and it can point you in new directions of where to research your ideas further.

3.) Look at the CPC (costs per click) for each keyword in Google Adwords and Microsoft Adcenter. By looking at what the competition is paying for traffic for all your relevant keywords, you can get a clearer picture of what kind of return you should expect for each visitor you can get to traffic your site. A low CPC shows that people searching for that term are not very likely to be qualified buyers. Testing different ad copy and targeting different keywords, then analyzing the results, gives you a better idea of which terms/aspects of your business and marketing message should be reevaluated.

4.) Browse the question and answer sites online for questions relating to your business idea. By searching sites like Yahoo! Answers, Ask, Quora, and a number of other Q&A sites, you can see if others are interested in the new service you are thinking about providing. You can even start looking in forums if you don’t immediately find what you’re looking for.

5.) Crowdsource your friends, family, and business contacts. Besides the obvious ways of calling and face-to-face contact, Twitter, Facebook, LinkedIn, and Google+ are great methods for crowdsourcing opinions from your contacts. Facebook even offers built-in polls that you can use for this very purpose. You could pay to outsource this service as well with things like focus groups and group surveys … but this should always be done last, because after doing the first four steps, you might not even need to!

If your results are still inconclusive, it might be a good idea to plan a small-scale product/service launch. For example, create an informational website about your product. Make it known that the product or service is coming soon and that visitors can join your mailing list to be notified once the product/service is available. (Offer some sort of discount or incentive as well.) Use PPC methods, email marketing, or even SEO to get traffic to the site. Depending on the result, you can determine the interest level of potential customers in your new business idea.

New business ideas are dreamt up every single day. The difference between going broke and becoming a multimillionaire is determining whether your business venture is viable and potentially profitable. Taking these five steps into account, you should be able to save time and money the next time you get a great idea for a new business.

Chris Wise is head of SEM at Customer Rave.

So, what’s the driving force behind all this change, the force that has marketers failing? It’s the buyer. Studies continue to confirm that, when it comes to making a buying decision, they have more access to technology, more interaction with their peers via social networks, less need for vendor-driven information, and less reliance on salespeople. Marketers are struggling with this new dynamic. Amid the morphing of the buyer and, subsequently, the B2B environment, marketers are failing to convince buyers to buy their products or services. Why? Because there remains one constant no matter how technology-driven or socially enabled we become. That one constant (and many fail to understand it) is that we market and sell to people.

As obvious as that seems, it is something that is lost in many of today’s marketing approaches. It’s as if we in the B2B space have failed to understand that behind the “B” in business to business are hosts of “Ps” (people).

A recent study by Televerde shows that even though some companies are making an attempt at nurturing the buyer, they are truly not connecting to the human element of B2B marketing.

• Over 60% of those surveyed stated incomplete data and poor data hygiene as their biggest marketing challenge when it came to data.
• Only 29% stated that more effective and relevant customer touches should be the highest priority for lead nurturing. (For that 29%, conversion rates ranked the highest. This indicates that  more effective and relevant touches lead to higher conversions.)
• 83% described their definition as providing educational and collateral (i.e., one-way communication).
• 57% make no distinction on how they nurture leads based on their buying stage. (All buyers are treated the same.)
• Of those that use nurturing, 54% do not use lead scoring. (Lack of lead scoring does not allow for one-on-one dialogue, but ensures that all buyers are treated the same.)

As seen above, many organizations are still not clueing into the fact that we are selling and marketing to people. People are the ones who make the buying decisions and vendor choices. Those people have thoughts and emotions that affect their buying decisions. We must remember that. To help move from a B2B mindset to a B2P mindset, here are a few quick tips.

Stop Trying to Sell

Randy Aronis of Ingenuity Sales Consulting has taught many that the first sales call should be focused on the building of rapport. In other words, start by getting to know your buyer. It seems crazy, but try it sometime. The first time I tried this was early in my career. I was selling into the largest IBM distributor in the world and had secured a call with one of the key decision-makers.

After the obligatory introductions and overview, we spent the next 25 minutes talking about golf, our favorite courses, etc. (Keep in mind the call was scheduled for 30 minutes.) We ended the call by scheduling a follow-up onsite that, of course, included a round of golf. Long story short: They were one of my best accounts for my remaining two years at the company. Why? My contact told me, “You guys were not the lowest cost nor did you have the largest staff, but I liked your company and knew I could call you directly if we were ever going to have a problem. I felt that you would personally address it.” Instead of selling, I built a relationship. Marketers would do well to begin thinking this way.

Put the Social Back in Social

The term “social media” is interesting to me. Yes, there is indeed a component that is social. Yet, social media also provides a false sense of relationship. Too many marketers are using this channel to provide one-way communication, doing nothing but sending out facts, figures, and business information. They forget to use social media to engage. What does that look like? Well, for starters, don’t be afraid to have some fun with social media. Comment on posts, answer questions in forums, and engage in online conversations. This will significantly enhance relationships with your buyers online and socially. Buyers want to know there are people behind the walls. So, listen and respond.

Act Like a Politician

OK, before you stop reading, don’t worry. By suggesting you act like a politician, I am not suggesting you give yourself a pay raise every year, slander your competition, or tell lies to divert attention from your mistakes. What I am suggesting is, like many politicians, seek to get personal with your buyers. This doesn’t necessarily mean a marketing/sales bus tour. However, it does mean adding face-to-face interactions to the mix. Marketers erroneously think that the face-to-face stuff is for sales to handle. Not so! Marketers need to stop hiding behind technology and to start meeting with buyers. This can be done by attending sales calls, conferences, and even one-on-one meetings. Nothing will replace a face-to-face interaction and connection. Sitting down across the table from your buyers (or playing a round of golf) will go a long way.

Be Slow to Speak

This goes hand in hand with the “Stop Trying to Sell” tip. My brother tells his kids all the time, “God gave you two ears and one mouth to remind you that listening is more important than speaking.” He may have something there.

By listening to your buyer and asking questions, you will gain (as will they) more from them than from all the things you so desperately want to tell them. You will also show them that you are genuinely interested in them and in meeting their needs. In the course of building a relationship, you’ll have plenty of time to talk about your product or service. So, be patient.

