Tag Archive for RTES

The Importance of Loyalty Programs to Retail Omnichannel

Retailers are playing catch up to consumers with omnichannel.  Many shoppers are already behaving in a seamless manner across channels either through webrooming (research online and buy in store), showrooming (research in store and buy online) or using their mobile device to do both while in store.  In other words, consumers are already embracing omnichannel.

Loyalty Programs Role in Omnichannel

For retailers, data limitations and technology complexities have slowed their ability to execute omnichannel.  The single biggest challenge to implementation is building a customer centric view that associates all activities of a shopper (e.g., transactions, web searches, store visits, mobile app usage).

The data simply doesn't exist for most retailers to build this customer centric view and the challenge remains how to "mark" the shopper as he or she interacts with them.  This goes beyond a customer just leaving a footprint.  It requires identifying the shopper while he or she is interacting with the retailer in real time in order to achieve optimal impact of omnichannel.

Although there is hype about potential new technology innovations, for the foreseeable future, the best answer to build this customer centric view is through a loyalty program.  The loyalty program provides the unique identifier to link customer activity to a shopper.

Design of Loyalty Programs for Omnichannel

In order to best deliver omnichannel, a new way of thinking about loyalty program design is required.  Traditionally, loyalty programs reward shoppers for transactions - e.g., "buy 5 and get 1 free".  The reward motivates shoppers to purchase more than he or she would have done otherwise.  This is the traditional "value of loyalty" business case for loyalty programs.

With omnichannel, loyalty program design must reward shoppers for identifying themselves while interacting with the retailer (e.g., "checking in" when entering a store, saving online research to your cart).   Essentially, the retailer puts the burden of data capture on the shopper but if the loyalty rewards are designed appropriately, he or she will do so willingly.

The business case for the omnichannel loyalty program design is built upon optimizing customer engagement in real time.  Because the retailer knows who the shopper is and his or her prior interactions, the retailer will improve closing sales leads (e.g., webrooming, showrooming), more effectively cross / upsell and entice new transactions.

It's worth noting that traditional loyalty program design can work synergistically with an omnichannel focused program.  In fact, if a traditional loyalty program exists, it accelerates implementation of the omnichannel component because core loyalty infrastructure already exists.

However, in either case, new capabilities, especially technology related, are needed to enable an omnichannel focused loyalty program (e.g., rewarding geo-location).  As an alternative to building proprietary technology, companies like Bckstgr provide these capabilities in a turn key fashion for retailers.

Once the loyalty program in place, the retailer has the platform to effectively execute omnichannel.  This coupled with real time customer engagement capabilities provides the retailer the ability to achieve the full benefits associated with omnichannel - that is, improve closing sales leads, more effectively cross / upsell and entice new transactions.

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.

 

 

What Retailers Can Learn From Airlines About Omnichannel

The financial troubles of airlines are well documented. So what can retailers learn from a historically struggling industry about how to deliver omnichannel, an innovation that has promise to drive incremental revenue and deliver a fantastic customer experience?  Well, actually quite a lot.

Airline Omnichannel Requirements

Airlines have been executing omnichannel long before the omnichannel buzz word existed.  Fulfillment of an airline's product (getting a person from point A to point B) is arguably the most complex, high touch consumer experiences for relatively frequent purchases.  After a ticket is purchased, a traveler could have 10 or more customer service interactions before the final destination is reached on issues like

  • Seat assignments
  • Upgrades
  • Special requests (e.g., meals, wheelchair)
  • Changing travel plans
  • Flight delays and cancellations
  • Buying ancillary products
  • Flight check in
  • Boarding plane
  • In flight experience
  • Lost baggage and other baggage claims.

It gets even more complicated when considering the number of channels which must be in synch with one another, including:

  • Distributors (i.e., travel agencies like American Express and Expedia)
  • Website
  • Mobile app
  • Call center
  • Email and text for proactive communications
  • Airport kiosks
  • Airport customer service reps
  • Flight attendants.

Each of these channels perform some combination of selling air travel, selling ancillary products and handling a myriad of customer service issues.

Further, a customer often interacts with multiple channels for a specific need.  For example, if a flight is delayed, the traveler may visit the website, check the mobile app, contact the call center, receive an email, look at the airport kiosk and speak with a gate agent all for the same issue.

Putting aside everyone's travel horror stories (and we all have them), airlines do a lot of things right in this challenging environment.

Airline Lessons for Retailer Omnichannel

There are many lessons a retailer can learn from airlines about omnichannel.  We'll share our four biggest insights.

1. Effective use of unique identifiers

Like other retail sectors, airlines have a unique transaction ID for purchased tickets called a passenger name record or PNR.  The PNR ensures linkage of customer service interactions across the channels and through the travel experience. In addition, loyalty frequent flier numbers connect transactions over time to customers.  The loyalty ID has been used effectively for personalization and customer life cycle management.  Leveraging these identifiers, especially loyalty IDs, can be an effective enabler to deliver omnichannel for retailers.  For example, loyalty IDs can be used to link customer research of products in digital channels with a purchase transaction in a brick & mortar store.

