LeadSpace Offers A No-Memory Approach to B2B Lead Scoring

My discussion last week of Infer, Mintigo, and Lattice Engines raised the question of what other B2B data vendors might be considered Customer Data Platforms. It’s easy to exclude companies that provide basic B2B lists (D&B, Data.com, Netprospex, ZoomInfo, etc.) since they’re clearly in a different business. But there’s another set of vendors that look very much like Mintigo, Infer, and Lattice Engines building detailed profiles by extracting data from Web sites, social networks, and other sources. This group includes InsideView, OneSource, SalesLoft and LeadSpace. So far as I know, none of them maintains a permanent copy of a client’s own customer file, which is the essence of being a Customer Data Platform. But if you’re a marketer needing to identify and score B2B prospects, you’d still want to give them a look.

I bring this up because a colleague suggested reconsider classifying LeadSpace as a CDP, which prompted me to learn more about them. Here’s what I found.

- LeadSpace, like the other vendors, scans Web sites, blogs, Twitter feeds, LinkedIn profiles, job hunting sites, and other sources to build a picture of a company’s business, managers, technologies, and similar attributes. Of course, every vendor argues it does this better than anyone else.  I  suspect there are indeed significant differences.  But I haven’t done any testing or seen anyone else’s test results – so all I can say is that wise buyers will test for themselves before making a choice.

- LeadSpace does build lead scores, something its Web site doesn’t reflect. This is one of the major points of differentiation among vendors in this space, so it’s worth understanding exactly what kind of scores each company provides. In LeadSpace’s case, the company builds “ideal buyer profiles” that measure how similar a lead is to a sample of existing customers provided by a client. Most clients have multiple profiles for different products or customer segments. Other companies in this group build different types of scores: say, for response to a specific campaign, or becoming a sales accepted lead, or having a high lifetime value. Some also estimate the incremental financial value of taking an action. It’s easy for buyers to gloss over these differences, but that would be a big mistake: they largely what kinds f applications a system can support. So be sure to explore them in detail (or read our explanations once we release the CDP Report itself.)

- LeadSpace doesn’t maintain its own permanent master database of all companies on the Internet. Rather, it conducts a fresh scan as each client requests research into its target audience.  This is another big difference from its competitors, who do run continuous scans and keep the results. LeadSpace argues that its approach avoids outdated information, saves the cost of storing and updating a persistent database, and lets the system collect precisely the right attributes for each situation – which can’t be known in advance. The company also points out that even a new scan will capture some history: the public Twitter feed goes back one year, as do job site listings. I have doubts about these arguments – I think older data can show important trends, am sure there’s plenty of outdated information on current Web pages, and suspect there’s the important attributes are pretty similar from one project to another.  Perhaps LeadSpace is really making the subtler argument that the incremental value of older information doesn’t justify the incremental cost of scanning and storing it, which is perfectly possible.  The company does store some old information, such as common job titles, to help analyze and classify inputs.

- LeadSpace doesn’t load a copy of its clients’ customer names, either. That’s essential for a CDP, which by definition has the potential of evolving into a primary marketing database. But it's not essential for LeadSpace's primary business of lead scoring, where even can be built on just a sample of a few hundred records. The arguments for and against the permanent master database also apply here, so I won’t repeat them. In addition, LeadSpace says its clients care more finding prospects with the right attributes, such as industry, company size, and technology fit, than trends in their behaviors or new job titles. Again, I’m not sure I agree, but should point out that LeadSpace mentioned combining their own scores with behavior data captured in marketing automation: so LeadSpace itself is at least implicitly acknowledging that behaviors are important.. LeadSpace's approach also means it can’t monitor a set of names and issue alerts when they do something interesting.  This is definitely something salespeople like to do. LeadSpace is closing that particular gap by developing a service, soon to enter beta testing, that will do a monthly scan of a client’s customer records.  It will feed the results back to the client's CRM or marketing automation, which themselves will highlight any changes.

- LeadSpace provides both prospect lists (i.e., new names) as well as data enhancement (i.e., information on names provided by the client). Most of its competitors also do both, but some do only enhancement. Again like its competitors, LeadSpace provides an interface for sales people to view the details associated with an existing customer. This is where its on demand approach comes in handy, since the interface can present information in categories tailored to each client’s needs. The system also lets sales people rate each lead with a thumbs up or thumbs down, providing feedback to fine tune the scoring model. I haven’t seen that particular feature in competitive systems but it’s not something I’ve specifically researched.

