Will New Marketing Automation Tools Let Sales Climb Back Up The Funnel?

Trends are signposts to the future: they point to where we’re headed. But the signposts are unreliable because trends are often interrupted – the classic example being the “Great Horse Manure Crisis of 1894”, when experts predicted that major cities would soon be buried beneath horse droppings. I’m beginning to suspect the much-cited trend of marketing playing a larger role deeper into the sales funnel has reached a similar peak. If the pendulum is really swinging the other way, then sales people will be taking a more active role earlier in the buying process.

This particular thought emerged quite unexpectedly during a vendor briefing yesterday. The talk had turned to the industry in general and I was ticking through my usual list of trends – external data, predictive modeling, sales enablement, advanced attribution, adtech and martech integration, local/partner marketing systems, all-in-one systems, content creation support, and of course customer data platforms. But I also had in mind another system I had just seen, MDCDOT, which gives marketing automation functions to sales people. For some reason, I suddenly saw a connection of this to external data: if vendors like NetProspex and InsideView could provide sales people with prospects and vendors like MDC provide tools to nurture those prospects, then sales people really don’t need marketing to do either of those things. Sales could then push marketing back to its traditional narrow role of generating promotional materials and doing research. That's pendulum swinging with a vengeance.

Once an idea like that pops up, supporting arguments quickly fall into place.  Integration of advertising with marketing technology potentially gives sales people another route for generating their own prospects. Advanced data enhancement and lead scoring make it easier for sales people to automate lead nurture processes without becoming marketing automation experts. All-in-one systems and customer data platforms both unify marketing and CRM technologies, making it easier to shift boundaries between marketing and sales responsibilities.  Those shifts could be permanent or vary dynamically based on fluctuations in needs, resources, and individual interests. Sales enablement tools like Velocify and Clari help salespeople pick the right treatment for each prospect, which can include sending them to automated campaigns.

In short, the conditions may be ripe for a counter-revolution: a sort of Terminator 2-style conversion where some machines defend the humans instead of trying to replace them. It even dawns on me that having marketers restrict their focus to content creation has some advantages, given how much more content is needed these days.

Let me be clear: this is a shiny new idea which could quickly lose its luster. It only applies in B2B and considered-purchase B2C relationships where actual salespeople are involved in the buying process. And it may be that most salespeople are happy to let marketers handle the lead generation and nurturing, which they never enjoyed in the first place. Or perhaps corporate management will decide it’s more effective and safer for marketing to handle those tasks, regardless of what sales people want.

On the other hand, sales departments are usually much larger, more powerful, and better funded than marketing departments, so it could be in the marketing automation vendors’ interest to serve sales people directly. This leads to the long-expected assimilation of marketing automation into CRM, a trend that has never quite happened but might finally take place.

Or maybe the future is really about machines selling to other machines, and the whole distinction between sales and marketing will no longer matter. Only time will tell. In the meantime, bear in mind that you never know whether a signpost is correct until you’ve passed it.*
__________________________________________________________________________________________
*I don’t know what that means, either, but it sounds pretty deep, don’t you think? I can definitely see the motivational poster.

New Frontiers in Data Driven Marketing

I recently gave a talk on New Frontiers in Data Driven Marketing, which managed to incorporate Barbie, Fred Astaire and Ginger Rogers, General Winfield Scott, and The Three Stooges. Let’s just say you had to be there. But even without celebrities, I think the list is worth a quick look as you start planning for next year’s marketing programs.

New Challenges

• Integrate ad tech and martech. We’ve seen this coming for some time but it’s now much more obvious as marketing automation vendors like Oracle and Adobe, display ad targeters like Bizo (now part of LinkedIn) and Demandbase, and even tag managers like Signal (formerly BrightTag) and Tealium come at the challenge from different directions. The core issue is that marketing campaigns in advertising, traditional outbound media, and new social and inbound media all target increasingly-identifiable audiences rather than anonymous cookies, site visitors, viewers, or prospect lists. This makes it more possible to work across all media to improve targeting, to coordinate messages for each individual, and to measure the incremental impact of each promotion. This, in turn, requires integrated systems to gather the necessary data in a single location, track interactions with individuals, send appropriate messages, and monitor results. Look for more integration along those lines from big platform players and for cooperation among specialized solutions as they seek to participate in the consolidated approach.

