Raab Report: B2B Marketing Automation Revenues to Hit $525 Million in 2012

I’ve just released the latest edition of my B2B Marketing Automation Vendor Selection Tool (VEST), which contains detailed analysis of all 22 B2B marketing automation systems. Serious marketing of the new edition is yet to begin, but anyone considering purchase of a marketing automation system can buy the VEST now at the www.raabguide.com Web site.

The new report contains a rich trove of industry information. The one item that people usually find most interesting is the size of the industry. I put this at $325 million for 2011, a 50% increase from 2010. With 2012 half finished, I can now make a reasonably solid estimate for this year. I find the growth rate has actually accelerated to 60%, for a total of about $525 million.

I come at these figures in two ways.

Installations by industry sector. Vendors in the VEST are asked for estimates of their client counts by company size. We distinguish four segments: micro-business with under $5 million revenue; small business with $5 to $20 million revenue, mid-size business with $20 to $500 million revenue, and large business with over $500 million revenue. Most vendors do provide the sector breakdown, although some are pretty rough estimates.  For a couple of vendors, I’ve used my own estimate based on past data.

Using the sector counts plus estimated revenue per client for each sector, I can calculate the revenue by sector and for the industry as a whole. Since the client counts are mid-year figures, they should roughly equal the full-year average. I’ve only included figures for vendors who specialize in B2B systems; none of the other vendors (Neolane, Oracle, Silverpop, Aprimo, MarketingPilot) are provided estimates of the B2B portion of their client base.  The table below shows my calculations:

The total comes to $362 million estimated 2012 revenue. I estimate the non-B2B specialists and other marketing automation vendors (IBM, SAS, SAP, etc.) who are not listed in the VEST will have another $165 million in B2B revenue, for a total of $527 million.

Revenue estimates for individual vendors. The second approach starts with the four largest B2B specialists: Infusionsoft, HubSpot, Marketo, and Eloqua. Each has announced revenue for 2011 (formally or in press interviews) and two, Infusionsoft* and Marketo**, have made forecasts for 2012. I estimated 2012 revenues for HubSpot and Eloqua based on their client counts and revenue per client.  I then estimated revenue for the other specialist vendors by combining results from two methods: estimated revenue per employee and estimated revenue per client. Finally, I’ve added figures for the non-specialist vendors, using the same assumptions as before. The table below shows the results.

As you see – and I swear I didn’t cook these numbers – this gives $525 million, almost exactly matching the other method.  

Of course, there's more to these figures than just the industry size.  One interesting point is that the “other specialist” vendors are actually growing faster than the big four vendors. This is a bit of a surprise, since we’d expect the industry to consolidate and squeeze out the smaller players. Still, remember that the big four control 75% of the revenue. 

The difference is client growth actually larger than the revenue estimates suggest.  The table below shows that the client base of the “other specialists” grew by 80%, which is faster than any of the big four.

One caveat is that a number of the smaller vendors didn’t provide updated client counts, and they may be vendors who were not growing much. But the reality is that the next three largest vendors (Pardot, Act-On, and Net-Results) did provide data, and each grew by well over 100%.  So the missing vendors don't have enough volume to affect the big picture.


I’ll share one final set of data that also points to industry strength. The table below shows revenue per client for the big four vendors over the past two years. These are actuals except for the 2012 figures for HubSpot and Eloqua, and I consider those to be educated, conservative guesses.

This table shows a consistent increase in revenue per customer across all vendors and all years. Given the intense competition within the industry, that’s pretty impressive: it shows that the big four vendors are managing to increase their revenue per client, which all must do to become profitable.  I suspect the increase is less the result of firmer pricing than of broader product lines that let the vendors sell more to each customer.  Nor does this mean that industry prices are rising: it’s possible – in fact, likely – that the smaller vendors are selling for less than their larger competitors, and that the average price in the industry is still dropping.

All told, this paints the picture of a healthy industry: still growing rapidly, still open to competition, and supporting sustainable prices.   It's a cheery bit of news.

