Conversions Are More Important Than Traffic With Google Ads and 3 Steps to Up Them

What’s the difference between successful (profitable) and unsuccessful (unprofitable) Google Ads advertisers? Successful advertisers focus more on conversions than traffic.

conversionsAnybody can spend money on Google Ads. But only a small percentage of advertisers earn a healthy profit from their investments in advertising. So, what’s the difference between successful (profitable) and unsuccessful (unprofitable) advertisers?

Unsuccessful advertisers focus on traffic. Successful advertisers focus on conversion. They understand that conversion is what really makes or breaks an advertising campaign.

In this context, we can define conversion as the ability to turn website visitors into customers. If you can convert on traffic at higher rates than your competitors, you’ll not only profit more, but you’ll be able to expand your reach and gain more market share.

Of course, traffic and conversion go hand-in-hand. You need traffic in order to get more conversions and customers. But in the scheme of things, conversion is way more important than traffic. Conversion is where all the leverage is. And to succeed with advertising, you must prioritize conversion over traffic.

You may be wondering, “Why is a conversion more important than traffic?” The answer is that your conversion rate places a limit on your traffic. For example…

  • If you have really low conversions … Only a tiny percentage of website visitors are converting into customers, and that means you’re probably losing money on your advertising, and you’ll have to stop or you’ll go broke.
  • If you have mediocre conversions …. Only a small percentage of your website visitors are converting into customers, and then you’ll have to “retreat” and find places where you can still earn a profit. Your traffic potential is very limited.
  • But if you have really strong conversions … You’re converting more website visitors into customers, compared with your competition. As a result, you’ll have the money to expand your advertising. Essentially, high conversion rates provide the funding to buy traffic.

Here’s the process you should take to create and fine-tune a high-converting advertising campaign:

Step 1: Always Run the Numbers Before You Run Ads

Never pull the trigger on an ad campaign before you run the numbers. You need to know how much you can really afford to get a click, generate a lead and acquire a new customer. Do some “back of the envelope” calculations to see what kind of conversion rates you’d need to have for your advertising to be profitable.

Step 2. Put Your Best Foot Forward

Once you run the numbers, you’ll probably need to make some improvements to your conversion systems in order to make the numbers work in your favor. You’ll want to create a high-converting landing page. You also may need to adjust your offer and pricing, improve your sales/closing process, put upsells in place and create a follow-up sequence.

The important thing is to do this before you buy traffic, so you’ll have confidence you can actually make money from your ads. Always avoid advertising into a “leaky” funnel, because that’s just asking to lose money.

Step 3. Never Stop Testing

Once you’ve got your ads live, that’s really just the beginning.

Now you need to track, tweak and test in order to get your campaign to be profitable. And once your ads are profitable, you should not “set it and forget it.” Instead, you should be continually tracking, tweaking and testing your marketing funnel. This is where advertising gets really fun, because with every conversion improvement you make, you’re giving yourself a raise. Plus, you’re widening the gap between you and your competition, making it harder and harder for anybody to catch up and compete with you.

Want more tips to improve your Google advertising? Get your free copy of our Ultimate Google AdWords Checklist.

 

How to Double Your Landing Page Conversion Rates With 6 Easy Tune-ups

One of the biggest mistakes you can make with your Google AdWords campaign is failing to optimize your landing page. No matter how carefully you fine tune your ad copy, tweak your keyword match settings and reallocate your budget, if your landing page conversion rates are low, you are literally giving away sales

One of the biggest mistakes you can make with your Google AdWords campaign is failing to optimize your landing page. No matter how carefully you fine tune your ad copy, tweak your keyword match settings and reallocate your budget, if your landing page conversion rates are low, you are literally giving away sales. Today, I will walk you through the steps to improve (even double) your current conversion rates.

What Is a Landing Page?
A landing page is the specific page on your website where prospects land after clicking on one of your ads. Note that you should never use your homepage as a landing page, because the homepage gives a general introduction to your company, while a landing page needs to be tightly geared to the ad copy. In fact, it is best to create a separate landing page for each ad. This allows you to clearly reiterate the main idea in the ad, improving the overall congruence, or harmony, of the prospect’s experience.

