Don’t Let Old Habits Dictate Your Marketing Thoughts

When marketers play with data, we often get confined within the limitations of the datasets that are available to us, or worse, tool sets through which we get to access data. Some bad habits live through an organization for multiple generations, as we all get trained in marketing thoughts, in the beginning of our careers, by others who have been doing similar jobs.

marketing thoughts
Credit: Pixabay by Mohamed Hassan

When marketers play with data, we often get confined within the limitations of the datasets that are available to us, or worse, tool sets through which we get to access data. Some bad habits live through an organization for multiple generations, as we all get trained in marketing thoughts, in the beginning of our careers, by others who have been doing similar jobs.

When a few iterations of such training go on through a series of onboarding processes, the original intents of data, reporting and analytics get diluted. And the organization ends up just using those marketing thoughts to go through motions of producing lots of reports that no one cares about or benefits from. I’ve heard some radical claims that the majority of decision-makers today won’t miss over half of automatically generated reports.

We shouldn’t really look at a single report or initiate data-related projects without setting a clear goal first. Often, the most important role of a consultant is to remind clients “why” they should do anything in the first place.

For example, why should we all watch clickthrough rates every day, often locked in a set frame of time parameters? As in, compared to the same time last year, the clickthrough rate went down by 0.8%! The horror! Why do marketers make a big fuss about it, when the clickthrough rate is just one of many indicators, not even the most effective one at that, of actual purchases? Because someone in the past set the KPI reports up that way?

In other words, sometimes marketers and analysts who help them needed to be reminded that the goal is to sell more things and retain customers, not live and die with open and clickthrough rates. I am not flatly dismissing those important metrics at all; I’m just pointing out that we need to have a goal-oriented mindset when dealing with data and analytics. Otherwise, we end up in a maze of metrics and activities that do not really help us achieve organizational goals.

What are those ultimate goals? Not that I want to be a smart ass who would say “From Earth” to an innocuous question “Where are you from?”, but let’s really go to that high level for a moment; we play with data (1) to increase the revenue, or (2) to decrease the cost. Since Profit=Revenue-Cost, well, we can even reduce this whole thing to just one goal: Increase the Profit.

Why am I pointing out the obvious? Because I’ve seen too many data players who just go through motions without questioning the original intent of the activity or key metrics, and blindly believe that all that hard work will somehow lead to success. Unfortunately, that is far from the truth.

If you run on an airplane midflight, would you get to the destination any sooner? Definitely not. In fact, the captain may even go back to the originating airport to drop such crazy person off, further elongating the length of the journey.

You may think this analogy is silly, but in the world of data and analytics, such detours happen all of the time. All because no one questioned how and why any activity set in motion in the distant past would continue to help achieve long and short-term organizational goals – especially when goals need to be constantly adjusted thanks to ever-changing business environments. Nothing in scientific activities, no marketing thoughts, should be carved in stone.

That is why the first question by a seasoned consultant should be what the organization’s long and short-term goals are. Okay, we can all easily agree that we are all in this data and analytics game to increase profit, but what are the specific goals, and what are the immediate pain points? Of course, like any good doctor, a consultant must remedy immediate pain points first. But what do we call those doctors who make the patient’s condition worse just to relieve immediate pain? We call them quacks.

Bringing back this discussion to the world of marketing, having the clear long and short-term goals for every data and analytical activity is a must. If you do that, you may never need an expensive consultant just to remind you that you are wasting resources digging wrong places. Clear business goals beget proper problem statements (not just list of all symptoms and wish lists), which beget appropriate measurement metrics, which in turn lead us to proper digging points in terms of data and methodology, which would minimize waste of time and energy to achieve predetermined goals. In short, we can avoid lots of mishaps and detours just by remembering the original intents of data and analytics endeavors.

Is Your Direct Mail Misunderstood?

Are your direct mail pieces engaging with your audience or are you talking over the audience? Do you use lingo that only people in the industry understand?

Are your direct mail pieces engaging with your audience or are you talking over the audience? Do you use lingo that only people in the industry understand?

Acronyms can quickly get you into trouble when people do not know them; especially in the age of texting, your acronym may be misinterpreted. What is obvious to you will not necessarily be obvious to them. This is a big problem if your audience is confused; the chances of you getting your important message across are significantly decreased. Basically, you have turned your direct mail piece into trash.

For the best results, create direct mail that is clear and concise. You have just a few seconds to be understood and engage them to read more rather than toss your mail piece in the trash.

So how can you be sure you are creating the best message?

  1. What Is Your Goal? Do you need to sell so many widgets or get so many phone calls? Clearly define your goal and how you will track results before you start writing.
  2. Write a List About Your Customers: What is their biggest problem? Who are they? What makes them happy? What makes them mad? Again, you need to be specific about them in order to create an actionable persona.
  3. Pick One Main Message: You should theme your entire message around one key idea. It needs to be easy to grasp quickly and be relevant to your audience.
  4. Benefit: Get specific on ONE benefit that they are in desperate need of. Consult your list about your customers to find which benefit will work best. The benefit sells your product or service, not features.
  5. Guarantee: Offer them some type of guarantee to alleviate any buying concerns. This shows buyer that you stand behind your product or service, because it really is the best and they should buy it.

We strongly suggest that you test message versions with different groups of your list. In order to test correctly, you will need to group like people together to get the right message. A benefit that works well for one group may be a dud for another. So take your time in creating the groups and which messages should go to which group. Make sure you can track your responses to see which ones are working best. You can make changes to the ones that had less traction.

Okay. Now you are ready to put it all together and write your messaging. Most of the time, there is still fluff in the message after the first couple of drafts. Go back though everything and eliminate any word that is not necessary. No extra words and no acronyms should be in your final copy. Make sure to have someone outside of your organization read your final copy. You need to see if they understand what you are saying, in the way you meant them to. Usually there is a need for a few more edits.

Your direct mail piece to should be easy to understand, targeted to the right people and with a clear call to action. Never use acronyms on your mail piece, they are too easily misunderstood. Remove long explanations and fluff from your message. You can provide links on the mail piece for them to look up more information if they want to, but most people prefer concise, straight-to-the-point benefits that make them want to buy. Are you ready to get started?

How to Outsource Analytics

In this series, I have been emphasizing the importance of statistical modeling in almost every article. While there are plenty of benefits of using statistical models in a more traditional sense (refer to “Why Model?”), in the days when “too much” data is the main challenge, I would dare to say that the most important function of statistical models is that they summarize complex data into simple-to-use “scores.”

In this series, I have been emphasizing the importance of statistical modeling in almost every article. While there are plenty of benefits of using statistical models in a more traditional sense (refer to “Why Model?”), in the days when “too much” data is the main challenge, I would dare to say that the most important function of statistical models is that they summarize complex data into simple-to-use “scores.”

The next important feature would be that models fill in the gaps, transforming “unknowns” to “potentials.” You see, even in the age of ubiquitous data, no one will ever know everything about everybody. For instance, out of 100,000 people you have permission to contact, only a fraction will be “known” wine enthusiasts. With modeling, we can assign scores for “likelihood of being a wine enthusiast” to everyone in the base. Sure, models are not 100 percent accurate, but I’ll take “70 percent chance of afternoon shower” over not knowing the weather forecast for the day of the company picnic.

I’ve already explained other benefits of modeling in detail earlier in this series, but if I may cut it really short, models will help marketers:

1. In deciding whom to engage, as they cannot afford to spam the world and annoy everyone who can read, and

2. In determining what to offer once they decide to engage someone, as consumers are savvier than ever and they will ignore and discard any irrelevant message, no matter how good it may look.