LeadSpace was founded in 2007 as a prospecting tool that let salespeople enter a company name and receive a list of individuals and their associated information and social conversations. The evolutionary path from there to the current system , launched in 2012, is fairly obvious. The company currently has more than 50 clients, mostly large B2B technology vendors. Pricing is based on the number of records either enhanced or provided in prospect lists, and starts around $25,000 per year.

Read More

The Marketing Funnel is Dead: Here's What Will Replace It

Okay, I freely admit that headlines like “the marketing funnel is dead” are a cheap trick to attract attention.
But I swear I came by this one honestly. Too tired to do any serious work on a recent plane flight, I scanned a random white paper that argued the traditional idea of a funnel didn’t capture the need to treat customers individually as they move towards a purchase. So far my head was nodding in agreement plus maybe a little drowsiness. But then came the punch line: instead of a funnel, marketers should think of managing each customer’s progress as a process, which is best represented – wait for it – as an escalator.

Now I was fully awake, and not in a good way. How is an escalator any less linear than a funnel? Have I missed some crazy new form of multi-path escalator networks? Maybe so: I don’t get out much, and who knows what these kids today are up to? But assuming that’s the not case – and I do after all read Twitter – the escalator analogy is no better than a funnel at illustrating today’s situation.

Nor, to be a bit more serious, is the concept of managing buyers as a process. It’s true that a process can have branches (although not the escalator-like linear process this paper described). But a process is still something the marketer controls. Whereas, the dominant fact of marketing today is precisely that marketers don’t have control: the buyer does. It’s the buyer who decides at every step what she’ll do next.

The picture that comes to my own mind is a tornado: a totally uncontrollable, unpredictable force that leaps across the landscape setting down wherever it wants. By this analogy, the best a marketer can do is to build storm-proof structures that will function successfully no matter what buyer does. I don’t think that’s quite the right image – after all, buyers are not destructive – but it does convey the frightening powerlessness of marketers in today’s world.

The better analogy is probably a maze. Marketers can build an environment that defines the options available to buyers, even though the buyers still make their own decisions about the path they take. The maze also shows how buyers can follow different paths and still end up at the goal, can go in circles indefinitely, and can exit without reaching the goal. It also implies correctly that the marketer’s skill determines quality and effectiveness of the buyer experience, just as the maze designer’s skill determines how much fun it is for visitors. If you really want to push the analogy, you can argue that we’re talking here about a corn maze – because ultimately customers can break out of the predefined paths if they want to.

I guess we’re all lucky that my flight didn’t last much longer, since I was beginning to think about how corn needs water (funding?) and if there’s a drought the ears of corn will have small kernels (customer value?). A more useful insight is that mazes have different regions, so the maze analogy replaces the notion of sequential lead stages with a more nuanced view of non-linear buyer states that can be the same distance to the goal yet differ in other significant ways. Buyers can also jump from state to state without necessarily moving through regions that are adjacent – like a tornado touching several spots within a maze, come to think of it. But enough with the metaphors.

Let’s just stick with the main point: the marketing funnel is really and sincerely dead. The purchase process is no longer linear and, even if it were, marketers couldn’t control how buyers move through it. The image of maze may not be perfect but it does show that buyers can follow many routes, that they’ll make their own choices, and that marketers still play an important role by defining the buyers’ environment. At least it’s a start.

Of one thing I’m certain: the buying process is not an escalator.

Read More

Marketing Automation Beer Goggles: What I Think I Learned at Dreamforce

I’m writing this on my way home from Dreamforce, the Salesforce.com user conference that has become the primary industry gathering for marketing automation vendors. With a reported 90,000 attendees (I didn't count them personally), the show is fragmented into many different experiences. My own experience was mostly talking to marketing technology vendors in the exhibit hall, private meetings, and maybe a party or two. I did attend the main keynote and the “marketing cloud” announcement, but neither contained  major product news and the basic story – that social networks change everything – was true but far from novel.

So what did I learn? On reflection, there were two themes that hadn’t expected when I arrived.

