Marketing Automation User Satisfaction: Clearly, There's Room for Improvement (and maybe a little vodka)

Last week’s post on marketing automation and its discontents prompted several questions about whether the level of dissatisfaction is any higher with marketing automation than other systems. To some extent, this is asking whether the glass is half empty or half full; and, as the illustration suggests, the answer matters less than the fact that there’s room for improvement. But I do have some data to share on the question of relative dissatisfaction.

The first insights come from G2 Crowd, a research firm that ranks software based on user ratings and social data. I have my doubts about comparing software this way* but users certainly know whether or not they're happy.  The folks at G2 were kind enough to reformat some of their data for me.**


According to the G2 figures, marketing automation users are in fact more enthusiastic about their choices than almost anyone else. CRM in particular has a vastly worse rating, but even email, Web analytics, and Web content management show more detractors and fewer promoters. I’m not sure how to interpret this – is the average marketing automation system really easier and better than those other types of software?  Or is something else going on: maybe satisfaction is lowest in the most mature categories, like human resources, enterprise resource management, and accounting, because experienced users are the most demanding?

A second set of insights comes from Ascend2 and Research Partners, which asked its panel which inbound marketing tactics they considered most effective and most difficult to execute. Here we see a very different story: marketing automation and lead nurturing (listed separately) are clear outliers in a bad way: among the less effective tactics and the hardest to execute. In fact, they are the only two tactics where the difficulty score was significantly higher than the effectiveness score (i.e., above the diagonal line in the chart below).***

The Ascend2 study also found that 18% of respondents used marketing automation extensively, while 43% made limited use of it, and 39% didn’t use at all. This is similar to the BtoB study I cited last week, which found that just 26% of marketing automation users had fully adopted their system.  I believe those effectiveness vs. difficulty ratings hint at the reason for those results: most marketers don’t fully deploy marketing automation because they find it too much work compared with the benefit they’d gain. In other words, the hurdle to marketing automation adoption is not laziness, but a rational evaluation of the return from investments in marketing automation vs. other activities.

That rational judgment could still be wrong.  After all, marketers who haven’t fully deployed marketing automation don’t know how effective it really is. Ascend2 addressed this by asking marketers to rate their performance and comparing answers of the 12% self-rated “very successful” with the 20% who rated themselves “not successful”.

Those answers contain some positive news: of the very successful group, 45% were extensive users of marketing automation, compared with just 9% of the not successful.

But even the very successful marketers gave marketing automation only the fifth-highest effectiveness rating, which doesn’t differ much from the sixth-highest rating in the not successful group.

Similarly, the very successful marketers rated marketing automation as sixth most difficult (actually, tied for fifth) while the not successful marketers ranked it as fourth-hardest. In other words, marketing automation is indeed a bit easier than it seems before you start, but even the most experienced and most successful marketing automation users consider it pretty darn hard and just modestly effective.

So what we have here is a mixed message: marketing automation does correlate with success and its users might even be relatively satisfied, but it's still a lot of work for limited results.  You read that as good news or bad, but, either way, it shows the need for more work before marketing automation can reach its full potential.


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* My basic objection is that users have different needs, so a system that satisfies one user may not be good for another.

** G2’s explanation: “The data for this chart comes from the over 7,400 enterprise software surveys users have completed on G2 Crowd as of Friday 10/18/13. For every product review we ask "How likely is it that you would recommend this product to a friend or colleague?" on a 0-10 scale. We segment reviewers that rate a product 9-10 as Promoters, 7-8 as Passives, and 0-6 as Detractors. The product segmentation data is aggregated to determine Net Promoter Score at a category level.”

***It's barely possible that the answers would be different if the Ascend2 study had asked about marketing in general rather than "inbound marketing purposes".  But I doubt it.

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Marketing Automation's Unhappy Users: Trouble in Paradise?

As I mentioned in last week's post, I’m writing a paper on stages of marketing automation deployment. Key findings will be presented in a Webinar next Thursday, sponsored by TreeHouse Interactive; you can register here. The paper itself will be available to Webinar attendees.