What Is Your Conversion Rate?
The most important conversion rate is the ratio of sales to visitors. However, that’s not always quick and easy to calculate, so advertisers measure other key sales actions, such as filling out a contact form or making a phone call. For example, let’s say that 1,000 people click through your AdWords ad to your landing page, but only 20 of them fill out the contact form on that page. Divide 20 by 1,000 to find that your “contact form conversion rate” is 2 percent. Your numbers might be very different, but remember that the conversion rate refers to the percentage of people who take further action toward making a purchase after landing on your page.

Why Should You Improve Your Landing Page Conversion Rates?
Simply put, improving your conversion rates means that you will get more leads or customers for fewer advertising dollars. Taking the example above, suppose that the action you want prospects to take is purchasing a product that you sell for $100. If 20 of 1,000 people who click on your ad buy the product, you make $2,000. If 40 of those same 1,000 people buy the product (4% conversion rate), then you make $4,000. That’s $2,000 extra revenue from the exact same investment in advertising!

What Are the Basic Keys to Improve Landing Page Conversion Rates?
Improving your landing page conversion rates is both a science and an art. Monitor your AdWords campaign closely at first to determine the results of the changes you implement, and be ready to tweak your landing page as needed depending on what you discover. These are the parts of the landing page that often need fine-tuning:

  1. Congruence: This is the overall harmony of the user experience. Your landing page should tightly reflect the message, tone, and feel of the ad that was clicked on. Your prospects clicked on the ad because something in it resonated with them, so follow up on that with the landing page. If you change nothing else, ensuring congruence can dramatically improve your conversion rates.
  2. Headline: The headline is the most important part of your landing page. People scan quickly and make snap decisions when reading online, so your headline needs to captivate them. Don’t try to close the sale in the headline, but do restate the offer or the most important point from your ad.
  3. Offer and Call to Action: Most people know that a strong offer is an important element in making a sale, but is your offer irresistible? Try offering something different from what everyone else in your line of business offers, or add an extra bonus. Make sure to give clear instructions on what to do next to make the purchase, and if possible, add a deadline to increase urgency.
  4. Copy: Make sure your landing page explains exactly how you can solve the customer’s current problem or fulfill a specific need. In other words, focus on benefits rather than features. Plus, add elements that make your business sound legitimate, such as testimonials, reviews, or industry affiliations.
  5. Reduce Risk: Prospects tend to be skeptical when shopping online, largely thanks to the frequent horror stories in the media. If your offer requires payment, reduce the perceived risk by providing a guarantee, adding third-party trust verification, and providing full contact details for your company.
  6. Layout and Aesthetics: Because people scan rather than reading in depth online, clearing out the clutter can improve your conversion rates. Make it easy for prospects to figure out what to do. Make the buttons they need to click bigger. Remove extraneous navigation menus. Avoid long blocks of text. Keep it simple and obvious, aesthetically pleasing, and congruent with your overall brand.

Want more Google AdWords tips and advice? I put together an AdWords checklist to help you get your campaigns set up for success. Click here to get my Google AdWords checklist.

Analytics Isn’t Reporting

Today, virtually all organizations have challenges in effectively leveraging analytics to drive business performance. Odds are pretty good that when you read that statement, you thought of at least one example in your organization. Perhaps you thought about the systemic contribution that analytics is making or a frustration you’ve had with analytics performance. If so, you’re hardly alone.

Today, virtually all organizations have challenges in effectively leveraging analytics to drive business performance.

Odds are pretty good that when you read that statement, you thought of at least one example in your organization. Perhaps you thought about the systemic contribution that analytics is making or a frustration you’ve had with analytics performance. If so, you’re hardly alone.

Here’s my home base for thinking about “analytics” in your organization.

