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.

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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!

Is Time-of-Day Sending Overhyped?

I can't recall the last time I waited in my inbox at 10:01 on a Tuesday clicking the "Send/Receive" button repeatedly eagerly anticipating the next edition of my favourite corporate newsletter. I'm not particularly bothered by what times emails arrive, as a recipient. I strongly doubt that I'm alone in that.

So why is the email industry enamoured by time-of-day sending optimization? I suspect it comes down to a combination of three factors:

1) Marketers are dying to find ways to improve their effectiveness at connecting with their audiences

2) As email service providers, we can easily build time-of-day sending control into our systems, and it seems like a compelling and simple answer to marketer's needs

3) Tactical metrics like open rates may even show a swing across emails sent at different times of the day, "proving" the effectiveness of this technique


However, this is avoiding the problem. The only true, long term way to better engage with an audience is to repeatedly deliver content that is interesting and relevant to their interests at the particular stage of a buying process they are in. This is not easy. As marketers, we need to work to understand the stage in the buying process each buyer is at and deliver content that is relevant to them. Unfortunately, it is much more difficult to understand buyer interests and needs than it is to simply time an email campaign.

So why does some of the data appear to show a real difference in effectiveness of email campaigns depending on when they were sent?

Much of that comes down to how email is handled in various situation and how that affects the measurement of data. Remember that measurements such as open rates are far from 100% accurate as they rely on the rendering of an image in the email to indicate that it has been opened.

If, for example, an email is sent to me before or during my commute to work, I'm likely to open it on my Blackberry. Images are not rendered and it does not show up as an "open". The effectiveness of the email has not changed, just our way of measuring it.

Time-of-day sending can be very relevant in certain situations, like with media types outside of email, such as voice or SMS, or if an email is being sent on behalf of a sales person, and would seem strange to appear at 2am. However avoiding the challenge of delivering relevant, compelling content in order to focus on time-of-day sending is spending effort in the wrong area.

I look forward to your comments. Are there situations where you have found time-of-day sending highly relevant?

The Goals of Lead Nurturing

One of the most common ways to use a marketing automation system is for lead nurturing. Also called “drip marketing”, “nurture marketing” or various other names, this is the art and science of keeping prospects “warm” until such time as they are ready to buy. At that level, there seems to be general agreement that it’s a great process to put in place. Similarly, the results are clearly showing that there is tremendous value in nurturing leads. However, there is often something of a lack of consensus on what the approach should be for nurturing leads.

At a high level, I would describe the goals of lead nurturing as three things, in order:

1) Maintain permission to stay in contact with the prospect: This is by far the most important goal of lead nurturing, and one that is most often overlooked. If a prospect emotionally unsubscribes, you have lost your connection with them, and you may in fact be marked as spam.


2) Establish key ideas, thoughts, or comparison points through education: A prospect you are nurturing may not enter a buying process for many months, if not quarters. However, if you can educate prospects, and by doing so, guide their thinking slightly to incorporate key requirements and ways of analyzing the market, when they do become buyers, you will be much better positioned


3) Watch for signs of progress through the buying cycle: As you nurture prospects, you can watch their digital body language to give you an understanding of when they are moving to a new stage of their buying process

Maintaining permission to stay in contact with the prospect is, as mentioned, the most critical aspect. As we’ve seen, unsubscribe rates, as measured by explicit clicks on your unsubscribe link can be very deceptive. Many recipients will emotionally unsubscribe instead. In order to manage your lead nurturing processes successfully, you need to ensure that you pay close attention to the engagement level of your audience through watching their response activity and manage the frequency with which you communicate with them in the lead nurturing process accordingly.

The need to maintain your audience’s permission to stay in contact with them is the key driver of why high quality, valuable, non-salesy content is crucial to your nurture strategy. However, that content can also guide thought processes and decision criteria. Often, buying decisions are influenced by how buyers think about the market, what “fault lines” they see as crucial in comparing vendors, and what they believe to be possible in the market. Depending on the buying process challenge you face, you can use this content marketing opportunity to educate buyers on what is possible, or on key buying criteria they may not have considered.

If you are successful in the first two goals, you can then begin to look for progress along a buying process through watching your buyers’ digital body language. You can include “teaser” content, such as content from an RSS feed and watch that for signs of interest in a topic that indicates direct buying interest, or can establish interim actions for prospects to take, such as signing up for a webinar or trial, that would indicate a deeper buying interest.

Lead nurturing can be a very powerful way to stay engaged with future potential prospects, and in doing so, successfully establish buyer preference and understand buyer timing. However, it only allows you to accomplish this if you are careful to maintain your audience’s permission to remain in contact with them.

What Exactly IS Digital Body Language?

I've been using the term "Digital Body Language" on this blog quite a lot for obvious reasons. However, I have not really taken a moment to define the term, as I realized recently after a presentation on the topic. An audience member came up to me and very hesitantly asked "I think I get the overall concept, but what exactly IS digital body language." It was a very fair reminder to me to not get so caught up in a topic that the basic concepts are overlooked.

So what is it?

