Four Interesting Trends from Dreamforce

Last week I had the pleasure of wandering the floor at Dreamforce, Salesforce.com’s annual conference. As the SaaS conference of the year, it’s a great time to get a pulse on how everyone is thinking about the next few years. This year, I spent a lot of time talking with the exhibiting ISVs, both large and small, and as I did so, I found a few key themes that resonated.

At Eloqua, we’ve been talking about Revenue Performance Management for a while now, and it was good to see that many of the themes that are driving that are common across other ISVs in the ecosystem. Here are four themes I noticed as I chatted with vendors on the show floor:

1) Data is Increasingly Critical: One clear trend was the increasing importance of data to those focused on driving revenue. Both as a source of new conversations, and as a source of continually updated insight into a buyer’s fit, data is one of the most important RPM stories of the next few years. Salesforce.com has clearly made a significant investment in this area with their acquisition of (and deep integration of) Jigsaw as a data cloud, but folks like Hoovers, D&B, and StrikeIron were very present on the show floor with data sourcing, append, and cleansing services.

2) Communication Contributes to Buyer Insight: There were many vendors who provided communication tools, ranging from PDF trackers to videoconferencing tools and Webinar providers. This was not new, but in each conversation with these providers, including Vitrium, ReadyTalk, iLinc, and more, their focus was on how the use of that communication tool by a buyer can provide rich insight into a buyers intentions. By leveraging attendance data as a key part of a buyer’s digital body language, marketers and sales people are much better armed.

3) Integration Must Be Seamless: Salesforce.com has long focused on the need to seamlessly tie together all interactions with customers, and this viewpoint is spreading throughout the entire buying process. Integration providers like Cast Iron, Informatica, and Pervasive had packed booths as visitors looked to understand how to seamlessly tie together every pre-purchase interaction, whether in social media, search, webinars or in any other communication vehicle.

4) 2011 will be the Year of Analytics: It is now beginning to be possible to see the entire buying process from end to end. With that possibility, revenue analytics jumps to the forefront as an extremely hot area. Both on the floor and in the track sessions, executives were asking about the right metrics, measurements, and KPIs to measure in order to ensure that their revenue engines were running in the most efficient manner possible

I have never been more excited about the RPM space, and seeing the breadth of solutions on display at Dreamforce tells me that others quite definitely share this excitement. 2011 will be an exciting year indeed.

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Is Foursquare Relevant for B2B Marketers?

Recently, I’ve been playing around with foursquare to get a better understanding of it, and think about how it might have a significant impact on B2B marketers. Whereas I’m far from a power user, I have unlocked a few badges (sadly, one of my first was the “Jetsetter” badge that is given for checking in at 5 airports), and I’m in strong contention for the Mayorship of Eloqua.

With South by Southwest happening recently, the number of foursquare announcements on Twitter, of people “checking in” made #SXSW a top Trending Topic. Considering that foursquare only launched a year ago at SXSW 2009, this is clearly a phenomenon worth looking at.

Lots of businesses, especially those selling to consumers, are experimenting with special offers via foursquare in order to motivate those in the area to drop in, make a purchase, or accept an offer. However, it’s not clear that there is any relevant parallel of this in the business to business environment. Business buyers for any given organization are much fewer in numbers, do not generally make purchases based on their current location, and are unlikely to be motivated by the style of offers (come in now, save $10) that are viable using location-based advertising.

Certainly, at tradeshows and events, many B2B organizations are experimenting with foursquare, setting up transient “locations” at their booth and offering prizes to people who check in. I suspect, however, that this way of using foursquare in a B2B marketing environment is temporary at best, and will quickly pass.

I was tempted to conclude that foursquare might, therefore, have limited relevance to B2B marketers, but as I looked into what businesses had been tagged in places as disparate as Toronto, Zurich, Brussels, Antwerp, and London, I began to realize that, very quickly, vast numbers of business venues are being tagged. Almost every venue I visited had already been entered into the foursquare database.

The motivation to do this is startlingly small. Users are able to unlock “badges” with colorful icons and creative names like “Far, Far, Away” and “Playa Please”. Whereas it may seem too small of a motivator to incite behavior, the badges are displayed to the world, and it clearly is driving 100s of Millions of venues around the world to be tagged. I will even admit, it’s a bit addictive, and I found myself looking into the meaning of the badges to see how I might “unlock” the next one.

And that’s where the true opportunity of foursquare gets revealed.

The badges are unlocked for all sorts of very specific behaviours, such as checking in at 25 pizza restaurants. In order to be seen as having done these specific behaviours, of course, the venues you visit must be tagged as such. Because of this, there is a motivation on the part of every user to correctly (and with great detail) tag each venue with its correct type. Foursquare uses up to three levels of increasing detail to tag each venue – a very detailed categorization.

As a B2B marketer, especially one selling to very small businesses that are owner-run and not adopters of technology, this (theoretically) makes available a highly targeted data set. Want to know how many ship’s chandlers are in the port of Zanzibar? (an example that came up in a recent conversation I had with ShipServ’s John Watton). Foursquare may soon have the best data set. Currently, they don’t collect contact information for those businesses, but it doesn’t seem unreasonable to apply the same model to acquire that data.

This proves a challenging problem for the classic providers of data who employ research teams to keep their data up to date. The more remote and small the businesses are, the worse the economics are in keeping this data accurate and current. Obviously, Google has been working on this problem too, from a different angle, by allowing business owners to update their own information on Google Maps. However, the dynamic is very different. In foursquare’s model, high-tech enthusiasts with iPhones and Blackberries update the data on multiple locations based on the motivations of a game, while in Google’s model, individual small business owners update their own information on Google based on their own business motivations.

Looked at side-by-side, the data provider models are very different:

Classic: Data provider employs researchers to update data on businesses

Google: Data provider allows business owners to update their own data

Foursquare: Data provider motivates population of enthusiasts to update data on local businesses

It’s not clear which one of these models will be able to collect the most up to date, accurate, and deep data on smaller businesses. It’s equally unclear whether foursquare will be able to leverage this data set to enable B2B marketers who target these micro-segments. However, as I think about what effect foursquare will have on B2B marketing, it is this effect that seems most promising.

Models that leverage the network effects of millions of people can be immensely powerful, and it appears that foursquare has hit on one of these models for gathering deep, location-based information on businesses around the world, that may be extremely valuable to B2B marketers who need this data set.

I would love to hear comments from other data providers, or anyone familiar with the data space, on how they see this model evolving. Read More...

Evaluating Marketing Automation - Data Management

Continuing on a theme that received great feedback, I wanted to provide another real, down in the details, way to evaluate the various claims in the marketing automation field. Last time we looked a way to ensure that a provider could have the performance needed for your marketing goals - a quick and simple upload that will test actual marketing automation system performance.