The premise of the paper and Webinar is marketing automation has a problem: clients who don’t move beyond basic email functions are unhappy. Last week’s post provided statistics that show how many marketers fail to make this transition, but it didn’t actually show why this matters. So let’s look at some more data that illustrates the trouble in marketing automation paradise.

First we’ll start with the paradise itself: B2B marketing automation has indeed been growing quickly, at about 50% per year over the past few years according to my estimates.  I do expect that to slow somewhat in 2014 as the core market of tech companies approaches saturation and adoption in other industries remains spotty. The great hope is that acquisitions by Oracle, Salesforce.com, Adobe, and other big software vendors finally push the industry across this classic Geoffrey Moore chasm from the beachhead niche to mainstream users, but that’s by no means certain to happen.

If and when that growth does occur, it will be fueled by positive experiences of previous users. But the news on that front is mixed: a survey by one of the industry’s best analysts, Jim Lenskold, found 60% of marketing automation users reporting increases in the key value measures of lead quantity and quality. That’s a happy majority, but it also means that about 30% found no improvement or even a decline.

Questions about satisfaction give a similarly ambiguous result: just over two-thirds of users in a Winsper Group survey reported themselves satisfied with the business value of their system, again meaning that nearly one-third were neutral or actively dissatisfied.

Even more scary (and just in time for Halloween, if you're still looking for a costume): yet another survey, this by Holger Schulze, found that 31% of current marketing automation users anticipate changing their system within the next two years, nearly always because they want better or different capabilities.

Although these figures come from different sources, they all point to the same conclusion: about 30% of marketing automation users are not happy with their systems. The Schulze survey suggests that most believe a different system will give them better results, so they’re not yet ready to give up on marketing automation entirely.

But will those users really do any better with a different product? I’d be the last person to say that all marketing automation systems are the same, but it's also true that the vast majority of systems purchased have all the functions needed to run a successful marketing program. Some fraction of users really did buy the wrong product, but I’ve no doubt that most have problems due to flawed deployment.

One final survey reinforces this point. This one, from BtoB Online, found that just 26% of users had fully deployed their system – and nearly 40% had only some or moderate adoption.

I’d guess that the dissatisfied users in the earlier surveys are concentrated in the low deployment groups in this survey.  But if that’s true, those marketers are abandoning their systems before giving them a real chance. The BtoB survey does show that strong and complete adoption have increased considerably from 2012 to 2013, which is good news.  It also shows that full adoption will double next year, which would be even better news if it happened – but those figures probably reflect aspirations more than reality.


All of this brings us back to where we started: rather than blaming their tools, marketers need to work harder at ensuring full deployment of the systems they’re already purchased. Join me at next week’s Webinar for a roadmap to making this happen.

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Which B2B Marketing Automation Features Actually Get Used? Here's Some Data.

I’ve been writing a paper on the stages that marketers go through when deploying their marketing automation systems, the basic point being it’s important not to stop with just one feature. That much is indisputable, but the next question seemed to call for some empirical data: Which features are used most often? Here’s where things got interesting.

Searching through my trove of published reports, I found four recent surveys that asked this question. Of course they differed in the precise categories used and their audiences, but they generally covered the major B2B marketing automation features: email, Web behavior tracking, landing pages, nurture campaigns, lead scoring, analytics, and social media marketing. They differ considerably in their findings.

The table below shows a summary of the results, with values all normalized so the highest ranked answer in each survey equals 100. (I’ve shown the original results at the bottom of this post.)

As you see, the only answer that’s truly consistent is that the most commonly-used feature is email – although even that wasn’t quite true in Holger Schulze’s report. This is exactly what you’d expect; indeed, my paper was inspired by the lament that many companies use marketing automation as nothing more than a glorified email engine.

The remaining rankings are nowhere near as consistent, either with each other or my expectations. I’d guess that landing pages and Web tracking would be relatively common, since they’re basic features that yield clear value and are easy to deploy. Yet both ranked towards the bottom of the list. On the other hand, nurture campaigns are often considered the most complicated and least used feature of marketing automation but ranked closer to the top. (I'll rationalize that one by guessing that people included simple newsletters and drip sequences along with more complicated nurture programs.)  Lead scoring, another advanced application, was closer to its expected position near the bottom. Analytics ranked somewhere in the middle but that hides a broad variance between surveys, which suggests it meant different things to different people.