OK, then. I hope you are sold on this idea by now. The next question is, who is going to do all that mathematical work? In a country where jocks rule over geeks, it is clear to me that many folks are more afraid of mathematics than public speaking; which, in its own right, ranks higher than death in terms of the fear factor for many people. If I may paraphrase “Seinfeld,” many folks are figuratively more afraid of giving a eulogy than being in the coffin at a funeral. And thanks to a sub-par math education in the U.S. (and I am not joking about this, having graduated high school on foreign soil), yes, the fear of math tops them all. Scary, heh?

But that’s OK. This is a big world, and there are plenty of people who are really good at mathematics and statistics. That is why I purposefully never got into the mechanics of modeling techniques and related programming issues in this series. Instead, I have been emphasizing how to formulate questions, how to express business goals in a more logical fashion and where to invest to create analytics-ready environments. Then the next question is, “How will you find the right math geeks who can make all your dreams come true?”

If you have a plan to create an internal analytics team, there are a few things to consider before committing to that idea. Too many organizations just hire one or two statisticians, dump all the raw data onto them, and hope to God that they will figure some ways to make money with data, somehow. Good luck with that idea, as:

1. I’ve seen so many failed attempts like that (actually, I’d be shocked if it actually worked), and

2. I am sure God doesn’t micromanage statistical units.

(Similarly, I am almost certain that she doesn’t care much for football or baseball scores of certain teams, either. You don’t think God cares more for the Red Sox than the Yankees, do ya?)

The first challenge is locating good candidates. If you post any online ad for “Statistical Analysts,” you will receive a few hundred resumes per day. But the hiring process is not that simple, as you should ask the right questions to figure out who is a real deal, and who is a poser (and there are many posers out there). Even among qualified candidates with ample statistical knowledge, there are differences between the “Doers” and “Vendor Managers.” Depending on your organizational goal, you must differentiate the two.

Then the next challenge is keeping the team intact. In general, mathematicians and statisticians are not solely motivated by money; they also want constant challenges. Like any smart and creative folks, they will simply pack up and leave, if “they” determine that the job is boring. Just a couple of modeling projects a year with some rudimentary sets of data? Meh. Boring! Promises of upward mobility only work for a fraction of them, as the majority would rather deal with numbers and figures, showing no interest in managing other human beings. So, coming up with interesting and challenging projects, which will also benefit the whole organization, becomes a job in itself. If there are not enough challenges, smart ones will quit on you first. Then they need constant mentoring, as even the smartest statisticians will not know everything about challenges associated with marketing, target audiences and the business world, in general. (If you stumble into a statistician who is even remotely curious about how her salary is paid for, start with her.)

Further, you would need to invest to set up an analytical environment, as well. That includes software, hardware and other supporting staff. Toolsets are becoming much cheaper, but they are not exactly free yet. In fact, some famous statistical software, such as SAS, could be quite expensive year after year, although there are plenty of alternatives now. And they need an “analytics-ready” data environment, as I emphasized countless times in this series (refer to “Chicken or the Egg? Data or Analytics?” and “Marketing and IT; Cats and Dogs”). Such data preparation work is not for statisticians, and most of them are not even good at cleaning up dirty data, anyway. That means you will need different types of developers/programmers on the analytics team. I pointed out that analytical projects call for a cohesive team, not some super-duper analyst who can do it all (refer to “How to Be a Good Data Scientist”).

By now you would say “Jeez Louise, enough already,” as all this is just too much to manage to build just a few models. Suddenly, outsourcing may sound like a great idea. Then you would realize there are many things to consider when outsourcing analytical work.

First, where would you go? Everyone in the data industry and their cousins claim that they can take care of analytics. But in reality, it is a scary place where many who have “analytics” in their taglines do not even touch “predictive analytics.”

Analytics is a word that is abused as much as “Big Data,” so we really need to differentiate them. “Analytics” may mean:

  • Business Intelligence (BI) Reporting: This is mostly about the present, such as the display of key success metrics and dashboard reporting. While it is very important to know about the current state of business, much of so-called “analytics” unfortunately stops right here. Yes, it is good to have a dashboard in your car now, but do you know where you should be going?
  • Descriptive Analytics: This is about how the targets “look.” Common techniques such as profiling, segmentation and clustering fall under this category. These techniques are mainly for describing the target audience to enhance and optimize messages to them. But using these segments as a selection mechanism is not recommended, while many dare to do exactly that (more on this subject in future articles).
  • Predictive Modeling: This is about answering the questions about the future. Who would be more likely to behave certain ways? What communication channels will be most effective for whom? How much is the potential spending level of a prospect? Who is more likely to be a loyal and profitable customer? What are their preferences? Response models, various of types of cloning models, value models, and revenue models, attrition models, etc. all fall under this category, and they require hardcore statistical skills. Plus, as I emphasized earlier, these model scores compact large amounts of complex data into nice bite-size packages.
  • Optimization: This is mostly about budget allocation and attribution. Marketing agencies (or media buyers) generally deal with channel optimization and spending analysis, at times using econometrics models. This type of statistical work calls for different types of expertise, but many still insist on calling it simply “analytics.”

Let’s say that for the purpose of customer-level targeting and personalization, we decided to outsource the “predictive” modeling projects. What are our options?

We may consider:

  • Individual Consultants: In-house consultants are dedicated to your business for the duration of the contract, guaranteeing full access like an employee. But they are there for you only temporarily, with one foot out the door all the time. And when they do leave, all the knowledge walks away with them. Depending on the rate, the costs can add up.
  • Standalone Analytical Service Providers: Analytical work is all they do, so you get focused professionals with broad technical and institutional knowledge. Many of them are entrepreneurs, but that may work against you, as they could often be understaffed and stretched thin. They also tend to charge for every little step, with not many freebies. They are generally open to use any type of data, but the majority of them do not have secure sources of third-party data, which could be essential for certain types of analytics involving prospecting.
  • Database Service Providers: Almost all data compilers and brokers have statistical units, as they need to fill in the gap within their data assets with statistical techniques. (You didn’t think that they knew everyone’s income or age, did you?) For that reason, they have deep knowledge in all types of data, as well as in many industry verticals. They provide a one-stop shop environment with deep resource pools and a variety of data processing capabilities. However, they may not be as agile as smaller analytical shops, and analytics units may be tucked away somewhere within large and complex organizations. They also tend to emphasize the use of their own data, as after all, their main cash cows are their data assets.
  • Direct Marketing Agencies: Agencies are very strategic, as they touch all aspects of marketing and control creative processes through segmentation. Many large agencies boast full-scale analytical units, capable of all types of analytics that I explained earlier. But some agencies have very small teams, stretched really thin—just barely handling the reporting aspect, not any advanced analytics. Some just admit that predictive analytics is not part of their core competencies, and they may outsource such projects (not that it is a bad thing).

As you can see here, there is no clear-cut answer to “with whom you should you work.” Basically, you will need to check out all types of analysts and service providers to determine the partner best suitable for your long- and short-term business purposes, not just analytical goals. Often, many marketers just go with the lowest bidder. But pricing is just one of many elements to be considered. Here, allow me to introduce “10 Essential Items to Consider When Outsourcing Analytics.”

1. Consulting Capabilities: I put this on the top of the list, as being a translator between the marketing and the technology world is the most important differentiator (refer to “How to Be a Good Data Scientist”). They must understand the business goals and marketing needs, prescribe suitable solutions, convert such goals into mathematical expressions and define targets, making the best of available data. If they lack strategic vision to set up the data roadmap, statistical knowledge alone will not be enough to achieve the goals. And such business goals vary greatly depending on the industry, channel usage and related success metrics. Good consultants always ask questions first, while sub-par ones will try to force-fit marketers’ goals into their toolsets and methodologies.

Translating marketing goals into specific courses of action is a skill in itself. A good analytical partner should be capable of building a data roadmap (not just statistical steps) with a deep understanding of the business impact of resultant models. They should be able to break down larger goals into smaller steps, creating proper phased approaches. The plan may call for multiple models, all kinds of pre- and post-selection rules, or even external data acquisition, while remaining sensitive to overall costs.