The first was data. I generally think of marketing systems as relying primarily on data from the company’s own marketing, sales, and operational systems. But the exhibit hall was filled with vendors offering information – mostly from Web crawling or social media – to supplement the company’s internal resources. Of course, this isn’t new but it seems that external sources are becoming increasingly important. The main reason is so much valuable public information is now available. A lesser factor may be that there’s less internal information, at least for sales and marketing, because so many prospects engage indirectly and anonymously until deep in the buying process.

But there’s more to data than the data itself. The theme includes easier connectivity to external data, via standard connectors in general and the Salesforce.com AppExchange in particular. A closely related trend is real-time, on-demand access to the external data: say, when a salesperson views a lead record or a lead is first added to marketing automation. This requires immediate matching to find the right person in the supplier’s database, and, sure enough, matching was another popular technology on the show floor. I also saw broader use of Hadoop to handle all this new data: as you probably know, Hadoop effectively handles large volumes of unstructured and semi-structured data, so it’s a key enabling technology for data expansion. A final component is continued growth in the reporting, analytics, and predictive modeling systems that make productive use of the newly-available data.

Some products combine all these attributes, others offer a few, and some just one. Obviously a single integrated solution is easiest for the buyer, but as Scott Brinker recently pointed out in an insightful blog post, platforms like Salesforce.com may actually make it practical for marketers to mix and match individual products without the technical pain traditionally associated with integration. It therefore makes sense to view the data-related systems as a cluster of capabilities that will develop as parts of single ecosystem, collectively raising the utility and importance of external data to marketers.

The second theme, considerably less grand, was lead scoring. I suppose this is really just a subset of the analytics component of the data theme, but I saw enough new lead scoring features from enough different vendors to treat it separately. In particular, predictive modeling vendor KXEN announced a free, cloud-based service to automatically score a new Salesforce.com lead’s likelihood of converting into a contact. (If you’re not familiar with Salesforce.com terminology: contacts are linked to an account, while leads are not. The conversion usually indicates the salesperson has deemed the person a valid prospect and is thus a critical stage in most sales processes.)

The KXEN service requires absolutely no set-up; users just install it from the AppExchange. KXEN then reads the data, builds a predictive model based on past results, and returns the scores on current leads. From a technical standpoint, the modeling is nothing new, and indeed the people I met at the KXEN booth seemed to feel the product was barely worth discussing. But I’ve long felt that an automated, predictive-model-based scoring service was a major business opportunity because it would replace the time-consuming, complicated, and surely suboptimal lead scoring models that most companies now build by hand, usually with little basis in real data. Of course, there are plenty of other predictive modeling systems available for marketers, but I’m excited because I don’t think anyone else has made model-based lead scoring as simple as the KXEN offering. Maybe I need to get out more.

Speaking of which, I met SetLogik at a loud party after several glasses of wine, so I may have been wearing the marketing technology equivalent of beer goggles. But if I understood correctly, it tackles the really hard part of revenue attribution by using advanced matching technologies to connect the right leads and contacts to sales (reflected in closed opportunities in Salesforce.com). Once you’ve done that, determining which marketing touches influenced those people is relatively easy.  It’s a unique solution to a huge industry problem. Come to think of it, correct linkages are also critical for building effective lead scoring models, which it turns out that SetLogik also does. (I'll admit it: I Googled them the next day.) So they're part of that theme as well.

As I mentioned earlier, data and lead scoring were themes that emerged for me during the conference. I did have some other themes in mind when I started, which are also worth sharing. I’ll do that another day.

Finally, it’s worth noting that the conference itself was tremendously well run. It sometimes felt that one-third of those 90,000 people were Salesforce.com employees hired to stand around and answer questions. Where they found so many cheerful people outside of the Midwest I’ll never know. Congratulations and thanks to the Salesforce.com team that made it happen.

Read More

The Marketing Funnel Is Dead. Let's Have Dessert.

Last week’s post on lead scoring attracted more positive attention than I expected. This was doubly surprising because first, I didn’t think lead scoring was such a hot topic and second, I don’t really agree with the approaches I described.

To clarify that second point, I’m not saying what I wrote was wrong or insincere. Rather, I consider it an accurate description of an approach I find problematic. The approach was using lead scoring as a way to define lead stages. My problem is the concept of lead stages themselves.