Social media, another very broad category, was only on two lists but did rank at the bottom of both. This also makes sense: it’s a relatively new application for marketing automation and many marketers don’t do it at all or use other tools.

The divergence of rankings leaves the results open to pretty much whatever interpretation you want.  Rather than sweating the details, it may be more useful to think of landing pages, Web tracking, nurture campaigns, and lead scoring as a single group of applications that are deployed after email but more-or-less simultaneously with each other. That’s how I do things in my own maturity model, which then adds two more layers: one for inbound marketing including social media and search marketing, and another for marketing management including planning, project management, and revenue attribution. Those don’t appear on my previous table because they’re not consistently included in the surveys, but you will find them in some of the individual surveys below.  They ranking towards the bottom in frequency, as you’d expect.

The paper I mentioned goes into the maturity model in more detail.  (I'll let you know when it's published).  It shows that each level involves new skills and organizational changes, so moving from one to the next takes a lot more than just turning on more system features. This is presumably why so many organizations get stuck at the first or second levels.
Here are details and links for the surveys I’ve summarized above:


Holger Schulze, B2B Lead Generation Marketing Trends, 2013 Survey Results.
More than 800 responses from the B2B Technology Marketing Community on LinkedIn.  Note that not everyone is a marketing automation user.

Aberdeen Group, Marketing Lead Management: From the Top of the Funnel to the Top Line, July 2012.  More than 160 respondents; the table below shows responses for “industry average” companies. One anomaly worth noting is that while the chart below shows lead nurturing as more common than lead scoring, the order is reversed among best-in-class and laggards.

Gleanster, Marketing Automation: Disrupting the Status Quo, August 2013.  Research from 1,396 B2B marketers. The table below shows consolidated results from top performers and others, kindly provided by study author Ian Michiels. The second table shows types of campaigns run by the same group of respondents.

Winsper, 2013 Marketing Automation Study.  132 responders who use a marketing automation system. Figures show “most utilized” features; total utilization is much higher – for example, 94% make some use of email automation.

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idio Does Sophisticated Content Recommendation

Systems in our new Guide to Customer Data Platforms range from B2B data enhancement to campaign managers to audience platforms. This may lead you to wonder whether there’s anything we actually left out.  In fact, there was: although the final choices were admittedly a bit subjective, I tried to ensure the report only included systems that met specific critieria including a persistent database, customer-level data, marketer control, and marketing-related outputs to external systems. In most cases, I could judge whether a system fit before doing a lot of detailed research. But a few systems were so close to the border that I only made the final call after I had evaluated them in depth.

idio was one of those. The company positions itself as a tool to deliver “personalized and relevant multi-channel communications”, which sure sounds like a CDP.  Indeed, it meets almost all the critieria listed above, including the most important one of building and maintaining a persistent customer database. But I ultimately excluded idio because it is tightly focused on identifying the content that customers are most likely to select, a function I felt was too narrow for a proper CDP. The folks at idio didn’t necessarily agree with this judgment, and pointed to planned developments that could indeed change the verdict (more about that later).  But, for now, let’s not worry about CDPs and take idio on its own terms.

The full description on idio's home page reads “idio understands your customer’s interests and intent through the content they consume and uses this to deliver personalized and relevant multi-channel communications” and that pretty much says it all. What idio does is ingest content – typically from a publisher such as ESPN, Virgin Media, Guardian Media, or eConsultancy (all clients) – but also from brands with large content stores such as Diageo, Unilever, and C Spire (also all clients). It uses advanced natural language processing to extract entities and concepts from this content, classifying it with the vendor’s own 23 million item taxonomy.

The system then monitors the content selected by its clients’ customers in emails, Web pages, mobile platforms, and some social platforms and builds an interest profile for each customer.  This in turn lets the system recommend which existing content the customer is most likely to select next. The recommendations are typically fed back to execution systems, such as email generators or Web content managers, which insert links to the recommended content into Web pages, emails, or newsletters.  Reports show selection rates by content, segment, or campaign, and can also show the most common topics published and the most commonly selected. Pricing is based on recommendation volume and starts around $60,000 per year for ten million recommendations.

