Next Gen Marketing Teams: From Silos to Systems

Automation has revolutionized marketing. It has brought new insights, capabilities, and methods of engagement. It has demanded new skills, thrust us into the omni-channel universe, and opened new levels of visibility and accountability. But these are all ripples in the pond, so to speak, only the most immediate after effects of a rather large splash down. The most profound change is just beginning to be felt. Automation has introduced the notion of an enterprise customer creation process, a horizontal function that cuts across all marketing activities. Effectively implementing and managing this process requires next generation marketing teams to be much more integrated and coordinated. 
Despite its mystique as a freewheeling, creative and dynamic function, corporate marketing is in reality a deeply fragmented hierarchical organization. Specialists typically function in separate domains moving from project to project with great urgency, rarely having time to consider the big picture. The need to be highly responsive to changes in direction has created a culture adverse to structured workflows. However, as marketing automation solutions consolidate into an enterprise system, a diverse set of marketing roles, process definitions, and data structures are brought together. In response, marketers are beginning to redesign their organizations around workflows instead of activities. Rather than having social, web, advertising, content, partner, analytics, systems admin, etc. in separate organizational buckets, these roles are being reformed into cross functional teams responsible for executing entire campaigns. 
Marketing solutions are starting to be designed around a multi-disciplinary community model. Adobe’s marketing cloud offers a collective view of the campaign workflow for each member of the team and unique workspaces for the various roles in content production, campaign management, analytics, etc. Each member can see what contributions have been made and why. They can communicate in real time on key issues and how they affect the overall process. IDC expects this trend to become pervasive. Providers such as Salesforce.com, Oracle, IBM, SAP, and others are driving their solutions around a vision of the “customer facing ERP” which integrates all customer facing functions in what will most likely be a hybrid cloud for managing customer experience. The implications for organizational design will be significant and CMOs should start instilling the culture of workflow based communities as soon as possible. 

Data Analytics wins 2012 US Presidential Election

Data analytics was the big winner in the 2012 US Presidential race. In fact, 11:17 PM (US ET) November 6th was the moment data analytics went mainstream. This was when Ohio was officially projected to go to Obama. It was the ultimate validation for Nate Silver and his data analytics approach to election forecasting. To much fanfare he accurately predicted the results of the election in all 50 states without doing any of his own polling. He used sophisticated analytic models based on data from as many third party polls he could find. To this he added the secret sauce of data analytics – a keen understanding of how different types of data from different sources relate to one another in context.

His FiveThirtyEight blog drove as much as 20% of the web traffic to the New York Times website – the 6th most visited US news site on the net – leading up to the election. As a result, data analytics is officially mainstream. Any business leader at any level that does not immediately embrace its power is putting his or her career and company in jeopardy.
Data analytics works. It does not produce miracles, but it does produce results that far outperform human judgment on its own. The Obama campaign employed an army of retail data analytics wonks to beat the Romney campaign in every battleground state. They did it by applying analytic techniques proven in the supermarket industry:
  • Standardizing records: Unifying the customer (voter) database
  • Widening perspective: Combining diverse data types: demographics; buying/voting history; response by media; donation/activity by trigger (celebrity dinner), model (contest) and method (mobile); group/church  membership, social networking activity (Reddit), etc.
  • Judicious targeting: Carefully identifying the potential for influencing voters that could influence the election. Not worth targeting easily influenced voters if they don’t live in a county that can help swing a state. Not worth targeting difficult to influence voters even if they live in a critical county. This is essential for achieving impact and ROI.
  • Media mix modeling: which media channels have the greatest impact on which kinds of voters?
  • Action oriented outreach: Understanding the specifics of why and how certain people act and designing multiple outreach experiments (progressive offers, channel mix, social references, etc.) based on that.
  • Openness to innovation: data driven models may point to approaches that are counter intuitive for some decision makers. They can seem risky and mysterious. They will not be right all the time. Controlled risk is part of the evolutionary process to effectiveness. Without a tolerance for experimentation however, you will not develop a data driven culture, you will in fact kill it.

Marketers in the world’s largest high tech companies are finally acquiring the enterprise data services needed to apply data analytics to long cycle B2B customer creation processes. We are already seeing signs of how significant the impact of these new approaches to marketing and sales can be:

  • $200M EU lift based on a sophisticated solutions recommendation engine
  • 45% more subscription revenue with no increase in a multi-million dollar marketing budget
  • Tens of millions of dollars in revenue uplift from simple web behavioral changes

Embracing data driven decision making is now a matter of survival. You simply cannot win against competitors that have faster, deeper market insight. They will beat you in every stage of the customer creation process. Your marketing will be months behind, your inside sales reps will be calling customers already committed to alternatives, your field sales reps will miss opportunity after opportunity to get more revenue from existing customers. Your funnel will collapse, your pipeline will dry up, your renewable revenue will shrink, and at that point it will be hard to recover. Hyperbole, you say? In the great A/B test of who uses data analytics and who does not, stay in the B group at your peril.
IDC EAG group has done extensive research on the key ingredients needed to create the enterprise data services that are a prerequisite for data driven customer creation and has ongoing research into how to create a data driven culture. To find out more please contact Gerry Murray – gmurray(at)idc(dot)com. 

Lead Distribution Scoring – a key differentiator for B2B marketers

Lead scoring is a well established technique for marketers to translate digital responses into levels of qualification for next stage outreach. For companies with no direct sales or sales cycles of 30 days or less lead scoring methodologies can be rapidly optimized around purchase behavior. For long cycle B2B sales processes, the optimization process goes only as fast as opportunity development which for many high tech companies can be 18 months or more. This is a crucial time for B2B marketers and they need to be just as exacting in how they manage the post-lead qualification journey as they are in getting prospects to the starting line.

B2B marketers need to segment, message, time, and target their communications with their direct sales reps just like they do with external prospects and customers. In my previous blog post Six Key Table Stakes for B2B Sales and Marketing Alignment marketers were tasked with three things:

  1. Treat the sales force like a market segment
  2. Market collateral (and leads) like solutions
  3. Take an account-centric approach to lead generation 

Lead distribution scoring touches on all three. Lead distribution scoring is a second stage scoring process for marketing qualified leads that enables marketers to “get the right information to the right sales rep in the right format at the right time to move an opportunity forward.” This is IDC’s definition of Sales Enablement and is a fundamental concept that should govern how marketing markets all of its output to direct sales (leads, campaigns, collateral, etc.) The days of posting to a portal or flowing and forgetting MQLs into the CRM are over. Lead distribution scoring incorporates dimensions such as:

  • What type of rep is this contact going to? 
    • By segment 
    • By tenure
    • By region
    • By product line, etc.
  • Does the rep need many leads or a very limited number of leads? 
  • What account is the lead associated with?
  • Is the sales rep meeting with this account in the next four weeks, next two weeks?
  • How is this contact connected to others in the account? 
  • Is this contact interested in the same solution as other contacts in the account?

Using a lead distribution scoring methodology will bring sales and marketing into much more direct alignment on a one to one basis. It can be applied not only to leads but to collateral, campaign training, and more. Marketing output can be “made to order” for sales reps so that it is not only highly qualified, it is also has high immediacy and relevancy to the reps’ call sheets. If the relationship between marketing and sales so bad that accessing call sheets is a non-starter, then look for friendly reps who might be willing to give a little more to get a little more from marketing.