HubSpot's Latest Marketing Software Sends the Right Message

Poor targeting

HubSpot yesterday launched a “completely re-envisioned and rebuilt” version of its marketing system at its Inbound 2012 user conference. The main thrust of the new release was dynamic customization of emails, landing pages, and forms based on each contact’s profile and behaviors. This is a major expansion beyond HubSpot’s original focus on “inbound marketing” to attract leads.

Specific changes include:

- a new contact database that is much more flexible than the original HubSpot database, allowing access to all types of email and landing page interactions within HubSpot and to social media activities imported to the system. The new database is built with HBase, which accesses Hadoop files. More on that later.

- “smart lists”, which are rule-based definitions of contact groups whose membership is updated automatically as contact data changes. Apologies if that’s a bit jargony; it just means the lists are always current.

- “smart calls to action” which are dynamic content blocks driven by the smart lists. That is, users define which contents go to members of different lists. The blocks are stored in a library and the same block can appear within multiple emails, HubSpot landing pages, or external Web pages.  In practical terms, this means things like: users who have already downloaded one piece of content can automatically be offered something else.

- “smart forms” (do you sense a pattern?) which don't repeat questions a client has already answered. This isn’t quite true progressive profiling, which would replace questions that are answered with ones that are not.  But it removes a major annoyance.

- workflows (hah! Bet you expected “smart flows”) that are triggered by smart list-style rules and can include multiple steps with multiple actions assigned to each step. Available actions include changing contact data, sending a record to CRM, updating a lead score, setting a lifecycle stage, and changing the call to action.

- social media tracking that captures responses to system-generated social media messages within the contact database.  The responses are associated with specific individuals, so they can be used in smart lists and workflow rules.

- iPhone apps to view some reports and individual contact data

This is all good stuff, although far from revolutionary. Dynamic email content, for example, is available in 14 of the 22 systems in our VEST report. HubSpot recognizes that these are not new features but argues they’ve made them easier to use than competitors. I’m not so sure – the rule builder underlying the smart lists and workflows looks pretty much like every other rule builder, and the workflows themselves are also similar to the sequential flows in other systems.

This isn’t a criticism of HubSpot, but just a recognition that these are inherently complicated features which plenty of smart people have already tried to simplify. Radically better approaches may yet be found – I had an interesting chat about some possibilities with HubSpot co-founder Dharmesh Shah – but so far, the state of the art is what it is.

The new release also includes improved email and landing page designers, A/B testing for landing pages (not available in the entry-level version of the system, alas), enhancements to the app and service marketplaces, and expanded training services. The company said the coming year will bring enhancements to existing components including the blogging and search engine optimization applications.

What’s really important about all these changes is not whether they’re unique, but how well HubSpot has pulled them together and how it teaches its clients and resellers to use them. This is where HubSpot has always been strongest, and the vision it set out this week – of highly relevant marketing messages for each individual – is indeed advanced. (It’s also one I agree with – see this post on why the marketing funnel is dead.) If HubSpot can get marketers to focus on that sort of targeting, which is quite different from traditional campaign-oriented promotions, they can indeed have a revolutionary impact on their clients and the marketing industry.

And what about HBase? Although HubSpot didn’t talk about it in its marketing materials, switching from a conventional relational database to the Hadoop-based system is almost certainly the most radical feature of the new release. So far as I know, HubSpot is the only marketing automation system using HBase.

I discussed this a bit with HubSpot Chief Product Officer David Cancel, who joined the company when it acquired Performable, which was itself built on HBase. Cancel said HBase takes more resources than a conventional database engine but provides direct access to all details of each contact’s behavior history. One immediate benefit is that HubSpot now allows custom fields – up to 1,000, in fact – which it didn’t previously.  Ad hoc reports against the HBase data isn't available yet but is due before the end of 2012.

Longer term, I suspect HBase will make it easier to add custom objects and to deal with unstructured and semi-structured data such as Web logs and text comments. This could make HubSpot fundamentally more flexible than most B2B marketing automation systems, whose data structures are tightly linked to CRM data structures. As I mentioned last week, the main exceptions to that rule today are the high-end marketing automation products, which were built for consumer marketing applications and assume a custom data structure. Having that flexibility in product for small-to-mid-size businesses could open up some possibilities that truly do make HubSpot unique.