In this post, it's worth taking it one step further. Getting marketing data into a platform is one thing, but if the data is messy (and what marketing data isn't), it will not be of much use. If, for example, your marketing database has 100,000 names in it, and the titles are just as they were written, such as:

• VP Marketing
• V.P. Mktg
• Vice Pres Marketing
• Marketing Vice President
• Mktg VP

and you are asked to build a list of Vice Presidents of Marketing to target, how many will you find? 300? 800? We've seen many situations where dirty data returned 300 names, but the same query against cleansed data returned 17,000 names. Proper management of data makes a huge difference in your marketing results.

So, how do you test for this when considering a marketing automation software investment?

Quite simply - ask, in a demo, for each vendor you are considering to run a quick test. Here is a sample CSV file with typical marketing data. Titles, states, and countries are as they would be in a normal marketing or CRM database. The data is kept simple, and the titles are mostly in sales, marketing, and finance, while the addresses are in Canada, US, and UK.

Have each vendor run the following test for you:

• Upload the sample file
• Clean up the country fields so that US, USA, U.S.A, as well as the variations of Canada, and England/UK are normalized
• Clean up the "raw" job title fields to two new fields for "level" (VP, Director, etc), and "role" (marketing, finance, etc) so you can properly segment
• As a bonus, see if they can correct the missing leading "0" on New England zip codes - removed by Excel in many marketers' data files

When it is uploaded and cleansed check the data to see the following:

• The only countries in the file are "USA", "GBR" and "CAN" or however you chose to normalize the country data
• The people can easily be filtered by role into "Marketing", "Sales", or "Finance"
• The people can easily be filtered by level into "SVP", "VP", "Director", or "Manager"

Many marketing challenges come from bad data. An inability to do proper segmentation, personalization, lead scoring, or analytics can quickly result if you are not able to standardize and normalize the data in your marketing database. To avoid getting into this situation, it's worth having the marketing automation provider you are thinking of choosing run through this quick test with real sample data. Read More...

The Foundation for Great Marketing is Great Data

Data is key to all your marketing efforts. Whether it is segmentation, personalization, lead scoring, lead routing, or marketing analysis, if you don’t have clean and consistent data, your efforts will be built on the shakiest of foundations. However, when thinking about your marketing automation efforts, data management can often be an afterthought.

However, some minimal upfront efforts to understand and improve the quality of your data can greatly improve your effectiveness as a marketer.

Current Database

First, you need to understand your current database. There may be a significant amount of data in your database, but unless it is data you can work with, it will not be adding value to your organization. Some simple analysis should give you a good sense of your current state:

- Growth and Total Size: The simplest of metrics; analyzing both the total size of your database and its growth over time gives you a clear sense of what you’re starting with. Net new contacts add to your total, while bouncebacks, and unsubscribes detract from it. In this measurement, be sure that you are truly measuring unique contacts, without any duplication. The overall database size should be growing in a healthy manner, although growth rates can vary depending on the growth rate of your company and your industry.

- Active/Inactive: Of equal importance to size of your marketing database is the analysis of what percentage of your database is active or inactive. A basic definition around “active”, such as a certain number of emails opened or clicked, visits to the website, or form submits will give you an objective definition of who is active. Those who are inactive may have “emotionally unsubscribed”, and are unlikely to be future buyers. It is more important that the active component of your database is growing over time than the overall size.

- Completeness: Each field that is of importance to you should be analyzed for its completeness. In many marketing databases, key fields may be only 30% or less complete, which leads to challenges in using those fields for marketing efforts. If your analysis shows that fields are less complete than ideal, you may want to use progressive profiling to add data to those fields

- Consistency: Even if a field is filled, if the data is inconsistent, it can be very difficult to derive value from it. Fields like Title, Industry, Country, State, or Revenue are very often extremely inconsistent as the data can be input in a wide variety of ways. Analyze each field for the breakdown of what values are in that field and their percentages to see if the data is generally consistent or inconsistent.




Some marketing automation platforms are able to perform this kind of analysis, but there is a lot of variation in the industry, so ask the tough questions if you are considering a marketing automation investment as this analysis will be key to your success.

Data Sources

With your own marketing database quality understood, you then need to begin understanding your sources of data to understand what will make your data challenges worsen if not controlled. Marketing data comes from many different sources, each of which has its unique opportunities and challenges.

- Other Systems: Marketing often sources data from CRM systems, data warehouses, or customer data masters. The data from these systems often must be brought in on a nightly (or more frequent) basis, and integrated into your marketing data. In many cases, there is limited opportunity to change the format or quality of the original data, and it must be dealt with on import automatically each time it is imported

- Continual Sources: Web forms, tradeshow leads, webinar registrants, and trial downloaders contribute a steady flow of data to the marketing database. The continual nature of these sources means that as a marketer, your database is being updated 24 hours a day, 7 days a week. This means that data cleansing must be done continually, and inline, rather than as a batch process once or twice a year

- Controlled vs Non-Controlled: Many of the sources you deal with are not sources that you are able to control. Lists from tradeshows, business cards, and many web forms are not sources that you are able to control, so the data from them is of varying quality and varying standardization

Given that you, as a marketer, are dealing with a variety of data sources, many of which are out of your control, and many of which are operating 24x7, keeping the data clean and consistent can be a significant challenge. The best way to approach this is to build a “contact washing machine” that standardizes and normalizes your data. Each time data is touched, whether from a web form, a list upload, or from your CRM system, it should flow to the contact washing machine.

Again, this is an area to ask tough questions in if you are looking at making an investment in lead management software as it makes a significant difference to your success. Look for contact washing machines that are a single, centralized point of data cleansing, and can handle standardizing and deduping data fields from industry to title to revenue. The best option is to have a pre-built structure out of the box, that you can then modify to meet the exact requirements of your business.


Data and the User Experience

In thinking about data, there can be a temptation to burden your audience of prospects with the data requirements of your marketing database. This is never a good idea. Many studies have shown that the more fields you add to your web forms, the more likely you are to see users drop off and not fill them out. Similarly, the more you restrict the input options that you provide to your audience (such as only allowing drop-down select lists for an individual’s job title), the more frustrated your audience will become.

The best option is to approach the challenge in two ways. Progressive profiling can be used to ask for a minimal amount of data at each interaction, never ask the same question twice, but continually add to a modular profile. This allows you to minimize the number of fields being asked per web form, and maximize the conversion rate. For the data itself, given the user frustration added by constraining their options, and the fact that many sources of data are beyond your control anyway, it is often better to allow free-form data while managing its quality via a contact washing machine once it enters your marketing database.


Data as a Foundation for Great Marketing

Today’s best marketers are building their creative campaigns, precise segmentation, accurate lead scoring, and relevant personalization on a base of great data quality. In fact, when top CMOs talked about their marketing dashboards, the focus on quality data was key to each of their successes. Whether you have made a marketing automation investment, and are looking to maximize the return you get from it, or are considering a marketing automation investment and want to know the right questions to ask, data should be front and center. It’s the foundation upon which everything else in marketing rests.