No matter how advanced technology becomes or how easy it gets to dole out information about our products and services, we’ll always be marketing and selling to people. That fact is something we cannot afford to forget.

Wow! Way to go? Or wrong way to go? While consumers “tsk tsk” about oversized meals and beverages in restaurants and fast food joints, many get more than a little pleasure, albeit guilty, in over-indulging. Let’s be truthful here. So the question remains: Is this a major marketing faux pas or a stroke of genius?

Nutritionists are undoubtedly alarmed at the possibility of consumers adding up to 200 calories per day—depending on their beverage of choice. And 200 additional calories per day adds up to 2 more pounds per month. Yikes!

And what about the amount of caffeine in 31 ounce coffee concoctions? While our culture has become increasingly caffeinated on high-voltage energy drinks and larger and larger mugs of coffee, I wonder when the human body’s tolerance is maxed out!

What do you think of this move? Is Starbucks going to cash in on our more-is-better consumer craze? Or are they going the wrong way now that consumers have said “no” to excess in every way, shape, and form? Or does Starbucks know something we don’t know? Are consumers saying “no” while their actions say “yes” to supersized portions still?

I’d love to get your take on Starbucks’ latest marketing move, Daily Fix readers, whether you’ve indulged in the 31 ounce Trenta or not.

A great way to evaluate your ideas is by categorizing them into what Edward de Bono in his book Serious Creativity: Using the Power of Lateral Thinking to Create New Ideas calls End Categories.

Understanding and using these categories will help you to focus, prioritize, take action, or reject the right ideas. The last quarter of your brainstorming agenda should be dedicated to categorizing ideas in these buckets.

1.) Directly Usable

These are your best ideas. You’ve determined they have value and could be used. These babies are worth exploring deeper and finding resources to support.

2.) Good Ideas, But Not For Us

These have value and support your objective, but are not a good fit. Maybe you lack skills or resources, but most often your brand filters eliminate these ideas. In a future round of thinking, you could explore these and ask,  “How could these be modified to be a good fit?” But more than likely, these ideas should be discarded.

3.) Good Idea, But Not for Now (Backburner)

These have value and are a good fit—but are not right at this time. Current resources, capacity, or priorities may not allow you to act on these. Put them on the backburner. Revisit them in a month or a quarter.

4.) Needs More Work

Ideas with potential, but are half-baked. With more work you can transform these into Directly Usable ideas. Get some folks working on these.

5.) Powerful, But Not Usable

These are usually great ideas blocked by some external force. Factors you can’t control that de Bono calls “regulations, environmental concerns, very high risk factors, cannibalizing existing products and so on.”

6.) Interesting, But Unusable

These are some of the most productive ideas. Not because they become usable, but because they spark other usable ideas. They often offer new ways about thinking of things. These are the ideas that start as “Hmmmm?” and spark “A-ha!”

7.) Weak Value

These ideas work and they fit your organization, but they lack value. The return on the effort invested in these may be disappointing. The danger with Weak Value ideas is that sometimes we accept and implement these just to “have something out there.” They support a “something is better than nothing” approach. Don’t fall into this trap. There may be ways to re-work these ideas so that way they work, are a fit, and become high contributors.

8.) Unworkable

These are fundamentally impossible. Not even if you worked hard on them.  They’re duds—and should be rejected. Period.

Finally …

Turn these ideas into action by assigning Action Steps to individual owners for each idea. This makes one person accountable for an idea.

• Begin exploring the Directly Usable ideas.
• Take a final look at the Good Idea, Not For Now before putting those into short-term storage.
• Get folks cracking on Needs More Work ideas.
• Tinker with your Interesting, But Unusable to see what other ideas they could spark.
• And make everyone promise not to monkey with the Weak Value or Unworkable ideas.

A previous column “Is the Speed of Decision Making Accelerating?” cited how a century ago, managers could take weeks or days to make important decisions. That’s because before the advent of the telephone, it would take a substantial amount of time for information to travel by courier. Fast forward to the 21st century, most executives now have a mobile device and can be reached at a moment’s notice.

Our global society is moving towards a zero latency world, where the reduction of time between decision and action is drastically reduced. And we need to look no further than Wall Street’s high frequency traders for evidence.

John Plender of the Financial Times recently defined high frequency trading (HFT) as a “type of computerized dealing (that) exploits the millisecond gap between news events and their impact on markets … such trading has expanded rapidly to the point where 60-70% of the trading volume is in U.S. equities. Much of this volume is conducted by a very small number of companies.”

So what’s wrong with HFT? Plender cites potential problems, such as the “ability (for high-frequency traders) to see orders before they are public” and the propensity for high-frequency traders to co-locate servers on the floor of stock exchanges for faster trading (something not available to the average investor). In addition, the race is on where the winner in high-frequency trading can close trades as fast as 250 microseconds—faster than you can blink your eye!

The speed of decision-making is accelerating. In HFT, the trend is unmistakable. Machines are trading with and against each other. They’re moving ahead of individual investors, leaving day traders in the dust. And as a Financial Times article notes, speed isn’t just confined to Wall Street:  “Technology has changed many other big markets around the world and tied them more closely together … Such changes has created winners and losers.”

For marketers, the implications of zero latency are clear. For example, did you know that “robots” are purported to perform text mining on press releases when they hit the wire? With analysis completed in microseconds, advanced algorithms then execute trades based on what they’ve learned. Your company’s equity price could go up or down in seconds, based on the words in your press release!

In a zero latency world, what marketers (and other employees) say, write, tweet, and announce can all be used as fodder by the machines to either raise equity prices or destroy shareholder value. Our ability to react and “fix” our mistakes before they are noticed is greatly diminished. All it takes is a bad press release, poorly written whitepaper or negative analyst report.

And it’s not just PR. To borrow a phrase from Thomas Davenport, companies are now “Competing on Analytics.” Marketers must understand that they are now engaged in an arms race with competitors mining their own (and third-party) data for insights—increasingly by the hour and minute, and then taking action to better connect with customers. Companies without these capabilities will increasingly face mammoth disadvantage.

