Channel Decision 鈥� Get it Right, the First Time
Proliferation of service channels as a result of social, economic, technological and regulatory forces are driving a digital transformation that is reshaping the global services industry. The emergence of pervasive connectivity, rising mobility, more intuitive interfaces and new interaction channels and methods are changing the services landscape for product delivery, adoption, utilization and access
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The expanding interaction landscape is leading to an increase in potential customer touch-points presenting more and different opportunities for organizations to interact with their customers. This, along with the increasing sophistication and empowerment of customers, is driving a need for organizations to use new channels and use them differently. And though great opportunities can be delivered by entering new channels, many organizations have not had successful experiences. Examples exist from disappointing results due to mismanagement of a new channel to complete misjudgment of the overall channel requirements, detrimentally affecting the top-line company results.
A channel strategy designed for the customer needs of your organization is imperative to ensure channel success. Understanding your customer鈥檚 channel requirements for their different possible interactions with the organization is a crucial strategy input. The customer may use multiple channel touch-points to complete a single transaction, and organizations need to be cognizant of this in their channel strategy. Customer centricity has become a strategic imperative, and organizations need to create a channel mix capable of delivering a valued and differentiated experience at each key consumer interaction.
Through the use of sophisticated solution like 鈥淐hannel Decision Engine鈥�, organizations can make logical channel decisions based on the data science. The model seamlessly correlates the unique needs of your end-customers with varied personal attributes and their Life-Time-Value (LTV) to the organization. This is followed by a liner approach to Discover, Optimize, Migrate, Rationalize and Integrate channels for increased revenues and reduced cost to serve whilst delivering a Wow! Experience or improved Net Promoter Score (NPS).
Overview
Multiple customer touch-points and varied interactions due to heavy channel proliferation within an organization have increased rapidly over the last decade.
Between 2000 and 2011 global internet usage grew 528.1%1 ; reshaping many aspects of a consumer鈥檚 lifestyle and unique selection of service sales.
Mobile telephony too has significantly penetrated the global population. Access to mobile networks is now available to 87% of the world2 with an ever-increasing trend of smartphone adoption. Smartphone accounts for almost half (49.7%) of all mobile phones in the US, as of February 20123 . There are over 100 Million4 smartphone owners in the US with Android and Apple accounting for 3 in 4 of all smartphones.
The continuing growth of social media is also having a significant channel impact; facilitating additional potential customer touch-points. New channels that have gained traction in the last decade include blogs, online communities and social networking. Social networking accounts for 1 of every 6 minutes spent online.5 These new transactional and information channels are changing the landscape of how the consumer buys, browses and socially connects. Social networking particularly means that an organization has less direct control.
The social channels are changing the dimension of the service industry from a 鈥減ush鈥� to a 鈥減ull鈥� environment as customers reach out through multiple social channels to get the information they need. Customers increasingly expect to be able to engage with companies through community-based interactions and best practices to do this have emerged.
Crowd sourcing is one such example, where customers can collectively influence new product and service ideas. AmEx moved from its costly innovation management program to an online ideas forum (American Express Labs 鈥� Crowd Voting Platform) open to all to submit innovative ideas for its Card members and Card products, awarding a prize to the winning idea6 . First Direct Bank (UK), a community-driven 听bank that prides itself as a customer-driven bank and offers awardwinning products using cutting-edge innovation uses first direct online lab that promotes online beta-concepts for innovation, thereby reducing fixed overhead.
Figure1:US Mobile Usage Overview
Channel Failure
Guided channel recommendation (deflection and or elimination) offers significant opportunities for impactful customer interaction, but many organizations have failed to get it right, leading to huge capital loss and irreversible Customer Experience.
Inability to gauge the optimum channel mix to meet the dynamic needs of the customer can lead to an enterprise-wide catastrophe.
While an organization can explore wide options for preferred channel of interaction, it should however be understood that a direct control of customer behavior and or interactions over a desired channel can never be established. This creates an opportunity for an innovative interaction strategy that is agnostic of channels of interaction. According to a data research (2011) by Fiserv 鈥� 56% of the banks have possessed a multi-channel sales and service strategy for at least 5 years. Yet, 6 out of 10 banks are only somewhat cross-channeled or integrated in their business processes that enable a unified customer experience or 360o view of their product/brand8 .
Failure to understand a new channel and applying a traditional strategy was the mistake made by Borders 鈥� a leading retailer in Oxford Street, but its subsequent fall was said to be 鈥渟ymbolic of an unforgiving decade for those retailers that failed to develop a multi-channel proposition. Borders struggled to compete with the rise of Amazon, which undercut its prices and offered the convenience of online ordering. In 2009, it collapsed into administration.
An inappropriate application of a new channel can cause significant customer discontent. Pepsi tried reaching out to the target male audience for its AMP energy drink with a 鈥済uide to women鈥� iPhone app. The application received such negative social media publicity about its derogatory nature that it had to be withdrawn and Pepsi issued an online apology9 .
To sum it up, it makes absolute sense to understand the changing customer, their behavior, preference, requirements - past, present and future should be thoroughly analyzed and built-into the overall channel strategy. The customer鈥檚 data (including transaction data) will feed into the multi-dimensional approach as described in figure 3 鈥揗ulti-dimension Approach.
Customer is always right! Focus on Unique Needs
Effective channel management is not necessarily operating in all channels. Each touch-point is unique to the customer based on their unique need. One of the largest US Retail Banks has focused on the customer acquisition online and has accomplished 17% incremental sales through predictive chat10. Another US bank that is one of the largest credit card issuers is able to accomplish 50% reduction in cost per contact by effectively migrating their customers from phone to web chat.11 To select the optimum channel strategy these companies understood their customer needs for their products, which is a crucial strategy input.
