New White Paper and Eloqua Prospect Profiler

Eloqua yesterday announced Eloqua Prospect Profiler , which makes it easier for salespeople to review prospect behaviors that are captured by the demand generation system. In honor of the event, they sponsored a white paper by Yours Truly on the general topic of, um, why it’s important to make it easier for salespeople to review prospect behaviors that are captured by the demand generation system. The paper, Restoring the Balance: Why Marketing Holds the Key to Effective Selling in a Changed Business World, is available for free on the Raab Guide site and will eventually show up on the Eloqua site as well.

Despite its origins, the paper itself is quite generic and I think makes a valid argument: basically, that salespeople have less contact with prospects today because the prospects can gather so much information on their own. This makes it harder for salespeople to understand prospects and build relationships with them. The behavior data captured by marketing automation systems restores the balance by providing an alternate source of insights into prospect interests and intentions.

Replacing the relationship-building is more difficult, but demand generation systems can help somewhat by responding appropriately to prospect behaviors. This gives prospects a generally positive feeling towards the company even if no personal relationships are created. At a minimum, it keeps the company in the consideration set during the early stages of the buying cycle. Once the prospect has been assigned to an actual salesperson, the demand generation system can also send a stream of emails “signed” by the salesperson, building something of a one-to-one relationship. Obviously those emails must be appropriate, but this is what clever campaign design and good predictive modeling are about, per my last two posts.

Back to Eloqua Prospect Profiler. There’s nothing new about demand generation systems making prospect behavior available to sales people. Pretty much every major system on the market does this, and in roughly the same way: they pass activity headers over to the sales automation system, where they can be viewed as part of the normal interface, and let salespeople drill into details that are stored in the demand generation system itself. This may sound a bit awkward but it’s seamless from the user’s perspective, and moving all the details into the sales automation system isn’t practical.

Prospect Profiler’s claim to fame, so near as I can tell, is that it also presents summaries and trends of the prospect activity, per this very nice screenshot from Eloqua:

I don’t recall the other vendors doing that. The idea is to make it easier for the sales person to see patterns and then to drill into the details. This seems like a nice enhancement, and perhaps (I’m speculating here; Eloqua didn’t mention it) will be followed by additional of other marketing-gathered data with the salesperson’s interface. That would seem to be the general path that Eloqua and the rest of the industry are headed down, as part of the larger trend towards more closely intertwining marketing and sales activities.

If this isn't clear, think in terms of data from directories (D&B, Hoovers, OneSource), news feeds (Google Alerts, Lexis-Nexis, Reuters), social networks (Jigsaw, Linked-In), and social media (blogs, Facebook, Twitter). These are already assembled by various vendors, so all that’s needed is a relatively simple integration. The demand generation / marketing automation system is the obvious place to do this, rather than asking each sales person to do it for herself. The sales automation system could also be an option, but marketing already needs the data for its own purposes so it’s arguably a stronger contender.

Anther feature of Prospect Profiler is that salespeople can define their own rules for behavior-related alerts. Again, other demand generation systems also allow alerts, but I don't think they allow each salesperson to configure the rules for herself. It would be done by system administrators instead. But whether this is truly unique to Eloqua, I can't say.

More on the Future of Demand Generation Systems

Summary: let's not forget that most companies are still not even doing simple demand generation. Systems for that might succeed even though advanced integration between sales and marketing is the long-run trend. And, my car hit a deer.

I hit a deer last Thursday while driving to Boston for the Sales 2.0 conference. I'm treating the four hour delay that followed as field research into customer relationship management, which ranged from great (the family-run auto shop that towed my car and took me in) to poor (the Enterprise car rental office that kept me waiting nearly two hours before admitting they didn’t have a vehicle available). It ended with the retired dad of the auto shop owners driving me into the next town to pick up a rental car there. The finishing touch was waving as we passed the local traffic cop, who had earlier stopped at the auto shop to chew the fat in true Mayberry RFD style. All told, my little visit to Plantsville (the actual town name) was straight from a grade B movie—city slicker makes an unplanned stop in a small town and learns about real life—except that I didn’t fall in love with a local shop girl.