Zero latency decision-making isn’t the future. It’s now. Are you ready?

Consider the following:
Regarding decision-making, how fast is too fast? What could go wrong at high speeds? What happens when the human element is removed?

But somewhere among the scoops of award-winning ice cream from Time Out New York readers, serving aces at the U.S. Open, and rolling out mobile ice-cream units, Blue Marble Ice Cream took on a new flavor. It added the philanthropic Blue Marble Dreams.

At a theater workshop at the Sundance Institute, Dundas—co-owner of Blue Marble who is also an actor— was approached by Odile Gakire Katese, a Rwandan drummer and playwright with a dream. She wanted to bring ice cream and self-sufficiency to the people in Rwanda.

“Because we struggle most of the time, we find ourselves aggressive against happiness, love, joy, life … Rwandan women want to reshape life in its simplest and sweetest form,” Katese told Dundas.  “We want to create a space where poverty, disease, illiteracy are not obstacles to happiness and barriers between human beings … The ice cream will have the power to reconcile people with life by providing privileged moments when life reminds them that it is also sweet.”

In response, Blue Marble created the non-profit venture Blue Marble Dreams. Through hard work; partnerships with women in Butare, Rwanda; even more hard work; and generous financial gifts, Rwanda’s first-ever local ice cream shop is opening in June 2010. The name? Sweet Dreams (Inzozi Nziza), of course.

Blue Marble Dreams came to be because Dundas and Miesen took a risk of pursuing a dream. So, when an opportunity arises for your own non-profit or for-profit venture, what should you do? Consider these tips.

• Know your focus. Blue Marble ice cream is sourced from an organic dairy co-operative in Lancaster, Penn., that includes Amish and Mennonite farmers. This leaves Dundas and Miesen to focus on the power of ice cream rather than the recipe. “We have no real interest in doing the culinary aspect. What we feel is our real strength is creating and building a brand that is going to have a major impact,” says Dundas.
• Do your research. Dundas and Miesen spent a considerable amount of time learning about Rwanda and where to open up a shop. They decided on Huye, Rwanda, a city known for its “cultural pulse.” Plus, Rwanda has a prosperous dairy industry. And they spent hours and hours learning about Rwanda from Katese.
• Consider any training or further education needed for people involved in the project. Blue Marble Dreams provided extensive English and accounting classes to 100 Rwanda women. Though not all were chosen for the shop, they all benefited from the education and expanded their skill sets to make them more employable. Then, Blue Marble Dreams brought representatives of the Business Council for Peace to spend an intense week helping the women work on customer service, running a business, and managing personal finances.

What advice would you give to someone considering an opportunity that has sprung up? Be sure to leave a comment. I enjoy hearing from you.

In May 2010’s issue of Wired Magazine, author Clive Thompson laments the poor mathematical literacy of his fellow citizens. For example, he cites people laughing at the concept of global warming as they face some of the harsher winters on record, or the extra-vocal debate on vaccines and possible links to autism. Mr. Thompson would tell us that it’s the trend lines that matter, and we too often look at the trees and miss the forest.

The problem, he says, is that “statistics is hard” and an overall understanding of this important discipline is severely lacking. He says, “If you don’t understand statistics, you don’t know what’s going on, and you can’t tell when you’re being lied to.”

Thompson is correct that statistics are difficult for most of us, and that thinking by the numbers takes training and much effort. It’s also true that one must understand statistical concepts, especially when percentages, populations, and probabilities are bandied about in business and technical press. However, broader acceptance of the power of statistics should be tempered with limitations of this mathematical science.

Before accepting any statistic, study or experiment as gospel, the following should be considered (there may be more…):

1. Assumptions: What are the assumptions underpinning the research? As seen from recent debate on CBO numbers for the U.S. health reform package, assumptions matter tremendously.
2. History: How much historical data was used in the study? What was the time scale? As seen from the 2008 financial crisis, the models used by Wall Street mavens often only took into account 10 years of data in judging the volatility and probability of failure of complex financial instruments.
3. Samples: Are the samples selected randomly? From what populations? Is there enough data for statistical significance?
4. Data Quality: The output is only going to be as good as the quality of data feeding the analysis. Garbage in, garbage out.
5. Survivorship Bias: Author Nassim Taleb points out that “losers are often not in the sample.” Does the analysis include a population of survivors and those who also failed?
6. Falsification and Omission: Yes, in an era of IPCC’s Climate Gate, one needs to ascertain if data are hidden, missing or outliers ignored.
7. Association equals causation fallacy: Correlation does not equate to causation (a common mistake made by marketing and finance executives alike).
8. Proper Application of Statistics: The effective use of statistics by insurance actuaries, scientists, and even casino managers is well-documented. However, real danger results when mathematical concepts are used to denote certainty indecision-making and divining behavior of markets.

Now, please don’t get me wrong. Statistical analysis is very important for many industries (e.g., health care, transportation, and manufacturing). Statistics, however, can give us an illusion of control in a world that’s much more complex than our models suggest. Nassim Taleb, author of the Black Swan likes to remind us that “(real) life isn’t a casino.”

Statistical analysis is definitely a powerful gadget in the business manager’s decision-making toolkit. But one needs to understand the limitations of this science.

After all, Taleb points out that many of today’s statistical models work as though we have “full knowledge of the probability of future outcomes.” And this just isn’t so, especially when it comes to fat tails, or the “ten sigma” event. Indeed, sometimes those rare events have extremely large impacts. Were he alive today, the former captain of the Titanic, E.J. Smith would wholeheartedly agree.

Questions:
• Clive Thompson calls statistics “the language of data.” How important is it for marketers to understand and apply statistical concepts?
• “Lies, damned lies, and statistics” is a phrase popularized by Mark Twain in the context of using statistics to unduly persuade, obfuscate or even swindle. Can statistics get its reputation back? If so, how?

It’s been said the phrase, “this time is different” is one of the most dangerous sentences in business. That’s because executives keep making the same mistakes again and again say economists Carmen Reinhart and Kenneth Rogoff; “We gullible humans (believe) that the laws of financial physics have been repealed for us.”