Describing idio’s basic functions makes it sound similar to other recommendation systems, which doesn’t really do it justice. What sets idio apart are the details and technology.

• Content can include ads, offers and products as well as conventional articles.
• The natural language system classifies content without users tagging each item, a huge labor savings where massive volumes are involved, and can handle most European languages.
• idio's largest client ingests more than 1,000 items per day and stores more than one million items, a scale far beyond the reach of systems designed to choose among a couple hundred offers or products.
• Interest profiles take into account the recency of each selection and give different weights to different types of selections – e.g., more weight to sharing something than just reading it.
• Users can apply rules that limit the set of contents available in a particular situation.
• The system returns recommendations in under 50 milliseconds, which is fast enough to support online advertising selection.
• It stores customer data in a schema-less system that can make any type of input available for segmentation and reporting, although not to help with recommendations.
• It can build a master list of identifiers for each individual, allowing systems to submit any identifier and access a unified customer profile.
• It can return a content abstract, full text, images, or HTML, or simply a pointer to content stored elsewhere.
• It captures responses directly as the content is presented.

Most of these capabilities are exceptional and the combination is almost surely unique. The ultimate goal is to increase engagement by offering content people want, and idio reports it has doubled or even quadrupled selection rates vs. previous choices. All this explains why a small company whose product launched in 2011 has already landed so many large enterprises among its dozen or so clients.

Impressive as it is, I don’t see idio as a CDP because it is primarily limited to interest profiles and  content recommendations. What might yet change my mind is idio’s plan to go beyond recommending content based on likelihood of response, to recommending content based on its impact on reaching future goals such as making a purchase. The vendor promises such goal-driven recommendations in about six months.

Idio is also working on predicting future interests, based on behavior patterns of previous customers.  For example, someone buying a home might start by researching schools, then switch to real estate listings, then to mortgages, then moving companies, and so on. Those predictions could be useful in their own right and also feed predictions of future value, which could support conventional lead scoring applications. Once those features become available, idio may well be of interest to buyers well beyond its current customer base and would probably be flexible enough to serve as as Customer Data Platform.

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Customer Data Platform Guide Reviews Tools to Build Marketing Databases

Raab Associates’ new Guide to Customer Data Platforms is now available (click here to buy).

You may not find that news to be fall-off-your-chair exciting. In fact, you’re more likely to wonder whether the world needs yet another report on anything at all. Fair enough. So before telling you what’s in the CDP Guide, I'll tell you why it exists.

Simply put, marketers need better databases. If you’re a working marketer, you almost surely know this from personal experience. But someone who only read industry news and vendor promotions might think all anyone had to do was to plug in the latest cool application and it would immediately be filled with fresh clean data like water from a tap. Dirty big data is our industry’s dirty little secret.


The problem isn’t new but it is getting worse. As customers interact across more channels, marketers need to not just meet them in every new location but recognize them and carry on a continuous conversation from one touchpoint to the next. Marketers can also become more effective by enriching that conversation with information from external sources such as Web pages, social media, and commercial databases. Both the carrot of better results and the stick of customer expectations are ever-more-urgently driving marketers towards building better databases.

The good news is that plenty of smart vendors have also recognized this need and are trying to help marketers on their journey. I call their systems Customer Data Platforms and define them as “a marketer-controlled system that supports external marketing execution based on persistent, cross-channel customer data.”

If there’s one absolutely critical point in that definition, it’s that CDPs put marketers in charge of building their own database. Taking control is the only way that marketers will ever get the databases they desperately need. It’s why CDPs are so important.

But too few marketers know who the CDP vendors are, what they do, and how they differ. The Guide to Customer Data Platforms is designed to provide this information. If the CDP vendors are tour guides on the path to better data, the CDP Guide is the reviews you read to decide which one you’ll hire. As far as we know, no other study serves this purpose.

Given its goal, the heart of the Guide is the vendor profiles: three to five pages on each vendor, describing capabilities for data management, predictive modeling, marketing campaigns, and message delivery, plus background on the vendor’s technology, clients, company history, and pricing. You’ll want to read those closely when you’re selecting a vendor. But first you’ll have to decide whether a Customer Data Platform is something to consider. Here is some information to help make that judgment.