“The promise of marketing analytics isn’t esoteric, or abstract — it’s fundamentally simple — analytics generates evidence of problem or opportunity that can be used to drive a specific business impact.”

Yet marketing analytics all too often fails to live up to its full potential. When it comes to the Web, almost a decade after the advent of mass adoption of Web analytics platforms like Google Analytics, engagement and conversion rates are still struggling to make methodical progress forward, and bring the business to materially greater profitability.

One of the biggest errors in strategy is the inadvertent substitution of “reporting,” or even “dashboards,” for a robust analytics process. It helps to first appreciate how subtle that difference is and why it happens:

  1. Analytics Is Interesting. Analytics can be intellectually stimulating, but some individuals and organizations spend too much time in the rapture of how interesting all that data can be. I was recently at an event where a smart young woman had a name badge on that said “I love data” below her name. I was tempted to write “I make money with the data” under my own.

    While I’ll be the first to express a life-long affair with the database and discovering “interesting” things in the data, that’s just not enough. So we have to monitor when analytics isn’t producing the evidence we need to affect change and deliver a business impact. While that can take a tremendous amount of work, the purpose itself must remain clear to create value.

  2. Reports Don’t Always Have the Right Questions Behind Them. Most of us came up in business generating and reading reports. I confess that I remember craving a report we used to call “the blue book” (if you still remember paper). I looked forward to every week when I ran my business line off of it in a large company that razed many a forest generating blue books. Thankfully, they email them now — but these reports are the same static, one-dimensional view of the business, many years later.

    The problem comes when we see our “standard reports” as the answer, even if the question we should be asking has changed.

    When you’re dealing with fickle consumers, and infinite choice is a click away, those questions sometimes change faster than “reporting standards” can realistically keep up with.

  3. The Relevancy Is Gone. Better than 80 percent of the time, I see marketing organizations with ample “stats” on their historical activity — yet they often fundamentally lack a strategic big picture and framework to consistently improve marketing and business decision-making. Frequently, the same organizations struggled with aligning the technical implementation of analytics and metrics required to drive business growth.

  4. Continuous Business Improvement Sometimes Requires a Cultural Shift. Cultural shifts of any size aren’t trivial, of course. I recently attended an all-day digital commerce strategy summit at a large brand I’ve done strategy work with during the past year. Dozens of staff, vendors and executives attended. The ultimate revelation for some of these executives who made the six-figure investment in the event was, “this requires patience, and is very methodical and testing-based” — it took a huge amount of effort, resources and time. To the credit of the executive who sponsored this event, a necessary cultural shift was recognized. While all in attendance knew intuitively about “test-optimize-learn” and had a large investment in their analytics software platform — she recognized that her organization was playing catch-up culturally — an achievement in itself.

5. Prioritization Is Key. Many large and more traditional organizations have very deep roots in a task- and reporting-based culture. This stifles Data Athletes from doing their jobs. Prioritization is key. As the old saying goes, “If everything is a priority, nothing is a priority.” Executive sponsors need to make choices on where to dial effort back; focus can then be applied to build a point of view based on evidence, and the opportunity to create and discover the context of opportunity and problems.

Forward vs. Backward Analysis.
Very frequently, I’ve helped organizations that started analytics processes or programs by looking “backward” at tactical reports; these reports can only show if a past tactic has or hasn’t worked. You cannot tell if a different tactic or mix of tactics would have done better, and by how much. Worse yet, the very volume of these “reports” often obscures the bigger picture. The solution … Look forward.

Analytics Should Be Forward-Looking. It’s driven not only by analyzing the past, but by creating a framework for planning and creating future performance. In other words, what to test, how to test it, and how to use the results of those tests to drive continuous improvements in the business.

In short, analytics done well creates visibility into what you should be doing and suggests the delta with what you are currently doing. Think about the aforementioned necessity for prioritization — Analytics done well helps you set those priorities.

Analytics professionals and and the executive team must all work together according to one principle:

Analytics is the process of identifying truths from data.
These truths inform decisions that measurably improve business performance.