(*this post was originally posted on the Focus.com marketing community)

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Evaluating Marketing Automation - System Performance and Usability

Evaluating the various claims in the marketing automation space is an interesting challenge. There are a variety of players, and many of the claims overlap quite a lot. It can be daunting to tell truth from fiction. I often get asked how one should independantly verify many of the claims, and I'm a big believer in the idea that the more educated a marketing automation buyer is, the better they do when they purchase a platform.


In light of that, I wanted to provide a series of actual tests that you can do when evaluating marketing automation systems. Real data (like this CSV file of sample contacts for this exercise), things to look for, and expected results are provided.


One of the basic elements to look at is system performance. It can be tempted to think of system performance as a "hygiene" factor that is common across the industry, but it's worth taking a deeper look at. The reason that this is the case is that marketing is, somewhat surprisingly, a very high scale activity. Think about the numbers for a second:

- An email campaign is sent out to your database of 50,000 people
- 30% of those people open the email, many of them multiple times, leading to 20,000 unique opens
- 3% of the email recipients visit your website, along with another 2,000 anonymous visitors, each seeing on average just less than 3 pages, for a total of 10,000 page views
- Each person in your database is scored against their title, their industry, their email opens and clicks, and their web activity (5 criteria x 50,000 people)
- The score for each person is updated in your CRM system

Very quickly, we have generated 380,000 transactions on a single day, and that is with a medium-sized database of 50,000 contacts only doing a very simple task. As soon as you begin to use your marketing automation platform more heavily, these numbers can grow quickly. If you don’t adequately assess the performance of the marketing automation platform you select, you may end up with slow performance affecting the experience of your marketing users

But how can this be assessed without a deep technical dive into each marketing automation platform? Quite simply, in fact.

In order to assess system performance, you need to see the system actually operating under conditions that you are interested in. Claims, quotes, and architecture diagrams will never compare with actually seeing the system do what you need it to do.

Here is a simple test that you can ask any of the marketing automation software vendors you are interested in evaluating to try during your demo with them.

Here is a link to a sample contact data file of 50,000 randomly generated contacts. Each one has basic information you would expect; name, address, company, email address. There are some duplicates in here, leaving just under 45,000 unique names. It’s not a huge file (only 5MB), so it should be easily accessible by anyone with Internet access.

When you are reviewing a demo of a marketing automation provider, give them the file (or the link) and ask them to do the following:

1. Upload the file to the marketing automation database (should take less than a few minutes, and you should be able to continue with the demo while it is being uploaded)
2. Ensure that the duplicates in the data are automatically handled (look for Claudia.Patterson@RainGolfEquipment.co.com as an example) to see if her data is clean (there are two records for her, which should be merged seamlessly)
3. Run a report that shows the breakdown in Job Titles within that set of data (to give you a sense of the platform’s ability to run rules against the data without impacting performance)
4. Export the full list again as a .csv file to test the system performance when doing a bulk export.
5. Delete the data that had been uploaded to ensure good performance when deleting in bulk


These 5 steps will tell you a lot about the performance of the marketing automation platform you are about to select. Things to look for are the following:
- How long does it take to upload the data?
- Is the platform usable while the upload is taking place?
- Are duplicates handled smoothly and accurately?
- Can the data be explored and reported on while in the marketing automation platform?
- Can data and reports be exported quickly and without causing platform performance issues?
- Can unneeded data be deleted in bulk, allowing you to keep your marketing database clean?

System performance is one of the most critical aspects of usability, and is very much worth evaluating. If you are considering an investment, this quick test is an easy one to perform and will give you very rich insights into whether the platform you are about to select will meet your needs. Read More...

Marketing Automation and B2B Marketing Predictions for 2010

It’s coming around to that time of year again when we all offer up predictions for what the coming year will offer. As with any of these, it’s a guess, and entirely my own opinion. Here are some of the trends and changes I think we’ll see in the coming year. If, at the end of the year, it turns out I guessed right on a few of these, I will be happy.

Overall Prediction Trend:

Buyers continue to gain control of their own buying processes, and marketers respond by building "revenue engines" to understand, facilitate, measure, and predict these buying processes.


1) Data is Free, Relationships are Not: As contact data becomes more and more available, approaching free in many cases, the value of the relationship will increase. As part of a continuing trend, the ability of your audience to block, prevent, and ignore communications that they don’t desire will increase, with reduces the value of the data (who they are), and increases the value of the relationship (their perception of you). Marketers who think this way, and truly work to provide valuable information to their audience, will do well.


2) Relationships, Education, and Nurturing: The trend in data becoming free (prediction 1) will work in parallel with a trend in information, of relevance to buyers, being expected to be free. Buying relationships will be more and more built on a foundation of buyer education, and the B2B organizations who can educate and nurture prospective buyers over time, without annoying them will win. This means an investment not just in the rich educational content, but also in systems to automatically understand buyer interest and deliver (or have discovered) educational content appropriate to the buyer’s next step.


3) A Degree in Marketing Engineering?: As marketing shifts towards a discipline whereby prospective buyers are understood based on their behavior, and the right content, according to where they are in their buying cycles, is delivered, marketing skills become more operational, process, and data oriented. Marketing hiring will swing towards backgrounds that have these data, operations, and process skills more so than copy and creative skills. Marketing education likely won’t change in this regard during 2010, but the initial discussions will start.

4) Mobile Thinking vs Mobile Devices: Every year in living memory has been talked about as the year that mobile will become big. I think that this coming year will finally see that claim begin to disappear. Sort of. Now that various devices such as the iPhone, are truly able to support a rich, interactive, graphical environment, interacting with your audience in places where they are mobile becomes very possible, but the device, and mobile-specific technology, almost becomes irrelevant. Thinking about what will motivate an audience to act, when they are at a show, see a poster, or are at a venue, how they can connect (text message, or short URL), and what the interaction should be will become part of many B2B marketers’ thinking. Specific mobile technologies, however, will not move beyond niche applications.

5) Brand Promise/Reality Gap Reduction: The trend that social media kicked into high gear in 2009 will continue into 2010; any organization that has a significant gap between their brand promise, and their brand reality will have that gap mercilessly exposed through social media and community-created content. 2010 will see aggressive adoption of basic listening techniques by marketers in order to understand where they are falling short. The gaps here will lead to a much broader discussion of what “brand” means to a B2B organization, that goes beyond logos, taglines, and colors.


