In this era of economic growth, the Sales Enablement Trends 2015 Report (registration required) by Qvidian was sobering. While B2B companies continue to grow at any cost, sales teams still struggle with quota attainment and can’t ramp up fast enough. Companies are trying to fix this by automating virtually every marketing process, sales process and touch point.
The result? We’re drowning in data and analyses. Big data and predictive analytics are providing a lot of information, but scant meaningful insights that can be acted on 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 it. 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 persists because companies don’t really know their buyers — a fact that they are finally coming to terms with.
Answers, But Not the Right Answers
Businesses currently approach customer alignment by comparing 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.
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 can always provide an answer, it’s not necessarily the right answer. He would rather give you a high-confidence answer even if that means sometimes not having an answer at all. When trying to keep and grow existing customers, trustworthy recommendations is critical to loyalty.
Mounier says that the problem 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 does real time pattern matching based on commonalities in customers who are or 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 significantly accelerates customer identification, issue resolution and upsell/cross-sell.
It comes down to data quality and breadth. 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 customer actions and tagging them with its patent pending “action rank” technology, CustomerMatrix quickly assesses the action’s impact on the customer relationship and lifetime value. The solution analyzes the patterns in real time to tell if the customer is happy or at risk of defection, and more importantly, what to do about it. Companies can then zero 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.”
Customer Matrix 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 experiences professionals will agree a reductionist approach improves results but not to levels that are possible. Understanding environmental cues and how they affect buyer behavior can deliver more sustainable results. Up until now, deciphering emergent behavior work was hard, required unique skills mostly found in academia, and few companies were willing to invest in it. It just doesn’t fit the silver bullet mindset so prevalent today.
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This post originally appeared on CMS Wire.
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