Analytics Must Be Purpose-Driven.
Here’s a simple approach to create focus and align the specific implementation of analytics to serve you and your business growth:

  • Your business’s Purpose drives specific Business Objectives.
  • Those Business Objectives, in turn, inform Goals.
  • Your Goals are tracked via KPIs.
  • The KPIs are continuously compared against Benchmarks.

It’s easy to dive into the weeds, get lost in the data, lose patience with the process, and begin a bottom-up approach. This deceptively simple framework I’ve suggested will help you take a top-down approach to analytics that ensures you are measuring the right things — correctly. When you do, you will become a true analytics-driven organization.

Doing so will help your organization grow faster, more consistently and reliably — and that makes for a valuable and happier organization. Be a Data Athlete, not an analytics nerd — and you’ll make all the difference in your organization.

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.

Subject Lines in Sheeps’ Clothing: A Go or a No?

I’m sure you’ve seen it, if not used it yourself: Marketing emails wearing a friendly disguise, boasting “RE:” or “FW:” in their subject lines, usually with a real person’s name in the from line rather than a publication or company name. Obviously, the objective is to give the recipient a sense of familiarity. But is it worth the risks?

I’m sure you’ve seen it, if not used it yourself: Marketing emails wearing a friendly disguise, boasting “RE:” or “FW:” in their subject lines, usually with a real person’s name in the from line rather than a publication or company name.

Obviously, the objective is to give the recipient a sense of familiarity, or curiosity about whether this is a correspondence they were previously involved in, thus hopefully prompting an open.

I can tell you that in my three years copywriting for the Target Marketing Group’s marketing department, I’ve used subjects like these several times, as have most of my colleagues—and to be perfectly honest, we’ve seen impressive results as far as response and conversion rates.

Many marketers feel strongly that this method is simply too dishonest, erring on the devious rather than the clever side of crafty. Integrity and ethics are never negligible factors in what we do, even when a high open rate seems like the most important goal.

After some consideration, our marketing department decided to stash away the “RE”s and “FW”s for a while. Still, I thought I’d check out the stats for a few of these emails, to see if it was at all possible that the benefits outweighed the risks. Here’s what I found at a glance:

Subject 1
Re: Your Direct Marketing Day @ Your Desk Registration

Subject 2
Re: 2014 email marketing plans

Subject 3
FW: Reasons to register

Registrants:

340

Registrants:

336

Registrants:

15

Open rate:

28%

Open rate:

18%

Open rate:

21%

Unsubs:

372

Unsubs:

309

Unsubs:

90

Spam Complaints:

6

Spam Complaints:

7

Spam Complaints:

4

The first two examples were used in promotions for free virtual conferences, while the third promoted a paid workshop. You can see that the open rates were rather good, especially the first of the three. You wouldn’t know from the table, but I can tell you that these registration numbers were among the highest of any email in these events’ respective campaigns.

Now for the bad news: Example No. 2 had the highest number of unsubscribers and spam complaints in its campaign by far. Nos. 1 and 3 were not the “winners” in this respect, but certainly too close to the top to be in the clear. We also received a small handful of, shall we say, colorfully phrased (so colorful they’d have been bleeped on network cable) criticisms from offended readers.

So, what’s the conclusion? Does the fact that all of these emails were huge successes purely in terms of conversion mean that a large majority of recipients were fans, or at least not bothered by the tactic? Or are those unsubs, spam complaints, or simply the principle of the thing too significant to handwave?

As of now, I treat them as I treat wasabi: Use sparingly and with extreme caution. I’d love to hear what you think, or if you’ve done some testing with it yourself!

How Much Should You Spend on Google AdWords?

One of the most frequent questions I receive about Google AdWords is, “How much should I be spending on my AdWords campaign?” That’s a great question, and the short answer is, “It depends.”

Editor’s Note: Don’t miss Phil Frost’s upcoming webinar “Old School SEO Is Dead: What you can do to adapt to Google and the new world of search marketing,” live on February 25. Click here to register.