6) Marketing Owns The Brand: 2010 may see some very early reshuffling of the decks in terms of what Marketing owns. Social media (prediction 5) will broaden the discussion of “brand” from logos and taglines to the full offering including service and product. A few very early examples will arise of organizations in B2B who have changed their definition of the Marketing team to truly provide them with greatly enhanced ownership or influence into the product/solution areas of the business as well as the services areas around it, as those are both critical to overall company brand and reputation. We likely won’t see mainstream shifts in this ownership until 2011 or beyond.


7) Follow me, Friend me, or List me: in 2010 we’ll see a dramatic shift in the definitions of influence in social media as we move from raw counts (like Followers or Friends) which are almost entirely ignorable, and to a model that comes one step closer to true influence. Each social media site will implement things differently, but an “inner circle” model of people who one actually pays attention to, versus just being a connection, will rapidly grow in relevance. Social media sites that do this well will balance the need for public awareness of how influential a person is (how many inner circles, they are part of) without making it so public that it is widely manipulated or gamed (like Twitter follower counts, for example).


8) Information Discovery Measurements: As marketers begin to cede control of the messages they push out, and begin to act more like publishers without assuming control of the distribution, they will begin to look for measurements of information discovery as their key driver. How, where, and from whom, did an individual discover information relevant to key stages in her buying process. Rather than measure individual outbound campaigns, measuring the likelihood of individual messages to be discovered, ideally through sharing and forwarding, will become more key to marketers.


9) Social Activity, Lead Scoring, and Lead Nurturing: As buyers begin to get more and more of the messages, information, and education through their peers, and through social media, these channels will quickly grow in their relevance to lead scoring and lead nurturing approaches. Awareness of how an individual discovered a message, where, and from whom (prediction 8) will feature very prominently in the lead scoring and lead nurturing routines of leading marketers.


10) Digital Body Language vs. the Discovery Call: As buyers gather most, if not all, of their early information (prediction 2) on the vendors they are considering speaking with online, the role of the salesperson’s “discovery call” shrinks and changes. In 2010, in sales organizations, we will see a rapid recognition of the new reality that buyers are less willing to take an initial call from a vendor salesperson with an assumption of only exchanging basic information. If sales people want to engage with buyers they will need to read buyers’ digital body language, and ensure that they are able to add more value than Google on the initial sales call. If sales teams do not have access to this information they will begin to push their marketing teams to provide it.

11) Measuring the Revenue Engine: As the above trends evolve in 2010, marketers will begin to develop their ability to predict revenue trends well in advance of sales, by measuring and analyzing the overall revenue funnel. Marketing leaders who can do this will become among the most strategic executives at the board room table.

What do you think? Are these predictions likely to happen? I look forward to your thoughts on these.

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Data Management and Marketing Automation - Video

In order to successfully move beyond the most basic drip marketing, it’s crucial for B2B marketers to effectively manage the data that they are working with. There are two main reasons that data has become more critical than ever before. First, it is with us for longer. As we engage with our prospective buyers earlier in the buying cycle, and nurture them throughout it, we are using the data for longer than ever before. Secondly, as we work to use marketing automation software to understand buyers, and then communicate with each buyer based on his or her unique needs, we end up using the data for various rules and automated systems.

When data is used by a marketing automation system for rules or automated systems, it needs to be clean and consistent. For example, in building a lead scoring algorithm, defining a target segment, or reporting on results, it is crucial that a title, an industry, or a country is represented in a consistent way so no contacts are missed.

In this quick but instructive video, Chris Petko, Eloqua’s Director of Marketing Operations talks about how we at Eloqua manage data, and how it is captured, cleansed, and analyzed. Chris introduces his 3 C framework and gives some great recommendations on how best to manage data as a marketing organization:



(if the video above does not load, please click here to watch Chis speak about marketing automation and data management)


Chris also discusses the Contact Washing Machine concept that automatically manages each and every data touch-point, and ensures that the data is cleansed and normalized. Chris discusses why this must be done inline, rather than as a one-time manual effort, given the way in that marketing data is continually updated via web forms, list uploads, and data flowing from other systems such as CRM.

I hope you enjoyed watching the video, and found Chris’s experience useful in your marketing operations. As someone who deals with marketing data on a daily basis, Chris has a lot of experience in exactly what aspects of data management matter, and how to approach it. Read More...

Marketing Automation in Europe and Asia - for North American Marketers

Many organizations with a history in North America are legitimately concerned about what they need to consider when engaging with their European and Asian teams on the topic of marketing automation. In this information-packed video, Stuart Wheldon, Eloqua’s Director of Client Services for EMEA and Asia-Pacific walks through some of the important factors to consider.

Stuart looks at how data models may need extra consideration in modeling prospective buyer information, how language plays a role in more than just your marketing content, and the team structures that generally work best to achieve success.



(if the above video clip doesn't load, click here for the Marketing Automation in Europe and Asia (for North American marketers) video)

Stuart’s experience in both the North American and non-North American markets give him a unique perspective on what marketers from North American need to think about when considering marketing automation software rollouts globally. His views on how teams localize marketing campaigns, well beyond just translation, and how this affects both team structure and rollout planning are very insightful. I hope you enjoy the video as much as I enjoyed talking with Stuart on this subject.

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Sales/Marketing Integration - The Technology Stack

Integration between Marketing Automation systems and CRM systems allows a very powerful and valuable flow of data, and business alignment, between marketing and sales. It forms the technology and data basis for a new relationship between your marketing team and your sales team. This is a very powerful concept, and worth digging into in some depth as there are a lot of questions worth asking as you evaluate potential solutions.

First, and most critical, is a look at what actually needs to be integrated between marketing and sales.

I think of it as a three-layer system, in order to keep things simple. This is obviously a somewhat simplified view, but it allows some clarity into the discussion of the integration.

Data:
The first layer is the data. Your marketing team and your sales team are communicating with the same audience in many cases. The data should be synchronized between sales and marketing in order to ensure that when a field is updated, both systems know about it. You will want to make sure that you can synchronize all key data; contacts, accounts, purchase history, etc. Any data that is meaningful for segmentation whether it is directly accessed (like contact data) or relational data (like purchase history).

Unless you have a very simple structure, you will want to allow flexibility in both what data passes between marketing and sales, and what data is tracked on each individual. The data model will share many common elements (name, address, title, etc), but most marketing and sales organizations begin to quickly evolve their data model with elements that are unique to their business. Marketing may store information on prospects’ campaign history, event attendance, meal selection, and communication preferences, while sales may store information on whether certain contract, budget, and commitment milestones had been met. Requiring both marketing and sales to use the same data model is a recipe for significant frustration.

You will also want good control on which data is moved to sales. Generally marketing deals with a broader universe of suspects than you want to pass to sales. If your CRM system becomes filled with this lower quality data, your sales team will become frustrated with invalid and poorly qualified entries. It’s worth keeping a “wheat and chaff” model whereby only the good quality data is passed to sales and the lower quality data is kept, and cleansed, in the marketing database.