Why do humans keep making the same mistakes? Perhaps it’s because over optimism—and resulting speculation—is very much a part of the human psyche. We like to believe those who have previously failed just didn’t have the right information, or that a new paradigm has emerged. And sometimes changes are so fundamental and drastic that they do create new markets. But more often than not, we’ve exchanged our money, time and hope for worthless swamp land.

Now what does any of this have to do with marketing?

An important role for marketing executives is to provide direction to our business leaders regarding trends, white space, and best areas in which to compete or avoid. We do this via a thorough understanding of competitive, social, governmental, and economic forces within a market.

In adding a potential new product or service to our portfolios, we need to ask ourselves, is this market sustainable —or does it depend on unstable factors? How long will this market exist? At what stage of the lifecycle is the market? Does my company have the capabilities to compete? Can my company make a profitable impact?

And this is where diagnosis of a market bubble comes into place.

Now let’s be clear. Not everyone believes in economic market bubbles. Some economists are convinced that people have all the information they need and therefore always make rational decisions. Efficient and rational market theorists from the Chicago School of Business, in particular Eugene Fama, don’t believe in unstable and wild market inflations. “I don’t know what a bubble means,” Fama recently declared to writer John Cassidy.

However, since there’s an abundance of evidence for market euphoria, let’s assume economic bubbles do in fact exist. The next step is identifying whether the market in which you plan to participate is in fact prone to speculative behavior (even mania), and if so, should your company compete or walk away from the opportunity?

These are a few questions that could have been asked by senior management at Lehman Brothers as they jumped headfirst into frenzied markets.

In the book, “A Colossal Failure of Common Sense; the Inside Story of the Collapse of Lehman Brothers,” former Lehman Brothers vice president, Larry McDonald cites how then CEO Dick Fuld and his second in command Joe Gregory made bet after bet, first in derivatives such as collateralized debt obligations (CDOs) and credit default swaps (CDS) and then grandiose real estate purchases.

These purchases—with borrowed money—were made with the following inherent assumptions:

1. the market would keep rising indefinitely,
2. there would always be a market for securitized debt, and
3. what’s profitable for competitors must also be the same for Lehman Brothers.

Sadly, we know how the story ends. McDonald relates, “When a high rolling market goes wrong, history tells us that it happens with lightning speed, as everyone stampedes for the door at the same time.”

Indeed, as the market for derivatives self destructed, Lehman was stuck with a bag full of product than nobody wanted, to the tune of sixty billion dollars. Senior management failed to ask themselves, “how long can this market sustain itself?” or even “what’s our current position and what happens if this bubble pops?”

It seems that it’s quite easy to get caught up in the euphoria of a new market, especially when everyone appears to be making boatloads of money. An ebullient market looks like it will never end.

However, it’s very possible to enter at the very top of the market and not know it, effectively joining the party just as the host removes the punchbowl. And this is where very careful analysis from the marketing function can come into play.

While a frothy market may be pretty easy to identify, it’s difficult to know when it’s going to end. Participating in a market bubble is a risky proposition and timing (getting in and out) is everything. And for those analytical types, even if deep market analysis is performed, it’s possible your timing may be off by just a bit, leaving you short or long. After all, as John Maynard Keynes once said, “The market can stay irrational longer than you can stay solvent.”

One thing is for certain, history repeats, or as others have said, it rhymes. Lehman Brothers stood for 158 years but participation in one of the largest asset bubbles in history brought this noteworthy firm to the steps of bankruptcy court. Lehman rode the bubble and didn’t “get out”. The musical chairs stopped with nary a seat.

It really wasn’t different this time.

________________________________________________________

Marketers, do bubbles exist? Is it possible to discern a bubble? How can one discern when to “get out” of a frothy market before it implodes?

• Declare the decision you’re evaluating,

• Identify and prioritize the qualities of the decision,
• Enter a selection of potential Options or choices (what you’ll ultimately choose), and
• Rank those options on how well they satisfy your needs.

The spreadsheet uses this information and calculates a weighted score for each. The Option with the highest score is your most desired choice.
You will find this Decision Making Tool helps at the office and at home with any decision you need to make. Using it you will have more confidence and feel better about your decisions.
Click to download the tool and get started:
Decision Making Tool (Excel Spreadsheet, 28 kb)

A Bit More Detail…

What makes this approach helpful is that it requires you to identify decision criteria – the values and qualities – you use to compare one option to another.
At work, your team may be debating which new product to launch in the Spring. Qualities you may consider for this decision may include: being market ready, ease of implementation, seasonally appropriate, minimal training required, etc.
If you’re thinking about buying a car, what are the qualities or the values you are using that will help make up your mind? Perhaps you’re considering: safety, fuel efficiency, comfort, space for 3 people and +1 dog, payments at a certain level, etc.
This tool is technically called a “Weighted Criteria Decision Matrix” and is used by professionals to make decisions using both quantitative (measurable) and qualitative (subjective) information.

Sometimes marketers believe that customers want more choice. According to an MIT Technology Review article, however, in the online dating market new research shows that, “users presented with too many choices experience cognitive overload and make poorer decisions as a result.”

The Technology Review article cites research from two professors from National Sun Yat-Sen University in Taiwan where in an experiment they presented online dating users with wide and deep selection of potential matches. After all, customers want more choice–right?

Not so according to the study: “More search options (led) to less selective processing by reducing user’s cognitive resources, distracting them with irrelevant information, and reducing their ability to screen out inferior options.” In effect, users suffered from data overload where too many choices prohibited them from making an optimum decision.

Here’s where statistical analysis can help reduce choice overload.

Online dating sites often attempt to use sophisticated computer applications and proprietary algorithms to divine appropriate partner matches based on user inputs such as preferences for race, religion, eye or hair color and more. EHarmony’s matchmaking algorithm, for example, helps select potential partners based on a 258 question personality test.

With a deep historical data set of what EHarmony determines as “success” (236 marriages a day according to the site) this online company believes they can predict matches with a high degree of probability.