One of the most frequent questions I receive about Google AdWords is, “How much should I be spending on my AdWords campaign?” That’s a great question, and the short answer is, “It depends.” One of the great things about AdWords is that it is highly customizable, allowing you to make the decisions that best fit your business needs. The downside is that it is not easy to see at a glance how best to manage your AdWords budget.

Fortunately, we have developed a formula that allows you to plug in your numbers and calculate a realistic budget. It breaks down into two phases: Testing and ROI.

Phase 1: Testing

When you begin your Google AdWords campaign, you will need to test several ideas to see what works for you and what doesn’t. While some campaigns are profitable right out of the gate, many others are not. Consider your testing phase to be a form of market research, and plan to invest those dollars without the expectation of getting them back.

Before you begin, gather the following information:

  • Target Keywords Cost Per Click (CPC): Google AdWords follows a pay per click (PPC) model. No matter how many times your ad appears, you only pay when a prospect actually clicks on it. For each keyword, you will pay a different amount of money for that click. This is known as the CPC, or cost per click. For example, Google estimates that “coffee shop” costs $2.90 per click, while “mortgage broker” costs $13.76.

Make a list of the keywords that you want to test, and then use the Google AdWords Keyword Planner Tool to estimate the CPC for each of those keywords. Remember that this is just an estimate, so your actual cost may be higher or lower.

  • Time Frame: How long can you spend in the testing phase before you need to see your results? This is partly dependent on your industry and the keywords you choose. Some keywords have a higher search volume than others, making it easier to get results in a shorter time frame. Also consider your normal sales cycle. Do customers tend to purchase in one day, or does it take months for them to make up their minds? The lower your search volume and the longer your sales cycle, the longer it will take for you to obtain accurate data.
  • Sales Conversion Rates: As a general rule of thumb it’s safe to estimate that 1 in 100 people (1 percent) who view an AdWords ad will click on it, and 1 in 100 clicks (1 percent) will convert into a paying customer. These are estimates, and your ads might drive more or less traffic, but they work for planning purposes in the testing phase.

Now you are ready to put together your testing budget:

  • Per Keyword Cost to Test: If you can turn 1 in 100 clicks into a customer, then the estimated cost per sale is the cost per click (CPC) divided by 1 percent. For example, a keyword that costs $3 per click will cost you an estimated $300 for one sale. Go through the same process for each keyword you want to test, and add up the results to get your total budget.
  • Monthly Testing Budget: To generate a per-month Google AdWords budget, divide your total keyword costs to test by the number of months you want to allot to the testing phase. For example, if your total costs calculated earlier are $2,000, then you could budget $500 per month for 4 months. Or if you wanted to test faster, then $1,000 per month for 2 months.

Phase 2: ROI

Once your testing phase is complete, and you have generated a handful of sales from your ads, then it’s time to move into the ROI phase. The goal here is obviously to maximize return on investment from AdWords.

What should your budget be in the ROI phase? If your ads are profitable, then the answer is you should ditch your budget altogether! If every dollar you spend nets you more than a dollar in sales, it only makes sense to invest as many dollars as possible.

While many businesses focus on writing better ads, which improves the AdWords quality score and reduces the cost per click (CPC), that’s only half of the equation. The real magic comes from the EPC, or earnings per click.

To find your EPC, just multiply your customer value times your conversion rate. Your Customer Value is the average amount that one customer spends on your product or service minus your fulfillment costs. Your conversion rate is the percentage of clicks that become paying customers. So if the customer value is $100 and you have a 1 percent conversion rate, your EPC is $1.00.

Why Is EPC so important?

Well, it tells you exactly how much you can afford to pay per click for every single keyword in your account! If you pay more than your EPC, then you’ll be unprofitable. If you pay less, then you’re profitable. It’s as simple as that.

That means the key to AdWords success is to maximize your EPC by increasing both your customer value and your conversion rates.