In order to do this, and in order to manage a marketing database that sees data from many sources, it is also necessary to have good control over the priority of data that is flowing into your marketing database. If, for example, you have the same contact in multiple data systems, all of which are synchronized with your marketing database, you need to be able to select which of those sources will be treated as a priority in updating your data.

Activity:
The second layer of an integration between marketing automation and CRM is the marketing activity and the prospect’s response. This is critical to an understanding of the individual’s digital body language, and is key to allowing your sales team to understand the individual, the company, and their overall territory.

It’s key in integrating activity to have a very flexible model for configuring what shows up where. The main goal of providing the activity information is to provide for sales enablement, which involves ensuring successful sales adoption. Being able to show prospect activity in a rich, interactive, visual manner is as critical for sales rep adoption as the data itself is.

Similarly, being able to configure what data is presented, and how it shows up is key for sales adoption. In some environments, prospect activity can be presented in an activity history record within the CRM system, whereas in other environments it may be more successful to send real-time email notifications, and in other environments, a weekly report is found to be more effective. The key to this level of the stack is configurability in order to maximize sales adoption.

Process:
The third, and final layer of the integration stack is the process layer. This layer is where the lifecycle of a lead is defined. When a lead is qualified, how is it presented to sales? Is it presented through a task, through a lead record, or through a more customized way? Similarly, if a lead is not followed up on, or is not turned into a live opportunity, what happens next? Is a lead that is not followed up on clawed back and re-distributed? If a follow-up attempt only resulted in a voicemail being left, is the lead automatically nurtured for a period of time before prompting sales with another follow-up attempt?

This process layer is where there is a great opportunity to differentiate your business. By optimizing how leads are passed to sales, in a way that makes sense for your business, you can drive noticeable improvements in your revenue. However, to do this, it’s crucial to be able to have your technology match your exact business process. If it makes sense to create structured follow-up tasks for sales so you can manage and monitor follow-up times, you will need to be able to automatically create and allocate tasks. If you need to focus on clawing back leads after they go quiet in order to plug leaks in your revenue funnel, you’ll need to be able to pull in opportunity history data in order to ensure that your marketing is aware of the sales process stage that individual lead is in.


Sales Alignment and the Technology Stack

Aligning sales and marketing in a new relationship is a challenging task that relies on many changes in people’s daily lives, and your overall business processes. In order to be successful with this new form of alignment, you will need your marketing automation system and your CRM system carefully aligned. This relies on alignment and flexibility at all levels, from data, to activity, to process. If you build your sales and marketing processes on a technology and data foundation with sufficient ability to map to your business processes, it will allow you to learn and grow over time and continually enhance the alignment between your sales and marketing teams. Read More...

Marketing Automation Weekly Wrap-up - 2009/09/11

In this week's marketing automation weekly wrap-up, there seems to be a theme of "lists" as lists are published, and list nominations are called for. At the same time, some recent listings of great B2B and Marketing Automation related blogs have surfaced some great writers I did not have on my own reading list previously.

Galen De Young (@GalenDY) from Proteus B2B Marketing published the 2009 list of B2B marketing blogs, many of which are classics, and many were new to me. It’s a tremendous (and comprehensive) list, and I was able to add a few new ones to my own list of blogs to read:

http://www.proteusb2b.com/b2b-marketing-blog/index.php/big-list-b2b-marketing-blogs/



The B2B Marketing Zone is also worth introducing, on our theme of "lists", for those who haven't come across it yet. Managed by Tom Pick and Tony Karrer, it aggregates many of the best B2B marketing blogs, and provides their content in an easily accessible way:

http://www.b2bmarketingzone.com/


Kate Brodock (@just_kate) from B2B Voices makes a push for video as a key part of the B2B marketer’s tool kit, if nothing else, because of its SEO effects. I like the fact that Kate avoids the idea that B2B videos must “go viral” to be successful:

http://www.b2bvoices.com/2009/09/using-video-as-a-b2b-marketing-tool/


Susan Fantle on B2B Marketing Smarts looks at resurrecting the dead with a discussion of the merits of direct mail in a B2B environment. It’s definitely a media type that has been under-utilized recently and has some compelling benefits:

http://b2bmarketingsmarts.com/?p=393


Stuart Wheldon on B2B Magazine with a post about SaaS, Social Media, and the economics of educating buyers. Makes for an interesting read in looking at the economic reasons behind why we are more apt to invest in buyer education than ever before:

http://www.b2bm.biz/blog/2009/09/saas-social-media-and-the-econ.html


Simon Salt (@incslinger) from Inc Slingers provides an Essential Social Media bookshelf of must-reads for social media, and it’s not the list you might expect as he goes beyond pure social media titles. (note: I’m biased here, as Digital Body Language was included – thanks Simon!):

http://www.theincslingers.com/2009/09/the-essential-social-media-bookshelf/


James Obermayer from the Sales Lead Management Association continues the theme of “lists” this week as he has opened nominations for the 50 most influential lead management professionals list:

http://blog.salesleadmgmtassn.com/2009/09/01/slma-seeks-nominations-for-first-annual-50-most-influential-sales-lead-management-professionals-list.aspx


Craig Rosenburg (@funnelholic) from The Funnelholic looks at webinars and what makes them work, with his recent post on the ABC’s of highly converting webinars:

http://www.funnelholic.com/2009/09/03/the-abcs-of-highly-converting-webinars/


Tim Wilson (@tgwilson) from Gilligan On Data covers data, business, math, and the irrational quirks of human psychology in this post on data cleansing processes – worth a read for anyone wondering why data always seems to be a mess:

http://www.gilliganondata.com/index.php/2009/09/04/the-inertia-of-the-status-quo/


Kipp Bodnar (@kbodnar32) from Social Media B2B looks at 3 examples of B2B companies using Facebook as part of their marketing efforts. Lots of experimentation in this area, but it’s hard to see the ideal path. Kipp’s examples are interesting success stories:

http://socialmediab2b.com/2009/09/b2b-company-facebook-lead-generation/


I hope you enjoyed this week's writings on marketing automation and B2B marketing as much as I did. It's great to have a new set of writers to keep track of. Read More...

Data Analysis in Marketing; What Google and the Flu Can Teach Us

I saw an interesting tool the other day from Google, that analyzed raw data on searches related to the flu in order to predict the severity and timing of flu outbreaks. Available at http://www.google.org/flutrends/, this tool is an interesting example of approaching data in a unique way in order to understand a problem.

Whereas an individual person searching for a flu-related topic is clearly not a diagnosed flu victim, the correlation between those searches and an actual outbreak is quite good, and more importantly, the data is available much faster than the medical diagnoses precisely because it is not relying on a properly diagnosed flu victim as the base data point it works from.

