Let’s face it — we’ve all gone to website pages that take forever to load. Sometimes, the load times are so long, I just close the window or hit return and hope a competitor’s website page was designed and optimized to load faster. So, what’s the culprit here? Typically it’s a large Flash file or just a page with one or more images that have not been optimized for the web. Non-optimized images and long page load times adversly affect conversion rates and marketing campaign results. Whether you’re building a simple webpage, a marketing oriented microsite or an email, images must be optimized for the web.

Optimizing images for website design and email is the act of finding the sweet spot between a great looking image that takes too long to download and a grainy image that downloads in a second at dial-up speeds of 56kps. The idea is to modify the image to retain a nice rendering while decreasing the overall file size. Optimization becomes more and more critical as we add more and more images to any microsite page.

There are a number of software packages out there that allow you to resize images – Photoshop, Fireworks and Paint Shop are a few of the most popular. By resizing, I mean changing the absolute size in pixels, as well as the file size itself. Changing file size refers to changing the amount of data compression used for an image. The most common image files used for the web are JPEG, GIF and PNG. The difference in JPEG, GIF and PNG is the way they compress data. GIF and PNG compression work almost exactly the same, but PNG often produces slightly smaller files. JPEG compression is designed to optimize images with fine gradiations of color, while GIF and PNG are better at compressing images with large areas of color, such as illustrations. The more you compress JPEG files, the more artifacts you see. This is because you are actually removing “data” from the file. Here are a few JPEG examples I created using Fireworks.

Look at the captions below each of the images. This a good/bad example of compression. WordPress compressed my origianl image as I uploaded it to this blog. You can’t even read the capations here, so I’ll have to tell you what they say. The original uncompressed image on the left is 96K and would take 15 seconds to download if you were using a dial-up connection. The image on the right manitains almost the same overall quality but has been compressed to less than 25% of the original file size, resulting in only a three second download. Now imagine that there are four images on this page that are these sizes. A webpage using non-optimized images would take 60 seconds to fully render, while a webpage using optimized images will only take 12 seconds. This is the difference between losing and gaining customers that visit your web and microsites. As you can see, “size” really does matter!

While GIF and PNG compressions do not actually lose “data” like JPEG compression, they do lose color fidelity. GIF and PNG files are limited to 256 colors or less. When compressing these files, we typically move to 32, 24 or 8 colors. Here are a couple of examples of compression using PNG files.

The above images are virtually identical in appearance. However, by looking at the captions, you can see that the 16 color, 8-bit image on the right is only about a third of the size of the original.

Many companies today are adopting marketing management technology that allows marketers to easily create their own marketing campaigns, emails and microsites. This is great, but companies need to also put safeguards in place to protect their brand. This is where marketing asset management comes into play. That is, the notion of creating assets like logos and other images that have been optimized and approved for use in marketing campaigns.

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Forrester’s recently published study on Interactive Marketing (email, social, dialog, banner, etc.) reveals 68% of survey respondents expect to achieve increased email marketing effectiveness over the next three years. Furthermore, survey respondents also indicated they would increase interactive marketing budgets by 60% by shifting funding away from traditional channels: direct mail (40%), Newspapers (35%) and Magazines (28%). The picture that is emerging here is one where marketers have high expectations on interactive marketing and expect to focus less on traditional channels. A lot will be riding on this reallocation of marketing budget — so what will marketers have to do right to fulfill their hopes and expectations? This particular blog will address best practices that must be followed by email marketers. Future blogs will address social and dialog marketing in detail.

I don’t know about you, but I’m not sure I can handle more emails coming into my professional and personal inboxes. I get so many from the same companies that I don’t even open them — not even when they come from companies I opted into. Companies that email too frequently create so much “white-noise” that it affects their open rates as well as the open rates for other companies. In addition to white-noise emails, I also get many others that made me think — “why did I even get this?…I don’t smoke, so why am i offered a smart smoker trial?”…”I have only rented mystery and adventure movies from you, so why are you telling me about The Lion King release?” You experience the same things and feel the same way too. So, what can email marketers do to ensure success and rise above the noise and mediocrity we see everyday? It takes only three things — relevancy, segmentation and testing. These three tactics are the key building blocks to optimizing your email marketing efforts.

Relevancy – A blog I posted a couple of weeks ago spoke to email relevancy — that it’s about personalizing the email, segmenting your audience and testing your content (copy, images, subject lines, etc).