For sites like EHarmony, Match.com or others, the challenge isn’t showing all relevant results (like a Google search that delivers 2,000 hits) but just the top ten and maybe worst case–twenty. This of course, assumes that the algorithm is based upon the right “predictors” of successful match making, and that the science behind the scenes can be trusted.

The lessons for online dating companies–much less any business–is due to carrying costs or customer confusion, less choice can actually increase sales and profitability! In fact, some large U.S. retailers are starting to investigate this idea by reducing the variety of different products carried by up to 15%.

The experiment run by the Taiwanese professors shows that when it comes to choice …. less is more. If this hypothesis is true, how then does a marketer decide which choices to reduce?

Reduction in customer “options” must be based on carefully considered variables and analytical analysis. For example, a category manager at a retailer–let’s say the toothpaste aisle–shouldn’t automatically assume that the products with the lowest sales should be removed.

Careful analysis including variables such as year-over-year sales comparisons, seasonality, pricing, profitability, trade promotion dollars, etc should be considered. In addition, market basket analysis may inform the retailer that a brand of slow selling toothpaste is in fact often purchased with other very profitable items. Indeed, assortment optimization and shelf space allocation can be a very scientific exercise. A company should also set up control groups for experimental purposes to test a hypothesis before making any permanent changes.

The use of algorithms and careful analytical analysis are two ways that companies are reducing and optimizing customer choice. In the end, customers may not ultimately want more choice–just more relevant options.

Questions:

• In online dating, some users may want more results (choices) presented because they feel that they can judge a “match” more effectively than a computer. Are there instances where delivering more choice options is the better strategy?
• Are there dangers of optimizing customer choice–especially when it comes to encouraging new innovative products/services with no sales history?
• Predictors of a “good match” in online dating can be highly subjective. How would you counsel online dating companies to improve their matchmaking capabilities?

Related article: Less is More in Consumer Choice

Let’s face it, the ability to peer around the corner and forecast the future (tomorrow, much less next month) is one of the biggest challenges for global business executives. If you have made forecasting mistakes in the past, take heart, you’re not alone. You can however, learn to forecast a bit better by avoiding a common mistake–anchoring.

The concept of anchoring in decision making was made famous by psychologists Daniel Kahneman and Amos Tversky. In a well known experiment, they asked a group of people to estimate the percentage of African countries in the United Nations.

Before each person guessed, they were asked to spin a wheel with the numbers 0 to 100 clearly labeled. According to a Money magazine article, “When the wheel landed on a low number, people tended to guess that African nations made up a small percentage of UN members; when it landed on a high number, they guessed that Africa accounted for much more of the UN’s membership.”

A recent Wall Street Journal article also highlights the problems of anchoring. The article mentions that in December 2008, Barron’s asked a dozen experts to forecast the level of the Standard and Poor’s 500 stock index at the end of 2009.
Despite a tumultuous 2008, where the stock market would regularly jump 500 points in a single day, not one expert predicted a down year for 2009. And while 2009 still has yet to play out, each of the twelve experts predicted a 5-38% increase with a median of 13%!

The author of the WSJ article, Jason Zweig, believes the stock picking experts are guilty of anchoring. He says we tend to over-extrapolate in forecasting when we base our decisions mostly on what happened in the past. Zweig writes, “(With) the fat five years from 2003-2007, when stocks shot up by an annual average of 12.8%, who expected 2008 to be a bloodbath?”

Who indeed? With the exception of Nouriel Roubini and a few others, most market prognosticators missed the mark.

In fact, accurate forecasting can be hard–or easy. Let me explain.

Years ago, as a divisional manager for a regional telecommunications firm, I was responsible for presenting my annual division revenue forecasts to the company president. These forecasts were very important, as staffing decisions and budgets would be initially based on the “acceptance” of the forecasts.

When visiting the office of another divisional manager, I was shocked to find out he was finished with his forecasting processes–in record time. He finished quickly by taking last year’s revenue numbers and adding 10%! Adding insult to injury, his last year plus ten percent forecasts were accepted by the company president!

Alas, the above story would be much richer if my colleague’s exercise in anchoring ultimately cost him dearly. He was lucky–in this instance his predictions came pretty close to reality. His gut decisioning (or perhaps laziness) came through for him.

However as levels of volatility and systemic failures increase in marketplaces and economies, and wild swings become the norm–anchoring and basing forecasts on what happened last year can ultimately lead to disaster. Need proof? Just query the terms, “investment bank” or “hedge fund” in the search field of the online Wall Street Journal.

To avoid anchoring in decision making, carefully consider assumptions and take no probability off the table. Think about your “reference point” for making a decision and then consider reasons why this particular point is your anchor. Does it take into account the possibility of an extreme outcome?

If you think it couldn’t happen–pay special attention to that scenario. It’s pretty wild out there–anything is possible!
Questions for DailyFix readers:

* In a tough economic environment, the penalty for poor forecasting increases. What tools and methods are you using to anticipate events and predict the future?
* Author Peter Bernstein says, “The successful businessperson is a forecaster first–all other activities follow.” Do you agree with this statement?
* What “experts” via news, commentary, consulting etc, are helping to guide your decisions on the future? Are these experts getting more or less reliable?
* What is the one event–if it happens in 2009–that will shake up the game board and change all the rules? Is this event on your radar screen?

Like it or not, important marketing decisions regarding forecasting, budgeting, hiring and resource allocation must be made for the coming year and beyond.

Marketing executives who sit on the sidelines and watch/wait could be missing some valuable opportunities to stake a claim in new markets, build market share, or capitalize on competitor weakness. Then again, sitting on the sidelines may be the smarter approach.

A common form of decision making is using probability to determine potential outcomes. In a casino–with games of chance–it’s pretty easy to figure out the “odds” of beating the house. Outside of the casino, life gets a little messier. We can however, use statistical analysis based on historical data to help us divine the probability of certain events happening (assuming a normal distribution and independence).

In an Edge essay, Dr. Nicholas Nassim Taleb tells us, “Statistical and applied probabilistic knowledge is the core of knowledge. Statistics is what tells you if something is true, false or merely anecdotal. It is the logic of science, and the instrument of risk taking.”