- CDPs are something new. CDPs are systems that help marketers build and update customer databases, and make those databases available to support marketing programs. That may not sound very new, but most B2B marketing automation products today build very limited databases while most B2C marketing automation products rely entirely on an external data warehouse. The systems that do build databases are designed to be used by IT departments, not marketers. And many CDPs provide predictive modeling or best-treatment recommendations that go well beyond the storage functions of a basic data warehouse.

- You still can’t do this at home. CDPs may be tools for marketers, but that doesn’t mean that marketers build the databases themselves. Rather, CDP vendors provide services that build the database with varying degrees of marketer involvement. The difference is that the marketers work directly with the CDP vendors, instead of relying on IT staff that often has other priorities and an imperfect understanding of marketing needs. This makes it much easier and quicker for marketers to get the database they need.

- CDPs are an outgrowth of existing system types. Most CDP systems were created for a purpose that happened to require the same database-building capabilities as a CDP. These purposes fall into three groups which I discussed in last week’s post, so I won’t repeat them here. They’re work understanding because vendors in each group have a different set of skills, one of which will probably come closest to your needs.

- Convergence is coming. Even though the CDP vendors started with different applications, their shared abilities for identity matching, database management, analytics, and integration will allow them to support more of the same functions over time. As marketers understand the value of their databases more clearly, CDP vendors will be able to focus on selling their data platform features rather than applications the platform supports. Of course, once the platforms themselves are common, vendors will climb the value chain by offering better predictive analytics and cross-channel treatment optimization.

- Details count. CDP features may eventually converge, but for now the systems differ in many small ways that make a big difference. To take one example, nearly every CDP creates predictive models. But some can only predict response to specific promotions, based on who has responded before. Others can do the much more sophisticated analysis needed to predict which offer will best advance a long-term goal such as becoming a new customer. And even among those that model against long-term goals, some can actually estimate the incremental impact of a specific offer and others can just see most common correlations. We found similarly subtle differences in how data is collected (via the vendor’s own Web tags or by importing from other systems), the range of data sources (just marketing automation and CRM or those plus many others), natural language processing to extract useful information from text sources such as Web pages, how much history is kept and how it’s used, program execution, and end-user control. The CDP Guide clarifies these distinctions, but it’s still up to marketers to evaluate which differences will matter in their own business.

The CDP Guide itself contains quite a bit of other useful information, including a formal definition of CDPs, detailed explanations of what to look for in a CDP, and a history of marketing databases starting with the Sumerians (don’t worry, I skipped the boring parts). Again, the goal is to provide one package with everything you need to get started along the path of buying a CDP system.  From there, it's up to you.

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New Study: Three Types of Customer Data Platform Address Cross-Channel Marketing Needs

My detailed study of Customer Data Platforms should be released next week. Now that the information is assembled, I can at last pull back and get a good overview of what I’ve found.

Perhaps the most interesting discovery has been that the CDP vendors cluster into three main groups.

• B2B data enhancement. These build a large reference database of companies and employees, which they match against records imported from their clients. They generally return corrected and enhanced data and lead scores based on models built from the client’s customer files. Their reference databases are built from multiple public, commercial, and proprietary sources, and are assembled using sophisticated matching engines. Most also perform their own scans of Web sites and social networks to extract sales-relevant information such as technology use and changes that suggest buying opportunities. These vendors vary considerably in the data they return, ranging from lead scores only to recommended marketing treatments to full customer profiles. Some also provide prospect lists of companies that are not already in the client’s own database. CDP vendors in this group include Infer, Lattice Engines, Mintigo, and ReachForce.

These systems compete with non-CDP products which also add or enhance prospect records but do not maintain a database with their clients’ customers. These include Web scanning systems such as InsideView, LeadSpace, and SalesLoft, and general data compilers including NetProspex, Demandbase, Data.com, ZoomInfo, and OneSource. The predictive modeling features also compete to some degree with end-user-oriented marketing analytics and modeling software such as Birst, GoodData, Cloud9 Analytics, AutoBox, and Predixion Software. Data cleansing competitors include services from firms such as D&B, as well as data management software for technical users such as Informatica, Experian QAS, and FullContact.