(Wondering about the alligator man picture?  Well, one of the sessions at the HubSpot conference said that having pictures in your blog posts increases readership, so I thought I'd give it a try.  If you want me to justify that particular image: she's getting a message she doesn't want.)

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Survey Looks for Hostility to Behavioral Targeting, and Finds It

Summary: a new survey found that most Americans oppose behavior-based Web targeting. The authors clearly had an agenda, but the industry still needs to present its side of the story.

A recent survey conducted by professors by UC/Berkeley and University of Pennsylvania concluded (to quote its full title) that “Contrary to what marketers say, Americans Reject Tailored Advertising and Three Activities That Enable It.” Is it just me, or do I detect a bit of hostility?

The headlined finding of the survey was that 66% of respondents said they did not want Web sites to show ads tailored to their interests. As eMarketer pointed out in its article on the study, this conflicts with other surveys have found consumers receptive to targeted Web ads.

The Berkeley/UPenn study claims it is more accurate because it is the first nationally representative telephone (rather than Internet-based) survey on the topic. I doubt that really had much impact on the results. As a detailed critique by the business-friendly Progress & Freedom Foundation points out, the answers were more likely influenced by the structure of the poll itself, which asked a variety of somewhat tendentious questions leading up to the final answers.

My own critique is that the questions all asked whether people wanted to see tailored ads, not whether they preferred tailored ads vs. non-tailored ads. People may have interpreted the unstated alternative as no advertising at all. In that case, their rejection had less to do with tailoring in particular than with the near-universal dislike of advertising in general.

Still, the answers I found most interesting have received relatively little attention. This was a set of three questions that found:

- 67% of Internet users agree they have “lost all control over how personal information is collected and used by companies”, but

- 58% feel “most businesses handle the personal information they collect about consumers in a proper and confidential way” and

- 54% agree that “existing laws and organizational practices provide a reasonable level of protection for consumer privacy today”.

In other words, people have finally accepted Scott McNealy’s famous advice from 1999: “You have zero privacy anyway. Get over it”.

I’d like to end the story there, if only for artistic reasons. But the survey also asked several questions about consumers’ understanding of current privacy laws, which basically found that most people think they have more protection than they really do. Another series of questions found support for laws giving consumers rights to insist that companies delete their information.

I don’t take the results too seriously because the questions didn’t indicate these would be new laws or balance the laws against reduced free content or the cost of more regulation. But they do suggest that people might support stronger regulation if they understood how poorly they are now protected. So there continues to be a real need for marketers to both do a good job of protecting consumer privacy and of educating the public and legislators about the benefits provided by easy access to consumer information.

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For Behavior Detection, Simple Triggers May Do the Trick

I was in the middle of writing last week’s post, on marketing systems that react to customers’ Web behavior, when I got a phone call from a friend at a marketing services agency who excitedly described his firm’s success with exactly such programs. Mostly this confirmed my belief that these programs are increasingly important. But it also prompted me to rethink the role of predictive modeling in these projects.

To back up just a bit, behavioral targeting is a hot topic right now in the world of Web marketing. It usually refers to systems that use customer behavior to predict which offers a visitor will find most attractive. By displaying the right offer for each person, rather than showing the same thing to everyone, average response rates can be increased significantly.

This type of behavioral targeting relies heavily on automated models that find correlations between the a relatively small amount of data and subsequent choices. Vendors like Certona and [X+1]tell me they can usually make valuable distinctions among visitors after as few as a half-dozen clicks.

At the risk of stating the obvious, this works because the system is able to track the results of making different offers. But this simple condition is not always met. The type of behavior tracking I wrote about last week—seeing which pages a visitor selected, what information they downloaded, how long they spent in different areas of the site, how often they returned, and so on—often relates to large, considered purchases. The sales cycle for these extends over many interactions as the customer educates herself, gets others involved for their opinions and approvals, speaks with sales people, and moves slowly towards a decision. A single Web visit rarely results in an offer that is rejected or accepted on the spot. Without a set of outcomes—that is, a list of offers that were accepted or rejected—predictive modeling systems don’t have anything to predict.

If your goal is to find a way to do predictive modeling, there are a couple of ways around this. One is to tie together the string of interactions and link them with the customer’s ultimate purchase decision. This can be used to estimate the value of a lead in a lead scoring system. Another solution is to make intermediate offers during each interaction, of “products” such as white papers and sales person contacts. These could be made through display ads on the Web site or something more direct like an email or phone call. The result is to give the modeling system something to predict. You have to be careful, of course, to check the impact of these offers on the customer’s ultimate purchase behavior: a phone call or email might annoy people (not to mention reminding them that you are watching). Information such as comparisons with competitors may be popular but could lead them to delay their decision or even end up purchasing something else.