As we implement lead scoring algorithms, and other predictive approaches within our marketing automation systems, we face a similar challenge. The best way to look at whether we have scored the leads the right way is to look at whether the leads we passed to sales became qualified opportunities and eventually closed revenue. However, similar to the medical diagnoses in the flu example, this can take a significant amount of time. However, the raw data can show us some very interesting trends and give us immediate insights.

When you have built out an initial algorithm, incorporating the best practices for lead scoring, the simplest thing to do is to pass your entire dataset through the algorithm to see how they would score. This “bottoms-up” look at the data gives a very quick view of the potential results. This technique is best used when looking at the explicit score (the “who” that indicates the right buyer in the right organization) rather than the implicit score (the “how interested” that indicates the level of current buying interest. The reason for this is that the “who” is not likely to change over time, while the “how interested” will obviously vary significantly over time.

With your entire database scored through your new algorithm, the results will tell you some very interesting things.

- Were the final numbers what you expected? If you scored 100,000 contacts on the explicit dimension of lead scoring, and only 0.1% ended up as A leads, was this what you would have anticipated? If you were expecting significantly more, it could easily be a data problem. For example, if your scoring algorithm looks for a key title, such as “Vice President of Marketing”, and you have not cleansed your data, you may miss most, if not all, of the contacts you are looking for. In our own experience, that search returned only 300 results before cleansing, and over 17,000 results after data cleansing to standardize all the other ways of writing “VP. Marketing”, “Vice Pres Mktg”, etc.



- Does the sales team like what they see? If you show your sales team a sample of the leads who scored well in your new algorithm, do they think that these are the right set of leads they should be speaking with? If you show them leads that did not score well, do they agree that these are not leads they would like to speak with? Remember, of course, that you are only looking at explicit information, not buying activity in this example, so it is assumed you would only be passing your sales team these leads when buying activity was detected. Balancing your sales team’s intuition with your objective lead scoring algorithms is as useful here as it is in highlighting flaws in the process through sales “cherry-picking” of leads.


- Does history agree with your hypothesis? When Google looked at their flu data, they compared it carefully with CDC data on actual flu outbreaks to ensure that there were minimal false negatives and false positives. Similarly, your lead scoring results need to match history accurately. If you look at contacts who were scored highly, and who have also been around for a long time, is there a higher number of them who have become customers? It not, what is missing?

Marketing data can be a true gold mine of insight if you use it carefully, much in the same way that search data shows extremely interesting predictive insights when looked at in certain ways.

What insights have you found in your marketing data that surprised you? Read More...

Marketing Automation - What does it mean?

Consistently, the term “marketing automation” is applied to our industry. I find it a term that is less than ideal when it comes to describing what the industry does, but nonetheless, the term has stuck. I often get the question of “can marketing really be automated” and I think that there’s an opportunity to clarify what aspects of marketing truly can be automated, and what aspects cannot. Also, given that the industry’s growth is surging, there’s an opportunity to look at the dynamics that are driving this desire to automate marketing.

If asked for a definition, I would say:

Marketing Automation is the art and science of automatically managing the targeting, timing, and content of your outbound marketing messages.

Let's look at why.

The Changing Buyer

First, let’s look at our buyers. Over the last decade, their ability to self-educate and manage their own buying processes has increased astronomically. Whether it is through vendor websites, analyst websites, social media, or peer reviews, buyers can acquire the information that they need in order to move towards a purchase decision.

However this transition in our buyers has meant that we as marketing organizations need to work with our buyers differently than we did previously. If we hope to facilitate their buying process, and guide them to consider, prefer, and select the products and services we are offering, we need to provide them with the precise information they need as part of their buying process.

The most critical factor is relevance. If we are able to deliver information that is relevant to the buyer’s role in the buying process, their stage as a buyer, their level of interest, and the areas of decision making of interest to them, we will establish a connection, and our message will get through. If not, our message will be lost in the clutter.

So where does marketing automation come in?

If you look at what is required, we need to first understand each of our prospective buyers individually, then we need to provide a message to them that has ideal timing and content based on their interests and stage in the buying process. This level of precision on targeting, timing, and content is nearly impossible without having a solid underlying platform to work from. The art of marketing, when it comes to creating persuasive, compelling copy, great offers, and elegant positioning cannot be automated, and likely won’t be in our lifetimes. However delivering the right selection of those messages to the right person at the right time is something that can no longer be done without automation.

Marketing Automation and Timing

Marketing automation gives us the ability to work on the exact timeframe of the buyer. This is best understood in the context of marketing initiatives like downloadable free trials. In downloading a free trial, the buyer has indicated that, at that exact moment in time, they are at the stage of their buying process where spending time with a trial is their most appropriate use of time. Your communications, as a marketer, need to reflect this buyer timing in order to best connect with this buyer.

Without automation, if a marketer is to attempt this with batch communications, the more closely one tries to align with the buyer’s timing, the smaller and smaller your batches must become, and the larger your workload as a marketer grows. Only through automation can a marketer effectively deliver a message on day 1, day 15, day 30, and day 90 to each individual prospective buyer.

Marketing Automation and Personalization

As one communicates with prospective buyers, each communication should ideally contain content that is precisely in line with their interests. The best way to do this is with dynamic content that automatically matches content to their interests. However, most prospective buyers will not explicitly declare their interests. If they do, the data is likely to be inaccurate. Marketing automation, by letting you tie web activity into buyer insight allows you to understand buyer interests based on what they do, not what they say.

On top of this foundation of buyer understanding, marketing automation gives you a platform from which to have that insight automatically personalize outbound content. Manual processes to personalize the content would quickly prove impossible, and the impact of not personalizing the content is a significant decrease in its relevance to the buyer.

Personalization and the Sender

However, the content itself is not the only aspect of personalization that impacts the buyer’s likelihood of engaging. Who it comes from is equally important. Recipients are 30% more likely, in most cases, to interact with content if it comes from a known person, rather than from a company. A marketing automation system can automatically have each communications come from the appropriate member of your sales team, building the relationship while increasing the response rate. To send your communications on behalf of 5 or 10 sales people might be possible if done manually, but to send on behalf of 50 or 100 requires automation.

Marketing Automation and Sales

As buyer progress through their own buying processes, they eventually reach the point when they would be willing to talk with someone in your sales team about pricing, contracts, or other elements of the purchase process. Knowing when they have reached this stage involves understanding their digital body language. Signs of buying activity can be seen and with the appropriate lead scoring algorithm, sales ready leads can be identified and passed to sales.

Whereas this analysis of leads has in the past been done manually or with spreadsheets, the need to identify and follow up with leads when they are most ready means that it must be done quickly. Using automation allows marketing teams to objectively and automatically score the leads in their marketing funnel in real time, identifying those that are sales ready and those that are not. Those that are not yet ready can be kept warm over time through lead nurturing, again a process that automation greatly facilitates.


