The Open Space Between Marketing Automation And CRM

In this era of economic growth, the Sales Enablement Trends 2015 Report by Qvidian, was sobering.  While B2B companies are growing at any cost their sales teams still struggle with quota attainment and can’t ramp up fast enough.  In an attempt to fix this, companies are automating virtually every marketing and sales process and touch points.

The result is we’re drowning in data and analyses.  We have a lot of information from Big Data and predictive analytics but scant meaningful insights that can be acted upon with consistent successful results.   The problem is data is siloed and analyzed as such. Even when broader data is analyzed, it’s never all of the data.  Some gets left out because it’s outside the organization’s four walls or it wasn’t considered relevant. That creates a condition of being directionally correct but precisely wrong.

The problem stubbornly persists because, as companies are coming to terms with, they don’t really know their buyers.

The current approach to customer alignment is to compare buyer behavior to past behavior patterns to identify which segment they are ‘most like’.  Once identified, the segment’s historical behavior pattern drives the recommended marketing or sales interactions.

According to Guy Mounier, co-founder and CEO of CustomerMatrix, a cognitive intelligence engine for CRM, “that amounts to guessing what the best next action is at every step of the customer interaction.”   While predictive analytics will give you an answer every time, it’s not always the right answer. He would rather give you a high-confidence answer even if that means sometimes not having an answer at all. When dealing with keeping and growing existing customers, trustworthy recommendations is critical to loyalty.

The problem according to Mounier is that predictive analytics works off static, point-in-time data. Meanwhile the customer is constantly evolving and changing their behavior in response to cues and triggers.   CustomerMatrix’s approach is to do real-time pattern matching based on commonalities in customer who are/have been in similar situations.  The differentiated benefit, according to Mounier, is “significantly improved accuracy in next step recommendations … by computing context in real-time and not trying to fit one-size-fits-all rules on the data.” This precision is the main driver for significantly accelerating customer identification, issue resolution, and upsell/cross-sell.

It all comes down to data quality and breadth. And when looking to enrich and maximize revenue from existing customer relationships, the key is to connect the dots across disparate sources of customer information. By scooping up every single customer action and tagging it with its patent pending “action rank” technology, CustomerMatrix quickly assesses the action’s impact on the customer relationship and lifetime value.  In analyzing the patterns in real time the solution tells you if the customer is happy or at risk of defection, and more importantly what to do about it.   From there companies can improve their customer experience by zeroing in on what processes need to be optimized or re-engineered.  Something traditional predictive analytics can’t do.

Mounier refers to his technology as “cognitive intelligence” designed to empower front line employees with “next step recommendations based on evidence”

CustomerMatrix isn’t the only vendor taking a divergent path from predictive analytics.  InsideSales, a sales acceleration platform, is on the same path. Both vendors are early entrants in the shift to an emergent view of customer behavior.   The underlying premise is that similar people behave similarly.

Emergent behavior recognizes that people act on and to environmental cues instead of being rational and predictable.  Cues directly influence and shape the actions and decisions people make.  A good example is how customers’ definition of value evolves post-purchase.  They are responding to cues and experiences which trigger them to change (or not) the bar by which value is measured.

Current approaches to understanding and acting on customer behavior follow reductionist theory.  Sales predictive analytics, journey mapping, customer experience management, lead scoring, and CRM take the approach that if we deconstruct and understand what the buyer does at every step we can increase our success rate by optimizing campaigns, websites, social selling and sales strategies.   That belief underpins the mantra of ‘be at the right channel at the right time with the right content for the right buyer’.

Most experienced customer experience professionals will agree a reductionist approach will improve results but not to the levels that are possible.   By understanding the environmental cues and how they affect buyer behavior, you’ll get much better results that are sustainable.  Up until now, deciphering emergent behavior work was hard, required unique skills mostly found in academia and few companies were willing to invest; it just doesn’t fit the silver bullet mindset.

InsideSales is the most advanced in building an emergent system.  By aggregating and analyzing a mind blogging amount of data – everything from weather and traffic patterns to contextual data points on individual personas and personal demographics with over 13 Billion sales interactions across all channels – InsideSales can tell sales people when to call a prospect, what to say, and what to offer as next steps.   Based on machine learning, the system has figured out that when it rains in Phoenix in the morning people eat lunch at their desks and are more inclined to answer their phone.  A traffic accident will result in people working later in the office even if they don’t know about the accident.

It is like the butterfly effect in chaos theory where a small change in one state can result in a large unexpected change in something totally different. We’ve all heard the saying, “a butterfly flaps its wings and a monsoon develops on the other side the world.”

Dave Elkington, CEO and Founder of InsideSales, calls his approach “interactive cognizance”.  The technology is used to not only understand target prospects along 110 different dimensions but also sales people.  He profiles sales people along 64 different categories to determine if they should be hired and how to customize gamification to match their personality so the right cues trigger the desired behavior.

InsideSales’ customer results are impressive: 30-300% increase in sales productivity and 30% uplift in revenue within 90 days.  For Elkington it’s all about sales efficiency, for Mounier it’s about customer satisfaction and cross-sell/upsell.  Both are pioneers in a new paradigm of understanding and engaging customers by addressing the space between marketing automation and CRM.

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