Of course, predictive modeling is not an end in itself, unless you happen to sell predictive modeling software. The business issue is how to make the best use of the information about detailed Web (and other) behaviors. This information can signal something important about a customer even if it doesn’t include response to an explicit offer.

As I wrote last week, one approach to exploiting this information is to let salespeople review it and decide how to react. This is expensive but make sense where a small number of customers to monitor have been identified in advance. Where manual review is not feasible, behavior detection software including SAS Interaction Management, Unica Affinium Detect, Fair Isaac OfferPoint, Harte-Hanks Allink Agent, Eventricity and ASA Customer Opportunity Advisor can scan huge volumes of information for significant patterns. They can then either react automatically or alert a sales person to take a closer look.

The behavior detection systems monitor complex patterns over multiple interactions. These are usually defined in advance through sophisticated manual and statistical analysis. But trigger events can also be as basic as an abandoned shopping cart or search for information on pricing. These can be identified intuitively, defined in simple rules and captured with standard technology. What’s important is not that sophisticated analytics can uncover subtle relationships, but that access to detailed data exposes behavior which was previously hidden. This is what my friend on the phone found so exciting—it was like finding gold nuggets lying on ground: all you had to do was look.

That said, even simple behavior-based triggers need some technical support. A good marketer can easily think of triggers to consider: in fact, a good marketer can easily think of many more triggers than it’s practical to exploit. So a testing process, and system to support the process, is needed to determine which triggers are actually worth deploying. This involves setting up the triggers, reacting when they fire, and measuring the short- and long-term results. The process can never be fully automated because the trigger definitions themselves will come from humans who perceive new opportunities. But it should be as automated as possible so the company can test new ideas as conditions change over time.

Fortunately, the technical requirements for this sort of testing and execution are largely the same as the requirements for other types of marketing execution. This means that any good customer management system should already meet them. (Another way to look at it: if your customer management system can’t support this, you probably need a new one anyway.)

So my point, for once, is not that some cool new technology can make you rich. It’s that you can do cool new things with your existing technology that can make you rich. All you have to do is look.

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Demand Generation Systems Shift Focus to Tracking Behavior

Over the past few months, I’ve had conversations with “demand generation” software vendors including Eloqua, Vtrenz and Manticore, and been on the receiving end of a drip marketing stream from yet another (Moonray Marketing, lately renamed OfficeAutoPilot).

What struck me was that each vendor stressed its ability to give a detailed view of prospects’ activities on the company Web site (pages visited, downloads requested, time spent, etc.) The (true) claim is that this information gives a significant insight into the prospect’s state of mind: the exact issues that concern them, their current degree of interest, and which people at the prospect company were involved. Of course, the Web information is combined with conventional contact history such as emails sent and call notes to give a complete view of the customer’s situation..

Even though I’ve long known it was technically possible for companies can track my visits in such detail, I’ll admit I still find it a bit spooky. It just doesn’t seem quite sporting of them to record what I’m doing if I haven’t voluntarily identified myself by registration or logging in. But I suppose it’s not a real privacy violation. I also know that if this really bothered me, I could remove cookies on a regular basis and foil much of the tracking.

Lest I comfort myself that my personal behavior is more private, another conversation with the marketing software people at SAS reminded me that they use the excellent Web behavior tracking technology of UK-based Speed-Trap to similarly monitor consumer activities. (I originally wrote about the SAS offering, called Customer Experience Analytics, when it was launched in the UK in February 2007. It is now being offered elsewhere.) Like the demand generation systems, SAS and Speed-Trap can record anonymous visits and later connect them to personal profiles once the user is identified.

Detailed tracking of individual behavior is quite different from traditional Web analytics, which are concerned with mass statistics—which pages are viewed most often, what paths do most customers follow, which offers yield the highest response. Although the underlying technology is similar, the focus on individuals supports highly personalized marketing.

In fact, the ability of these systems to track individual behavior is what links their activity monitoring features to what I have previously considered the central feature of the demand generation systems: the ability to manage automated, multi-step contact streams. This is still a major selling point and vendors continue to make such streams more powerful and easier to use. But it no longer seems to be the focus of their presentations.