“You cannot be a modern intellectual and not think probabilistically,” Dr. Taleb declares. But he also counsels us that there are many instances where “statistics don’t work–where stats are unreliable, where your knowledge is no longer valid.” And judging from the volatility of events in 2008, we just might be in new and uncharted territory where the usual tools and methods just plain don’t work.

If you believe 2008 has ushered in a new paradigm where the old rules no longer apply, one is essentially left with two choices: Pattern recognition of a different kind–(intuition) and thereby making decisions “by the gut”, or inaction–doing nothing, at least for now.

Is gut decisioning the best approach to plan for 2009? Gary Klein, author of “The Power of Intuition” says, “Analysis doesn’t work well in challenging situations where information is scarce, time is short, and stakes are high.”

While it is hard to argue that information is scarce, time is definitely of the essence and for many companies; the stakes (i.e. survival) have never been higher.

Lastly, there’s always inaction as a completely valid alternative. Many companies are maintaining the status quo, hoarding cash, letting the bodies pile up, and waiting for a better day before committing to major investments.

Every company is different, and every industry has their own challenges right now. But it has been said that challenge is only one side of the coin. Turn it over, and “opportunity” might be staring back at you.

Key questions:
* Given the events of 2008, are we in a new paradigm? If so, do the old rules no longer apply?
* Consumer confidence is at levels unseen since the early 1990s. Is the marketplace, (both B2C and B2B) overly pessimistic?
* In your forecasting models (or mental processes), are you “weighting the events of 2008″ more heavily than previous years?
* How closely are you monitoring daily/weekly events to determine whether to ramp up/down your spending plans for next year? What “key event” are you seeking that will be a major variable in your decision making?

Suppose you are a marketer at a wireless telecommunications company in Europe. Your company has a deep historical record of customer transactions, products purchased and billing history. Three months ago, your company also finished an implementation of an activity based costing project complemented with a profitability management application that now shows customer spend, margins and life-time value.

Deciding it’s high time to determine which of your customers are creating and potentially destroying value, you start analyzing a key segment of customers.

On one particular day, while finishing the last sip of your Monster energy drink, you’ve zeroed in on customer “Thomas Smith”.
The analysis shows that Mr. Smith is chronically late on his bills. He does pay but often late, and while you appreciate the incremental revenues associated with late charges, you also examine that he constantly uses your call center to ask mundane questions (bypassing the automated systems). You also see from your product analysis that he likes to switch phones frequently, bringing back product just before the thirty day exchange policy expires.

In the initial analysis, it appears Mr. Smith is costing your company a lot of money. Now, as a marketer, should you keep Mr. Smith as a customer, or gently pass him onto the competition?

What might be obvious is in fact a very complex decision, especially when you include social network analysis.

Leading edge companies are using Social Network Analysis to detect and interpret the patterns of social ties within a customer base. Authors Stanley Wasserman and Katherine Faust in their book “Social Network Analysis: Methods and Applications“, mention that SNA is “based on an assumption of the importance of relationships among interacting units. The social network encompasses theories, models, and applications that are expressed in terms of relational concepts or processes.”

In this particular marketing example, social network analysis can be used to determine the “importance” of Mr. Smith, especially in relation to other paying customers.

So let’s get back to the analysis on Thomas Smith. By adding call detail records to your data warehouse, and using social network analysis techniques, you now see that Mr. Smith is a “node” in a pretty complex network of customers. In fact, through a “Fab Five” campaign you concocted a year ago (where customers can call five friends for free in-network), you also see that Thomas is linked to five very profitable customers.

Since Mr. Smith is well connected to five very profitable customers, treating him poorly on his next customer visit, or jettisoning Mr. Smith altogether could lead to the defection of his five closest friends … customers on your network, and customers that pay their bills and produce positive cash flow.

Let us suppose you also had the divine prescience to add net promoter scoring to the mix. Now you notice that while Mr. Smith has trouble paying his bills, he is in fact a “promoter” of your company. He likes your friendly customer service representatives, and also is very generous in telling his friends about your willingness to “bend over backwards for him” to meet his changing needs.

With this analysis in hand, you determine that Mr. Smith isn’t a customer to jettison, and in fact, is probably one that deserves a closer look.

Before we get too caught up in the hype, social network analysis isn’t a savior to marketing decision making.

Social network analysis–done right–requires a lot of data from myriad sources. In the hypothetical marketing example above, simple call detail records are used, but to say–find a terrorist–the National Security Agency of the United States (NSA) would require call detail records, credit card transactions, car rental receipts, and many other digital markers. Even then, the output of the analysis isn’t always accurate.

Accurate data is a key factor in reliable results, but so too are the assumptions used in the model. Is Mr. Smith really that “important”? Should we assume that “associations” are actual close relationships? Should we also assume that if Mr. Smith leaves our company, his “Fab Five” connections will follow?

Social network analysis–hype or help? It’s up to you to decide.

Questions:
* It is well documented that Social Network Analysis (SNA) can be the source of “false positives”. Would you trust the output of SNA to make marketing decisions in your organization?
* Call detail records show Mr. Smith calls five contacts quite often. Does “activity” … in this instance a phone call–denote a “close relationship”?
* SNA can also be used to model employee connections in an organization–in effect to determine the “importance” of an employee? Good or bad idea?
* If you were the marketer in the situation above, what other tools might you use to determine if you should keep Mr. Smith as a customer?

Data, by itself is of little value. The real value lies in the capturing, cleansing, management and analysis of data thereby making it more useful for decision making.

One of the ways companies analyze data is to build or “program” models to simulate, test, learn and predict outcomes. Indeed, models built upon statistical techniques are helping companies identify fraud, predict next best offers, determine customer churn, and assess credit risk among other valuable applications.

But modeling isn’t a panacea, and a recent New York Times article, “How Wall Street Lied to Its Computers“, shows us how companies can get it wrong.

Writer Saul Hansell notes that Wall Street traders have long had very sophisticated models on market behavior–devised by quantitative analysts–that were supposed to help them hedge their positions and allow them to essentially manage their risks (or enable them to take bigger risks).