The net result, beyond some new anecdotes, was that I missed much of the conference. My only extended conversation was with a sales manager who was just recognizing that cold calls were not the most efficient use of his time, and was quite excited to learn that many vendors can provide qualified lists and do the appointment setting for him. He was also starting to think that maybe the company Web site could play a role in attracting leads. In other words, he far behind the times. Yet he also appeared to be seasoned, competent and generally successful. In its own way, this conversation was as much an intrusion of the real world into my bubble as the stopover in Plantsville.

But then it was back to the bubble (so much more interesting than reality) with vendor meetings. Much of the conversation related to my blog post of the day before, which argued that self-adjusting statistical models will replace manually-generated business rules for alerts, lead scores, segmentation and message selection in demand generation systems. Discussing this idea let me to refine the presentation, which I now describe in terms of marketers catching up with changes in their role. That is, most marketers understand their job as generating leads and are just starting to implement systems to do this better. But their job today is actually to manage relationships deep into the buying process. Industry leaders have recently recognized this. The next step, which has barely begun, is for demand generation vendors to deliver solutions that support the new role. My specific argument is that self-adjusting models must replace rules because only models let the systems handle their expanded responsibilities and still be simple enough for marketers to actually use them. My broader argument is that self-adjusting models, rather than a variety of other new capabilities, will be the really important features for vendors to add.

My vendor discussions also touched on the other side of this coin, which is that demand generation systems to perform the original marketing tasks are quickly becoming commodities. Interestingly, I’ve spoken with more than one vendor who sees this as an opportunity, so long as they get to be the dominant commodity provider. Whether these vendors know how to make this happen is another question. I don't know myself, although I’m guessing the primary requirement (beyond a suitable product) is deep pockets for extensive marketing to grab share quickly.

Nor am I convinced that commoditization is a very good strategy, since even the dominant player may not make much money. But, with my little reality check still fresh in mind, I don’t want to underestimate the market for old-style demand generation systems. Perhaps a simple, low-priced system really can be a major success even though marketers will eventually need something more advanced. (Yes, the obvious strategy is to offer one product that can start simple and expand. But I’m very skeptical that this is actually possible.) It’s an interesting strategic puzzle. Fortunately for me, I don’t have to solve it. I just observe and report.

Prediction: Statistical Methods Will Replace Conventional Rules for Marketing Decisions

Summary: basic demand generation features are close to a commodity. Vendors who replace conventional decision rules with automated statistical methods may gain a key competitive advantage because the automated methods produce substantially and measurably better results.

One of the most popular posts ever on this blog is Low Cost Systems for Demand Generation, which listed several options that started at under $500 per month. But it seems that nearly every day brings yet another possibility to my attention. Some really frugal alternatives include Genoo starting at $199 per month; Net-Results starting at $79 per month; and Nurture starting at $495 per month. I haven’t looked closely at any of these but they all seem to promise the core demand generation capabilities of email, landing pages, automated nurturing, lead scoring, and sales system integration.

The question this raises in my mind is where the industry goes from here. Basic demand generation is on the verge of becoming a commodity if it isn’t one already. The more sophisticated vendors will of course continue to add features, but it’s not clear that most marketers will be interested in the additional capabilities or be able to handle the added complexity. Perhaps the key competitive battleground is the ability to add that complexity without making the systems harder to use. But even though there are certainly substantial differences in usability among today’s systems, it’s hard to see why everyone won’t eventually be able to do roughly equal jobs of simplifying their interfaces.

Another possibility is that vendors will compete on their ability to help marketers use their systems – that is, by providing marketing training, usage reviews, and professional services. In other marketing automation segments, including MCIF systems and campaign management for consumer marketers, the ability to provide such services was the single most important difference between winners and losers. The same applies to CRM systems – it was Siebel’s partnerships with big system integrators that ultimately let it pull away from the pack. I do think these services will be a key success factor in the demand generation market, but there’s a big difference: because demand generation systems are offered as on-demand services rather than on-premise software, the actual deployment is much simpler. This means independent consulting firms can more easily learn to work with multiple systems. Because it’s much harder for vendors to build a loyal, locked-in base of resellers, it’s easier for new players to duplicate the service infrastructure of established competitors.