Perhaps contact streams are no longer a point of differentiation simply because so many products now have them in a reasonably mature form. But I suspect the shift reflects something more fundamental. I believe that marketers now recognize, perhaps only intuitively, that the amount of detailed, near-immediate information now available about individual customers substantially changes their business. Specifically, it makes possible more effective treatments than a small number of conventional contact streams can provide.

Conventional contact streams are relatively difficult to design, deploy and maintain. As a result, they are typically limited to a small number of key decisions. The greater volume of information now available implies a much larger number of possible decisions, so a new approach is needed.

This will still use decision rules to react as events occur. But the rules will make more subtle distinctions among events, based on the details of the events themselves and the context provided by surrounding events. This may eventually involve advanced analytics to uncover subtle relationships among events and behaviors, and to calculate the optimal response in each situation. However, those analytics are not yet in place. Until they are, human decision-makers will do a better job of integrating the relevant information and finding the best response. This is why the transformation has started with demand generation systems, which are used primarily in business-to-business situations where sales people personally manage individual customer relationships.

Over time, the focus of these systems will shift from simply capturing information and presenting it to humans, to reacting to that information automatically. The transition may be nearly imperceptible since it will employ technologies that already exist, such as recommendation engines and interaction management systems. These will gradually take over an increasing portion of the treatment decisions as they gradually improve the quality of the decisions they can make. Only when we compare today’s systems with those in place several years from now will we see how radically the situation has changed.

But the path is already clear. As increasing amounts of useful information become accessible, marketers will find tools to take advantage of it. Today, the volume is overwhelming, like oil gushing into the air from a newly drilled well. Eventually marketers will cap that well and use its stream of information invisibly but even more effectively—not wasting a single precious drop.

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Eventricity Lets Banks Buy, Not Build, Event-Based Marketing Systems

As you may recall from my posts on Unica and SAS, event-based marketing (also called behavior identification) seems to be gaining traction at long last. By coincidence, I recently found some notes I made two years about a UK-based firm named eventricity Ltd. This led to a long conversation with eventricity founder Mark Holtom, who turned out to be an industry veteran with background at NCR/Teradata and AIMS Software, where he worked on several of the pioneering projects in the field.

Eventricity, launched in 2003, is Holtom’s effort to convert the largely custom implementations he had seen elsewhere into a more packaged software product. Similar offerings do exist, from Harte-Hanks, Teradata and Conclusive Marketing (successor to Synapse Technology) as well as Unica and SAS. But those are all part of a larger product line, while eventricity offers event-based software alone.

Specifically, eventricity has two products: Timeframe event detection and Coffee event filtering. Both run on standard servers and relational databases (currently implemented on Oracle and SQL Server). This contrasts with many other event-detection systems, which use special data structures to capture event data efficiently. Scalability doesn’t seem to be an issue for eventricity: Holtom said it processes data for one million customers (500 million transactions, 28 events) in one hour on a dual processor Dell server.

One of the big challenges with event detection is defining the events themselves. Eventricity is delivered with a couple dozen basic events, such as unusually large deposit, end of a mortgage, significant birthday, first salary check, and first overdraft. These are defined with SQL statements, which imposes some limits in both complexity and end-user control. For example, although events can consider transactions during a specified time period, they cannot be use a sequence of transactions (e.g., an overdraft followed by a withdrawal). And since few marketers can write their own SQL, creation of new events takes outside help.

But users do have great flexibility once the events are built. Timeframe has a graphical interface that lets users specify parameters, such as minimum values, percentages and time intervals, which are passed through to the underlying SQL. Different parameters can be assigned to customers in different segments. Users can also give each event its own processing schedule, and can combine several events into a “super event”.

Coffee adds still more power, aimed at distilling a trickle of significant leads from the flood of raw events. This involves filters to determine which events to consider, ranking to decide which leads to handle first, and distribution to determine which channels will process them. Filters can consider event recency, rules for contact frequency, and customer type. Eligible events are ranked based on event type and processing sequence. Distribution can be based on channel capacity and channel priorities by customer segment: the highest-ranked leads are handled first.

What eventricity does not do is decide what offer should be triggered by each event. Rather, the intent is to feed the leads to call centers or account managers who will call the customer, assess the situation, and react appropriately. Several event-detection vendors share this similar approach, arguing that automated systems are too error-prone to pre-select a specific offer. Other vendors do support automated offers, arguing that automated contacts are so inexpensive that they are profitable even if the targeting is inexact. The counter-argument of the first group is that poorly targeted offers harm the customer relationship, so the true cost goes beyond the expense of sending the message itself.