The target definition is the core of all these considerations, which requires years of experience and industry knowledge. Simply, the wrong or inadequate targeting decision leads to disastrous results, no matter how sound the mathematical work is (refer to “Art of Targeting”).

Another important quality of a good analytical partner is the ability to create usefulness out of seemingly chaotic and unstructured data environments. Modeling is not about waiting for the perfect set of data, but about making the best of available data. In many modeling bake-offs, the winners are often decided by the creative usage of provided data, not just statistical techniques.

Finally, the consultative approach is important, as models do not exist in a vacuum, but they have to fit into the marketing engine. Be aware of the ones who want to change the world around their precious algorithms, as they are geeks not strategists. And the ones who understand the entire marketing cycle will give advice on what the next phase should be, as marketing efforts must be perpetual, not transient.

So, how will you find consultants? Ask the following questions:

  • Are they “listening” to you?
  • Can they repeat “your” goals in their own words?
  • Do their roadmaps cover both short- and long-term goals?
  • Are they confident enough to correct you?
  • Do they understand “non-statistical” elements in marketing?
  • Have they “been there, done that” for real, or just in theories?

2. Data Processing Capabilities: I know that some people look down upon the word “processing.” But data manipulation is the most important key step “before” any type of advanced analytics even begins. Simply, “garbage-in, garbage out.” And unfortunately, most datasets are completely unsuitable for analytics and modeling. In general, easily more than 80 percent of model development time goes into “fixing” the data, as most are unstructured and unrefined. I have been repeatedly emphasizing the importance of a “model-ready” (or “analytics-ready”) environment for that reason.

However, the reality dictates that the majority of databases are indeed NOT model-ready, and most of them are not even close to it. Well, someone has to clean up the mess. And in this data business, the last one who touches the dataset becomes responsible for all the errors and mistakes made to it thus far. I know it is not fair, but that is why we need to look at the potential partner’s ability to handle large and really messy data, not just the statistical savviness displayed in glossy presentations.

Yes, that dirty work includes data conversion, edit/hygiene, categorization/tagging, data summarization and variable creation, encompassing all kinds of numeric, character and freeform data (refer to “Beyond RFM Data” and “Freeform Data Aren’t Exactly Free”). It is not the most glorious part of this business, but data consistency is the key to successful implementation of any advanced analytics. So, if a model-ready environment is not available, someone had better know how to make the best of whatever is given. I have seen too many meltdowns in “before” and “after” modeling steps due to inconsistencies in databases.

So, grill the candidates with the following questions:

  • If they support file conversions, edit, categorization and summarization
  • How big of a dataset is too big, and how many files/tables are too many for them
  • How much free-form data are too much for them
  • Ask for sample model variables that they have created in the past

3. Track Records in the Industry: It can be argued that industry knowledge is even more crucial for the success than statistical know-how, as nuances are often “Lost in Translation” without relevant industry experience. In fact, some may not even be able to carry on a proper conversation with a client without it, leading to all kinds of wrong assumptions. I have seen a case where “real” rocket scientists messed up models for credit card campaigns.

The No. 1 reason why industry experience is important is everyone’s success metrics are unique. Just to name a few, financial services (banking, credit card, insurance, investment, etc.), travel and hospitality, entertainment, packaged goods, online and offline retail, catalogs, publication, telecommunications/utilities, non-profit and political organizations all call for different types of analytics and models, as their business models and the way they interact with target audiences are vastly different. For example, building a model (or a database, for that matter) for businesses where they hand over merchandise “before” they collect money is fundamentally different than the ones where exchange happens simultaneously. Even a simple concept of payment date or transaction date cannot be treated the same way. For retailers, recent dates could be better for business, but for subscription business, older dates may carry more weight. And these are just some examples with “dates,” before touching any dollar figures or other fun stuff.

Then the job gets even more complicated, if we further divide all of these industries by B-to-B vs. B-to-C, where available data do not even look similar. On top of that, divisional ROI metrics may be completely different, and even terminology and culture may play a role in all of this. When you are a consultant, you really don’t want to stop the flow of a meeting to clarify some unfamiliar acronyms, as you are supposed to know them all.

So, always demand specific industry references and examine client roasters, if allowed. (Many clients specifically ask vendors not to use their names as references.) Basically, watch out for the ones who push one-size-fits-all cookie-cutter solutions. You deserve way more than that.

4. Types of Models Supported: Speaking of cookie-cutter stuff, we need to be concerned with types of models that the outsourcing partner would support. Sure, nobody employs every technique, and no one can be good at everything. But we need to watch out for the “One-trick Ponies.”

This could be a tricky issue, as we are going into a more technical domain. Plus, marketers should not self-prescribe with specific techniques, instead of clearly stating their business goals (refer to “Marketing and IT; Cats and Dogs”). Some of the modeling goals are:

  • Rank and select prospect names
  • Lead scoring
  • Cross-sell/upsell
  • Segment the universe for messaging strategy
  • Pinpoint the attrition point
  • Assign lifetime values for prospects and customers
  • Optimize media/channel spending
  • Create new product packages
  • Detect fraud
  • Etc.

Unless you have successfully dealt with the outsourcing partner in the past (or you have a degree in statistics), do not blurt out words like Neural-net, CHAID, Cluster Analysis, Multiple Regression, Discriminant Function Analysis, etc. That would be like demanding specific medication before your new doctor even asks about your symptoms. The key is meeting your business goals, not fulfilling buzzwords. Let them present their methodology “after” the goal discussion. Nevertheless, see if the potential partner is pushing one or two specific techniques or solutions all the time.

5. Speed of Execution: In modern marketing, speed to action is the king. Speed wins, and speed gains respect. However, when it comes to modeling or other advanced analytics, you may be shocked by the wide range of time estimates provided by each outsourcing vendor. To be fair they are covering themselves, mainly because they have no idea what kind of messy data they will receive. As I mentioned earlier, pre-model data preparation and manipulation are critical components, and they are the most time-consuming part of all; especially when available data are in bad shape. Post-model scoring, audit and usage support may elongate the timeline. The key is to differentiate such pre- and post-modeling processes in the time estimate.

Even for pure modeling elements, time estimates vary greatly, depending on the complexity of assignments. Surely, a simple cloning model with basic demographic data would be much easier to execute than the ones that involve ample amounts of transaction- and event-level data, coming from all types of channels. If time-series elements are added, it will definitely be more complex. Typical clustering work is known to take longer than regression models with clear target definitions. If multiple models are required for the project, it will obviously take more time to finish the whole job.

Now, the interesting thing about building a model is that analysts don’t really finish it, but they just run out of time—much like the way marketers work on PowerPoint presentations. The commonality is that we can basically tweak models or decks forever, but we have to stop at some point.

However, with all kinds of automated tools and macros, model development time has decreased dramatically in past decades. We really came a long way since the first application of statistical techniques to marketing, and no one should be quoting a 1980s timeline in this century. But some still do. I know vendors are trained to follow the guideline “always under-promise and over-deliver,” but still.

An interesting aspect of this dilemma is that we can negotiate the timeline by asking for simpler and less sophisticated versions with diminished accuracy. If, hypothetically, it takes a week to be 98 percent accurate, but it only takes a day to be 90 percent accurate, what would you pick? That should be the business decision.

So, what is a general guideline? Again, it really depends on many factors, but allow me to share a version of it:

  • Pre-modeling Processing

– Data Conversions: from half a day to weeks

– Data Append/Enhancement: between overnight and two days

– Data Edit and Summarization: Data-dependent

  • Modeling: Ranges from half a day to weeks

– Depends on type, number of models and complexity

  • Scoring: from half a day to one week

– Mainly depends on number of records and state of the database to be scored

I know these are wide ranges, but watch out for the ones that routinely quote 30 days or more for simple clone models. They may not know what they are doing, or worse, they may be some mathematical perfectionists who don’t understand the marketing needs.