• Campaigns. These systems build a multi-source marketing database from the client’s own data and either recommend marketing treatments to execution systems or execute marketing campaigns directly. These are primarily used for consumer marketing although they also have B2B clients. Most have sophisticated matching capabilities. This group includes Silverpop with its Universal Behavior feature, NICE’s Causata, AgilOne, and RedPoint.

This group competes with conventional consumer marketing automation products, which provide similar campaign management abilities but lack the CDPs' database flexibility, database management, and customer matching features.

• Audience management. These systems build a database of customers and their responses to online display advertisements. They then build models that predict the customers’ probability of responding to future advertisements and provide recommendations for how much to bid and which content to display. These systems perform the same basic functions as standard online audience management systems (Data Management Platforms, or DMPs) and provide the same very quick responses needed for real time bidding (usually under 100 milliseconds). The major difference is that they also recommend messages in other channels, such as Web site personalization or email campaigns. Like DMPs, they work primarily at the Web cookie level, can link cookies known to relate to the same customer, and can be linked to actual customer names and addresses in external systems. This group includes IgnitionOne, [x+1], and Knotice.

This group overlaps with recommendation and ad targeting engines and DMP systems. Those products provide similar functions but do not track identified individuals and are often limited to single channel executions.

Given that each group addresses a different business need, you might wonder why I think they should all be lumped together under the CDP label. Quite simply, it’s because they are all addressing a portion of the same larger problem, which is how marketers can get a complete view of their customers and use that view to coordinate treatments across channels. What marketers truly need is a combination of the features from each group: data enhancement from external sources, for consumers as well as B2B; sophisticated customer matching and treatment selection; and integration of online advertising audiences with traditional customer databases. Each of these systems has the potential to grow into a complete solution, and the normal dynamics of software industry growth will push them towards pursuing that potential. So I expect the categories to overlap increasingly over the next few years and eventually merge into complete Customer Data Platforms as I envision them.

Incidentally and tangentially related: I'll be giving a Webinar with ReachForce on October 2 on Data Quality for Hipsters, a name that started as a joke but does make the point that data quality is essential for cutting-edge marketing.  YOLO, so you might as well attend.  I'm already working on the mustache.

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How Raab Associates Converted to ZohoCRM In One Weekend: a B2B CRM Success Story

Raab Associates is really two businesses: the technology consulting practice run by Yours Truly, and a marketing agency specializing in children’s books run by my beautiful and brilliant wife Susan. We keep them largely separate, but I am inevitably involved in her technology decisions. So when her ancient Goldmine CRM system finally crashed last week, we both scrambled to pick a replacement.

From my usual lofty perch in enterprise software world, Susan's requirements seem stick-figure simple: accounts, contacts, opportunities, lists, and mass emails. So our first thought was to find a system that offered those plus some cool new things like social media profiling. But a quick scan of the market showed that none of the neat new systems also offered the basic functions with with enough refinement and flexibility to meet Susan's needs.

This pushed us back to the more standard CRM options.  To my dismay, we found ourselves ruling out one after another for various. I even briefly suggested we reconsider Goldmine, an thought that was quickly rejected.  Eventually we took an unhopeful look at ZohoCRM, which I know as a popular small business system but had never considered particularly advanced. Happily, the system has a very thorough online user manual, so I was able to check it out in detail.

Even more happily, the answers all came back positive as I imagined working through Susan’s basic business processes in Zoho. Build contact lists, check. Mass emails, check. Opportunities linked to campaigns, check. Pull-down status list and callback date on opportunities, check. Custom filters across all field types, check. End-user report writer, check. Multi-field search, check. A bunch of other details that I no longer recall, check check check. Reasonable cost, double check: we would have grudgingly paid a couple hundred dollars a month for a solution, but Zoho’s mid-tier Professional edition costs all of $20 per month with no limits on database size (Susan has about 14,000 contact records – well above the minimum for many small business systems). We may even splurge for $35 per month enterprise edition, which provides some advanced automation features but is probably overkill for most small businesses.  Just call me Diamond Jim.