What all event-detection vendors agree on is the need for speed. Timeframe cites studies showing that real time reaction to events can yield an 82% success rate, vs. 70% for response within 12 hours, 25% in 48 hours and 10% in four days. Holtom argues that the difference in results between next-day response and real time (which Timeframe does not support) is not worth the extra cost, particularly since few if any banks can share and react to events across all channels in real time .

Still, the real question is not why banks won’t put in real-time event detection systems, but why so few have bought the overnight event detection products already available. The eventricity Web site cites several cases with mouth-watering results. My own explanation has long been that most banks cannot act on the leads these systems generate: either they lack the contact management systems or cannot convince personal bankers to make the calls. Some vendors have agreed.

But Holtom and others argue the main problem is banks build their own event-detection systems rather than purchasing someone else’s. This is certainly plausible for the large institutions. Event detection looks simple. It’s the sort of project in-house IT and analytical departments would find appealing. The problem for the software vendors is that once a company builds its own system, it’s unlikely to buy an outside product: if the internal system works, there’s no need to replace it, and if it doesn’t work, well, then, the idea has been tested and failed, hasn’t it?

For the record, Holtom and other vendors argue their experience has taught them where to look for the most important events, providing better results faster than an in-house team. The most important trick is event filtering: identifying the tiny fraction of daily events that are most likely to signal productive leads. In one example Holtom cites, a company’s existing event-detection project yielded an unmanageable 660,000 leads per day, compared with a handy 16,000 for eventricity.

The vendors also argue that buying an external system is much cheaper than building one yourself. This is certainly true, but something that internal departments rarely acknowledge, and accounting systems often obscure.

Eventricity’s solution to the marketing challenge is a low-cost initial trial, which includes in-house set-up and scanning for three to five events for a three month period. Cost is 75,000 Euros, or about $110,000 at today’s pitiful exchange rate. Pricing on the actual software starts as low as $50,000 and would be about 250,000 Euros ($360,000) for a bank with one million customers. Implementation takes ten to 12 weeks. Eventricity has been sold and implemented at Banca Antonveneta in Italy, and several other trials are in various stages.

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Enough About LTV: Let's Talk Mobile

One final thought on last week’s string regarding LTV vs. product-based metrics. The precise relationship between LTV and conventional measures such as profit and cash flow is this: profit and cash flows are constraints, while LTV is what you optimize.

Now that we’ve cleared that up, I’d like to point out that today’s New York Times has not one but two articles on mobile marketing. One is on the front page of the business section (“Hollywood Loves the Tiny Screen. Advertisers Don’t.”, The New York Times, May 7, 2007, Business Day, page C1) and the other is inside (“Cellphones Tailored for Any Organization”, The New York Times, May 7, 2007, Business Day, page c7). This follows a piece last month in BusinessWeek (“The Sell-Phone Revolution”, BusinessWeek, April 23, 2007).

The BusinessWeek piece was still in the “gee-whiz, they can do ads on mobile phones” stage of thinking. The two Times pieces were a little more evolved, addressing the business challenges in mobile content and the idea of private-label cell phones for affinity groups or businesses (being offered by Sonopia).

I could note here that the private-label cell phone concept is yet another example of monetizing a customer relationship: in this case, by getting a consumer to commit to carrying your own cell phone, which then gives you a channel to beam them messages—your own and other people’s. But I think I just talked about that last week, and wouldn’t want to repeat myself (unless the topic is LTV. Have I mentioned that lately?)

So let me make another observation: the idea of private-label cell phones leads to the idea of people having more than one. The Times article mentions companies giving their phones to employees; this might easily be extended to favored customers and suppliers. I’m not sure there’s much business sense in this, although of course companies do already often provide non-branded phones to employees as regular business tools. But if some sort of revenue base evolves that makes it profitable for groups to offer phones to consumers for next to nothing, I can certainly see consumers carrying multiple phones in the same way they carry multiple credit cards.

In fact, I rather like the concept because it will break the emerging notion that cell phones are identical with their owners: each person has one phone and each phone has one person. This is almost true today but will probably be less true in the future. So it’s good for marketers to think ahead about how they’ll deal with many-to-many relationships.

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