However a key challenge emerged when, “The people who ran the financial firms chose to program their risk management systems with overly optimistic assumptions and feed them oversimplified data.”
Even worse, many of the products (read: derivatives and derivatives of derivatives) weren’t understood by the creators of the products and thus it was near impossible to accurately assess the risk of these products with a mathematical model.

Modeling isn’t just for risk management, and can be a very valuable tool for companies assessing future scenarios, determining cause and effect, and allocating scarce resources. However the New York Times article highlights a great case study of pitfalls and key challenges when attempting to model a system, phenomenon or behavior.

First, understand that a model will only be as good as your assumptions. For example, in many risk management systems, models are designed to assume rational decision makers, a stable and relatively volatile-free marketplace, and that outliers generally have a limited effect on the entire population. Anyone who’s invested in a 401K and tracked their stock portfolio recently knows the futility of these assumptions (Nassim Nicholas Taleb explains why here).

Second, mathematical modeling is only as good as your data. Hansell’s article points out that it was in the best interest of traders to ensure the models didn’t warn them of impending danger, so they took efforts to smooth the data and manipulate the amount of historical data their risk management systems could analyze so as to take more aggressive trading positions.

Third, modeling is only as good as the design and designer of the model. “There was a willful designing of the systems to measure the risks in a certain way that would not necessarily pick up all the right risks,” says Gregg Berman of software company Risk Metrics. The design of an model should be checked for accuracy–not only of the accuracy statistical concepts used, but also that the model is not “tweaked” to produce desired results.

Last point: a model might be based on fair and accurate assumptions, sourcing clean and legitimate data, and designed properly–however it is of little use of politics stands in the way of recognizing and acting upon the output. All the analytical systems in the world are of little use if corporate politics dictate an outcome that is different than what the model prescribes.

No mathematical model is perfect–a model is just that–a model and not a silver bullet. Also such models are support tools that should be combined with good judgment, experience, and the input of others to effectively drive decisions.

That said, the time, energy, and investment dollars spent on mathematical modeling is close to worthless when poor assumptions, faulty/dirty data, bad design and corporate politics get in the way of good decision making.

Questions:
* Mathematical models are used by companies for customer segmentation, risk management, propensity to buy, loyalty management etc. Are you using models to help you make better decisions? If so, how?
* Do you think we often try to model things that are too complex–things that can’t be modeled effectively? What might be the ramifications when we get it wrong?
* In the business world, do you think the use of mathematics sometimes overrides “common sense”?
* Mathematical modeling–done right–can be a powerful business tool. How can we teach future generations of business leaders to use these tools ethically?

In the past, managers could take weeks or days to make important decisions, however to effectively compete globally, some companies are making critical decisions in hours, minutes or even seconds. With windows for decision making closing faster than ever–are your decision making processes setting you up for success–or failure?

While most would agree that strategic decisions require thoughtful consideration that should rightly stretch out months or weeks, the financial market turmoil of the past year plainly shows that decision making windows can open and close quite rapidly. In fact, as marketplaces grow more complex, and financial markets interconnect in ways analysts still struggle to understand, strategic decisions (even those involving M&A) sometimes need to be made in 24-48 hours.

The window for operational decisions is also shrinking. Companies now need the ability to detect and respond in real-time or near real time when fraud is occurring, products are out of stock, lines at store checkout are too long, online shopping carts are abandoned, or customers are calling with product/service quality issues.

There can be significant financial benefit to speeding operational decisions. Case in point is the financial services industry.

As early as the 1990s, trades were conducted on a system called SuperDot which still exists today. However, according to Richard Bookstaber, an equity fund manager and author of “Demon of Our Own Design“, there was nothing super about the system. “Orders were sent using primitive 386s communicating via Hayes micromodem,” he writes. “Between short sale restrictions and bottlenecks from excessive volume, there was no guarantee orders would get executed at all.”

Now let’s fast forward to the future. In Technology Review, an article titled, “The Blow Up” mentions that many high frequency financial services traders make 1,500 or more trades a day, whereas the computers at some brokerage firms execute “hundreds of thousands of trades everyday”–most of which are automated by computers following complex business rules and require no human intervention.

The same article details how the “science of event processing” allows computers to “read, interpret and act upon news” such as making a trade in response to an “FDA announcement–in milliseconds!”

The ability to act upon information faster than others–in this instance to execute a trade faster than other market participants–can make a huge difference in profits or loss. Creating business value via faster and better operational decision making extends to other industries as well.

In retail, analytical systems are enabling workforce and inventory optimization to ensure plenty of staffing and products, notifying managers of stock outs, and even helping speed up the checkout process.

According to an article in the Economist titled, “Watching While You Shop“, one very large British retailer is using a system to sense the number of shoppers that enter and leave the store, and then use that data to predict how many check-stands should be open. Systems predict, “up to an hour in advance and monitor average waiting times and queue lengths.” Since most of the point of sale systems at this retailer are self service, the system can detect when lines get too long and then open check-stands accordingly.

Faster and better decision making can infer a competitive advantage for companies, but those advantages don’t traditionally last very long. Competitors can invest in the same technologies and copy workflows. However, those companies that create a culture based on analytical decision making are hard to imitate as “thinking by the numbers” becomes a way of life.

Getting back to the original premise, I believe that in a complex and global economy, there is less room for error as economies, companies and even individual actions are more tightly coupled. Nothing happens in a vacuum anymore. This means there is less time to react as single events often start chain reactions.

To thrive in a global economy, companies must be able to make the best decisions based on accurate data sources that present as complete a picture as possible. Windows of opportunity are opening and closing faster than ever before. The ability or inability to capitalize on those open windows could be the difference between sustained competitive advantage and obsolescence.

Questions for DailyFix readers:
* Do you see the “windows for decision making” closing at a much faster pace?
* What other drivers besides technology, globalization and interlocking financial markets are driving a reduction in time between “event” and “action”?
* With decision making windows closing faster, will “gut decisioning” play a bigger role going forward?
* When decision making opportunities arise, how are you preparing yourself or your company to best act on those opportunities?
I’d love to hear your comments!