Google AdWords is a highly customizable and extremely powerful advertising network, but it can be a bit overwhelming for newcomers. That’s why I put together an AdWords checklist to help you get your campaigns set up for success. Click here to get my Google AdWords checklist.

5 Mobile Marketing Trends You Can’t Ignore in 2015

I don’t have to tell you that we are living in a mobile-first world that continues to drive brands to explore new ways to engage consumers. This ever-changing mobile landscape requires marketers to determine the best ways to connect with their mobile consumers with interactions that will resonate across varying screen sizes.

I don’t have to tell you that we are living in a mobile-first world that continues to drive brands to explore new ways to engage consumers. This ever-changing mobile landscape requires marketers to determine the best ways to connect with their mobile consumers with interactions that will resonate across varying screen sizes. Below you will find a handful of trends that brands should consider as they continue to evolve their mobile strategies:

1. The ROI for Mobile Marketing and Advertising Is No Longer a Guessing Game
Every second of the day mobile devices create copious amounts of actionable data for marketers. This data include call detail records, Short Message Service (SMS) data, and geo-location data. The volume of mobile data and the speed at which it is created continues to increase as the global population increases, as mobile device penetration rates rise, and as the consumer usage rate for social media grows.

2. Mobile Videos—the Time Is Now
During the last few years, the use of videos for marketing purposes has been steadily growing. According to eMarketer, more than one-third of all U.S.-based mobile phone users—about 29 percent of the country’s population—are expected to watch video on their mobile devices. This gives advertisers an ideal way to get in front of their target audience.

The reality is that mobile video is growing and will continue to do so. Stats show that consumers will continue to access, share and interact with video on their smartphones and tablets. The time to start developing a mobile video strategy is now.

3. Make It Personal
Effective digital strategies take a cross-channel approach that integrates the various mobile channels, such as SMS, app, Web and social. The value comes behind the scenes, as brands can learn useful information from each mobile interaction. For example, customers reveal their operating system when they download an app or open their Web browser. Smart marketers collate such data points into one centralized customer profile—an ideal asset to maximize personalization for mobile.

4. Going All In on Mobile
The importance of mobile will grow in each and every aspect of business. People use mobile devices all day long and in various contexts, allowing marketers to target them in a longer stretch of time and during different phases of the day.

All agree that the popularity of mobile devices will only grow in 2015, driven by smartphones, tablets and wearable technology requiring marketers to consider how they target and approach their mobile initiatives.

5. Hyper-localized Targeting
The proliferation of mobile devices, primarily smartphones, has created a major opportunity for marketers to deliver contextual advertisements. These mobile-specific ads target the right people at the right time. For instance, through the combination of social data and location data, stores that shoppers are near and might be interested in can send out ads offering percentage discounts or other incentives. Delivered by shops to their shoppers in real time, these ads get consumers to walk through their doors. Hyper-localized advertising has been shown to increase customer engagement and conversion rates.

People everywhere are becoming more reliant on their mobile devices to provide them with instant access to product information, deals and the opportunity to purchase in an easy, straightforward manner. These trends should inspire brands to think about how they can evolve their marketing strategies to enhance their mobile consumer engagement in 2015.

Tracking Mobile Ad Effectiveness With Real-Time Data

The volume of mobile data and the speed at which it is created continues to increase as the global population increases, as mobile device penetration rates rise, and as the consumer usage rate for social media grows.

The volume of mobile data and the speed at which it is created continues to increase as the global population increases, as mobile device penetration rates rise, and as the consumer usage rate for social media grows.

When analyzed effectively, this data can provide business insight on user sentiment, behavior and even physical movement patterns. Due to the sheer number of mobile devices in use, Big Data practitioners can tap mobile Big Data analytics to better understand trends across vast populations and sub-segments of users. This understanding helps business to improve engagement tactics and to optimize the delivery of services.