At this point, we were ready to sign up for the free trial account, which was a simple process and didn’t ask for a credit card. Let me point out that I purposely hadn’t signed up sooner because I didn’t want to waste time exploring a system that I wasn’t pretty confident would meet my needs. Diving in too soon is a classic mistake among software buyers – and, in this instance at least, I actually followed my own advice.  (While I'm patting myself on the back, I'll also point out that we evaluated the software against our actual business process, not an arbitrary feature checklist.  That's another best practice that too few buyers follow.)

We now pulled a small set of test records from Goldmine to test the import function. The online manual guided me through the exact steps necessary, complete with a handy checklist of preparatory tasks.  When I went to load the file itself, I got the first of many delightful surprises: Zoho took a guess at mapping the input fields, based on their names, and got about half right. That’s a pretty sophisticated function and a big time-saver. It’s the sort of refinement you don’t see in a new system because it’s not essential to get the product into market, but gets added after enough users request it and the developers have some breathing room. Zoho has actually been around since 1996 (although CRM came later), so they’ve had time to add a lot of those little helpers.

In any event, the test import worked perfectly the first time out, which was a great feeling of accomplishment. Susan and I played with the system a bit more now that we had some real data in it, and found all sorts of nice little options, like being able to rename objects (she calls an opportunity a “pending record”), rearrange the fields on each screen, change the order of sections, and move fields from one section to another.  Again, none of these is cutting edge, but they’re not always available and make a big difference in making the system more usable.  The interface itself was also highly intuitive – lots of nice dragging to move the fields around, for example. There were plenty of other unexpected goodies that I would have otherwise needed to configure or live without, like automatically listing the associated contacts when you view an account record, and listing the associated opportunities – I mean, pending records – when you look at a contact. And, oh yes, you can control which fields are displayed on those related records.

At this point we were feeling pretty good about actually pulling off the conversion, so I spent all day Sunday manually cleansing those 14,000 contact records to ensure the critical data was populated. Even Zoho couldn’t help with that one. I finished around midnight and had a moment of panic when I saw that Zoho would only import 5,000 records at a time.  But it turned out to accept all three batches without waiting for the first batch to finish, so I was able to submit them and get some sleep.

I woke up bright and early (well, actually, late and cranky), feeling pleased that Susan could start using the system without missing a business day.  Alas, we found that somehow there were twice as many account records as expected. A quick call to Zoho support pointed us to a rollback function that should have cleaned up the problem in a few seconds. Sadly, it rolled back one set of records but not the other (remember, there had only been one import).  I spoke again with Zoho support, who promised to look into it but hadn’t accomplished anything several hours later.  At that point, I realized – duh – that it would take about two minutes to delete the records manually (you can only delete 100 at a time, but it’s three keystrokes for each batch, so you can probably do about 50 batches per minute). Once I figured that out, I cleaned out the old records and reimported everything, and we had a clean set of data.

Susan has been working with the system for the past two days, and I’ve been peeking over her shoulder and poking around a bit myself.  ZohoCRM is certainly not perfect – there are bunch of little things she would like to do, such as preview a template-based email with the variables populated. There are also some oddities like two unrelated sets of email templates, a vestige of Zoho's earlier separate systems for CRM and mass mailings. Those quirks take a bit of getting used to but are far from show-stoppers. There are some other tasks that cumbersome at the moment, but I suspect we’ll be able to automate once we have time to explore those functions. And, yes, there are some things it doesn’t do that Susan would like, such as associating multiple email addresses with the same contact. I wouldn’t exactly say they’re trivial – certainly not to Susan – but she can live with them.

We're generally satisfied with customer support: phone calls aren’t always answered immediately, but after about a minute on hold, a very nice lady picks up the line and offers to take a message. I appreciate the human touch, and, more important, the opportunity to get immediate help if something is truly urgent. We do get callbacks in an hour or two and the agents have been pleasant and helpful, which is about all I can ask. There’s a “how’d we do?” email after each interaction, which is a good sign that Zoho is trying to do a good job.

Bottom line: We’re still in the honeymoon period, so I may find Zoho isn’t really as great as I think.  On the other hand, I proposed to Susan almost immediately after meeting her and that's worked out just fine.  So I'd say ZohoCRM is worth a close look for small business CRM, even for people who think it may be too simple for their needs.

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