6. Pricing Structure: Some marketers would put this on top of the checklist, or worse, use the pricing factor as the only criterion. Obviously, I disagree. (Full disclosure: I have been on the service side of the fence during my entire career.) Yes, every project must make an economic sense in the end, but the budget should not and cannot be the sole deciding factor in choosing an outsourcing partner. There are many specialists under famous brand names who command top dollars, and then there are many data vendors who throw in “free” models, disrupting the ecosystem. Either way, one should not jump to conclusions too fast, as there is no free lunch, after all. In any case, I strongly recommend that no one should start the meeting with pricing questions (hence, this article). When you get to the pricing part, ask what the price includes, as the analytical journey could be a series of long and winding roads. Some of the biggest factors that need to be considered are:

  • Multiple Model Discounts—Less for second or third models within a project?
  • Pre-developed (off-the-shelf) Models—These can be “much” cheaper than custom models, while not custom-fitted.
  • Acquisition vs. CRM—Employing client-specific variables certainly increases the cost.
  • Regression Models vs. Other Types—At times, types of techniques may affect the price.
  • Clustering and Segmentations—They are generally priced much higher than target-specific models.

Again, it really depends on the complexity factor more than anything else, and the pre- and post-modeling process must be estimated and priced separately. Non-modeling charges often add up fast, and you should ask for unit prices and minimum charges for each step.

Scoring charges in time can be expensive, too, so negotiate for discounts for routine scoring of the same models. Some may offer all-inclusive package pricing for everything. The important thing is that you must be consistent with the checklist when shopping around with multiple candidates.

7. Documentation: When you pay for a custom model (not pre-developed, off-the-shelf ones), you get to own the algorithm. Because algorithms are not tangible items, the knowledge is to be transformed in model documents. Beware of the ones who offer “black-box” solutions with comments like, “Oh, it will work, so trust us.”

Good model documents must include the following, at the minimum:

  • Target and Comparison Universe Definitions: What was the target variable (or “dependent” variable) and how was it defined? How was the comparison universe defined? Was there any “pre-selection” for either of the universes? These are the most important factors in any model—even more than the mechanics of the model itself.
  • List of Variables: What are the “independent” variables? How were they transformed or binned? From where did they originate? Often, these model variables describe the nature of the model, and they should make intuitive sense.
  • Model Algorithms: What is the actual algorithm? What are the assigned weight for each independent variable?
  • Gains Chart: We need to examine potential effectiveness of the model. What are the “gains” for each model group, from top to bottom (e.g., 320 percent gain at the top model group in comparison to the whole universe)? How fast do such gains decrease as we move down the scale? How do the gains factors compare against the validation sample? A graphic representation would be nice, too.

For custom models, it is customary to have a formal model presentation, full documentation and scoring script in designated programming languages. In addition, if client files are provided, ask for a waterfall report that details input and output counts of each step. After the model scoring, it is also customary for the vendor to provide a scored universe count by model group. You will be shocked to find out that many so-called analytical vendors do not provide thorough documentation. Therefore, it is recommended to ask for sample documents upfront.

8. Scoring Validation: Models are built and presented properly, but the job is not done until the models are applied to the universe from which the names are ranked and selected for campaigns. I have seen too many major meltdowns at this stage. Simply, it is one thing to develop models with a few hundred thousand record samples, but it is quite another to apply the algorithm to millions of records. I am not saying that the scoring job always falls onto the developers, as you may have an internal team or a separate vendor for such ongoing processes. But do not let the model developer completely leave the building until everything checks out.

The model should have been validated against the validation sample by then, but live scoring may reveal all kinds of inconsistencies. You may also want to back-test the algorithms with past campaign results, as well. In short, many things go wrong “after” the modeling steps. When I hear customers complaining about models, I often find that the modeling is the only part that was done properly, and “before” and “after” steps were all messed up. Further, even machines misunderstand each other, as any differences in platform or scripting language may cause discrepancies. Or, maybe there was no technical error, but missing values may have caused inconsistencies (refer to “Missing Data Can Be Meaningful”). Nonetheless, the model developers would have the best insight as to what could have gone wrong, so make sure that they are available for questions after models are presented and delivered.

9. Back-end Analysis: Good analytics is all about applying learnings from past campaigns—good or bad—to new iterations of efforts. We often call it “closed-loop marketing—while many marketers often neglect to follow up. Any respectful analytics shop must be aware of it, while they may classify such work separately from modeling or other analytical projects. At the minimum, you need to check out if they even offer such services. In fact, so-called “match-back analysis” is not as simple as just matching campaign files against responders in this omnichannel environment. When many channels are employed at the same time, allocation of credit (i.e., “what worked?”) may call for all kinds of business rules or even dedicated models.

While you are at it, ask for a cheaper version of “canned” reports, as well, as custom back-end analysis can be even more costly than the modeling job itself, over time. Pre-developed reports may not include all the ROI metrics that you’re looking for (e.g., open, clickthrough, conversion rates, plus revenue and orders-per-mailed, per order, per display, per email, per conversion. etc.). So ask for sample reports upfront.

If you start breaking down all these figures by data source, campaign, time series, model group, offer, creative, targeting criteria, channel, ad server, publisher, keywords, etc., it can be unwieldy really fast. So contain yourself, as no one can understand 100-page reports, anyway. See if the analysts can guide you with such planning, as well. Lastly, if you are so into ROI analysis, get ready to share the “cost” side of the equation with the selected partner. Some jobs are on the marketers.

10. Ongoing Support: Models have a finite shelf life, as all kinds of changes happen in the real world. Seasonality may be a factor, or the business model or strategy may have changed. Fluctuations in data availability and quality further complicate the matter. Basically assumptions like “all things being equal” only happen in textbooks, so marketers must plan for periodic review of models and business rules.

A sure sign of trouble is decreasing effectiveness of models. When in doubt, consult the developers and they may recommend a re-fit or complete re-development of models. Quarterly reviews would be ideal, but if the cost becomes an issue, start with 6-month or yearly reviews, but never go past more than a year without any review. Some vendors may offer discounts for redevelopment, so ask for the price quote upfront.

I know this is a long list of things to check, but picking the right partner is very important, as it often becomes a long-term relationship. And you may find it strange that I didn’t even list “technical capabilities” at all. That is because:

1. Many marketers are not equipped to dig deep into the technical realm anyway, and

2. The difference between the most mathematically sound models and the ones from the opposite end of the spectrum is not nearly as critical as other factors I listed in this article.

In other words, even the worst model in the bake-off would be much better than no model, if these other business criterion are well-considered. So, happy shopping with this list, and I hope you find the right partner. Employing analytics is not an option when living in the sea of data.

Smart Data – Not Big Data

As a concerned data professional, I am already plotting an exit strategy from this Big Data hype. Because like any bubble, it will surely burst. That inevitable doomsday could be a couple of years away, but I can feel it coming. At the risk of sounding too much like Yoda the Jedi Grand Master, all hypes lead to over-investments, all over-investments lead to disappointments, and all disappointments lead to blames. Yes, in a few years, lots of blames will go around, and lots of heads will roll.

As a concerned data professional, I am already plotting an exit strategy from this Big Data hype. Because like any bubble, it will surely burst. That inevitable doomsday could be a couple of years away, but I can feel it coming. At the risk of sounding too much like Yoda the Jedi Grand Master, all hypes lead to over-investments, all over-investments lead to disappointments, and all disappointments lead to blames. Yes, in a few years, lots of blames will go around, and lots of heads will roll.