Segmentation – Your audience will differ by demographics, personality, shopping habits, geography, etc. Simple segmentations where different messages are sent to each segment can deliver huge marketing ROI. A recent Marketing Experiments webinar offered a case study on American Greetings.com (AG). AG’s goal for their email campaign was to increase individual Ecard purchases as well as Annual Subscriptions. They created two segments — Segment A contained customers that purchased humorous Ecards in the past, while Segment B contained customers that purchased traditional Ecards. Each segment got an email that spoke to their interests based on this past purchase behavior. This simple use of segmentation resulted in a 70% improvement in conversion rates when compared to a control group — that’s HUGE! Just imagine what more sophisticated segmentation schemes might produce!

Frequency – Ok, so I have a real issue with this particular topic. I can’t begin to tell you how much junk I get in my inbox. I don’t even open emails from some marketers and yet I still get an email every day from them — please do some analysis on open rates and realize, I’m just not into Chocolate Covered Strawberries — OK?! Oh yes, back to the informational part of my message… The same webinar by Marketing Experiments (I suggest you Google them!) provided another case study on a very large anonymous Ecommerce company. They segmented their customers into seven segments. Each segment got a different number of emails over a 60 day period. At the extremes, one segment got an email every other day, while the other got an email every 15 days. During the webinar, the audience was polled to see what they thought the optimal number of emails would be. They chose 3-4 per month based on their own experiences and readings. Well, the actual results were quite surprising. Their test showed that customers that received emails every two days produced 3X the revenue of the segments that got 2-4 emails per month. In fact, there was a significant positive correlation across all segments based on the number of emails they received (see below graph).

You would think this is illogical. Most email marketers believe we face the tradeoff shown below — that there will be an increase in revenues at first, but then we’ll experience more unsubscribes or non-opens as the frequency increases.

So, what is the disparity between the experience of the webinar audience and the results of this study? Well, we are simply seeing that each company has a unique customer base and a unique relationship with them. You can’t just assume your optimal frequency should be what is best “on average” or for a specific company they read about. It means that every company must do segmentation and testing to determine the right frequency for their unique audience.

Caveats? — there is always one or more:

1) Tell your ESP that you’ll be doing experiments and they may see greater volume than normal. After all, you don’t want to be blacklisted.

2) Also look at open rates and unsubscribes during your testing. The anonymous email marketer in the 2nd case study saw no correlation between frequency, and open rates or unsubscribes per email sent. But your experience may be different. Remember, an unsubscribe doesn’t just effect revenue from a given campaign, but it also erases expected/future customer lifetime value.

Feel free to follow my other postings related to the broader topic of Marketing Automation.

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The key to achieving your desired conversion rate is relevancy — pure and simple. It’s more than using your microsite software to support specific campaigns. It’s about testing and delivering personalized emails with relevant content that drives customers to a personalized and relevant experience on your microsite.

A few words stand out in the above paragraph that merit additional attention.

Personalize – This means many things to many people. It can be as simple as embedding the customer’s name in the email message. A recent study by Aberdeen found that personalizing an email with a name increased conversion rates by 200-300% over non-personalized emails.

Relevant – The message/offer needs to resonate with the customer. Relevancy can be driven by events, prior purchases, and/or through segmentations.

•Events – A customer that downloads a whitepaper or article about a product or service could be ripe for a follow-up email or call. A dramatic increase in bank account balance could signal a call-to-action from a bank about investment options. A very personalized email could be triggered to drive customers to personalized microsites with a relevant message that speaks to the customer’s need or interest. Lead nurturing applications can play a key role in supporting your marketing efforts related to customer events.

•Prior purchases – Simple cross/up-sell campaigns can be driven by product purchases. For instance, a customer that purchases a water filter could receive an email that drives them to a microsite that attempts to enroll them in scheduled deliveries (recurring sales!) of replacement filters. Data mining can also use information about prior purchases (RFM type data) to predict the likelihood of a customer’s interest in other products or services. Then we simply communicate to customers about the products they are most likely to purchase (based on a statistical probability to respond). We won’t always be right, but more times than not, this type of personalized communication will increase conversions and improve our campaign results.

•Segmentations – There are many ways to create segmentations. One is based on industry, product and customer knowledge that is accumulated over time. For instance, “I’ve worked in this industry for 10 years and know that females, aged 21-25 are the best targets for my product.” Another interesting segmentation approach that improves campaign results is customer clustering. Clustering is a data mining technique that creates customer segments where everyone in one segment is similar to each other based on customer attributes (e.g., gender, age, prior purchases, geographic location, income class, etc.). While everyone in a given segment are similar to one another, each segment in general is quite different from any other. Once we profile each segment, it is easy to develop a personalized message that goes beyond first name. The actual copy/text of the email can be personalized to be perceived as even more relevant. If just using first name for personalization leads to a 2-3 X conversion improvement versus mass email, just imagine what affect personalized copy will have. Let’s look at an example.