This verges on heresy, but I’m having an increasingly hard time with lead stages as a way to organize a marketing program. Of course, stages make perfect intuitive sense, and they’re ultimately based on the AIDA (Awareness, Interest, Desire, Action) model of the sales process that has been around for more than 100 years.*

But we all know in our heart of hearts that real buyers don’t follow such an orderly sequence. Indeed, there has been a fair amount of research questioning the validity of AIDA and similar “hierarchy of effects” models. The fundamental criticism is that decision making isn’t as rational as AIDA suggests because emotions play a much stronger part than AIDA allows. I’d also add – without a shred of empirical proof, thanks for asking – that B2B decision processes flit among stages in no particular sequence, depending on who asks what questions at any given moment. This randomness is abetted by the Internet, which makes information appropriate to all stages equally accessible on demand. But I suspect the process was always more chaotic than marketers cared to admit.

I’d further argue that buyers’ interests are especially fluid early in the purchase process, which is where marketers are involved. It may be more structured towards the end where salespeople can shepherd buyers through a defined set of stages. No, I don’t have any evidence for this either.

The point is this: if buyers don’t move through a fixed set of stages, then it doesn’t make sense to use lead scoring to determine which stage a buyer is at. Nor, for that matter, does it make sense to structure lead nurturing programs to lead (or follow) buyers from one stage to the next. As I said, heresy.

But any jackass can kick down a barn.** I wouldn't discard the funnel model without offering a better alternative – and by better, I specifically mean more effective at producing productive leads. Here’s my two-part modest proposal:

- within nurture programs, leads should be offered whatever materials they are most likely to select next, based on their recent behavior. This is exactly the same as offering customers the products they are most likely to buy (think Amazon book recommendation or Netflix’s movie suggestions) and it can be based on similar advanced predictive modeling technology. And, just as Amazon and Netflix offer more than one option, nurture programs should also offer several items – within limits, since too many choices can depress response. There’s an important humility in offering choices: it recognizes how poor we are at predicting what people want.

- for lead scoring, the goal is to predict which leads the sales force will like. I chose that word carefully – it’s not a question of whether sales will accept a lead, but whether they’ll decide it’s worth sustained effort. Yes, there could be a “like” button that lets sales rate the leads, but don't be so literal-minded.  It would be simpler and more effective to check how much activity sales has invested in the lead within, say, thirty days after they received it. Leads that sales is working are, by definition, leads that sales thinks is worthwhile. Leads they don’t work should never have been sent to them. This approach doesn’t magically solve the problem of connecting marketing leads to sales results, but it’s easier than tying leads to actual revenue.

Of these two proposals, the first one is the more radical since it implies a change in the structure of nurture campaigns. Today, sequential campaigns are the gold standard and complex branching structure are the mark of sophistication. A campaign that just presented the most relevant materials would have a vastly simpler structure – essentially a big loop that kept coming back with more messages, which would only differ in which offers they included. The sophistication would lie in the offer selection, not the campaign logic. Lead scoring's only role would be to run in the background and continuously assess whether a lead is ready to send to sales.

Even this choice-based approach doesn’t fully discard a sequential model. You need something to help decide what kinds of content to create, and the most logical tool is the content matrix that marketers already use to ensure they have content for all personas at all buying stages. But while you’re still cooking a full range of dishes, you’re offering them as a buffet rather than a fixed-course dinner. If a customer wants to eat dessert first, why argue?


______________________________________________________________________________

* Usually attributed to Elias St. Elmo Lewis in 1898, although there is some controversy.

** Sam Rayburn, although I bet he didn't originate it.

Read More

3 Ways to Use Lead Scoring Within Your Marketing Automation Programs

I wrote last week about the difficulty of linking marketing leads to sales results. One reason the topic was on my mind is I’m also thinking a lot these days about lead scoring. The practical use of lead scoring is to decide which leads to pass from marketing automation to sales, or, even more pragmatically, to predict which leads will be accepted by sales.* But the ultimate goal is to identify the leads most likely to generate revenue. Building an accurate scoring model therefore requires an accurate view of how leads and revenue are connected.

For all the reasons I discussed last week, that lead-to-revenue connection is hard to make. This is one reason that most lead scoring projects focus instead on the criteria that salespeople use in judging which leads to accept. The other reason is that salespeople can decide which leads they’ll work on – so giving them what they want, regardless of whether it’s what they really need, is the key to lead scoring being considered a success.