So, why would I stay on the troubled side? Well, because, for now, this Big Data thing is creating lots of opportunities, too. I am writing this on my way back from Seoul, Korea, where I presented this Big Data idea nine times in just two short weeks, trotting from large venues to small gatherings. Just a few years back, I used to have a hard time explaining what I do for living. Now, I just have to say “Hey, I do this Big Data thing,” and the doors start to open. In my experience, this is the best “Open Sesame” moment for all data specialists. But it will last only if we play it right.

Nonetheless, I also know that I will somehow continue to make living setting data strategies, fixing bad data, designing databases and leading analytical activities, even after the hype cools down. Just with a different title, under a different banner. I’ve seen buzzwords come and go, and this data business has been carried on by the people who cut through each hype (and gargantuan amount of BS along with it) and create real revenue-generating opportunities. At the end of the day (I apologize for using this cliché), it is all about the bottom line, whether it comes from a revenue increase or cost reduction. It is never about the buzzwords that may have created the business opportunities in the first place; it has always been more about the substance that turned those opportunities into money-making machines. And substance needs no fancy title or buzzwords attached to it.

Have you heard Google or Amazon calling themselves a “Big Data” companies? They are the ones with sick amounts of data, but they also know that it is not about the sheer amount of data, but it is all about the user experience. “Wannabes” who are not able to understand the core values often hang onto buzzwords and hypes. As if Big Data, Cloud Computing or coding language du jour will come and save the day. But they are just words.

Even the name “Big Data” is all wrong, as it implies that bigger is always better. The 3 Vs of Big Data—volume, velocity and variety—are also misleading. That could be a meaningful distinction for existing data players, but for decision-makers, it gives a notion that size and speed are the ultimate quest. But for the users, small is better. They don’t have time to analyze big sets of data. They need small answers in fun size packages. Plus, why is big and fast new? Since the invention of modern computers, has there been any year when the processing speed did not get faster and storage capacity did not get bigger?

Lest we forget, it is the software industry that came up with this Big Data thing. It was created as a marketing tagline. We should have read it as, “Yes, we can now process really large amounts of data, too,” not as, “Big Data will make all your dreams come true.” If you are in the business of selling toolsets, of course, that is how you present your product. If guitar companies keep emphasizing how hard it is to be a decent guitar player, would that help their businesses? It is a lot more effective to say, “Hey, this is the same guitar that your guitar hero plays!” But you don’t become Jeff Beck just because you bought a white Fender Stratocaster with a rosewood neck. The real hard work begins “after” you purchase a decent guitar. However, this obvious connection is often lost in the data business. Toolsets never provide solutions on their own. They may make your life easier, but you’d still have to formulate the question in a logical fashion, and still have to make decisions based on provided data. And harnessing meanings out of mounds of data requires training of your mind, much like the way musicians practice incessantly.

So, before business people even consider venturing into this Big Data hype, they should ask themselves “Why data?” What are burning questions that you are trying to solve with the data? If you can’t answer this simple question, then don’t jump into it. Forget about it. Don’t get into it just because everyone else seems to be getting into it. Yeah, it’s a big party, but why are you going there? Besides, if you formulate the question properly, often you will find that you don’t need Big Data all the time. If fact, Big Data can be a terrible detour if your question can be answered by “small” data. But that happens all the time, because people approach their business questions through the processes set by the toolsets. Big Data should be about the business, not about the IT or data.

Smart Data, Not Big Data
So, how do we get over this hype? All too often, perception rules, and a replacement word becomes necessary to summarize the essence of the concept for the general public. In my opinion, “Big Data” should have been “Smart Data.” Piles of unorganized dumb data aren’t worth a damn thing. Imagine a warehouse full of boxes with no labels, collecting dust since 1943. Would you be impressed with the sheer size of the warehouse? Great, the ark that Indiana Jones procured (or did he?) may be stored in there somewhere. But if no one knows where it is—or even if it can be located, if no one knows what to do with it—who cares?

Then, how do data get smarter? Smart data are bite-sized answers to questions. A thousand variables could have been considered to provide the weather forecast that calls for a “70 percent chance of scattered showers in the afternoon,” but that one line that we hear is the smart piece of data. Not the list of all the variables that went into the formula that created that answer. Emphasizing the raw data would be like giving paints and brushes to a person who wants a picture on the wall. As in, “Hey, here are all the ingredients, so why don’t you paint the picture and hang it on the wall?” Unfortunately, that is how the Big Data movement looks now. And too often, even the ingredients aren’t all that great.

I visit many companies only to find that the databases in question are just messy piles of unorganized and unstructured data. And please do not assume that such disarrays are good for my business. I’d rather spend my time harnessing meanings out of data and creating values, not taking care of someone else’s mess all the time. Really smart data are small, concise, clean and organized. Big Data should only be seen in “Behind the Scenes” types of documentaries for manias, not for everyday decision-makers.

I have been already saying that Big Data must get smaller for some time (refer to “Big Data Must Get Smaller“) and I would repeat it until it becomes a movement on its own. The Big Data movement must be about:

  1. Cutting down the noise
  2. Providing the answers

There is too much noise in the data, and cutting it out is the first step toward making the data smaller and smarter. The trouble is that the definition of “noise” is not static. Rock music that I grew up with was certainly a noise to my parents’ generation. In turn, some music that my kids listen to is pure noise to me. Likewise, “product color,” which is essential for a database designed for an inventory management system, may or may not be noise if the goal is to sell more apparel items. In such cases, more important variables could be style, brand, price range, target gender, etc., but color could be just peripheral information at best, or even noise (as in, “Uh, she isn’t going to buy just red shoes all the time?”). How do we then determine the differences? First, set the clear goals (as in, “Why are we playing with the data to begin with?”), define the goals using logical expressions, and let mathematics take care of it. Now you can drop the noise with conviction (even if it may look important to human minds).

If we continue with that mathematical path, we would reach the second part, which is “providing answers to the question.” And the smart answers are in the forms of yes/no, probability figures or some type of scores. Like in the weather forecast example, the question would be “chance of rain on a certain day” and the answer would be “70 percent.” Statistical modeling is not easy or simple, but it is the essential part of making the data smarter, as models are the most effective way to summarize complex and abundant data into compact forms (refer to “Why Model?”).

Most people do not have degrees in mathematics or statistics, but they all know what to do with a piece of information such as “70 percent chance of rain” on the day of a company outing. Some may complain that it is not a definite yes/no answer, but all would agree that providing information in this form is more humane than dumping all the raw data onto users. Sales folks are not necessarily mathematicians, but they would certainly appreciate scores attached to each lead, as in “more or less likely to close.” No, that is not a definite answer, but now sales people can start calling the leads in the order of relative importance to them.

So, all the Big Data players and data scientists must try to “humanize” the data, instead of bragging about the size of the data, making things more complex, and providing irrelevant pieces of raw data to users. Make things simpler, not more complex. Some may think that complexity is their job security, but I strongly disagree. That is a sure way to bring down this Big Data movement to the ground. We are already living in a complex world, and we certainly do not need more complications around us (more on “How to be a good data scientist” in a future article).

It’s About the Users, Too
On the flip side, the decision-makers must change their attitude about the data, as well.

1. Define the goals first: The main theme of this series has been that the Big Data movement is about the business, not IT or data. But I’ve seen too many business folks who would so willingly take a hands-off approach to data. They just fund the database; do not define clear business goals to developers; and hope to God that someday, somehow, some genius will show up and clear up the mess for them. Guess what? That cavalry is never coming if you are not even praying properly. If you do not know what problems you want to solve with data, don’t even get started; you will get to nowhere really slowly, bleeding lots of money and time along the way.

2. Take the data seriously: You don’t have to be a scientist to have a scientific mind. It is not ideal if someone blindly subscribes anything computers spew out (there are lots of inaccurate information in databases; refer to “Not All Databases Are Created Equal.”). But too many people do not take data seriously and continue to follow their gut feelings. Even if your customer profile coming out of a serious analysis does not match with your preconceived notions, do not blindly reject it; instead, treat it as a newly found gold mine. Gut feelings are even more overrated than Big Data.