This brings us back to features. Certainly there is a list of hot items right now: Webinar integration, digital asset management, dedicated IP addresses for outbound email, APIs to post data from external forms, integration with Google Adwords, providing contact names from external databases when a visiting company is recognized by its IP address, pulling data from social networks to flesh out a prospect’s profile, and interacting through social media in addition to traditional channels.

The question is which of these features will turn out to be really essential. The only one I personally see as important to a large number of marketers in the immediate future is the Webinar integration, because Webinars are widely popular and integration makes the marketer’s life significantly easier. Everything else on that list strikes me as either of interest to a relatively small fraction of marketers or as simple enough to add that it won’t be a competitive advantage.

So is there something else that could be really important? Well, I wouldn’t ask the question if I weren’t leading up to something.

My particular insight, if it is one, is that consensus has crystallized within the past month that marketing now remains dominant much deeper into the buying cycle, and that sales and marketing must work much more closely together as a result. The idea itself isn’t new, but I suddenly see it referenced everywhere I turn. Part of the reason may be that I’m paying more attention because I wrote a paper on the topic myself (see When Best Practices Go Bad: New Rules for Sales and Marketing Management) although I’m under no illusion that my paper was anything other than one voice among many. It’s simply one of those ideas whose time has come.

As I and others have written, the immediate implication of this change is that marketing systems should provide salespeople with more information about prospect behaviors – what Steve Woods of Eloqua elegantly calls “digital body language”. This gives the salespeople insights into customer interests, replacing to some extent the information that they previously gathered for themselves when dealing with prospects directly.

But those direct interactions also built a relationship between the salesperson and the prospect. Watching their behaviors doesn’t do that. To the extent that anything does build the early relationship today, it’s the automated nurturing programs and behavior-driven responses executed by marketing systems. I don’t really believe that even the cleverest marketing systems can really replace the trust built by a good salesperson, but at least the automated programs can educate prospects and leave a positive impression about the company’s responsiveness to their needs.

I haven’t seen much written about the burden that this change places on the marketing systems. We’re not talking about some simple drip marketing to keep leads warm and educate them a bit until they move closer to their purchase. Rather, marketing must come as close as possible to simulating the interactions between a prospect and a good salesperson to build an essential relationship. This means that the marketing system has to be really smart. And I think providing this sort of intelligence might be a major competitive battleground for the vendors.

That last sentence was a bit of a leap, so let me fill in the blanks. Today’s demand generation systems are largely rule-driven when it comes to selecting prospect treatments. Whether those rules are embedded in list definitions, campaign flows or dynamic content doesn’t matter. The problem is that rules are hard to build and remain unchanged until somebody writes a new one. They’re generally based on somebody’s best guess about how the world works and they tend to be fairly simple. As a result, rule-driven systems just can’t be very smart, in the sense of reacting appropriately to subtle clues or changes in behaviors.

The limits of rule-driven systems don’t matter when there isn’t much data to work with and there aren’t many choices to make. That was arguably the case in the past when lead management systems worked with only a small amount of data from a postal reply card or brief telephone survey. But today’s demand generation systems are dealing a flood of behavioral data related to emails and Web visits. Rules can’t deal optimally with that much information. In addition, the demand generation systems have many more decisions to make, since every personalized email and Web page involves many choices for information to display. No one can create enough rules to handle all the possibilities.

Nor is the challenge limited to rules for selecting messages. Demand generation systems also use rules to decide when to alert salespeople about prospect behaviors. Lead scoring formulas are essentially rules as well. In addition to the fact that these rules are all defined manually and pretty much arbitrarily (that is, based on users’ best judgments), there is little feedback to check whether they are effective.