_____________________________________________________________

*Infusionsoft "expected revenue of $40 million in 2012" (Customer Experience Matrix, April 14, 2012)

**Marketo "revenues last year grew 140% over the year to $35 million. Management expects revenues to double during this year" (Sramana Mitra blog interview with Phil Fernandez, July 21, 2012)

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

Dell To Resell Pardot Marketing Automation

Dell announced today that it has added  Pardot  marketing automation to its list of Dell Cloud Business Software applications.  Other products in the suite include Salesforce.com for sales automation and customer service, Adobe EchoSign for e-signatures, AppExtremes Conga Composer for proposal creation, Dell’s own Boomi for application integration, and a Dell-built analytics platform.  That is some pretty good company to keep.

Beyond the Pardot system itself, the Dell offering includes pre-built integration with the other Dell products and with Microsoft Dynamics CRM, and fixed-price implementation packages (from free to $5,000) including training, campaign development, site search setup, CRM integration, and Google AdWords integration.  The Pardot system costs from $1,000 to $3,000 per month depending on the email volume, file size, and numbers of forms, landing pages, programs, and automation rules.  This is the same as Pardot’s direct-sold prices.  Dell also offers a 30 day free trial of Pardot.

The details of the deal are probably less important than its potential for market penetration.  Dell hasn’t been on my list of potential entrants into the marketing automation space, but it certainly has a huge presence among small and mid-size businesses.  This gives it the capability to add thousands of clients to Pardot’s existing base, which has just recently passed 1,000.  Like Intuit’s acquisition last month of local marketing vendor Demandforce,  a well-executed rollout could quickly establish a firm whose market share dwarfs existing competitors.  In some ways, the Dell/Pardot deal is even more interesting than Intuit/Demandforce, because it touches the small to mid-size businesses that form the core of the B2B marketing automation client base.  Intuit/Demandforce will serve many micro-businesses, while other recent deals (FICO/Entiera and Experian/Conversen) are aimed at larger, business-to-consumer marketers.

This doesn't mean an effective Dell/Pardot rollout is guaranteed.  These sorts of relationships often fizzle quickly, typically because the larger company’s sales force can’t be bothered to sell the new partner’s product.  That seems a bit less likely to happen in this case, since Dell’s cloud business group offers just a handful of applications and Dell has traditionally been a very effective marketer – although its recent performance has been spotty.

Whatever the result of this particular deal, it is more evidence that the B2B marketing automation industry is rapidly approaching consolidation.  As deep-pocketed outside companies become active, they battle each other on a grand scale and little firms get crushed almost accidentally.  It may be some time before a single victor emerges – if ever – but it’s hard to imagine many of today’s small companies remaining successful as elephants stampede all around them.

Read More

3 Ways to Connect Marketing Activity to Revenue

Discussions of revenue attribution often remind me of the famous recipe* that begins “First, catch your hare”.  Specifically, they assume that marketers know which marketing-generated lead is associated with each bit of revenue, and then go on like medieval theologians to debate how credit should be shared among promotions to that lead.  The missing hare is that marketers often can’t link leads to revenue in the first place.

The issues will be painfully familiar to anyone who’s ever tried this. For those who haven’t, let’s start with the mechanics.  In most configurations, leads are created in marketing automation and later transferred to Sales, which creates an opportunity that eventually becomes a closed sale with revenue attached.  If all goes smoothly, the original marketing campaign and marketing-generated lead are named on the opportunity to provide the lead-to-revenue connection. 

But – spoiler alert! – things don’t always go smoothly.  When Sales creates the opportunity, it often links it to a contact record other than the original marketing lead.  Perhaps the salesperson was already working with someone else, perhaps the marketing lead wasn’t the real decision maker, or perhaps Sales just doesn’t want to give acknowledge Marketing’s contribution.  The original marketing campaign is often lost for similar reasons.


All those beautiful attribution recipes are moot if you don’t know which lead is linked to which revenue.  So let’s put down the cooking pots and go hare hunting.



The first approach is simply to get Sales to retain the marketing information when it creates the opportunity.  Let’s not dismiss this out of hand – yes, salespeople can be uncooperative, but appropriate training and management support can convince them it’s important to retain the information.  So it’s worth a try.

But let’s say you don’t have time to wait for better data or can’t get Sales to do what you need.  Now you’ll need to work a bit harder with the data on hand. 

One approach is to look for matches at the account level: build a list of marketing-generated leads, find the accounts associated with them, find the revenues associated with those accounts, and assume there’s a connection.  This could hugely overstate marketing-related revenue, since it potentially takes credit for sales that had nothing to do with marketing activity.  So you’ll probably want to put some parameters on the matches such as only including accounts with no pre-existing contacts, leads that Sales followed up on, and opportunities created soon after the marketing lead was submitted.  Setting these rules may take some serious discussion between Sales and Marketing, but that’s a good thing.