What we are referring to when we talk about Digital Body Language is the aggregate of all the digital activity you see from an individual. Each email that is opened or clicked, each web visit, each form, each search on Google, each referral from a social media property, and each webinar attended are part of the prospect's digital body language.

In the same way that body language, as read by a sales person managing a deal, is an amalgamation of facial expressions, body posture, eye motions, and many other small details, digital body language is the amalgamation of all digital touchpoints.

The best physical representation of digital body language that I can think of is as seen through Eloqua's Prospect Profiler tool (pictured), which shows the spikes and valleys of activity, across all digital media, from web and email to search and forms. You can see how a perspective of what is happening with that individual can be gained from seeing this amalgamated digital insight.

However, the raw information that digital body language provides is often only the foundation. Much as each facial muscle contributes to our reading of a person's body language, the raw digital information is mainly of use when looked at through the lens of lead scoring to understand whether an individual is ready for sales, or what buyer role they play in the process.

Hopefully this clarifies what Digital Body Language is. As always, I welcome your questions or comments.

Sybase: Buyer Profiling for Micro Segment

We all know that it is often best to engage with our markets by segment. However, in some industries, this can be extremely challenging as the segments can be so small that they are extremely difficult to target. Sybase faced this challenge, as one of their products was of most interest to an extremely small audience - the "data elite".

To tackle the challenge, the Sybase team catered to the data elite's natural competitiveness - Peter Kim would call it an "ego trap", but regardless of what you call it, it's great marketing.

Enjoy the case study, it's from Digital Body Language:

Sybase: Buyer Profiling for Micro Segment

For one of its key data-management products, Sybase IQ, Sybase needed to engage with a specific set of its customers: “the data elite” – people who needed fast response times in a solution to tackle extremely high volumes of data. The Sybase IQ product leveraged a new approach to data storage and querying that resulted in performance improvements of many orders of magnitude. The target buyers, however, in many cases were not aware that such a solution was possible, and may have been grudgingly purchasing ever larger hardware in order to tackle the problem.

To connect with this audience, the Sybase team leveraged the naturally competitive nature of administrators of huge volumes of data, and their desire to compare themselves against their peers. The campaign targeted a scrubbed list of existing Sybase contacts and asked them for information on the extreme challenges they were tackling - data volume, response time, or both. Based on their answers, one of three cartoon icons guided them through an information-gathering process where they were ranked as a Pro, an Expert, or Elite by comparing them to their peers.

With this basic knowledge, the campaign guided them through five stages - from collecting basic information through to fully engaged, through sharing thought leadership from industry gurus and case studies of similar professionals becoming corporate heroes through delivering massive performance increases. At each step, the content and detailed information provided was tightly matched to the individual’s biggest challenge and rank. By observing their interactions with available content, the campaign transitioned the customer from one buying stage to the next.

By cultivating that competitive spirit among database experts as to who tackles the larger data challenge, Sybase engaged with the “data elite” in ways that enabled the company to better understand who would be an ideal audience for the product. By catering to this competitive spirit, Sybase was also able to develop the opportunity to present to them possible solutions, that they had never thought possible, to a very real challenge they were having.

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.

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.

Analyzing B2B Marketing: Balance Sheet and Income Statement

Consistent in every discussion I have with B2B marketing execs is the topic of analysis. We're all very familiar with the tactical metrics on each marketing type; open rates and click through rates on emails, landing page conversion rates, cost per click metrics, traffic statistics, and so on. However, these metrics fall short when it comes to understanding marketing's overall contribution to the performance of the business.


Much recent writing has focused on a campaign-up view of the world, with analysis focused around an attempt to determine the return on investment for each campaign. These ROI or ROMI analysis frameworks tend to run into challenges in a B2B marketing environment as the buying cycle is often of significant length, and influenced by many touch-points.


An alternative, and much more actionable framework, is a top down view that uses a "balance sheet" and "income statement" view of the marketing world to understand marketing's overall effect on top-of-funnel activity. Within this top-down view, marketing actions and campaigns can facilitate buyers moving from one stage in their buying process to another, but the complexity of each campaign's individual influence is smoothly aggregated into an overall view that shows the current view of the top of the funnel, and the transitions that have been made over the last period of time.


The first step in looking at this view of the B2B marketing world is to map the buyers' buying process. Each step from education and awareness through to vendor discovery and validation should be understood and mapped, as these are phases that are influenced by marketing, and thus highly relevant in an overall marketing analysis picture.


In order to understand which buyers are at which phase of their buying process, the techniques of lead scoring can be used in order to map each prospective buyer to their individual stage of the buying process by looking at their digital body language.


With this in place, it is possible to build a "balance sheet" view of the B2B marketing funnel. This is a moment-in-time view of where leads are in the funnel, much as a financial balance sheet is a moment-in-time view of where dollars are in an organization at the end of a reporting period.


To have this view relevant and accurate, it is of course necessary to ensure that lead scores degrade over time. Buying interest is, in most cases, a transient phenomenon, and if a lead has not turned into a sales qualified opportunity (SQO) within a certain amount of time, it likely will not, so ensuring a time-based decrease in score is key.


