Saturday, September 22, 2012

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.

6 comments:

Bruce Gregoire said...

David - I have read your lead scoring posts for some time, so perhaps I have brought this question to your attention before. Q: does it make sense to use a modified RFM approach to web site browser behavior, for lead scoring. I am not schooled in this area, but as I understand from a Google Analytics guru, the cookies dropped by visitors to a site will register repeat visitors. So it would seem that a Recent, Frequent and some sort of proxy for Monetary Value would be very workable. The proxy could be time on specific pages, that correlate with probability to convert. What are your thoughts?

David Raab said...

Hi Bruce,
Yes, recency and frequency of visits to specific Web pages are important parts of most lead scoring formulas. They're usually combined with other activities, such as email opens, and with traditional static attributes such as budget, authority, need, and timing.

The greater challenge with automating the model creation is what to use as the target (dependent) variable. Leads in the marketing automation system are often not associated with the final sale, so you can't just point the modeling engine at a list of buyers vs. non-buyers. KXEN cleverly avoided this by using lead-to-contact conversion as its target because it is easier to identify. But it's not the ultimate solution because it is working only within Salesforce. That's why SetLogik intrigued me, since they did seem to working on making the connections needed for more comprehensive modeling.

Kyle Porter said...

I think your two points can actually be combined to one in the same. Internal data is just one side of the coin & the external data is vast and virtually untapped.

External data should be part of lead scoring as well. While it's important to know a prospect's visitor behavior (did they click a link, visit the site, go to the pricing page, etc?), it's equally importnat to know external information (their growth position, what technologies they use, executive changes, competitors, etc).

The future of marketing automation is not just internal behavioral data, it's that + data points (quantitative and qualitative) mined from external sources on the web.

Do you agree?

- Kyle Porter SalesLoft

David Raab said...

Hi Kyle,
Indeed, lead scoring can and should use both internal and external data. Both replace the information that in the past would have been gathered directly by salespeople.

AlisaG said...

Curious about using the KXEN product with SFDC instead of using Lead Scoring through Marketo? We currently have scoring go through Marketo but I'm about to revamp the whole model so curious if I should look at KXEN?

David Raab said...

Alisa, I did a deeper dive into the KXEN product in my October 2 post. It's pretty limited in the type of data it can read (only what's on the lead record) and only predicts lead-to-contact conversion, which may not be what you want. So it probably won't replace your Marketo scoring. On the other hand, it's free, so you could use it just to see which variables it finds are important: you might get some new ideas to use in your Marketo model.