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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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...

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!

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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.

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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.

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Unsubscribes and Content Relevance in B2B Marketing

Another great chart from MarketingSherpa shows very clearly what we as marketers have long known. Relevance is key. 58% of those who stop reading, disengage, or unsubscribe quote a lack of relevance as a key factor.

Too many people are still looking at unsubscribe rates as a relevant metric to determine whether marketing messages are connecting with an audience. The fact is that only some of your audience will unsubscribe. The rest will tune out, emotionally unsubscribe, or even report your message as spam if it loses relevance.

So what is relevance and what can we do as marketers to better align our communications with what is relevant to the audience. There are four main areas we need to focus on in order to connect with our buying audience:

Relevance to their Business: This is one of the more often focused on aspects of relevance, and in many discussions around segmentation, this is all that is considered. Industry information can tell us whether they are likely to be experiencing pains we can solve, company size will give an indication of the resources they may have to tackle that pain and the size of a challenge it might be for them. Geography can give us an indication of whether the cultural or regulatory environment makes the business pain more (or less) acute. To do this, we first need to get our marketing data cleansed continually so that we can easily define our target segments based on industry or geography.

Relevance to their Role: Now it gets interesting. Knowing what role a buyer plays in the buying process allows you to target your message much more accurately. Are they a technical evaluator? If so, product details, devoid of marketing speak may be best. Are they an economic buyer? Perhaps ROI oriented case studies might be best.

Relevance to their Stage in their Buying Process: We’ve all received marketing communications that were driving towards a deal when we were just educating ourselves on the industry, and vice versa, we’ve received introductory, high level content when we were almost finished a detailed evaluation. The mis-match is painful as the content is not relevant even though we do have a certain amount of interest. Matching stage in a buyer’s buying process is crucial to relevance, and to do this, we need to map the buying process and what aspects of Digital Body Language indicate a buyer is at each stage.

Relevance of Style: Each audience responds to different styles and content. Where should the call to action be? What copy or subject line works best? There is no better answer to this than actual prospect response, and the use of A/B testing is your best option to understand which style is most relevant and effective with your audience.

Keeping unsubscribe rates low is great, but keeping audience engagement high, and emotional unsubscribes low is even better. The only way to accomplish this is through a relentless focus on making your message relevant to your audience across each of the key dimensions. Read More...

Why the Contact Washing Machine must be In-House

As B2B marketers, we all deal with the same reality; we receive a continuous stream of dirty data, but yet realize that success requires us to work with clean data. I wrote some time ago about the Contact Washing Machine concept, a data cleanliness program that standardizes and normalizes data within a B2B marketing platform.

Since that time, however, I've had a lot of conversations where marketers have suggested that they don't need to establish the discipline of the contact washing machine to keep their data clean, as they have a service (either a tool or an agency service) that they send their marketing data to once in a while to have it cleansed.

The challenge with this approach is that the data we are dealing with is continually being used, and continually being added to and edited. Every source of data that flows into your marketing data base has an opportunity to dirty the data that is within it. Whereas you may be able to control some sources of data, many you cannot, and if a data source is contributing dirty data, very quickly your marketing database will soon have a percentage of data that cannot be relied upon.

The sources of data to most of today's B2B marketing platforms are quite varied, and many of them either are not, or cannot be, rigorously controlled in terms of the data they pass into your platform.

CRM systems and data marts will have their own data rules and data standards, and may contribute data of varying qualities. Event registrations may be provided to you by third party vendors and thus have non-standard ways of collecting data. Web forms may allow free-form text fields for titles, industries, or address fields, and in doing so contribute dirty data. List uploads, whether purchased, or from legacy data stores are often of dubious quality, and any integration with your sales team's desktop email environment means that they will be contributing data as they chose to type it.

In short, the variety and breadth of data sources in most marketing environments means that we cannot control what data is coming in, and thus, our data quality begins to degrade as soon as we have finished a bulk data cleansing process.

The only viable solution, and the concept behind the Contact Washing Machine, is to bring it in house. In order to have clean data in your marketing platform, you must have your data being continually cleansed at each touch point. Each time that data is sourced or changed, it should be put through the cleansing process of the Contact Washing Machine in order to remain clean and standardized.

Whereas bulk data cleansing can offer additional cleaning to what the automated standardizing, cleansing, deduping, and normalizing that a Contact Washing Machine can provide, both are necessary. If you think of clean data as being like the oil that keeps your marketing engine running smoothly, you can think of bulk data cleansing as being like an oil change, and the Contact Washing Machine as being like an oil filter within the engine. An oil change may do more to clean the oil, but you won't get far from the mechanic's garage if your engine does not have an oil filter to keep things continually cleansed.
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The Contact Washing Machine

I think we can all agree that B2B marketing has shifted from a purely creative discipline to a much more operational, process-oriented, data-centric, analytical discipline. It's a long road though, as the data we get to work with is often... well... terrible.

Data comes in through so many sources, most of which are not controlled in any way - free-form text. Web forms, tradeshows, lists, internal systems, etc. We're then expected to use that data for analysis, segment targeting, lead scoring, etc.

The best thing that anyone in Demand Generation can do, to set up for long term success, is to build a "Contact Washing Machine" that standardizes and normalizes this data as it comes in. For Eloqua users, I'll talk about some of the pieces of a good Contact Washing Machine on this blog's sister blog - Eloqua Artisan (http://eloqua.blogspot.com/2008/12/whats-in-name-job-titles-and.html).

If not, the things I typically see addressed on all incoming data are:

• Standardize Title to allow it to be used for Segmentation or Scoring
• Standardize Country to a 3-letter or 2-letter code
• Fix Zip codes from New England... they have a leading 0 and Excel drops that if the list has ever been in Excel
• Map to sales (or field marketing) territory
• Validate physical address (if you'll be using this)
• Match company to existing company list (or use a standard code such as DUNS)
• Map industry to SIC, NAICS or other industry code (if this is important for you)
• Any other data standards or fields that are key to your business

Putting this in place, and having all incoming data flow through it, is a great way to avoid the cycle of data continually degrading over time until a major undertaking (at great cost) is done to cleanse it, whereupon it immediately begins degrading again. Time is too short for bad data...

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