All of this absolutely guarantees that demand generation systems will produce suboptimal results. That would be annoying under any circumstances, but if the demand generation system takes on the primary responsibility for early relationship building, it’s more than merely annoying. It could destroy your company.

There is an alternative. Marketing systems can deploy automated statistical techniques to select messages, issue alerts and send leads to sales. Consumer marketers have used such methods for years with proven success. In addition to dealing with many more options than rules can handle, such systems can automatically learn from past results to improve their accuracy and adjust to changes in behaviors. Nicer still, marketers and salespeople can actually observe the success or failure of the decisions by watching objective criteria such as return visits and close rates. This last point is critical because it means marketers have a way to actually compare the value of decisions made by different systems. This means that vendors can meaningfully compete to offer the best decision-making capabilities, and marketers can choose the system that does a better job. And, unlike a feature that appeals to just a small fraction of marketers, better decisions are important to everyone. A system that could show it made better decisions would therefore have a very major competitive advantage.

So far, everything I’ve written here is just my private little theory. I haven’t heard any vendor, pundit or client suggest anything similar. This could well mean that I’m wrong; after all, I do like fancy automated systems with their cool bells and whistles. But I think maybe I’m right. Demand generation systems are getting more and more complicated, and something is needed to radically simply them before they collapse into chaos. Given that the stakes are nothing less than the sales process itself, allowing this to happen is unthinkable.

Eloqua Adds Free Implementation Offering

On Monday, Eloqua announced a new free deployment service for its clients. This is part of a larger industry trend to offer free deployment. It follows last month’s free deployment offer from Eloqua reseller Pedowitz Group, which generated quite a bit of comment on this blog. The new service, called QuickStart, will also be delivered by Eloqua partners, giving them an opportunity to start a relationship that could lead to future paid business. Crafty.

Eloqua Senior Vice President Paul Teshima, who is in charge of post-sales support, said the new program includes system configuration, CRM data integration, setting up an email template, landing page, three-touch lead nurturing program and a lead scoring discussion. It is delivered remotely and can be completed in two days to two weeks, depending on how much time the client has available. Advance preparation involves filling out a survey and receiving (if not reading) simple documentation. Clients fill out a workbook during the sessions and are the consultant leaves behind a 90 day plan for future action.

Teshima said the new program was developed in response to customer requests for a fast way to get some immediate use from their systems. It is a subset of the company’s year-old SmartStart program, which take five days or longer but includes more extensive email set-up; data posting from an external Web form; deeper CRM integration including lead flow, activity-triggered sales alerts, lead assignment, and email opt-outs; creation of either a lead scoring or lead nurturing program; and several types of marketing assessments and planning. SmartStart involves on-site consulting and costs $3,000 to $8,000.

The difference in scope between QuickStart and SmartStart provides a useful reminder of the importance of digging into the details of vendor claims about deployment. The question isn’t whether it’s free or can be done in one day, but what’s included and how much your company must do in advance.

The reality is that a complete demand generation program is something you develop and expand over time. A good start is important but it’s only a start.

Another reality is that most companies need help with improving their programs. Teshima pointed to Eloqua's customer success managers, who meet with each client quarterly to review system usage and develop a plan for improvements. They are compensated solely on retention rates, so their focus is on making better use of existing components rather than selling new licenses.

Eloqua also has its professional services group and consulting partners to provide more hands-on assistance. Other vendors also provide such services, either with their own own staff or through partners.

My point is to recognize that you’ll very likely want to purchase such services to get the most value from your demand generation investment. If that sounds like bad news, I guess you don’t absolutely need to. And while you’re saving money on that, you can also change your car’s oil and cut your own hair to save money on mechanics and stylists.

Sarcasm aside, a few companies already have skills to deploy a demand generation system effectively, but most do not. The reason you pay money for these systems is because they’ll help you do a better job. Not investing in the training and consulting means you’ll get less value than you should. Of course, you still need to invest wisely, in the sense of getting the right training and consulting. And, yes, you can probably get some value even without outside help.