Many companies today have inserted a phone call between marketing automation and the sales department, screening every plausible lead before sending them to actual salespeople. This reduces the need for scoring accuracy because the phone call will clarify whether the lead is sales ready.  Since the cost of a missed opportunity is much higher than the cost of a wasted phone call, scoring in this situation must simply find all leads with a reasonable chance of success.

In short, scoring programs face two scenarios:

- for scores that directly determine which leads are sent to sales, accuracy is needed but data on past results (necessary to build a good model) is scarce

- for scores that determine which leads get a screening call, accuracy isn’t very important.

Perhaps this is why so few companies use lead scoring (just 19% in a recent MarketingSherpa study) and why the scoring models tend to be simplistic. Investment in more sophisticated techniques, such as statistically-based predictive models, is rarely worth the cost.

There is, however, another use for lead scoring: assigning leads to stages as they move through the marketing funnel.**

Conceptually, assigning leads to funnel stages is quite different from calculating their probability of making a purchase. A funnel stage is defined by meeting specific criteria such as BANT (budget, authority, need and timing) and engagement (downloading a paper or providing contact information). This is more like a checklist than a numeric score, although items like the number of specified behaviors may be calculated. Still, it's sometimes convenient to use score ranges as stage definitions.

In this context, scoring can be used in three ways.

- assign points  to directly to stage criteria.  For example, imagine a three-stage funnel of Respondent (replied to an email), Qualified Respondent (meets BANT conditions) and Sales Ready Lead (demonstrates engagement). If the scoring rules give 100 points for a response, 100 points for meeting BANT criteria, and 100 points for demonstrating sufficient engagement, then people with 100 points are Respondents, people with 200 points are Qualified Respondents, and people with 300 points are Sales Ready Leads. This is a common approach, although it’s not much different from applying the same rules to classify leads directly.


- treat the score as a probability estimate of reaching the final goal (sales readiness, sales acceptance, or revenue). Under this approach, a Respondent might be someone with a goal probability of under 10%; a Qualified Respondent might have a goal probability of 10% to 50%, and Sales Ready Lead might have a goal probability above 50%. This method avoids the need to define specific lead stage criteria, replacing them with objective predictive modeling methods that are likely to be more accurate.

- treat the score as a probability estimate of reaching the next stage (Respondent, Qualified Respondent, etc.). This retains the explicit stage criteria, which may help marketers visualize who is in each stage and how best to treat them. The predictive model provides additional segmentation within each stage, so marketers can focus their efforts on the most promising leads. Since linking leads to stage movement is easier than linking them to revenue, these predictive models are easier to build.

Today, most companies probably do a hybrid of the first and second options. That is, they assign points based on specified criteria (first option) but assign stages based on point ranges (second option). This combines the familiarity of criteria-based scoring rules with the convenience of numerical stage definitions, making it the easiest method available. But it is also doubly arbitrary, since neither the point values nor the range boundaries can be measured against an objective standard.

I’d suggest that marketers move towards a purer version of the second method, building statistical models that predict the final goal (revenue if available; sales acceptance or sales-ready lead criteria if not). Stage definitions can be arbitrary ranges but correlated against existing stage criteria. Eventually, marketers may want to move toward the third method, with separate models for each stage. This makes it easier to focus on advancing leads from one stage to the next while retaining the rigor of a statistically based approach.


______________________________________________________________________________

* For example, Marketo’s Definitive Guide to Lead Scoring defines lead scoring as “a shared sales and marketing methodology for ranking leads in order to determine their sales-readiness.”

**Eloqua’s Grande Guide to Lead Scoring puts it nicely: lead scoring “helps marketing and sales professionals identify where each prospect is in the buying process.”

Read More

CallidusCloud Buys LeadFormix Marketing Automation for $9 Million Cash

The list of independent marketing automation systems shrank by one yesterday when Leadformix  was purchased by sales enablement vendor CallidusCloud for $9 million.

The price is surprisingly low for an established marketing automation vendor. In my VEST report from last July, LeadFormix reported 210 clients, concentrated among mid-size firms, and 82 employees. This would translate to around $7 million revenue, for a price of just over 1x revenue, compared with 4 to 5x revenue in other recent acquisitions.  I suspect the actual LeadFormix revenue was considerably lower than $7 million, but, even so, the price may give pause to investors in other marketing automation firms who are hoping for a great payout.  But bear in mind that LeadFormix was largely self-financed, so they may have sold at a bargain price because they couldn’t afford to compete with better-funded competitors.