3. Be logical: Illogical questions do not lead anywhere. There is no toolset that reads minds—at least not yet. Even if we get to have such amazing computers—as seen on “Star Trek” or in other science fiction movies—you would still have to ask questions in a logical fashion for them to be effective. I am not asking decision-makers to learn how to code (or be like Mr. Spock or his loyal follower, Dr. Sheldon Cooper), but to have some basic understanding of logical expressions and try to learn how analysts communicate with computers. This is not data geek vs. non-geek world anymore; we all have to be a little geekier. Knowing Boolean expressions may not be as cool as being able to throw a curve ball, but it is necessary to survive in the age of information overload.

4. Shoot for small successes: Start with a small proof of concept before fully investing in large data initiatives. Even with a small project, one gets to touch all necessary steps to finish the job. Understanding the flow of information is as important as each specific step, as most breakdowns occur in between steps, due to lack of proper connections. There was Gemini program before Apollo missions. Learn how to dock spaceships in space before plotting the chart to the moon. Often, over-investments are committed when the discussion is led by IT. Outsource even major components in the beginning, as the initial goal should be mastering the flow of things.

5. Be buyer-centric: No customer is bound by the channel of the marketer’s choice, and yet, may businesses act exactly that way. No one is an online person just because she did not refuse your email promotions yet (refer to “The Future of Online is Offline“). No buyer is just one dimensional. So get out of brand-, division-, product- or channel-centric mindsets. Even well-designed, buyer-centric marketing databases become ineffective if users are trapped in their channel- or division-centric attitudes, as in “These email promotions must flow!” or “I own this product line!” The more data we collect, the more chances marketers will gain to impress their customers and prospects. Do not waste those opportunities by imposing your own myopic views on them. Big Data movement is not there to fortify marketers’ bad habits. Thanks to the size of the data and speed of machines, we are now capable of disappointing a lot of people really fast.

What Did This Hype Change?
So, what did this Big Data hype change? First off, it changed people’s attitudes about the data. Some are no longer afraid of large amounts of information being thrown at them, and some actually started using them in their decision-making processes. Many realized that we are surrounded by numbers everywhere, not just in marketing, but also in politics, media, national security, health care and the criminal justice system.

Conversely, some people became more afraid—often with good reasons. But even more often, people react based on pure fear that their personal information is being actively exploited without their consent. While data geeks are rejoicing in the age of open source and cloud computing, many more are looking at this hype with deep suspicions, and they boldly reject storing any personal data in those obscure “clouds.” There are some people who don’t even sign up for EZ Pass and voluntarily stay on the long lane to pay tolls in the old, but untraceable way.

Nevertheless, not all is lost in this hype. The data got really big, and types of data that were previously unavailable, such as mobile and social data, became available to many marketers. Focus groups are now the size of Twitter followers of the company or a subject matter. The collection rate of POS (point of service) data has been increasingly steady, and some data players became virtuosi in using such fresh and abundant data to impress their customers (though some crossed that “creepy” line inadvertently). Different types of data are being used together now, and such merging activities will compound the predictive power even further. Analysts are dealing with less missing data, though no dataset would ever be totally complete. Developers in open source environments are now able to move really fast with new toolsets that would just run on any device. Simply, things that our forefathers of direct marketing used to take six months to complete can be done in few hours, and in the near future, maybe within a few seconds.

And that may be a good thing and a bad thing. If we do this right, without creating too many angry consumers and without burning holes in our budgets, we are currently in a position to achieve great many things in terms of predicting the future and making everyone’s lives a little more convenient. If we screw it up badly, we will end up creating lots of angry customers by abusing sensitive data and, at the same time, wasting a whole lot of investors’ money. Then this Big Data thing will go down in history as a great money-eating hype.

We should never do things just because we can; data is a powerful tool that can hurt real people. Do not even get into it if you don’t have a clear goal in terms of what to do with the data; it is not some piece of furniture that you buy just because your neighbor bought it. Living with data is a lifestyle change, and it requires a long-term commitment; it is not some fad that you try once and give up. It is a continuous loop where people’s responses to marketer’s data-based activities create even more data to be analyzed. And that is the only way it keeps getting better.

There Is No Big Data
And all that has nothing to do with “Big.” If done right, small data can do plenty. And in fact, most companies’ transaction data for the past few years would easily fit in an iPhone. It is about what to do with the data, and that goal must be set from a business point of view. This is not just a new playground for data geeks, who may care more for new hip technologies that sound cool in their little circle.

I recently went to Brazil to speak at a data conference called QIBRAS, and I was pleasantly surprised that the main theme of it was the quality of the data, not the size of the data. Well, at least somewhere in the world, people are approaching this whole thing without the “Big” hype. And if you look around, you will not find any successful data players calling this thing “Big Data.” They just deal with small and large data as part of their businesses. There is no buzzword, fanfare or a big banner there. Because when something is just part of your everyday business, you don’t even care what you call it. You just do. And to those masters of data, there is no Big Data. If Google all of a sudden starts calling itself a Big Data company, it would be so uncool, as that word would seriously limit it. Think about that.

PPC Shockers and Secrets

Pay per click (PPC), particularly Google AdWords, is a marketing channel that can produce profitable results for your business, whether your goal is lead generation or sales. I have been managing PPC for businesses, as an in-house marketing leader as well as marketing consultant, for over a decade now. Though the years, I have noticed many secrets to success that I wanted to share—especially with business owners and marketers that haven’t tried PPC yet.

Pay per click (PPC), particularly Google AdWords, is a marketing channel that can produce profitable results for your business, whether your goal is lead generation or sales.

I have been managing PPC for businesses, as an in-house marketing leader as well as marketing consultant, for over a decade now.

Though the years, I have noticed many secrets to success that I wanted to share—especially with business owners and marketers that haven’t tried PPC yet.

First, I’d like to clear the air about a big shocker … or actually a fallacy … that you need a big budget to run an effective PPC campaign.

You don’t. If you happen to have a large budget, your ads will be shown more and you can spread out your ad groups and test different types. With a smaller budget, you do need to be more judicious with your efforts. But if you market smarter, not broader, your campaigns can still produce positive results.

I have run PPC campaigns with total monthly budgets of $1,000. I have run campaigns with total daily maximum budgets ranging from $25 to $50. These campaigns brought in both sales and leads, despite their limited spending. But they do require active management, strategic thinking, deep PPC knowledge and refinement/optimization.

The PPC Tri-Pod
What is going to determine the cost and return of your campaign are three simple things I call the “PPC Tri-pod”, as it supports your entire PPC efforts:

  1. Keywords
  2. Creative (or banner ad, if it’s running on the display network)
  3. Redirect URL

So in order for you to get the most bang for your buck with PPC, you should be aware of a few things regarding the PPC Tri-pod:

Keywords. The more popular the keyword, the more cost per click (CPC) it’s going to have. So it’s very important to do your keyword research before you start selecting your keywords as you’re setting up your campaign.

I like to use Keywordspy.com. The “lite” version is free, but you can also upgrade to the full version and see more results and have more capabilities for a monthly fee. Google used to have its Keyword External Tool, which has since morphed into Google AdWords Keyword Planner. You need a Gmail account to access this free tool.

Either of these tools will allow you to enter keywords or keyword phrases and then view popularity (actual search results), as well as what the average CPCs are. This is important for your keyword selection and bidding. You can also type in your “core” or focus keywords and get additional ad group/keyword ideas. To help refine your search terms, you can also choose broad match, broad match modifier, phrase match, exact match and negative match.

If you pick a word that is too vague or too under-searched, your ad will not see much (or any) action. Impressions will either not be served, or if they are served (in the case of a vague word), it may cost you a high CPC. In addition, a vague keyword may not be relevant enough to get you a good conversion rate. Because you pay by the click, your goal is to monetize that click by getting an instant conversion. And conversions, my friends, will be the role of the landing page. I’ll talk about that more in a moment.

