ai:sight > Volume 1 > Consumers are ready to chat


Consumers are ready to chat

Behavioral science, backed by artificial intelligence, can help connect with consumers.

Personalized engagement is the key to customer satisfaction and loyalty in the age of digital transformation. Enterprises like Amazon, Facebook, and Netflix have shown the world what can be achieved with intuitive content and communication that fits each customer’s habits, moods, and preferences at any point in their journey.

But many Fortune 500 enterprises today face challenges in achieving those levels of proactive and personalized engagement. Blocking their way are typically four obstacles:

  • Lack of interconnected data,
  • Decision siloes,
  • Lack of insight agility, and
  • Lack of orchestration.

Let’s start with interconnected data and emotional insight. Most major enterprises have grown to include several lines of business, each with its own set of customer data. For example, a bank has separate departments to manage credit cards, debit cards, auto loans, mortgages, digital experience, etc. Together, that data could provide a comprehensive view of the customer. Instead, it often sits in silos around the enterprise. In addition, traditional data models don’t capture dynamic influences – the context, emotions, preferences, and bias that drive customer decisions – so even connected data often provides an incomplete picture.

Enter Customer Genomics 3.0, a Fractal designed solution that resolves these challenges by integrating data harmonization with our behavioral sciences toolkit. The harmonization layer quickly stitches data from across the organization to create a single view of each customer, including products, transactions, and browsing history. Behavioral science tools work with that data and design gamified labs to identify the emotional levers behind those decisions.

With a complete, dynamic picture of the customer’s journey in hand, the next step is to craft a bespoke experience that will connect with them. Customer Genomics enables the agility for informed, on-the-spot decisions by using powerful, self-learning AI models that predict the customer’s intent and recommend the best intervention. These recommendations are used to orchestrate targeted customer communications using the company’s preferred sales, marketing, and CRM tools. Meanwhile, a feedback loop ensures the system continually learns from the results of each action and reports full explanations of the reasons behind its decisions.

By integrating these capabilities with their existing technology investments, enterprises in any industry can provide a ‘Next Best Action’ framework for engagement.

A leading healthcare insurer in the US, for example, found that smarter targeted personalization has improved patient outcomes, customer satisfaction, and engagement – while reducing the number of communications it sends.

A top US retail bank uses self-teaching algorithms to democratize data and automate decision-making across the enterprise with exhaustive reporting. This helped the users ensure the models aligned with their KPIs and metrics.

Meanwhile, a global asset management firm uses the platform to understand how its financial advisors behave under different market conditions and refine its conversations accordingly.

In the media, entertainment, and communications sector, technology is helping to minimize churn. Enterprises use intelligent models to identify the customers they’re at risk of losing and drive engagement that enriches their experience with the brand.

These examples highlight the growing importance of emotionally intelligent engagement in every sector. Combining AI with behavioral science in a modular solution provides a flexible way for enterprises to drive customer interaction at speed and scale, meeting individuals at a point where the relationship can move forward.

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Rahul Desai

Client Partner, Fractal

Rahul Desai leads the firm’s AI practice of Algorithmic Decision Making, including Customer Genomics, Fractal’s flagship solution for ‘Next Best Customer Action.’ He is interested in disrupting the status quoto drive deep impact through contextual problem solving and data science at an enterprise scale.

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