The New Importance of Data

What we’re seeing in the above discussion is a shift away from batch communications that are not highly differentiated to true one-to-one personalization. However, as we do this, and we have marketing automation platforms, rather than people looking at the data to make decisions, the importance of data quality takes on a new priority.

Data, in order to be used by rules for personalization of content, segmentation of lists, and scoring and routing of leads, needs to be clean and consistent. Marketing teams are being tasked with ensuring this consistency is maintained at all times, even though marketing data may be touched by web forms, list uploads, CRM synchronizations, or sales input. The only way to consistently and constantly maintain a clean and standardized set of data is to use automation to manage marketing data quality in-line within the marketing database.


What Can’t be Automated?

Marketing remains as much art as it ever has been, even as the new buyer requires elements of science in automating how the targeting, timing, and content of a message is delivered. Compelling offers, captivating visuals, great positioning, and elegant copy are as difficult as ever to create. Likewise, the understanding of market segments, buyer journeys, stages of a buying process, and what moves a buyer along their buying process still differentiate excellent marketers from merely good marketers.

However, as today’s marketers shift from outbound messaging to understanding a buying process and facilitating it, they can only do so if enabled with a platform that automates the conversations, timing, and personalization needed. That is where marketing automation comes in. Read More...

Data Management Is as Sexy as a High Quality Mattress

I'm excited to have Tim Wilson from Gilligan on Data contribute today's guest post. Tim is one of the smartest guys on data management and data quality in the industry and brings a great perspective on what works in the real world. He also has one of the wittier writing styles out there, that makes his posts fun to read. I enjoyed this one, and I hope you do too.

=======================================

When Steve asked me to write a guest post about marketing automation and data quality, I couldn't resist, as we've been going back and forth on our respective blogs exploring the issue. It really started with Steve's Contact Washing Machine post late last year, which he followed up with in April of this year with a post about the need for that washing machine to be managed in-house, largely due to the diversity of sources of contact data. I added my own thoughts about the teeter-totter of customer data management a month later. That back and forth led to Steve thinking I might have a worthwhile direct contribution to his blog.

So, here it is:

Data management is like a mattress. It's not nearly as interesting as what gets done with it (on it)...but it's still awfully important!

The truth is, you can ignore the mattress and still get some interesting things done, but, eventually, as you wake up with a sore back, as you don't sleep well in the first place, and as you get shoved into awkward positions by pits and valleys...the interesting stuff just isn't going to be as interesting and effective.

Let's see how far we can push this analogy before it absolutely collapses under its own metaphorical weight.

Know What's Important about Your Mattress

Imagine the scenario: you're a spastic sleeper, flailing about on the calmest of nights; your significant other is a very light sleeper and wakes up at the slightest of touches. What's important? A mattress with enough room for you to roam about. That may be way more important to you than, say, the firmness of the mattress, which may be very important to someone with a chronically sore back.
It's easy to shoot for the stars with your contact data by trying to ensure that every contact attribute you capture is complete, accurate, and current. The problem is that shooting for a star is overly ambitious -- NASA is only now getting close to pulling that off for the first time. The same goes for your contact data. If you expect to have all of your data 100% clean, you will wind up with all of your data equally dirty, and it will hurt you. Prioritize your contact attributes so that you know what data is most important. The most important data will always be your core communication details: email address, mailing address (if you use direct mail as a communications channel), phone number, etc. After that, it really depends on your long-term marketing strategy -- focus on the data that matters most.

Start with a Good Mattress

Steve's contact washing machine is one example of this: at every point where you are capturing contact data, do what you can to capture it accurately. Be prepared to invest more -- in internal technology development as well as in third-party tools -- to ensure the highest accuracy of your most critical data. For instance, check that the e-mail address the prospect provides is well-formed. If the mailing address is a high priority, then, for U.S. addresses, consider validating the address provided against a CASS-certification tool. Build in other logical checks -- can the user put in that they have 5,000 employees at their company but have annual revenue of less than $1 million of revenue?

Be careful: it can be tempting to build in all sorts of logic to check that you are capturing good information, but that can be risky for two reasons:

• Faulty logic in your checking -- we've all been to a web site at one time or another that tells us we've entered something incorrectly...when we haven't. I've been on the inside of a company that had this happening with one of their most highly-trafficked lead acquisition points. It's not pretty. It's better to get 95% perfect data quality and have 100% of the visitors to your site get to the information they want than to have 99% data quality and 10% of your visitors getting caught in an endless (flawed) validation loop that leads them to give up and leave (with a bad taste in their mouth about your company).
  



• Losing sight of your priorities -- have you ever been to a web registration form with the "Red asterisks denote required fields" note...and then every field has a red asterisk? This is bad. Yes, you want your data as clean as possible, but you want the data that is most important to really be clean. Prioritization sucks, but you've got to do it.

Flip Your Mattress

"Will everyone in the room who has flipped their mattress in the past six months as per the manufacturer's instructions please stand up? Wow. There's one guy. Usually no one stands up when I ask that question. Oh. He's just taking a call on his cell phone."

Data management cannot stop at the point that you've got your data capture mechanisms set up. This is where the mattress analogy breaks down a bit, as ensuring that you are constantly working on the quality of your data is wayyyy more important than your mattress-flipping schedule.

Here's the contact data-equivalent mental exercise to the mattress-flipping survey above:

• How many people are in your department at work? How many of those people joined the department in the last year? How many people were in the department a year ago and are not any longer? How many people have had a change in job title or responsibilities in the last year? Given your answers to these questions, roughly speaking: what percentage of your department has had key attributes of their contact profiles change in the last year? 10%? 20%? More?
  



• Now look at your database. What percentage of your contacts have had no updates to their key profile data in the last year?

Do you see where this is heading?

The point: we tend to be wildly optimistic about the quality of our contact data, because we underestimate how rapidly that data decays. We assume that the rest of the business world is more static than our own immediate environment.

This is where marketing automation, and your overall marketing program, really start to show their symbiotic relationship with the management of your contact data. All too often, we live with some cognitive dissonance, in that, when we talk about the quality of our customer data, or when we manually inspect a handful of records, we quickly realize that much of the data is old or incomplete. We then turn around and build automated marketing programs that pretend the data is perfect. We reconcile this by telling ourselves that it's the best data we have, it's better than nothing, and there's nothing we can do about it. This is not true.

While there is no magical, easy way to maintain your customer data quality on an on-going basis, you do have opportunities in many of your marketing activities to fight off the beast of data decay:

• When known users hit a registration form on your web site, prepopulate it with the data you have about them and include a simple note asking that they confirm the accuracy of the information before submitting the form



• Alternatively, or in conjunction with the above, add a persistent element throughout your web site that shows the 3-5 most critical fields about the visitor with a clear "Update my information" link



• In direct mail and direct e-mail campaigns, include the explicit information (including information you have determined based on implicit/behavioral data, when applicable) about the person, with a secondary call to action for them to update that information. (For four years in a prior role I regularly received direct mail from Microsoft targeted to me because I was an "IT executive" who, apparently, had responsibility for IT infrastructure -- if there had been a way for me to tell them I was woefully misflagged in their database, I would have done so.)