Training and consulting are ultimately business decisions about where you can spend money to get the greatest return on your investment. A small investment in using your system effectively is likely to be a wise choice.

Demand Generation Deployment Survey: Preparation Saves Two Months

Summary: My survey of demand generation deployments found that some companies deploy many features immediately, while others take two or three months to reach the same stage. A fast start depends on ample preparation. A white paper on the Raab Guide site explores the results in detail.

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I finished my detailed analysis of the demand generation deployment survey results yesterday and posted it to the resource library of the Raab Guide site. This turned out to be a major project (the analysis, not the posting) because I revisited the data from a company perspective. The analysis in my earlier blog posts looked at average deployment rates by feature, without relating those to particular companies.

As often happens, averages gave misleading results. For example, one of the original factoids that most impressed me was that 80% of features ever deployed are deployed by the second month. This seems to suggest that people deploy quickly and then are largely done. But analyzing data by company, I found a very wide divergence in behaviors: some companies deploy nearly all features immediately, while others start very slowly.

Specifically, I grouped the companies into four quartiles, ranked by the number of features they deployed during the first week. This figure itself varied hugely, from 0.6 features per company in the lowest quartile to 8.8 features/company in the highest. But what I found is the companies who start with very few features will add them steadily over time, while the ones who deploy many features immediately quickly reach the maximum. So, that average of 80% deployment by the second month really is a combination of rates ranging from 57% to 97% for the different quartiles.

table 10 [table numbers refer to tables in the paper]
% of final features deployed by time period (companies stay in original quartile over time)
quartile	first week	first month	second month	third month	later
1 (tortoise)	0.05	0.32	0.57	0.72	1.00
2	0.28	0.55	0.70	0.77	1.00
3	0.49	0.76	0.84	0.86	1.00
4 (hare)	0.68	0.93	0.97	0.97	1.00
average	0.39	0.66	0.80	0.84	1.00

In other words, we have a classic tortoise vs the hare race, with some fast starters and others moving slow but steady. Looking at the number of features per company rather than percentages, we see the tortoises (quartile 1) never quite catch up, but do greatly narrow the gap.

table 9
average features per company by quartile (companies stay in original quartile over time)
quartile	first week	first month	second month	third month	later
1 (tortoise)	0.6	3.4	6.1	7.7	10.7
2	3.0	6.0	7.7	8.3	10.9
3	5.6	8.7	9.7	9.9	11.5
4 (hare)	8.8	12.0	12.6	12.6	12.9
average	4.5	7.6	9.2	9.6	11.5

My fundamental interpretation is that the companies who deploy many features immediately have done their homework and are ready to go from day one, while those who start slowly did little advance preparation. The figures above suggest it takes the tortoises about three months to approach the initial deployment levels of the hares - so it seems this is the length of the delay from lack of preparation.

I actually tightened the analysis even more by looking separately at deployment rates for basic, advanced and optional features within each quartile. (Basic features are needed for simple email campaigns; advanced and optional features are more complex and less common. The paper describes the definitions in detail.) Looking just at the basic features, you'll see they're deployed sooner than average, and that even the tortoises finish implementing them by the second or third month. (You'll also note that, even among basic features, the tortoises never quite deploy as many as the hares.)

table E-1
cumulative features deployed by quartile (based on first period rank)
% of final features deployed by period							average features deployed
quartile	feature category	first week	first month	second month	third month	later
1 (tortoise)	basic	0.10	0.53	0.85	0.93	1.00	4.4
2	basic	0.52	0.79	0.88	0.90	1.00	4.7
3	basic	0.71	0.88	0.92	0.92	1.00	4.8
4 (hare)	basic	0.84	0.98	1.00	1.00	1.00	5.0
avg		0.56	0.80	0.91	0.94	1.00	4.7

The paper draws a number of other conclusions from the data and makes some helpful if generic recommendations (select the right system, prepare in advance, plan for expansion, test and measure). That's all good stuff but far from world-changing. What's really interesting is the details themselves - go ahead and dig into the paper and see what you find.