Unfortunately, there’s no guarantee that Sales will retain the leads sent by marketing or attach them to the correct accounts.  Nor is it certain that the companies listed in the marketing automation system will match the accounts listed by Sales.  In this case, you may need to build an even looser relationship, looking at company names in both systems – or even ignoring the Sales system altogether and taking data from accounting records.  Because the same company may be listed differently in different systems, this sort of matching requires either knowledgeable people or comprehensive reference databases that can make the non-obvious connections.  Fortunately, this is a well understood problem and plenty of resources are available to help.

Company-to-company matching casts an even wider net than lead-to-account matching, so it’s correspondingly harder to give marketing credit for every connection.  But you can rate how likely it was that marketing played a role in a given opportunity by looking at factors like timing, pre-existing relationships, and amount of marketing activity.  This could translate into allocating a fraction of the revenue to marketing, ultimately a more realistic, if less satisfying, approach than taking full credit for some deals and no credit for others. 

If fractional allocation strikes you as too complicated, you can also start with a much simpler question: did companies that interacted with marketing programs show more sales than similar companies that didn’t interact with marketing programs?  You won’t be able to prove that any particular contact generated any particular deal, but a strong correlation between marketing programs and revenue growth is good evidence that marketing had an impact.  Once you’ve captured that hare, you can think about the details of how you’ll cook it.

__________________________________________________________________________

*Jugged Hare in Hannah Grasse’s The Art of Cookery Made Plain and Easy, although it apparently  doesn’t include the “catch your hare” part.

Read More

Salesforce.com and Oracle Buy Social Marketing Systems: Not the End of Marketing As We Know It

Salesforce.com yesterday announced agreement to buy social media publishing vendor Buddy Media for $689 million, thereby adding another big fluffy piece to its “marketing cloud”. Oracle followed suit this morning  with an acquisition of social media monitoring and semantic analysis vendor Collective Intellect. This followed Oracle’s $300 million acquisition last month  of social publishing system Vitrue. Just for symmetry, it’s worth pointing out that Salesforce.com acquired its own social monitoring system, Radian6, in March 2011.

What are marketers to make of all this activity, not to mention Marketo’s acquisition in April  of social marketing vendor Cloud Factory? Is this the death of marketing as we know it?

In a word, no. Social media are certainly a new way to hear what buyers are saying and send them marketing messages. But only the most besotted booster would argue that it will replace, rather than supplement, traditional methods. Every serious marketer already recognizes this, so I’m not even being boldly contrarian by saying it out loud.

The more interesting question is whether social media can be the foundation of a company’s marketing infrastructure. Both Marketo and Oracle already offer robust marketing platforms, so they presumably see social media as a supplement rather than a replacement. (It’s possible that Marketo hopes to reinvent itself as a social media specialist, which is surely more attractive to investors than marketing automation. The key positions taken by Cloud Factory executives might even support the theory.  But Marketo hasn’t hinted at this approach.)

Salesforce.com is another story.  They’ve always avoided traditional marketing automation, so perhaps they feel a complete “marketing cloud” can be built without it.

The gaps in this approach are obvious to anyone familiar with standard marketing automation systems: no Web behavior tracking, no multi-step nurture campaigns, no marketing resource management. But Salesforce.com could close those gaps by gradually extending its existing products. This might actually be easier than acquiring a separate marketing automation system and shoe horning it into other Salesforce.com components.

One thing I don’t see is social media systems themselves expanding to be marketing automation platforms. So far as I know, the data structures within the social media systems are simple contact profiles – little more than flat files – which can’t easily be extended to store and analyze detailed activity histories across multiple channels. Nor does a standard social media publishing or monitoring platform have the multi-step, branching campaign flows that are the heart of marketing automation. It’s probably easier to add social marketing functions to a marketing automation platform than the other way around. Indeed, many marketing automation vendors have already started.

So, back to the original question: what do these acquisitions mean? I’d say they’re good news for marketers, who will increasingly find social marketing functions available within core marketing platforms, ending the need to integrate separate products. The acquisitions are more problematic for marketing automation vendors, who now need to build, buy, or connect with social marketing systems to remain competitive. This will make it still harder for smaller vendors to compete, hastening the industry consolidation we all know is coming anyway. Nothing boldly contrarian about that prediction either, but it’s still worth bearing in mind.