• Extract meaning from big data. Everybody loves big data but few people talk about the downside: sloshing huge buckets of information into a giant data lake means that everybody has to do their own refining before they can do anything useful. Of course, analysts have always spent a lot of time on data prep and veterans will scoff at the implication that most data warehouses are pristine. But the ease of adding new feeds to big data stores, especially of unstructured data, means that users now face a “do it yourself data quality” challenge that's much greater than before. To make things even harder, direct access to data has expanded to many business users who don’t have the data management skills or sensitivity of expert analysts. This is a problem I haven’t seen discussed very much, but you can be certain it is coming to a desktop near you.

• Translate offers across media and campaigns. All that cross-channel coordination means marketers have more ways to present the right message to each individual, which turn means each message much be available in the format of each touchpoint. “Responsive design” addresses one piece of the problem, making it easy for the same Web content to render effectively on different devices. But there are plenty of other touchpoints that responsive design doesn’t reach, including display ads, call centers, and social media. So far, most of the energy related to this issue has been spent in making it easier for a single system to send messages to multiple channels, not in automatically adjusting messages to account for different amounts of content or user mindset in a given context. This is another area that has received little attention so far, especially in terms of refinements like testing and optimization.

New Technologies

• Predictive everywhere.  Most marketers are now familiar with basic predictive modeling applications like lead scoring and content recommendations. But big data and multiplying channels offer them opportunities to do so much more – and, given the alternative of poor customer treatments, they really have no choice.  Happily, the technology to build predictive models has kept up with marketer needs, so it’s increasingly possible for automated systems to build and deploy dozens or hundreds of models with almost no marketer input. This means programs can be designed to incorporate predictive models in all kinds of treatment decisions, from content recommendations to sales call prioritization to banner ad selection. In fact, the technology in this area is probably ahead of marketers, who need to learn how to identify modeling opportunities, to structure programs to use models effectively, and to monitor model results.

• Natural language processing for unstructured data management. Natural language processing (or NLP, as the cool kids say) and unstructured data are different things and both relatively established. I’m listing them here because unstructured data must become at least semi-structured to be useful, through processes such as tagging and indexing. Doing this efficiently at big data volumes requires automated solutions, which is where NLP comes into play. There are plenty of other NLP applications, such as sentiment analysis, speech processing, data gathering, and even some slick “copy generation” methods (for example, Persado and Captora, which I described briefly last June ). But I think making sense of unstructured data is NLP’s killer app.

New Opportunities

• Mobile/local marketing. Okay, maybe not so new. But still at the frontiers, since marketers are struggling to take advantage of what’s unique about mobile systems rather than just treating them as tiny desktops. Mobile apps are one part of this, since they’re separate from regular Web sites and emails. Location- and context-aware programs are another aspect: the potential is obvious even though it’s not yet clear how to best exploit it. There are some pretty serious privacy concerns to address here, although it’s never clear whether those will be real obstacles or evaporate as customers overcome their initial surprise at how much marketers can tell about them and get back to playing Clash of Clans.

• Advanced attribution. I’m talking here about attribution based on a nearly complete view of all customer interactions with a brand: Web and email messages, of course, but also search, display, broadcast and print advertisements, in-store and near-store* interactions, purchase and service histories, social messages and networks, device telemetry, and only the NSA knows what else. Once you have all that data and have managed to link identities across different sources, you can apply some truly whiz-bang analytics to estimate the incremental impact of different messages on short- and long-term customer behaviors. This goes beyond the simplifying assumptions of first-touch, last-touch and fractional attribution approaches. If it works properly, it promises to revolutionize how marketing budgets are managed and to give a substantial business edge to companies that master it first.

• Journey mapping. Another old concept, but one that’s gaining a lot of new attention. I’ll give a shout-out to my friends at SuiteCX who have built some slick mapping tools that I never quite get around to reviewing. If I had to speculate why journey mapping is suddenly so popular, I’d guess it’s because it’s become so obvious that the traditional purchase funnel has exploded into maze of hopscotch courts, with customers leaping from one spot to the next like crickets on a frying pan. Journey mapping is one way to make sense of it all, or at least apply a bit of order to the natural chaos. It relates closely to multi-channel programs, attribution and mobile/local marketing as well, if you think about it. No wonder it’s climbing to be king of the buzz hill.

_______________________________________________________
* I just made that up.