Understanding Behavior - Preferences Per Channel
Figure 2: Channel Preferences
Classic Customer Journey
The initial phase of customer analysis focuses on a multi-dimensional view of their potent interactions across the channels correlated with their value to the company and impact on their critical experience (as described in Figure 3鈥� Problem-type identification in a channel). Insights are drawn through the problem-type identification model including but not limited to:听
These insights feed into the overall recommendation strategy covering:
Integrated User Experience
It is imperative for the companies to provide unified experience across wide channels of interaction (branches/distribution centers, contact centers, Kiosks, Digital (including Mobility) 鈥� Web Chat, Social Media and Email).
Strategic objectives focused on customer acquisition and revenue optimization through a preferred or an introductory channel should be scientifically analyzed on the performance primarily focused on the听end-users experience as one of the primary parameters.
Although there exists a variety of cheaper alternate channels like web transactions, utmost caution needs to be exercised before migrating customers to such channels. Every customer must be treated uniquely based on the unique requirements at a given point of time for personalized interaction and on an optimized channel based on data analysis
Problem-Type Identification
Impact On Customer Value
A multi-dimensional contact landscape analysis that provides a road map to reduce call volume, while maintaining / improving customer service
D1听Frequency Of Occurance
D2听Contact Type
D3听Enagement Stage
D4听Channel
D5听No. Of Contacts PerCustomer / Year
D6听Customer Type
Channel Decision Engine
The framework (see Figure 4 鈥� Channel Decision Engine) begins with correlation of an organization鈥檚 objectives with each channel of interaction in business terms. Core to this is an understanding of the customer鈥檚 channel requirements. 鈥淪ingle Source of Truth鈥� obtained from data mining/fusion using multivariate analysis forms the core of the engine before making a recommendation. The complexity of the customer channel interactions for Sales, Service or Hybrid is听determined and correlated with multi-dimensional parameters. While parameters for Service interactions are provided in the Figure 4, similar methodology is applied to the sales interactions as well. The Channel Decision Engine approaches the recommendation strategy through deep analysis of multivariate scientific data and filters the analyses through 5 phases as described, before making an optimized recommendation.
Phase 1.0:
Discover The phase focuses on establishing the existence of all possible channels of interaction irrespective of the volumes per channel. Customer鈥檚 adoption rate for all the channels are established and correlated with the values like preference, interaction-type (sales or service or both), value to organization, customer experience etc.
Phase 2.0:
Optimize This phase measures the performance of each channel by establishing the right matrices across the interaction-type 鈥� Sales, Service or Hybrid. There should be both specific sales and customer interaction targets, as a particular channel鈥檚 strategic intent may be to drive sales to other channels.
Performance against the targets should be monitored continually and used to drive the processes and system changes needed to meet the channel objectives. Online Travel Agency (OTA) Priceline.com, for example, found that name-your-price business model that allows customers to bid for airline tickets, hotel rooms, rental cars and mort- -gages, lead to improved revenue realization and help passengers save significantly. By adding this feature to its website, Priceline saved on shipping, inventory and customer service costs.
Phase 3.0:
Migrate Upon identification of the right channels and its performance, a correlation approach across the profitability, profile match and cost-to-serve is factored. This will require analyzing each channel in isolation to study operational efficiencies and associated costs. Customers can then be 鈥淕uided鈥� to the appropriate channel through focused strategies like incentives, promotions, additional services and awareness generation. Cautious measures should be implemented during the migration. Each customer-segment should be studied for channel adoption based on their expectation and perceived fear of using a new channel due to complexity or non awareness on feature usage. For example a swift web navigation with enabled features is likely to appeal to only a set of segment -Generations Z (18-22 years) and X (32-45 years) as per the North American Technographics庐 Customer Experience Online Survey, Q4 2011 (US). Therefore a sensitive approach with strong consideration to the customer segments is a must.
Phase 4.0:
Rationalize With the increased channel proliferation, eliminating a channel may be counter intuitive to some organizations but strategically this may be the right course of action. During this phase you will have identified the strategic channel goals and understood the costs to serve for each channels. Terminating under-performing channels will reduce complexity and cost, enhance revenue and reduce cost to serve whilst improving the overall experience. Metrics for each channel should be established and aligned to the overall objectives. Web Chat for example can be evaluated on incremental sales however it should be noted that such chats could be reactive, proactive, collaborative and or predictive. In some cases preemptive chats may disrupt a self-serve journey therefore cannibalizing the cost and customer experience.
Phase 5.0:
Integrate Once the right channel is identified and analyzed, it is critical to facilitate a robust integration strategy to avoid disruptive services and seamless migration. The phase focuses in integrating isolated channels to the hub for a holistic delivery that is channel agnostic.
Conclusion:
Proliferation of channels is leading to a sensational shift in interaction paradigm both for the organizations and the customers. Organizations need to stay ahead of the changing interaction ecosystem and should therefore deploy predictive techniques and data science across customer analytics, product/service offerings, weigh intrinsic value associated with each interaction across the channels and corroborate with individual satisfaction level for a personalized interaction and on a preferred channel of choice. With 鈥淨uick鈥� and 鈥淐onvenient鈥� as the new mantra for success, global organizations are evenly poised to enable innovative techniques and technologies for making each interaction a 鈥淲ow!鈥� experience that is agnostic of channels of interaction with the help of data science.