Creative. This is your text ad (or banner ad, if you’re running in AdWords’ display network). For Google to rank your ad favorably, and more importantly, for you to get the best conversion results possible—there needs to be a relevancy and synergy between your keyword, text ad and landing page. Google will let you know if you’re not passing muster by your ad’s page position and quality score. Once you’ve carefully researched and selected your ad group keywords, you’ll want to make sure those keywords are consistent across the board with your ad and landing page. Your text ad has four visible lines with limited character count:

  1. Headline (25 Characters)
  2. Description Line 1 (35 Characters)
  3. Description Line 2 (35 Characters)
  4. Display URL (35 Characters)

Your keyword must appear in your text ad, as well as follow through and appear in the content of your landing page.

This will give you a good quality rank with Google, but also help qualify the prospect and carry the relevancy of the ad through to the landing page. Why is this important? It helps maintain consistency of the message and also set expectations with the end user. You don’t want to present one ad, and then have a completely different landing page come up.

Not only is that a “bait and switch,” but it’s costly. Because you’re paying for clicks, a great ad that is compelling and keyword rich, but not cohesive to your landing page, will not convert as well as one that is. And your campaign will actually lose conversions.

Redirect URL. This is your landing page. Different goals and different industries will have different formats. A lead generation campaign, which is just looking to collect email addresses to build an opt-in email list, will be a “squeeze page.” This is simply a landing page with a form asking for first name and email address in return for giving something away for free—albeit a bonus report, free newsletter subscription or similar. It got its name because it’s “squeezing” an email address from the prospect. Some retail campaigns will direct prospects directly to e-commerce sites or catalog pages (as opposed to a sales page). Direct response online marketers will drive their traffic to a targeted promotional landing page where it’s not typically a Web page where there’s other navigation or distractions that will take the prospect away from the main goal. It’s more streamlined and focused. The copy is not technical, it’s compelling and emotional, like promotional copy you would see in a sales letter. The anatomy of your redirect URL will vary on your goal and offer. It will take optimization and testing to see what’s working and what’s not. And that’s par for the course. If you’re testing, I suggest elements that scream and not whisper, such as long copy vs. short copy, or headlines and leads that are different themes. However, no matter what your goal, whether it’s going for the sale or the email address, you still need keyword consistency between all creative elements.

Tips And Tricks For Maximum ROI
Whether you have a big or small budget, there are a few things I’ve learned during the years that help the overall performance of a PPC campaign. Some of these are anecdotal, so if you’ve seen otherwise, I suggest testing to see if it makes a difference to your particular industry.

Ad and Landing Page. In general, I have noticed that shorter, to the point, landing pages produce better results. And the rationale is quite obvious. People searching the Web are looking for quick solutions to a problem. This means your creatives have to not only be keyword rich, but compelling and eye-caching. You have seconds to grab a Web surfer’s attention and get them to click. In the same sense, the landing page has to be equally relevant and persuasive, and typically shorter in copy. Keep in mind Google has many rules surrounding ad copy development. So write your text ads in accordance to its advertising policy.

Price Point. Again, in my personal experience, most Web surfers have a price threshold. And that’s items under about $79. When running a PPC campaign, think about price points that are more tolerable to “cold” prospects; that is, people who haven’t built a relationship with you or know anything about you. They have no brand loyalty. They don’t know you from Adam. So getting a sale at a lower price point is an easier sell than a product you have that costs hundreds of dollars. Luxury items or items with strong recognition and brand loyalty are the exception to that rule. As a direct response marketer, I urge you to price test and see for yourself.

Campaign Set-up. There are a few tactics I notice that help with ad exposure, clicks and saving money. When you’re setting up your campaign you can day-part, frequency cap and run ad extensions. Day parting allows you to select the hours of the day you’d like your campaign to run; ad extensions allow you to add components to your text ad to help visibility and call to action—such as location, site links, reviews and more; And frequency capping lets you set a threshold on how many times you’d like a given person to see your ad (based on impressions).

PPC Networks. It’s smart not to put all your eggs in one basket. In addition to Google AdWords, try running campaigns on other PPC networks, such as Bing/Yahoo, Adroll (retargeting through Facebook), Advertising.com/AdSonar.com, SiteScout.com (formerly Adbrite.com), and Kanoodle.com. Then see where you get the best cost per click, cost per conversion and overall results.

I’ve only touched the surface here. There are more tactics and features that can help a PPC campaign’s performance. So get yourself familiar with it, read up on the best practices, and don’t be afraid to put your toe in the water. As with any marketing tactic, some channels will work for your business, and some won’t. But you won’t know unless you test. Just remember the foundation of success hinges on the PPC Tri-Pod. The possibilities are endless.

LinkedIn Profile Makeover for Sellers

Are you appealing to emotional and tangible desires of buyers on your LinkedIn profile—in ways they cannot resist acting on? Reinsurance broker, Paul Dzielinski is. That’s how he’s enticing prospects to talk about buying his products. Dzielinski is generating leads with his LinkedIn profile using a system to get the job done faster. Once again, the process is rooted in traditional direct response copywriting. There are three components.

Are you appealing to emotional and tangible desires of buyers on your LinkedIn profile—in ways they cannot resist acting on? Reinsurance broker, Paul Dzielinski is. That’s how he’s enticing prospects to talk about buying his products.

Dzielinski is generating leads with his LinkedIn profile using a system to get the job done faster. Once again, the process is rooted in traditional direct response copywriting. There are three components.

  1. Solving customers problems in ways that
  2. are designed to provoke a response and ultimately
  3. foster buying confidence in customers (convert the lead).

Give Prospects a Reason to Act
Dzielinski knows that prospects are lazy. That’s why he gives them a reason to take action. There is no better reason than a pain, fear or goal his customers have.

Smart sellers like Dzielinski are placing videos and Slideshare presentations on LinkedIn that invite customers to act—to be taken on a journey. A trip where the prospect identifies as a buyer and then chooses to steer toward or away from products.

As it turns out, engagement is not the goal. Response is. But you’ve got to give customers a clear, compelling reason to act.

Design Slideshare Decks to Provoke Response
Dzielinski ‘s customers are asking him questions—the questions he wants to answer for them. Here’s how he’s doing it. It’s all about what and how prospects encounter content on his profile. For example, buyers are asking for advice, short-cuts and practical know-how based on a Slideshare deck on his profile.

What makes Dzielinski ‘s Slideshare deck work? Success is all about how the content is structured around the three-step process. Paul is successful because he exploits classic copywriting techniques via Slideshare.

Dzielinski is giving prospects temporary satisfaction. He’s answering questions in ways that satisfy for the moment, yet provokes intense curiosity, which creates more questions.

“It’s Copywriting 101,” says Copyblogger Media founder, Brian Clark. “You know, in copywriting, the purpose of the headline is to get the first sentence read. The purpose of the first sentence is to get the second sentence read.”

Get Prospects to Lean Forward
Clark says, when you apply the idea to SlideShare, “the purpose of each slide is to get the next slide advanced … and the next thing you know, your finger is just moving. Advance, advance, advance.”

Clark wisely points out, “It’s very engaging because it’s not a lean-back experience. It’s a lean forward. I want to see what the next slide says. And when it’s really well done, it’s fascinating. The next thing you know, you’ve gone through 70 slides and read the entire thing.”

In Dzielinski’s case, he’s offering prospects pithy, useful advice about captive insurance. Do they need it, why they might benefit, why not (what’s the “best fit”) and the kind of costs involved.

Using his PowerPoint presentation, he’s getting buyers curious about the details behind his solution. At the end he makes a call to action for a free assessment.

Is a deadly simple idea. Plus, it’s effective and repeatable.