With a balance sheet view in place, it is then possible to begin looking at the motion in the funnel over a period of time, such as a quarter. As buyers progress through their individual buying processes, the transitions in the funnel can be viewed and analyzed, showing where in the funnel there is growth, and where in the funnel there is shrinkage.


If a large number of leads have been passed to sales as Marketing Qualified Leads (MQLs) and accepted as Sales Accepted Leads (SALs), this may be viewed as a very productive quarter, but if the top of the funnel has been neglected and there is a decrease in awareness and a lower number of inquires than normal, this is a warning sign for future funnel trouble.

With the funnel understood in these terms, our ability as marketers to assess the buyers toolkit becomes very valuable as we can understand what resources are contributing to transitions at each stage of the funnel. If there is a gap in funnel motion at a certain stage, and investment in the buyer's toolkit at that stage can be made.

Presenting a marketing funnel in a "balance sheet" and "income statement" view allows marketing to clearly demonstrate their relevance to the business, justify investments in top-of-funnel activities, and make meaningful contributions to revenue forecasting. Whereas approaching marketing analysis from a bottom-up ROI perspective runs into significant challenges as the complexities of the B2B buying cycle come into play, approaching it from a top-down perspective brings clarity and consistency to the task.






As marketing teams begin to present this "balance sheet" and "income statement" view of the marketing funnel, the ability for the business, and the board to objectively understand marketing's contribution increases. With that shift, the well known tenure challenges of today's CMOs and the compensation imbalances between marketing and sales may begin to slowly disappear.

Lead Scoring: Eight Critical Questions to Consider

Much of the conversation around how best to set up lead scoring tends to focus around the aspects of the buyer's digital body language that are most interesting. What whitepaper, excerpt, or download they last looked at, and what this means in terms of their propensity to purchase.

These are all great discussions to have, but there are eight critical questions that need to be contemplated and discussed in order to build a lead scoring algorithm that will truly work in a business environment:

1) What are Your Outputs?: are you using lead scoring to determine who to hand off to sales? what message to communicate to them? who to nurture further? which accounts to get deeper visibility into? all of the above? Understanding your lead scoring outputs first is key in understanding how you want to approach the scoring of leads.

2) How does Time affect your Scores?: it's necessary to think through how the relevance of your prospect actions, and hence their lead score, degrades over time. An action 6, 12, or 18 months ago will likely not have the same relevance as the same action last week. For scoring interest level, this time factor is crucial, whereas for scoring on buyer role, this may not change as rapidly over time.

3) What Dimensions are Critical?: In lead scoring, it is important to clearly define what the question is that you are asking, and to build your scoring algorithm to match that question. If multiple lead scoring dimensions are merged into one, you will likely run into a challenge. Two of the most commonly used scoring dimensions are who the prospect is (explicit data like title, industry, and revenues) and how interested the prospect is (implicit data like web interest, search, and downloads).

4) How do you Cap and Bucket Scores so they are manageable?: When building a lead scoring algorithm, there are often a few buckets of data that come into play. For example in scoring the lead explicitly (who they are), you may look at title, industry, and revenues to determine whether the individual is highly qualified. To do this, it is often best to cap the scores available for each individual bucket. For example, if you are looking at the title to find an executive responsible for content strategy, you may give 10 points for "VP", "Content", "Digital", "Media" or "Production". However, would you want to give 50 points for a "VP of Digital Content and Media Production". Likely not, so this is where caps are needed. 10 points for any of the key terms, up to a maximum of 20.

5) Are your Scores Loosely Mapped to the Ranks that Determine Follow-Up?: If you are going to teach sales to follow up with leads that are defined as "A leads", you need to build in the flexibility to slightly adjust the bar on what makes a lead an "A lead" over time, without retraining sales. The best way to do this is to have both a lead score (a number such as 0-100) and a lead rank (a letter or grade such as A, B, C). Mapping the lead score to the lead rank allows you to adjust your criteria while sales does not change their process.

6) Do you Allow Sales to Cherry-Pick More Leads?: In many environments, especially when lead scoring has been implemented and only good leads are passed to sales, the sales team will feel as though they need more leads. They will ask for the lead funnel to be opened up to them so that they can "Cherry Pick" leads that they deem to be good. Allowing sales to cherry pick has opportunities as well as significant risks, however, and should be discussed carefully upfront.

7) If Sales Does Not Act, do you Claw Back the Lead?: In an ideal world, sales follows up with all leads. However, this is not the reality that most organizations live in, so a process is needed for automatically clawing back the sales lead into marketing if it has not been followed up on in a short time period. Once the lead has been clawed back, it can be re-allocated to another sales person, re-entered into a nurture program, or passed to a partner channel.

8) Have You Provided Sales with Disposition and Nurture Options?: If leads are not being followed up on by sales, it is difficult to adjust your processes unless you know why. Providing sales with options for lead disposition, including automated nurture programs that will continue to educate the lead until it is again ready for sales, can provide both great insight into reasons for lead rejection and foundation for better nurturing of leads.


By thinking through these eight critical factors up front, your lead scoring process will avoid a number of challenges on its road to adoption and success, and will succeed in driving revenue for your organization.