A large print newspaper in the northeast was experiencing declining subscribers like many of it’s counterparts nationally. The newspaper appended Census data (number of residents, race, ethnicity, age, income, home value, average commute time and many other variables) at the zip+4 level to all of it’s subscribers. It then used clustering to create five different clusters of customers based on the Census data. Their idea was to profile each group and develop editorial zones based on these segments. Each editorial zone would receive it’s own unique newspaper content based on assumed interests as derived from the cluster profiles. One cluster was comprised of the highest proportion of customers with high home values, 4-year degrees and the lowest proportion of people with blue-collar jobs. This cluster also enjoyed the highest penetration in terms of current subscribers. You can see how the content this group would be interested in would differ from the cluster with lower education and income. By personalizing the newspaper content, the newspaper reduced the rate of subscriber loss from all segments/zones.

This information was also used to promote customizable online versions of the newspaper as well. Subscribers now opt-in/out to various content. As such, they are directly professing their interests in a topic or issue. This information is even more powerful from a marketing perspective than what we “deduce” via analytics, and can drive a circular process where we get to know the customer better and better over time. This increases customer loyalty and ROMI.

Many organizations have even taken this idea farther from a Social Marketing perspective. Customers can form their own clusters by opting in/out of particular forums or discussions. Creating customer segments based on the forums or discussion groups to which they belong is valuable low hanging fruit. Some leading edge companies are also applying Text Mining to customer posts to take proactive steps for customer loyalty/retention, cross-sell and acquisition efforts. More to come on Social Marketing and Text Mining in future posts.
Test – Testing is a best practice that cannot be ignored by Online Marketers. It’s often referred to as A/B or Champion/Challenger testing. The notion is to create two or more versions of your message. Perhaps version A uses a dark blue call to action that is italicized, and version B uses rich green that is bolded instead of italicized. The simplified notion here is to split your targets into two groups or segments. Segment A gets version A, and segment B gets version B. We’d then look at open rates and click-thru’s to see if one version outperformed another. We can then use the format of the winning version in future email campaigns. We can also utilize A/B testing on microsite pages as well. Testing can cover various combinations of: font size, font color, subject line text, images, etc. Testing is truly where the art of marketing meets the science of marketing square on to dramatically improve your campaign performance. I will develop a post dedicated solely to the subject of Testing in the near future. Keep an eye out!

There is soooo much that can be written on the many marketing topics I’ve covered at various levels in this post. Please write to share some of your valuable insights today and help others become marketing heros!

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Email marketing has great ROI when combined with a thoughtful “transactional email” strategy. But, its like taking aspirin — just because two makes you feel better, doesn’t mean you should take four, five or six over a short period of time. It’s not necessary, and can even lead to more problems down the road. Overdoing email can cause you problems as well. The most common problem is email fatigue from your customers standpoint.

Let me give you an example…

I am a customer of a well known vitamin retailer. I shop online and also do brick-and-mortar. About four months ago, I noticed I was getting a lot of email from them. So much in fact, that I stopped reading them. This happened to coincide with my being asked to develop a webinar about Aprimo’s Contact Optimization module (a part of Aprimo’s Multichannel Campaign Management solution). I decided to “go personal” and try to get each webinar attendee to really see for themselves how non-optimal contact strategies can hurt a company’s marketing efforts. I went into my personal Outlook and sorted my emails based on “From”. I then quickly looked to see how frequently I was getting emails from my vitamin company. What I found was surprising. Over a 20 day period, I had gotten 11 emails — about 1 every other day. And on top of that, the offers were not self-reinforcing. The offers were all over the place and confusing. As I continued to build out the webinar content, I decided to see who else was spamming me. Surprisingly, a well know CRM publication had sent me 14 emails in 14 days — I even got three emails in a 30 minute time period! My audience laughed-out-loud when they saw this slide — many mentioned they could relate. Humor aside — this kind of email blasting hurts everyone — from companies that do indiscriminate blasts to those that don’t.

What’s needed is the development and utilization of an optimized contact strategy across the enterprise. Aprimo Contact Optimization allows marketers to develop comprehensive contact strategies that are easy to maintain and manage. There are a couple of high level approaches to contact optimization — rules-based, and statistics-based (more on the details of these in another forthcoming post).