Mobile device data becomes particularly useful for analytics purposes when combined with extended data from outside sources. For example, weather and economic allow practitioners to correlate macro-level trends to a targeted set of users. These consumer segments have traditionally grouped users together based upon similarities. However, industry is increasingly focusing upon targeting towards individuals based upon their interests or upon their past behaviors.

Below you will find a number of ways you can apply real-time data analytics to your mobile marketing and advertising campaigns.

  • More Personalized and Targeted Ads
    Big data allows brands to better target users with more personalized interactions that drive engagement. We increasingly see ads featuring products and services we might actually want to use to make our lives better. These more personalized, more targeted ads are all based on massive amounts of personal data we constantly provide. Everywhere we go nowadays, we leave data crumbs. Following these trails reveals information about what we we’re doing, saying, liking, or sharing. Thanks to our mobile devices, this data trail now also hints at where we’re going.
  • Hyper-Localized Advertising
    The proliferation of mobile devices, primarily smartphones, has created a major opportunity for marketers to deliver contextual advertisements. These mobile-specific ads target the right people at the right time. For instance, through the combination of social data and location data, stores that shoppers are near and might be interested in can send out ads offering percentage discounts or other incentives. Delivered by shops to their shoppers in real time, these ads get consumers to walk through their doors. Hyper-localized advertising has been shown to increase customer engagement and conversion rates.
  • Leveraging Attribution to Achieve Mobile Engagement
    Leveraging Big Data about user behavior helps brands more accurately and completely quantify the effectiveness of their mobile marketing initiatives. Big data helps marketers determine whether their campaigns are creating the desired results. The ways users respond to branding assets can be used to literally create “rules of engagement” for each user or for each type of user. Marketers optimize their results through understanding varying levels of consumer engagement and through understanding the contributions of different campaigns across the path-to-purchase.
  • Real-Time Data Analytics Across the Complete Mobile Lifecycle
    In the past, conventional database solutions could be relied upon to effectively manage and analyze massively large data sets. But they did so at a snail-like pace, taking days and even weeks to perform tasks that often yielded “stale” results. By contrast, the big data analytics platforms of today can perform sophisticated processes at lightening-fast speeds, allowing for real-time analysis and insights. Shorter time to insight allows marketers to make real-time decisions and take immediate action based on fresh, reliable and relevant information.
  • Flip Traditional Consumer Profiling Upside-Down
    In the context of ubiquitous, real-time consumer data brands can now ask the data who their customers are. Contrast this to the erudite consumer profiling where consumers are targeted towards based upon their goodness of fit into an expected consumer picture. Rather than relying upon arcane consumer characteristics, instead we can now quantitatively choose targeting characteristics based upon the congruence of these characteristics with the desired call-to-action.

Brands are in desperate need for solutions that will help them understand the impact of their mobile advertising spend in the grand scheme of their broader marketing plan. This requires brands to go well beyond the click-through to be able to attribute their spend to in-store visits and better yet, sales.

5 Important Email Tips for Converting Prospects to Customers

The harder you make it for your prospects to become customers, the fewer will. Most marketers agree that lead generation and lead conversion are the bedrocks of their efforts. As you scrutinize your internal process to convert prospects to customers, remember that, in order to consistently convert, you must at least

(Editor’s Note: This is a preview of Cyndie’s presentation on the upcoming webinar “Email for Customer Acquisition: 5 Great Ways to Expand Your List, and Your Profits!,” with Yeva Roberts of Standard Register, airing Jan. 28 at 2 p.m. EST. Register here to watch the rest live tomorrow, or catch it on-demand starting Jan. 29.)

The harder you make it for your prospects to become customers, the fewer will.

Most marketers agree that lead generation and lead conversion are the bedrocks of their efforts. As you scrutinize your internal process to convert prospects to customers, remember that, in order to consistently convert, you must at least:

  • Provide a clear, concise path to becoming a customer.
  • Enable your prospect to become a customer.
  • Resolve any concerns your prospect has about becoming a customer.