• Factor in the "last updated" date for the contacts when developing your promotional lists. You may already be running some form of reengagement program on old leads -- don't assume that the job title or role is remotely accurate for these contacts. If this program includes a, "We haven't heard from you in a while" component, a non-aggressive tactic can be to ask them to update their information and interests so that you will not bother them with information in the future that is not useful to them.



• Don't assume that the humans in your company are thinking of data quality when they have direct interactions. Do some digging into your telemarketing and inside sales processes to ensure that they include steps to check for the currency and accuracy of the key data points when they interact with leads directly.

In short, flipping your contact data mattress is not something you can do with a few simple steps on a bi-annual basis. It really needs to be an on-going process that is embedded in small ways throughout your marketing programs, always keeping in mind that the burden on the contact himself/herself needs to be kept to an absolute minimum.

Sleep Well!

At the end of the day, you want your contact data to be as accurate as possible so you can drive more sales. A better mindset, though, is to recognize that "more sales" is the end, and the means to that end is "provide more value to your leads by better understanding their wants and needs." In other words, contact data management is about being customer-centric first, which will lead to improvements in your lead qualification process, which will improve the handoff of leads to Sales, which will lead to higher revenue...and a good night's sleep!

Read More...

Is Data Quality the "New Black"?

Anytime I talk about data quality with a marketer, I always get the answer “yes that’s really important, but i don’t know where to start as we have so many problems and we don’t have the resources”. Well I believe that now it is more important than ever to implement a data quality plan, as the success of your campaigns depends on it. In fact it is so important, that I believe data quality will be the “new black” for this season of marketing campaigns.

We have found that customers that focus on data quality generate 267% more leads that those who don’t.

Why would that be? Quality data drives your segmentation and targeting, personalization and more accurate lead scores. All of these things help deliver higher quality leads to your sales team.

Let me walk you through the top 3 things you should do to maximize data quality:

1. Identify the sources of all of your new data and prioritize the quality level of data from each of those sources:
   a. Your CRM system may be top priority
   b. But a list from a new sales rep may be lower priority
   
2. Standardize the fields and values you are getting from those sources – whether it is fields on a form, or the information you are capturing at a trade show
   
3. Finally put a system in place that cleanses new data to a minimum standard, “inline” as new contacts are added to your system – this is the critical part of the solution. Steve wrote a great article on the inline data cleansing concept or contact washing machine in April.

With these three steps you will ensure to be in vogue with this season’s marketing campaigns.

Read More...

Data Quality: Balancing the Customer Experience

I was in a conversation recently with Tim Wilson from Gilligan on Data about the balance between the client experience and data quality when it comes to semi-standard data like title or industry. On one side of the spectrum, the best user experience is often free-form text. Forcing a user to select from a defined set of choices often leads to a frustrating experience. A short list of titles, for example, will often be missing a good match for the visitor’s title, and lead to a poor selection. A longer list forces the user to select from many, many options, and impacts their ability to quickly use the form.

However, on the opposite side of the spectrum, demand generation relies on clean data. Rules for such activities as segmentation, lead scoring, and lead routing may be built on such data fields as title or industry. Personalized content rules might select a piece of content based on visitor data, and analytics may present results that build off of the underlying data. In all cases, having clean data is critical to the success of these initiatives.

So, how do we balance the requirement for the best possible visitor experience with the need for cleansed data to work with within our marketing database? The answer is through using secondary data fields for standardized data. The user is allowed to input free-form data on the web form, which provides them with an optimal user experience.

As the form is submitted, this data is fed into an inline data cleansing system (such as a contact washing machine) to scrub the data. The free-form data is compared against a standard list of titles in the contact washing machine. Because this step is automated, and not part of the user’s experience, the size of the list of titles used does not matter, and accuracy does not have to be sacrificed.

However, when a match is made, the resulting data can be fed into a secondary field, rather than back into the original field, leaving the user’s free-form data intact. In many cases, it may be useful to feed the data into more than one field. For example, when looking at a visitor’s title, it may be useful to split it into a “level” component (Vice Presidente, C-level, Manager, Director), and a “department” component (sales, marketing, finance, human resources).

As an example:

• User Inputs: "V.P. Marketing"

which is then split into three data fields:

• Raw Title is Maintained as "V.P. Marketing"
• Level is Standardized as "Vice President"
• Area is Standardized as "Marketing"

The personalization, scoring, segmentation, and routing rules that are needed can be built on the cleansed and standardized data, giving maximum accuracy and ease of use to the marketer. At the same time, the visitor is able to submit free-form data, which provides them with an excellent user experience.

Read More...

National Instruments: Multiple Activities Leading to Multiple Responses

National Instruments has done a great job of creating an information rich web presence that provided relevant and useful information to their audience of scientists and engineers. They had implemented a very elegant equitable exchange of information process that asked for small amounts of information from their audience in exchange for access to the information resources, and were nurturing their prospects based on the information they had requested.

The challenge that Helena Lewis and the team at National Instruments needed to tackle though was what happened in a prospective buyer was very active on their site and accessed multiple information resources in different areas. Here is a case study on how they tackled the challenge, from Digital Body Language:

National Instruments: Multiple Activities Leading to Multiple Responses


National Instruments leveraged the rich information on prospects’ interests that it gleaned from prospect digital body language on its Web site to deliver highly targeted and relevant communications. The success of these communications was evident in the very high open and clickthrough rates discussed earlier. To achieve this, however, National Instruments had to overcome an operational challenge.

The mapping of online activities to communications was straightforward, but also created a challenge. What should happen if a site visitor performs multiple actions that warrant a communication? For instance, downloading four whitepapers should not result in four communications.

To ensure that prospects are not inundated if they perform a number of triggering activities, National Instruments built a waiting period of 24 hours into its scoring. If multiple actions were seen in a 24-hour period, the actions were scored individually and the most relevant communication was selected. Similarly, if an action had been performed before (for example, downloading an automated test guide), the prospect was not sent communications that had this as a call to action.

This solved the challenge of too many communications, but National Instruments also realized that certain key actions should bypass this logic. For example, if a visitor abandons a shopping cart, or saves the configuration of a product, a communication would be immediately triggered. The 24-hour delay was reserved for communications that were deemed less critical.

Since National Instruments is a global organization, each time it learned a better way to interact with customers and built processes for doing so, it replicated that logic and structure and separated it from the content. In this manner, it only needed to translate content and messaging to roll out its program to any of 35 countries.

Read More...