Archive for November 2013

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.

The Value of Big Data - Defining It Once and For All

Big Data is a huge buzz word.  Like so many "next big things", there is often misunderstanding about what it is and it's value.  This leaves many companies and industries with an open and lingering question about what they should be doing with Big Data.

However, unlike other "next big things", Big Data is not overhyped in its possibilities.  It truly opens up a tremendous frontier of business intelligence.  Unfortunately, given the misunderstandings, it is not always clear how to take advantage of it.

What is Big Data

Before answering the value of Big Data, it's worth a quick summary of what it is.  Big Data is generally thought of as the "3 Vs"; i.e., data that has

  • Volume (terabytes & beyond)
  • Velocity (streaming real time)
  • Variation (structured & unstructured).

Twitter is a great example.  It's data is very large, generated real time and unstructured (admittedly hashtags and handles provide some structure but no where near a traditional relational database).

It's also important to understand that not all "3 Vs" need to be present.  The concept of Big Data is relevant if Big Data processing technologies are needed to unlock the value of a company's data or take advantage of external data.

At a high level, Big Data processing technologies offer two key capabilities.  First, it is a novel way to store data that is especially well suited for any of the "3 Vs" with the focus of providing fast access in terms of queries and updates.  Second, it provides Map / Reduce functionality that identifies relationships between unstructured data elements and helps in building ‘keys’ or ‘indices’ that are needed for fast access and cross-data relationships.

Big Data's "4th V" - Value

It is critical to emphasize that processing Big Data is not an objective unto itself and value is not created by just implementing Big Data processing and storage.  Big Data must be directly used to help improve core objectives such as

  • Optimize offers (marketing offers, cross/up sell, product configuration)
  • Improve customer service (omnichannel, surprise & delight, recovery)
  • Predict and prevent customer churn
  • Improve inventory management / product forecasting.

Big Data can be leveraged to accomplish this and create value if used an input into any of the following business intelligence processes within your organization:

  1. Collection and maintenance of enterprise data assets
  2. Batch analytics and static decisions
  3. Real time analytics and decisions

Each process is described in more detail below as how Big Data can create value.  We've shared a Marketing related example for each but obviously there are many other operational processes that leverage business intelligence and can benefit from Big Data.

Enterprise Data Assets

By processing Big Data, intelligence can be extracted to build data assets over time which plug into existing enterprise data management solutions like customer relationship management (CRM).  These new data elements help improve performance for any process that leverages CRM data (e.g., marketing campaign management).  It is analogous to the objective of building marketing databases that contain customer profile and email address.  However, with Big Data, the profile information that is built contains detailed digital activity and social activity, sentiment and influence.  By knowing these attributes, marketing campaign target lists, channel selection and offer/content can all be improved.

Batch Analytics and Static Decisions

Batch analytics and static decisions are traditionally how companies make decisions.  Historical data is compiled and analysis is done to inform decisions like marketing mix / planning decisions.  However, with Big Data, a company can now bring in granular data about digital interactions that could be terabytes in size.  This granular, cross channel data enables much more sophisticated and accurate marketing optimization models leading to more effective marketing campaigns and resource allocation.

Real Time Analytics and Decisions

Real time decisions is the ability to intelligently engage customers and improve outcomes based on real time events. It requires processing events as they occur, combining the event with other valuable data, gaining intelligence from the data and deciding on an action to improve the customer interaction.  All of this done in real time.  Big Data opens up new data sources like granular data on digital interactions and external data like weather to feed algorithms that optimize which marketing offer to present on a customer by customer basis.

In the end, Big Data is similar to a lot of innovations.  It is a new and innovative way to improve solutions for existing core objectives like marketing offer optimization.  Perhaps, Big Data's value is not mysterious after all.

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