1. Be Timely, Relevant and Easy
Conversion begins at the moment of acquisition—waiting to engage is the kiss of death if you hope to hold the attention of your new prospect. We humans have very short memories—and attention spans—and marketers who allow the opportunity for one to forget a recent engagement will be saddled with lower retention and conversion rates over the customer’s lifecycle.

Your first touch to new prospects must be prompt and direct as you remind the recipient of how the relationship began and, ideally, lay out the path for becoming a highly valued customer. Using email, converting prospects to leads can be quite easy, and when you group likeminded prospects into segments, you can also create highly relevant content appropriate for this audience.

When relevant content is bolstered by personalization, your messaging can transcend a timid first step and become a flat stone skipping through sales ripples reducing necessary touches to a simple few.

Tracking clicked links and buttons within your email will enable you to appropriately respond to engagement with auto-responders recognizing specific engagement activities. Auto-responders are unique tools for reminding prospects they engaged with your brand and helping them resume the process if they’ve become distracted along the way.

2. Provide High-value Content
Inbound marketing represents one of the most successful approaches to converting prospects to leads, leads to subscribers, and subscribers to customers. Your content should be well-written and professionally designed while establishing your brand as an expert.

Your e-books, slide decks, videos, webcasts, demos and the like must be honest and forthright in order to establish your credibility, and should not shy away from areas where your competitors have you bested. Recognizing and addressing these areas will foster trust and help you to build upon these new, budding relationships.

When you post inbound content to your website, you will drive repeat visits; visits that naturally develop, deepen and nurture the relationship to the next stage.

Inbound content such as blogs, videos and online tools also extend the time of visit, and this is an important metric that contributes to your search-engine optimization effort.

Though content at your site is important for this reason and others, resist the urge to keep your content to yourself. Create partnerships with companies that will post your content or choose apps such as SlideShare, YouTube or edocr.com to syndicate your content beyond your own reach. Requiring a form submission to download your content will result in capturing some leads, but you will benefit far more from unrestricted content that is shared liberally.

3. Convey Urgency and Scarcity
Certainly not news to most seasoned marketers, urgency and exclusivity still motivate prospects to act more quickly. Procrastination is a sales killer, so text within your email reminding the recipient of how few widgets remain or how few days to buy the widget remains can dispel bouts of procrastination that grip many of us at one time or another.

Positioning your offer as time-sensitive, quantity-bound or event-based will boost your conversions, but lack of instructions for how to take advantage of your offer can easily negate the benefit gained.

4. Provide Instant Gratification
In email marketing, it’s key to first identify and then solve the customer’s problem—as quickly as possible. Your customers have come to expect and even demand instant gratification, not just in electronic platforms but physical as well. (It’s unbelievable that Amazon is currently testing same-day drone delivery and delivery before you’ve even ordered in order to meet such demands.) You must strive to deliver now.

In your emails, recognize that your clients want it now, and use words such as “instant,” “immediate,” and “now” as trigger words to put them in the buying mood. If your product doesn’t lend itself to being delivered via drone so they can get it now, offer an instant rebate or immediate download. By solving your customer’s problem more quickly than your competitor, you will be more likely to gain the coveted conversion.

As with urgency and scarcity, it’s imperative that you are clear on what steps must be taken in order to achieve instant gratification.

5. Test, Track and Tweak
Don’t guess at what it takes to reduce clicks and shorten your sales cycle, nor should you be a focus group of one. While your opinion about what works and what does not is important, you are not the customer. Use your opinion and expertise as the starting point for testing, but analytics must be used to prove or disprove your educated guesses.

As you begin to understand areas or components slowing your conversions, consider paths that provide information in a more compact and effective manner. Videos are a great solution and a preferred vehicle for many, but podcasts, self-running demos and other online options are also ideal for replacing overhead-heavy meetings, site visits and other person-to-person events.

There are myriad sales-funnel processes, but all can benefit from trusting relationships and consistent experiences. Your blast, drip and nurture emails should be professional, branded and graduated in order to nudge your constituents along. It’s important to remind your prospects why they should choose you—both explicitly and obscurely.