Let’s look at a real-life example. One of our customers (a large online retailer) applies different contact frequency rules to different customer segments. They restrict the number of communications as follows: Prospects get one per month; Active Customers can get one per week; and Lapsed Customers can get two per month. After implementing this type of throttling mechanism, our online retailer found significant improvements in open rates.

Tune in for more details on Contact Optimization and how it can help you build better customer relationships in my next post.

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If it’s difficult to gain new customers, you must do your best to retain and grow your existing customer base.

1) Add new complementary products/services and market them to existing customers
2) Create a web presence if you don’t have one. Studies show customers are significantly more likely to purchase from a company that has a website compared to companies that don’t.
3) Increase customer loyalty via informative marketing efforts. Send Post Cards, ePost Cards and emails on a periodic basis. If you don’t have email addresses — get them!
4) Use data mining to identify which customers are most likely to attrite/churn or become inactive. Use this information to launch pro-active retention campaigns and offers.

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Online marketing investment is predicted to increase for the sixth consecutive year as organisations begin to look to social networks as well as email, SEO and pay-per-click advertising.

Source: Alterian Annual Marketing Survey

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8 out of 10 consumers say they would contact a company that has a website first, over and above one that doesn’t.

What are YOU waiting for?!

BMRB (British Market Research Bureau) 2007

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Gartner claims in 2009, companies that invest in online marketing processes will experience at least a 10 percent increase in revenues within 6 months.

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RFM stands for Recency, Frequency and Monetary Value. It has been used by direct marketers for over 40 years as a segmentation tool to increase marketing ROI. The basic premise of RFM is that customers who have purchased more recently, more frequently and have spent more with your company are your best prospects for future direct marketing campaigns. Like data mining/response modeling, the goal of RFM is to increase marketing ROI by communicating (via direct mail, call center, etc.) only with customers that are likely to respond. Done well, you increase your ROI as you attain almost the same number of sales by contacting only a fraction of your customer base.

RFM, BI, data mining and optimization represent a common progression away from mass marketing for many organizations as their marketing efforts become more analytically based and targeted.

As depicted above, the adoption of each technique is a function of many factors. Consequently, a technique like RFM can still be a new and promising approach to many companies today. It is simple to understand, contributes to ROI, is inexpensive, and can be utilized as a reliable stepping stone to more advanced techniques like data mining.

RFM in Action

RFM was initially utilized by marketers in the B-2-C space – specifically in industries like Cataloging, Insurance, Retail Banking, Telecommunications and others. There are a number of scoring approaches that can be used with RFM. We’ll take a look at three:

RFM – Basic Ranking
RFM – Within Parent Cell Ranking
RFM – Weighted Cell Ranking

Each approach has experienced proponents that argue one over the other. The point is to start somewhere and experiment to find the one that works best for your company and your customer base. Let’s look at a few examples.

RFM – Basic Ranking

This approach involves scoring customers based on each RFM factor separately. It begins with sorting your customers based on Recency, i.e., the number of days or months since their last purchase. Once sorted in ascending order (most recent purchasers at the top), the customers are then split into quintiles, or five equal groups. The customers in the top quintile represent the 20% of your customers that most recently purchased from you.

This process is then undertaken for Frequency and Monetary as well. Each customer is in one of the five cells for R, F, and M (see below).

This process is then undertaken for Frequency and Monetary as well. Each customer is in one of the five cells for R, F, and M (see below).

Experience tells us that the best prospects for an upcoming campaign are those customers that are in Quintile 5 for each factor – those customers that have purchased most recently, most frequently and have spent the most money. In fact, a common approach to creating an aggregated score is to concatenate the individual RFM scores together resulting in 125 cells (5×5x5).

A customer’s score can range from 555 being the highest, to 111 being the lowest.

Future posts will cover RFM – Within Parent Cell Ranking and RFM – Weighted Cell Ranking techniques. A case study will also be featured.

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Successful data mining is really all about getting your data properly prepared. Data miners spend about 80% of their model building efforts on data preparation. Preparation includes:

1) Missing data analysis. What fields have missing values? Should you fill in the missing values? If so, what values do you use? Should the field be used at all?

2) Outlier detection. Is “33 children in a household” extreme? Probably — and consequently this value should be adjusted to perhaps the average or maximum number of children in your customer’s households.

3) Transformations and standardizations. When various fields have vastly different ranges (e.g., number of children per household and income), it’s often helpful to standardize or normalize your data to get better results. It’s also useful to transform data to get better predictive relationships. For instance, it’s common to transform monetary variables by using their natural logs.

4) Binning Data. Binning continuous variables is an approach that can help with noisy data. It is also required by some data mining algorithms.

These topics will be discussed in more detail in future posts.

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