The Truth About Sharing Content on LinkedIn
Your prospects don’t need engaging stories. Buyers have nagging problems and challenging goals that are far more important. What they need is a better way to achieve goals—or an insurance policy against risk. Thus, your job is to leverage this need and get customers curious about your remedy.

How can you help customers overcome the challenges they face, reduce the risks they need to take or find a short-cut to achieve a goal faster?

Make sure your words are making customers respond.

Make sure you LinkedIn profile is answering questions in ways that makes potential buyers think, “Yes, yes, YES … I should take action on that. That will probably create results for me. Now, how can I get my hands on more of those kinds of insights/tips?”

Need some help making this happen on your profile? View the 12-minute video training here.

Getting customers curious about you is the key to using LinkedIn for lead generation—effectively. This simple idea is the difference between wasting time on LinkedIn and having it pay you.

Good luck!

A Weird, But Effective Shortcut to Generate Sales Leads on LinkedIn

See what I just did? You chose to read this article—probably because the headline provoked curiosity. It’s one of the oldest tricks in the book, the basis of effective copywriting. True, there is no silver bullet for generating sales leads on LinkedIn. However, there is one habit that consistently brings my students and me more success generating leads online: Giving customers a reason to click and take action—relieve that nagging pain or take a step toward an exciting goal.

See what I just did? You chose to read this article—probably because the headline provoked curiosity. It’s one of the oldest tricks in the book, the basis of effective copywriting. True, there is no silver bullet for generating sales leads on LinkedIn. However, there is one habit that consistently brings my students and me more success generating leads online: Giving customers a reason to click and take action—relieve that nagging pain or take a step toward an exciting goal.

Yes, creating curiosity that lures customers to act seems like an obvious strategy. So, are you and your team doing it?

Engagement Is NOT the Goal: It’s the Entry Fee
At the simplest level these are our goals:

  • Grab attention, hold it long enough to…
  • provoke engagement in ways that…
  • earns response (generates a lead).

Will you agree with me? If you don’t get response to content placed on LinkedIn, you’re wasting precious time.

Will you also agree engagement is not the goal on LinkedIn? I know we’ve been told it is. It feels strange saying it’s not. But engagement is the beginning of a courtship process.

Whether it happens on your profile or inside LinkedIn groups, engagement is the entry fee. It’s your chance to create irresistible curiosity—or let your customer click away.

LinkedIn can be a big time-saver. It can scale your ability to generate leads. But only if you adopt a successful paradigm, one where engagement is the beginning, not the end. I’m talking about a world where it’s easy to get response—using a system to get customers curious.

3 Steps to Generating Leads on LinkedIn
Here are my best tips on structuring what to say and when—so you create hunger for more details in potential buyers. Remember, intense curiosity is the goal.

The idea is to give prospects temporary satisfaction. When you post updates, engage in LinkedIn groups or dress up your profile, answer customers’ questions in ways that satisfy. However, make sure your answers cause more questions to pop into their heads. That’s when you’ll hit ’em with a call to action that begins the lead generation journey.

Here’s where to start—either on your profile or in a LinkedIn group where prospects can be found: Answer a question your target market needs answered in a way that focuses on a nagging pain or fear. The idea is to directly or indirectly signal, “this discussion will help you overcome _____” (insert fear or pain).

If responding to an existing question make your comment suggest, “I’m here with a new point-of-view” or “I’m here with a fresh, new remedy to that pain.”

When you communicate follow these guidelines:

  1. Get right to-the-point. When you start or contribute to a LinkedIn group discussion be like a laser. Don’t make readers wait for the solution. Hit ’em with it. However start by…
  2. Revealing slowly. When it comes to all the juicy details of your remedy take it slow. Slow enough to encourage more questions—to create curiosity in the total solution. When you do this, make sure you are…
  3. Provoking response by leveraging customers’ curiosity.

Yes, be action-oriented and specific. But avoid being so complete that readers become totally satisfied with your words.

Make Your Answers Generate More Questions
Think of this like a successful dating encounter. Masters of the courtship process have always known the secret to creating intense curiosity: Being a little mysterious. Suggesting “I’ve got something you might want.” Holding a little information back. Strategically timing the sharing of information.

We’re trying to get the other person to be curious about us. So the best way to spark curiosity is to answer questions in direct ways that satisfy—but only for the moment. Answers should generate more questions … spark more curiosity in what we are all about.

Of course, we need to be credible. We cannot risk playing games with the other side. Yet being a little mysterious is fair play. It encourages more questions. This is how to generate leads on LinkedIn.

In business it works the same. Your ability to start generating sales leads on LinkedIn will be determined by an ability to answer questions in ways that provoke more questions from the buyer. Good luck!

‘I Can’t Because, I Need … ’

Does this sound like you? Have you ever set up a goal, but then realized (either quickly or too late) that it wasn’t possible due to some other dependency? This dependency could be outdated technology, lack of the “right” resource or an immovable deliverable date. Well, this is just reality.

Does this sound like you?

Have you ever set up a goal, but then realized (either quickly or too late) that it wasn’t possible due to some other dependency?

This dependency could be outdated technology, lack of the “right” resource or an immovable deliverable date.

Well, this is just reality. And if you haven’t come across this yet, bless your soul.

Having worked with both large brands and small businesses … basically, budgets of all sizes … I realized this can happen to you no matter who you are.

When it comes to mobile, I feel it happens even more due to a few reasons.

The technology is advancing so fast that older, more traditional companies have a hard time keeping their back-end infrastructure up-to-date. This means the technology they have in place may not, and most likely won’t, support some of the new and innovative tools you may want to use to meet your goals.

The experience needed to execute mobile programs the way you desire doesn’t currently exist within your reach … either internally, or affordably with a partnering organization. This means that you’re either going to force additional demand on your existing staff (or yourself) or just not meet your goals.

Both of these can be a bummer, as you can imagine. But there can be light at the end of the tunnel for you.

Instead of just putting your head down and saying “we just can’t do this” or “this isn’t fair” (I know some businesses say this. We’re all human.) you just need to shift your mindset.

If you can’t reach a specific goal due to a particular dependency and sometimes dependencies … just change your goal.

I know this sounds simple, but I’ve watched this with my own eyes at huge brands that generate billions of dollars a year to small business owners who generate a little over $100,000 a year.

Too many of us (yes, myself included) have become so reactionary that we don’t plan out our success. We just chase shiny objects that won’t even get us to our goals.

If a dependency is holding you back, you have two options.

First, your new goal is to remove the dependency. If this takes time, so be it. Put together a road map (I mean an actual calendar) working backward from when you want to achieve this goal. Then identify as many smaller milestones that will help you get there. I don’t care if one of those milestones is faxing a contract.

Do people still use fax machines? I digress …

Whether you’re a small business or a large business, small wins matter. I’m telling you, I’m speaking from firsthand knowledge. Small wins help you build momentum, show progress to the rest of the organization (or again … yourself).

Second, modify your goal based on the dependency. You’ll probably want to do this while you’re doing the option above too. 😉 If you can’t reach your desired goal because you can’t afford a certain tool or enough money for advertising (hopefully, you’re not wasting advertising dollars already, as you likely are) then change your goal.

It amazes me to see people get hung up on only one desired outcome or path to that outcome that they just take no action at all. Stop your pity party and just change your goal. You can dictate what success looks like and what path can get you there based on these dependencies.

Once you’ve figured out how to modify your path to work within your means, I want you to make that calendar again. Determine when you want to reach that goal and identify all of the small wins along the way. Celebrate each and every one in some way and report that back to the business or your employees or your family if you’re a “solopreneur.”

You can power-on and find your success in a variety of ways.

Don’t let dependencies create inaction.

Are you enjoying more of these thought pieces? Please let me know in the comments.

Share what you think is holding you back from success. Go!