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Marketo Secret Sauce - Shyna Zhang
 

Marketo Secret Sauce - Shyna Zhang

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Shyna Zhang, Sr. Product Marketing Manager at Marketo, dives into Marketo's "secret sauce" and discusses how you can achieve better marketing results.

Shyna Zhang, Sr. Product Marketing Manager at Marketo, dives into Marketo's "secret sauce" and discusses how you can achieve better marketing results.

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  • A case study on modern marketing <br /> Examples from how Marketo drives our own business <br /> Examples of our innovative customers driving their businesses <br /> <br /> Covers content marketing, generating new business, marketing automation, analytics and more for B2B and consumer marketers alike <br /> Meant to inspire and educate <br /> Share best practices between the B2B and B2C worlds <br /> Not a commercial for Marketo! <br /> Don’t have to do this all at once – we built this over years <br />
  • Let’s talk about where all these targets come from. How are we generating our targets. This is a report pulled right out of our analytics, with real actual data, and It shows which channels are generating targets for us at the top of the funnel. This is a screenshot out of Marketo, so across various channels, what are the number of targets generated, what is the investment per target for that channel (and I am only counting program dollars there, so not counting people’s time). <br /> <br /> The % opps, or the % of the targets that become opportunities. The index tells us how efficient each channel is at creating opportunities. <br /> <br /> Index is simply the % opp number presented as an index versus the average. So take the inbound web channel. The 2.5 index means that targets generated from the inbound channel are 2.5 times more likely to become an opportunity as compared to the average across channels. <br /> <br /> And then days to opp is how long it take them on average, how long do I have to nurture prospects from each channel, before they become an opportunity.
  • So how do we segment at Marketo. Well, first of all, we do it across 2 primary dimensions on a regional basis. One dimension we use is the buying stage, which maps really well to our overall revenue model, both to the high level funnel stages, TOFU, MOFU and BOFU, and also happens to map to the way we segment our content, EARLY, MID, LATE and CUSTOMER. The other dimension is the buying profile, which we typically define by personas. <br /> <br /> And by the way, we’ve kept it to 2 because even moving to 3 adds an exponential level of complexity. For instance, if we have 4 buy stages and 3 profiles, that’s 12 segments. If we add a 3rd dimension to segment off of, and that dimension had 3 options, now all of a sudden your taking your original 12, and muliplying it by 3, and now instead of 12 segments, we have 36.
  • What happens after the purchase? Well, in most cases, marketers have goals for that too. But many marketers aren’t executing on them. Those goals could be to drive someone to become a repeat purchaser, to become an active user, or to be an active visitor. And in most business, retaining that customer is far more important than capturing him the first time.
  • Customer version
  • Here are out actual metrics… <br /> <br /> Focus on the very low conversion from MQL to SQL… since we are lose about MQL definition and don’t want to miss any deals…. But strict about what we pass to sales. <br /> We want that 75% SQL to Opp, since you want sales to value marketing leads… if it goes below 50%, sales doesn’t jump on SQLs as fast.
  • Majority of leads NOT sales ready. This is OK since human interaction is part of developing the relationship (nurturing). These Lead are recycled back to Target for additional nurturing until Sales Ready.
  • Need to run a lot of programs to support this! <br /> Clone is so important <br />
  • … if all they wanted were names, you could give them the phone book. <br /> They want LEADS!
  • To get data for FIT / demographic scoring, don’t ask for it on forms. You’ll get crappy data….
  • Curves example – a 35 year old man may be on the website every day, but they’re not the right fit. <br /> <br /> Different types of demographic data and behavioral data have different weights. So someone visiting the blog gets a point, a product page 5 points, and someone going to our pricing page would get 10 points. And different demographic criteria also carry different weights. <br /> <br /> And as you can see on this matrix, someone who is considered a really good fit, doesn’t have to have a high behavioral score in order to be a lead. And on the flip side, someone showing strong buying intent doesn’t have to have as good a fit score to be considered a lead. Which makes sense. <br /> <br /> And once a person reaches 100 points, now they are no longer a name, no longer a target, but they are A LEAD. <br />