Or maybe these comparisons are irrelevant because LeadFormix was never a standard marketing automation system to begin with. While its feature list covers all the usual marketing automation categories, the company's real focus was always on providing the most useful information to sales people. In particular, LeadFormix infers visitors' “intent” (i.e., interests) and sales stage from the Web contents they choose to view. This is a clever and largely unique approach, although Right-On Interactive does something broadly similar.

LeadFormix combines its behavior analysis with anonymous visitor identification (inferring their company from the IP address), access to external prospect lists and enhancement data, and collaboration with partners and affiliates to share lead access. Indeed, the LeadFormix tag line is “aligning marketing with sales” – something that’s important to all marketing automation vendors, but never their primary benefit statement.

This is why Callidus is a good buyer. Callidus isn’t a sales automation system like Salesforce.com, but it does provide a range of other systems that help sales departments. These include products for hiring, training, collaboration, content distribution, proposals, incentives, quota management, and analysis. LeadFormix will give Callidus another offering for its existing 900+ customers and access to 200 more companies now using LeadFormix.  I don’t know whether Callidus will also try to expand LeadFormix sales among "pure" marketers.  But even if they back away completely from “marketing automation”, the deal makes sense.

A bit of background on Callidus: it’s a public company with about $80 million annual revenue, no profits, and a $200 million market value. It made at least five acquisitions last year, all software-as-a-service companies selling some type of sales enablement. These include:

• Salesforce Assessments (salesperson hiring /assessment) 03/28/11
• Litmos (learning management) 06/10/2011
• iCentera (on-demand portal software for sales enablement) 07/06/2011
• Rapid Intake (collaborative rapid e-learning authoring) 09/08/2011
• Webcom Inc. (product configuration, pricing, quoting, and proposals management) 10/04/2011

In short, LeadFormix fits nicely with Callidus from strategic, financial and operational perspectives.  Because this is such a unique match,  I don’t think the acquisition says much about larger trends in the marketing automation industry.  At most, it could be part of the long-expected shakeout as the industry consolidates around a small number of winners. But, while that consolidation is inevitable, it will take more than one deal to show it has started in earnest.

Read More

How Demand Generation Systems Handle Company Data: Diving into the Details

Back in early January I posted a discussion on treatment of Company-Level Data in Demand Generation Systems . At that time, I posed a set of specific questions to the demand generation vendors in the Raab Guide. Yesterday the final answer trickled in. Results are summarized in the table at the end of this post.

More than anything else, this exercise reinforced my understanding of how hard it is to answer a seemly simple question about a software product’s capabilities. My original approach in the Guide had simply been to ask vendors whether they had a separate company table in their system. In theory, this would imply that company data is stored once and applied to all the associated individuals, and that the demand generation system could aggregate data by company, use that data to calculate company-level lead scores, and change which company an individual is linked to. This turns out not to be the case. So I had to specifically ask about each of those capabilities, and even those questions don’t necessarily have simple answers.

This type of complexity is why I’ve always avoided simple summary grids that hide all the gory details. I’m perfectly aware – and people remind me quite often, should I forget – that most people find the details overwhelming and really just want a simple way to sekect a few systems to consider.

But it just doesn’t work that way. Yes, you can screen on non-functional criteria like cost, technical skills required, and vendor stability (not that those are exactly simple, either). But let’s say that leaves you with a dozen vendors, and you use some gross criteria to select the top three. If it turns out once you drill into details that two are missing some small-but-critical feature, you have to either go with the one remaining or start the process again with another set of candidates.

Starting again would be fine if you had the time, but let's face it: here in the real world you'll be under pressure to make a choice and will probably just choose whichever vendor remains standing. This isn’t necessarily so terrible, although your negotiating position will be weak and you might have missed another, better product.

But what if all three vendors fail on one detail or another? Now you’re really in trouble.

The only way to avoid these scenarios is to review the details up front. Thankfully, you don’t have to review all the details, but can limit yourself to the details that matter. Of course, this means you have to know what those details are, which in turn requires still more preliminary work. I was going to (and still may) write a separate post about this, but basically that means you have to lay out the details of the marketing programs you expect to execute with the system, and then identify the features needed to support those programs.

