A New Way to Engage Customers With Real Time Decisions

Most merchants miss a huge opportunity to intelligently engage in real time with their customers.  Capitalizing on this missed opportunity could deliver 3% to 5% revenue growth through increased transactions and reduction of customer churn.

Traditionally, merchants have engaged shoppers through pricing, promotion and loyalty program tactics delivered through marketing campaigns and customer service.  Highly effective marketing organizations would take the data and insight from the customer interactions to determine how best to optimize future marketing efforts (e.g., target audience, message, offers, promotion channel).

These practices are tried and true.  Improving execution against this tactics will continue to deliver results and no doubt should remain a focus area for marketing organizations, especially with access to Big Data as an input into the analytics.  However, this engagement model is static by nature with periodic batch updates.

Real time decision capabilities enable marketing organizations an additional way to engage customers.  This is based on "in the moment" intelligence that dynamically determines an optimal action to take in real time.  Although current personalization tactics are a simple example, robust real time decision capabilities go well beyond it, delivering significantly higher effectiveness.

Where Real Time Decisions Sit in the Customer Engagement Model

 

Entrigna Blog - Customer Engagement and Real Time Decisions

Role of Real Time Decisions in Customer Engagement

Whether initiated by a marketing campaign or the customer, "events" occur all the time where a customer interacts with the merchant.  A basic example of "events" is when a customer enters a merchant channel - i.e., visit website, use mobile app, enter store, call contact center.  At that moment, the merchant has the opportunity to optimize the interaction with the customer.  Desired outcomes could be any combination of

  • Close a sale that was initiated but not completed in another channel
  • Optimize offers presented to drive transactions
  • Recognize key customer lifecycle milestones
  • Deliver excellent customer service - either surprise & delight or recovery actions.

In addition, once a customer interaction reaches an outcome (e.g., purchase, loyalty accrual/redemption, customer service resolution, abandonment), the merchant has another opportunity to re-engage the customer to drive additional desirable behaviors or outcomes.  This cycle could repeat continuously.

Beyond just capturing the "event" itself, real time decision capabilities leverage additional data to help determine an optimal action.  This data could be from both internal and external sources - e.g., customer profile, transaction history, prior channel activity, weather, geo-location.  Sophisticated and flexible decision intelligence frameworks (e.g., machine learning algorithms, complex event processing, optimization techniques, rules engine) are prerequisite tools to derive intelligence (for more detail on required real time decision capabilities see How to Execute Real Time Decisions).

When you step back, real time decisions is an automated, algorithmic means to engage customers in a similar fashion that store clerks have been doing for ages.  However, because of the automation, the capabilities can pull in large amounts of data to feed "intelligence" and work in 21st century e-channels like mobile.

Tell us what you think?  We'd love to hear.

 

 

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