Archive for December 2013

Three Customer Buying Behaviors Omnichannel Should Solve

It seems like omnichannel is discussed everywhere in retail these days.  However, even with all this talk, there isn't significant content about the specific customer buying behaviors that omnichannel should solve.

So, as a way to get the discussion going on, we'll share three customer buying behaviors that omnichannel should focus on and by doing so, should generate incremental revenue for the merchant.

Definition of Omnichannel

Before we share the buying behaviors, let's ground ourselves with a definition.  Although there are several floating around, we define omnichannel as:

Omnichannel is a customer focused approach that works alongside the customer as he or she interacts with the merchant.  With omnichannel, when a customer engages in a particular channel, his or her prior interactions in other channels are known and that knowledge is used to optimize the interaction in the current channel.  Omnichannel should also leverage knowledge of one other huge "channel" - what's going on in the outside world.  This includes information like location, weather, traffic and national / local events.

Three Customer Buying Behaviors Omnichannel Should Solve

1. Buy online, pick up in store

Ok, this is definitely not new.  Many merchants, like The Container Store, have implemented this customer solution as it delivers real value to the shopper.  The shopper gains the efficiency of the website purchase process and same day fulfillment of an in store visit while minimizing the time spent at the store.  Implementation requires in store operational changes; namely, establish a process to receive these orders from online channel, have staff available to package order, and create a customer service line for customers to pick the orders up.  In addition, the merchant must work through supply chain and inventory management requirements in order to ensure items bought online are available at the local store.

2. Right offer, right time, right place

As the shopper fluidly moves back and forth and across channels, he or she seeks information during the buying process.  This includes marketing offers, as a way to identify saving opportunities, but it must be relevant and timely.

Ideally, a merchant would want a 360 degree view of all the shopper activity and be able to uniquely identify the shopper at each and every channel interaction.  This would enable the merchant to best select which offer to present and ensure consistency across channels.

However, the 360 degree view will not be available in all cases and for every merchant.  Fortunately, it's not needed to optimize marketing offers.  Classification algorithms coupled with real time decision capabilities can take whatever data is known at the channel interaction (e.g., customer transaction history, mobile identifier, IP address, profile data) and assign the customer to a micro segment.  Optimization algorithms will decide which offers are best for each micro segment. Again, knowing exactly who the customer isn't necessary because segment specific offers will perform far better than a "one size fits all" approach.

 3. Research one in channel, buy in another

More frequently, shoppers research items online and then go to a store to make the actual purchase - i.e., webrooming.   However, this behavior also works in the opposite direction where a shopper may touch, feel and try on a product in a store and then go home to shop online for the best deal - i.e., showrooming.

In the online to offline scenario, the merchant would ideally want to identify the customer as he or she walks into the store and greet the shopper with a message - e.g., "the product you've researched online is sold in aisle 10, here's 5% off coupon and you should consider purchasing this additional item with it".

To execute tactics like this, the merchant must possess exact knowledge and tracking of the customer across channels.  The Loyalty ID of a merchant's loyalty program is the best way to enable this.  Of course, the merchant must motivate the shopper through the loyalty program to "mark" themselves along his or her buying process.  In addition, real time decision capabilities are necessary in order to know when a shopper is interacting with a channel and intelligently decide the optimal action to take based on prior interactions.


What do you think?  Are there other customer buying behaviors omnichannel should solve?

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.