2. "Intelligent" middleware investments to speed cross channel deployment

Most companies have legacy technology systems that are difficult to enhance.   Airlines are no different and some have learned the hard way of not investing in "intelligent" middleware that can act as channel "brains".  Without channel "brains", new cross-channel features become a different project in each channel.  This drives significant costs and elongates timelines.  Since investment dollars are constrained, often functionality is implemented in one channel but not another.  This leads to missed revenue opportunities and poor customer experiences.  Instead with "intelligent" middleware, new functionality can be launched much more like a single deployment, enabled all at once.

3. Leveraging events for insights to trigger action

In several contexts, airlines have leveraged the occurrence of an event to take action in real time.  Events are essentially customer digital footprints that can be captured at every touch point and is usually initiated by a customer action; examples include,

  • Customer visits website
  • Customer uses mobile app
  • Customer visits store
  • Customer purchases product
  • Customer logs a complaint
  • Retailer geo-locates customer.

By applying predictive analytics and correlation techniques on events, intelligence can be extracted in real time that can be used for instantaneous decision making and subsequent actions.  For example, airlines might take several immediate actions if a flight delay causes a missed connecting flight - rebook the traveler on next available flight, ensure the bags of traveler are transferred accordingly, offer a food and hotel compensation voucher to the traveler.

4. Data management and data virtualization

Data management and access are critical to the execution of omnichannel.  Airlines have many legacy systems with each acting as a source of truth for different elements of essential data fields.  It is a natural tendency to undergo a large scale enterprise data management project to bring all of these sources of information together into one system (e.g., CRM or MDM ).  However, going down this path will significantly delay omnichannel delivery.  Instead, airlines that have enabled virtualized data stores fed from the different systems have improved time to market for critical omnichannel functionality like real time decisions and offer optimization.

Omnichannel is relatively new for retail and the sector is still trying define the opportunity.  But retail has the opportunity to learn from the lessons of airlines, an industry that has been executing omnichannel for decades.

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

How to Deliver Omnichannel Real Time Decisions

Omnichannel execution has become imperative for retailers as consumers increasingly combine their shopping activity across online, brick & mortar and mobile channels.

With omnichannel, when a customer engages in any channel, the retailer is aware of their prior interactions in other channels and uses that knowledge to optimize the interaction in the current channel.

A well executed omnichannel strategy requires real time decision capabilities - the ability to process events as they occur, combine the event with other valuable data, gain intelligence from the data and decide on an action to improve the customer interaction.  All of this done in real time.

Real Time Decision Process

 

Entrigna Blog - Omnichannel Strategy - Real Time Decisions Process

Real time decisions process

A Real Time Decision Process can be viewed as five distinct steps.

Events

Execution of omnichannel real time decisions is triggered by an event.  An event can be any number of things but is usually initiated by a customer action; examples include,

  • Customer visits website
  • Customer uses mobile app
  • Customer visits store
  • Customer calls contact center
  • Retailer geo-locates customer.

The event presents an opportunity to engage and must be captured in order to initiate the real time decision process.

Virtualized Data

Sometimes, knowledge of the event is sufficient information to take action.  More often, additional data must be leveraged to improve intelligence.  Many different types of information is potentially needed including

  • Customer profile
  • Transaction/sales history
  • Channel interaction history
  • Social activity history
  • External data like weather, traffic, national/local events.

Data virtualization is the technical process that integrates these disparate data sources in real time into a usable format.

Intelligence

Intelligence must be derived based on the event and virtualized data to determine the optimal action.  Predictive analytic capabilities are necessary and a wide range of decision frameworks must be available, including

  • Rules engine
  • Complex event processing
  • Classification / clustering
  • Optimization
  • Machine learning
  • Artificial intelligence.

The breadth of decision frameworks is necessary because different business objectives require different analytical approaches.  For example, a rules engine works great when recognizing a customer for a milestone.  Likewise, event processing is well suited for identifying potential customer dis-service scenarios.  Finally, optimization techniques work well when making decisions about which promotions to place in front of the customer.

Action

Once determined, the decision must be integrated with a customer facing channel or business process in order to impact the outcome in real time.  The types of actions should be related to achieving core objectives such as

  • Offer optimization
  • Winning/completing the sale
  • Customer lifecycle milestones
  • Customer service (cross channel coordination, surprise & delight, recovery).
Feedback

Feedback takes two forms.  First, the outcome of the action/decision is fed back to the algorithms used in the Intelligence step.  This can be done in real time for online learning algorithms or stored and leveraged in a batch mode for off line learning algorithms.

Second, data and insight from the Real Time Decision Process is fed into enterprise data management processes like customer relationship management (CRM) and customer data management (CDM).

It's important to note that real time decisions are related but separate to enterprise data management processes in which both rely on each other as inputs to one another.  As a consequence, implementation of real time decisions does not require a multi-year, multi-million dollar enterprise data management project to be complete.

Execution of real time decisions is a complex set of capabilities that include predictive analytics, data virtualization and real time decision software.  However, when done well, real time decisions delivers the full promise of omnichannel benefits.

What do you think?  We'd love to hear your thoughts.