The good news here is you’ll need to lay out those programs anyway once you start using the system, so this is just a matter of time-shifting the work rather than adding it. Of course, doing more work now isn't easy, since you probably don't have a lot of free time. But knowing what you need will actually make the project go faster as well, so the payback will come fairly quickly.

So, the bottom line is that you really do need to look at product details early in the selection process. That said, I do think it’s possible to produce summaries that are linked to details, so people can more easily screen vendors against the summary criteria and then only look at the details of the most promising. I’m working on incorporating something along those lines into the Raab Guide.

Ok, now for the company-level data itself. Here are the questions I asked, with summaries of answers from the five vendors in the current Raab Guide (Eloqua, Manticore Technology, Market2Lead, Marketo, Vtrenz) plus two I’ll be adding shortly (Marketbright and Neolane.) As usual, even though I’ve looked at these products in detail, I’m ultimately reporting what the vendors told me. ("SFDC" stands for Salesforce.com.)

Question	Eloqua	Manticore Technology	Market2 Lead	Marketo	Vtrenz	Market bright	Neolane
Is there a distinct company table linked in a one-to-many relationship with individual records?	yes, link set by Eloqua matching	no	yes, link set by SFDC	yes, link set by SFDC	no	yes, link set by SFDC	yes; typically use SFDC link but client could set own
Are changes in company-level data copied to CRM company (account) records?	yes, if client chooses	no	no	not now; next release will allow client to choose	no	yes, if client chooses	yes, if client chooses
Can the demand generation system establish or modify company-to-individual relationships, and have these changes apply to CRM records?	yes	no	no	not now; might be in new release	no	no	yes, if client chooses
Do demand generation reports give a consolidated company-wide view of activities (i.e., combined activities for all individuals associated with a company)	no; available in SFDC	no; available in SFDC	yes	no; available in SFDC	possible with special effort	yes for Web activities	yes
Can demand generation lead scores be based on company-wide data (i.e., create a company-level score in addition to individual level scores)?	yes for attributes, no for behaviors	no	yes	yes	no	yes	yes
If company-level scores are possible, can they be created within the normal score-building interface?	yes	n/a	yes	yes	n/a	yes	yes

As you see, the answers even at this level of detail are more than simple yes or no. In the case of the first question, which is a restatement of the original question about whether a separate company table exists, “yes” answers must be extended to clarify whether the link between that table and the individual records is imported from Salesforce.com or can be set within the demand generation system. I explored this in most depth with Marketo, who clarified that any individual NOT linked to a company by Salesforce.com will be given its own company record (a one-to-one relationship), even if the database contains several individuals from the same organization. Users can edit that company data, but not the company data imported from Salesforce.

Market2Lead and Marketbright also use the company data and links imported from Salesforce.com. But while Market2Lead matches Marketo's policy of not changing company data in Salesforce.com, Marketbright lets clients determine what do to a field-by-field basis and actually have rules for different cases for the same field. (For example, you might want to let a demand generation user add data to a blank field, but not overwrite data where it exists.)

Just to add a bit more confusion: Marketo itself is changing its system to let clients decide during implementation whether to let users to override the Salesforce links and company data. Apparently some Marketo clients really wanted to do this, while others were firmly opposed.

The other especially knotty question is the one about company-level lead scores. All vendors with company tables can generate scores based on the data attributes in the company records. But I had also intended that question to include aggregate behavior of all individuals associated with a company – such as total emails opened or the date of the most recent Web site visit by anyone in the group.

Eloqua volunteered that they couldn’t do this, which I appreciated. The only other vendor I explored this with in detail was Marketo. They can in fact use behaviors in company scores, but only for individuals linked in Salesforce.com and only by using separate rules to assign points to individuals and to companies. That is, a Web download would have one rule to assign points to individual-level scores and another to assign points to the company score. This isn’t quite the same as building the company calculation by examining each individual independently . For example, Marketo's method can't limit the impact of a single hyperactive individual on the company score.

This is pretty picky stuff, but that’s exactly the point: people who really care about these things tend to be pretty picky about the details. They should make sure they understand them before they buy a product, rather than risk unpleasant surprises after the fact.

Read More