Read More

Social and Mobile Features Head the List of New Marketing Automation Capabilities

I’m getting ready for the next edition of the B2B Marketing Automation Vendor Selection Tool (VEST). This is based on nearly 200 questions to vendors, mostly about product features. The first step in the process is to update the list of questions. This is based on a review of recent vendor announcements plus my own feeling for what’s important. What emerges is an interesting portrait of industry trends in product development.

You won’t be surprised to learn that most of the changes involve social and mobile marketing, today's two hottest areas in marketing in general. We’ll get back to those in a bit. But first, I’d argue the single most important result is just how few changes there really were. B2B marketing automation is far from mature in terms of market penetration, but the mix of product features is pretty well set. Most of vendor announcements I reviewed were about common features that particular vendors had been lacking or were enhancing.  Social and mobile are the exceptions, but both are still very small contributors to most B2B marketing programs. I saw much more activity around features that were new last year, such as dynamic content and integration with Webinar systems and with Microsoft Dynamics CRM.

So exactly what new social and mobile features are now on my list? The previous report already included basic social capabilities including sharing marketing content to social media, tracking responses generated from social media, and monitoring social media activity. The new VEST expands that list to include:

- track social media influence: individual-level tracking mechanism that can identify the number of times a recipient has shared a promotion to social media and the number of responses generated the shared promotions. This information is part of the contact profile of the individual.

- create social media posts: deliver messages through social media, such as Twitter posts and Facebook updates. These messages can be created and then scheduled for future delivery.

- create social forms: create forms that are delivered within a third-party social media system such as Facebook.

- create social promotions: create social promotions such as contests, polls, ratings, etc.

- social sign-on and data capture: recipients can register using third-party social credentials, such as their Facebook ID. This gives access to information stored within the third-party social media system and allows communication through that system.

- build social profile: capture information about a specified individual by searching public information across multiple social media systems. This information includes social media handles and social activity such as posts, comments, and questions answered. The information is added to the individual profile and activity history.

The broad range of these features represents both a maturation of B2B social marketing and uncertainty about what will ultimately prove useful. We can expect more social features in the near future, although I suspect some will later be abandoned when it turns out they’re not especially effective in a B2B context.

On to mobile.  My previous list of mobile features was limited to text messaging. I’ve expanded that to add:

- mobile formats: generate Web and email versions in formats tailored to delivery on mobile devices such as smartphones and tablets.

- mobile CRM: salespeople can access the system on mobile platforms such as smartphones and tablets.

- mobile reporting: users can access reports on mobile platforms such as smartphones and tablets.

- mobile administration: users can set up campaigns and create content on mobile platforms such as smartphones and tablets.

Only the first of these, mobile formats, is about delivering marketing messages. The others are all about marketers and salespeople accessing the system on their own mobile devices. That’s clearly the current focus on mobile marketing automation, although it’s safe to expect more mobile marketing in the future – such as location-based promotions, which are notably absent so far.

I also added three entries in other categories. These were:

- app marketplace: the vendor has a formal app marketplace that lets third party applications connect to its product without custom integration.

- real time recommendations: rules and/or predictive models can recommend the best treatment for a customer as an interaction takes place within system-managed content such as a Web page.

- real time interactions: rules and/or predictive models can recommend the best treatment for a customer as an interaction takes place within an external platform such as a call center or Web site. This requires features to collect information about the interaction from the external platform, to match this information against the system's own database of contacts profiles and history, to make recommendation using the available information, and to deliver the recommendation to the external platform. .

These features all expand the scope of B2B marketing automation, mostly be connecting it with other systems. In one sense that's the opposite of the previous new entries, which were about adding features to marketing automation itself.  But both approaches aim to place marketing automation at the center of a company’s customer management infrastructure. Since other products, including CRM and Web sites, are also reaching for that position, we’ll see how widely these features get adopted. My sense is they’ll be more successful at small companies, where the labor savings of a unified system are most important because technology resources are most constrained.

None of the features I’ve added are currently available in more than a handful of systems.  Some may not yet be present in any. Few marketers this year will choose a system primarily because these particular features are present.  But we'll find over time which are really important.

Read More