A recent article in Fast Company titled, “Going for the Gut,” details how even though we like our “heroes to crunch the numbers, we (also) like them to play their hunches.” Author Rob Walker laments that books like Malcolm Gladwell’s Blink, Jack Welch’s Straight from the Gut and others, give gut decision making first billing over a “careful, rational, empirical” approach.

Walker asks, “Are the narratives of popular culture dominated by super rational heroes triumphing over seat of the pants, gut-trusting bad guys? Actually, it’s the opposite: from Captain Kirk to Indiana Jones to Rambo to Tony Soprano–we’re drawn to the character who follows the hunch and wins.”

And business press and mainstream media largely agree. After all, wouldn’t you rather read about the business executive who had the right hunch and made millions, as opposed to the quant-jock who crunched the numbers and came up with the winning combination?

Gut decision making is “in” and for lack of a better word–cool.

Some senior executives have alluded there’s a mystique to gut decision making–those who have it have it, and those who don’t will never ascend the ivory tower of business success.

Case in point, Ralph Larsen, former CEO of a large consumer product goods company, states in a Harvard Business Review article, “When to Trust Your Gut,” that “very often people will do a brilliant job up through the middle of management levels, where it’s very heavy quantitative in terms of decision making. But then they reach senior management, where the problems get more complex and ambiguous, and we discover that their judgment or intuition is not what it should be.”

I do agree with Mr. Larsen that data driven decision making works best when there is in fact “data” to analyze. Sometimes, senior level decisions can be challenging because situations might be in uncharted territory–and there’s no past data, or too small a data set for analysis or prediction. That said, I’m not convinced that good judgment and intuition is the sole purview of senior management.

There is hope, however, that a data-driven approach can work just as good as gut thinking, and in most instances–compliment it.

Recent books, “Super Crunchers” by Ian Ayres and “Smart Enough Systems” by Neil Raden and James Taylor show case study after case study where companies and government entities of all sizes are using data analysis, experimentation and analytical applications to make smarter and faster decisions.

And the business press also seems to be noticing. In the same Fast Company issue (November 2007) executive Matt Carey cites how he’s attempting to create a “culture of analytics” at eBay, where experimentation, data and testing rule. “I want to eliminate feelings,” he says, “and get down to true math.”

If 2007 was “The Year of the Gut”, perhaps 2008 will be the “The Rise of The Quant Jock.”

Well, “quant jock” might not be the best terminology, and “analytics” might not be the sexiest noun, however running the company and making decisions based on the hard facts and numbers is something that I believe will never go out of style.

* Have you seen other examples of the “glorification of the gut”?
* How is decision making approached in your company (and in your marketing department)–by the numbers or by the gut? Do you see a trend one way or another?

No surprise to anyone, some upscale retailers and hotels are looking for visual cues to determine the service level they should provide to customers. According to a WSJ article, “The Gatekeeper: How Posh Hotel Sizes Up Guests”, May 10, 2007, some hotels are sizing up guests based on what car they park in valet, or what they’re wearing when they walk in the door.

In addition to keeping a record of the spending of hotel guests, the staff of the Peninsula Beverly Hills looks for signs of wealth and sophistication in guests. The article notes,

“The hotel’s managing director, Ali Kasikci, is something of an anthropologist of status signals. He is highly aware of the delicate hierarchy of fashion and symbols of influence, and he looks for small details to tell him what a pair of jeans and a T-shirt can’t.”

In the article, Mr. Kasicki spots a Hermes tie and a Charvet shirt among his wealthy guests and says, “It’s like a skunk. There’s enough scent being sprayed around that you can connect the dots.”

Mr. Kasicki is thin-slicing; segmenting and treating his customer’s differently based on his seasoned observations and intuition.
It’s also a dangerous strategy.

Malcolm Gladwell, in his best seller, Blink, defines the concept of thin-slicing as, “the ability of our unconscious to find patterns in situations and behavior based on very narrow slices of experience.” Essentially, it’s the ability to see patterns based on extensive experience in a particular field or discipline. In the case of Mr. Kasicki, his years of hotel experience are giving him visual cues and “distinctive signatures” of which guests can afford his services.

Solely relying on “at a glance” decision making, or decision making based on gut instinct can be very costly to our business and careers. For Mr. Kasicki to make better decisions on which guests should receive special attention, both observational data (visual cues) and hard numerical data are necessary.

It’s probably challenging in a service business like high-end hoteling, to not consciously or unconsciously segment and then treat customers differently based on how they dress or what they drive. However, even Mr. Kasicki admits that sometimes he gets it wrong when it comes to sizing up his guests. For example, the article notes a poorly dressed retired pharmaceutical executive is one of Mr. Kasicki’s wealthy guests!

It often makes sense to build loyalty programs, marketing campaigns and service/product offers to keep valuable customers spending money with your company. A good segmentation strategy, based on quantitative data, can help a company determine what customers to keep and which ones to let go to the competition.

For example, a data-driven customer profitability and life time value analysis could show that while an individual is a frequent guest to Mr. Kasicki’s hotel, they also tend to bargain for the lowest rates, berate the service staff, tip poorly, steal towels and swipe hotel fixtures.

In the case of Peninsula hotel, the best dressed customers might be the least valuable customers! Just because someone is wearing a Hermes suit doesn’t mean they won’t abscond with the light fixtures and conversely, the guest who is dressed like a slob might have a very large bank account!

The article mentions that Peninsula hotel tracks guest spend, but doesn’t say if it’s a manual or technology based approach. There are many solutions both custom and off the shelf that can help Peninsula hotel track customer interactions and provide analysis to help make sense of mountains of collected data. The Ritz-Carlton provides a great case study of service and technology happily married to each other in the hospitality business.

In an era of fierce competition, taking care of your most profitable and valuable customers has never been more important. Just don’t base your definition of a valuable customer on criteria such as he or she “looks the part.” Even Gladwell admits, “We are often careless with our powers of rapid cognition.”

Providing better levels of service to your top customers is a good strategy, but close your eyes for a moment and let your data speak to you for a comprehensive picture of who is “valuable.”