October 31, 2020

Recalibrating Your Customer Analytics Strategy

Businesses must adapt their approach to analytics and AI to factor in post-pandemic buying behavior

The pandemic-induced recession is the tipping point that is forcing businesses to accelerate the digital transformation of their commercial models.  New research from the Wharton Business School in collaboration with Forbes confirms business leaders are shifting their budgets and management focus in digital channels to gain access to buyers, support virtual selling, and replace traffic and eyeballs from face-to-face trade shows, events and storefronts.

The research into the strategies and spending patterns of 352 CMOs confirmed the pandemic is accelerating efforts to make the go-to-market model more digital, data-driven, and measurable. 88% of CMOs view the pandemic as a big opportunity to change the way they reach and engage customers. This is something they have been pushing their boards to do for years and now they are actively reshaping their budgets to achieve this. 81% are increasing investment in digital technologies to improve market coverage and client engagement. And over 80% are investing more in digital channels, media, and the content to support them at a time when two thirds of companies are cutting growth budgets overall.

A major factor behind this rapid pivot to digital is the

unique nature of this pandemic-induced economic downturn. The Wharton analysis found that the current recession is historically unprecedented because of the large role digital channels are playing in terms of mitigating the impact of the downturn and shaping the response of most organizations. Overall, “digital-first” businesses have weathered the pandemic better than businesses that rely on field sales, third party and omnichannel distribution. For example, CMOs of e-commerce businesses reported revenue forecasts that are 50% more optimistic than their peers that rely on traditional sales channels or hybrid selling models

So traditional businesses have responded by accelerating their efforts to make their commercial models more digital and data driven. “Digital transformation has become a very big issue because access to customer demand is a much bigger factor in this recession compared to every past recession,” reports Raghu Iyengar, Professor of Marketing at the Wharton School of Business who led this research effort. “Traditional businesses have lost the foot traffic and access to customers in retail, trade show, and face-to face-sales channels and are pivoting to digital channels and selling models to regain that access.”

Professor Iyengar views the pressure on senior business leaders to become more digitally savvy is perhaps the only silver lining from the pandemic-induced recession.  But he also warns that businesses will face a whole new set of challenges and unintended consequences as they shift investment and focus to digital channels, advanced analytics, and AI to transform their go to market models. “Businesses will face some new challenges as they shift budgets and management attention to digital transformation and try to capitalize on new digital channels and massive new sources of customer engagement data,” warns Iyengar. This is going to force business leaders to adapt their marketing analytics and data-driven selling strategies to reflect big changes in buying behavior and effectively deal with amplified data volume, privacy, and protection issues. “Even organizations with mature analytics operations are going to have to step back and revisit the models, algorithms and data management policies they have relied upon to allocate, optimize and target growth resources because the behavior and assumptions they are built upon has changed so much.”

While the digital transformation of selling will benefit all businesses in the long term –  it will require some big changes to the way they manage, analyze, protect, and monetize data. Some of these changes are obvious. Others are not.

In particular, Professor Iyengar warns that organizations with mature analytics functions must take a step back and examine the models and algorithms they use to allocate sales resources, map marketing response curves, and personalize, target and optimize customer engagement. “The events of the past six months have led to fundamental changes in buying behavior across every industry. This means past behavior is less likely to predict future behavior,” warns Professor Iyengar. “This is a big issue because it means the sales and marketing response curves that underlie models and algorithms that drive modern selling will be less accurate or predictive.”  

Raghu offers managers specific advice to get in front of these issues and adapt their approaches. He advises business leaders to:

·        Revisit their current models and better structure their approach to test and learn. This includes making new tests to understand to what degree customer behavior has changed and explore the potential of new data sources like conversational intelligence to improve selling performance.

·        Put in place more integrated and nimble approaches to collect, analyze, manage, and protect data. “With bigger data comes bigger responsibilities,” warns Professor Iyengar. “There is going to be much more customer engagement data to manage and there will be new laws to protect that customer data. Businesses should proactively put in place more agile and robust systems for collecting, analyzing, protecting, and stewarding all that data to get in front of these issues.”

·        Evolve their use of analytics to support Emotional Quotient (EQ) applications. The rapid growth of virtual selling is creating a need for AI and analytics that inform the EQ of selling to help sales reps better understand customer sentiment, response, and relationships in the absence of face-to-face conversations. Raghu forecasts that “businesses will need to find ways to use new data from customer transcription and digital engagement platforms like Zoom or Teams to understand customer emotions and all the non-verbal elements of selling.”

·        Better understand the true cost and value of their data to direct and prioritize investment. The digital transformation of the commercial model is going to require large investments in systems to better manage, distribute, monetize, and protect investment. They’ll need to understand what that data costs and how it creates financial returns in terms of profitable growth, customer lifetime value, and risk mitigation.  This understanding of how data creates value is essential to guide their efforts to knit together the many pieces of their legacy growth technology stacks into highly productive “ecosystems” that create value through data driven algorithmic selling.

Professor Iyengar will be addressing these issues in more depth at the Wharton Customer Analytics for Growth Using Machine Learning, AI, and Big Data executive education program – which in the spirit of the changes above, is being offered online to senior sales, marketing and analytics professionals.

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