Technological Tools Trends:

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Technological Tools Trends:

Prediction Models, Business Intelligence, Big

Data and Multichannel Communications

James Grace,

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What is Big Data?

Tema según el cronograma

• What is Big Data?

• Big data is used to describe massive volumes of both structured and unstructured data that is so large that it is challenging to process using

traditional database and software techniques. • Can Big Data be an effective prediction tool? YES:

• Big data has the potential to help companies improve operations and make faster, more intelligent decisions.

• Big Data can be used to predict results with reasonable accuracy.

• Big Data can be effectively used in scenarios with multiple variables and situations lacking certainty.

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How is Big Data relevant to

the contact center

environment?

• Using Big Data as a prediction tool:

• When dealing with larger datasets, organizations face difficulties in being able to create,

manipulate, and manage Big Data. Big Data is particularly challenging in business analytics because standard tools and procedures are not designed to search and analyze massive datasets. • Big Data’s accuracy in the prediction of desired

business outcomes:

• This data, when captured, formatted,

manipulated, stored, and analyzed can help a company gain useful insight to increase revenues, acquire or retain customers, and improve

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What Customers Expect in 2014

• A consistent experience –not necessarily to consistently reach the same person for each interaction (there is a difference!).

• The least path of resistance/effort during an interaction – ease in doing business.

• Organizations will look to address customer expectations through:

• Unified communication models • Unified view of the customer • Unified knowledge and data.

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Multichannel Customer

Expectations

• Customers channel entry:

• Customers expect the ability to enter using any channel provided by an organization, and expect the ability to change their entry preference at any time.

• Movement between channels:

• Customers expect to be able to move between channels, even within the same interaction. • Information flow between channels:

• Customers expected a seamless flow of information between channels; no repetition of information already provided.

• Customers expect a universal experience regardless of channel entry and the number of channels used.

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Multichannel Customer

Expectations

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Multichannel Customer

Expectations

0% 10% 20% 30% 40% 50% 60%

1 or 2 Channels 3 to 4 Channels 5 or More Channels

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Multichannel Access Points

• 85.2% of Contact Centers handle at least two media types.

• 32.8% handle at least three channels. • 10.2% up to 4 channels.

• 3.9% of centers answer 5 channels.

Source: Interactive Intelligence/Contact Center Pipeline, March 2013.

30.5%

20.3%

8.0%

7.0%

Channel Blending

_{Inbound and Outbound Calls}

Inbound Calls and Emails

Inbound Outbound Calls and Emails Inbound Calls and Web Chat

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Channel Growth

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Channel Growth

Source: “The US Contact Center Decision-Makers’ Guide (5th_{Edition -2012)”, ContactBabel}

Currently Using

Planning to

Implement

In Next 12 Months

Social Media Monitoring

40%

27%

Social Media Engagement

35%

26%

Web Chat

39%

27%

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Deciding Channels for

Deployment

• Research current and emerging trends in the industry. • Know what the competition is doing.

• Know the demographics and behaviors of the customer base.

• Determine the corporate strategy. • Ask your customers what they expect.

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Metrics for Multichannel

Management

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Challenges to Meaningful

Data Integration

• Fragmentation of efforts between different teams, tools and data sources across multiple channels, brands and regions.

• Political and fiscal turf protection. • Multiple sales funnels.

• Inability to identify the customer.

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Benefits to Data Integration

• Common Data Marketing Platform for reporting, analytics, targeting and media integration.

• A channel and customer view of success.

• Metrics that measure the entire customer journey – not just the parts.

• Decision-making through actionable insights.

• A common language for performance across different teams, brands and markets.

• Better use of the team’s time to focus on what matters most to their business.

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How does Big Data Support

Multichannel Deployment

& Management?

• Identify predictable customer behaviors/patterns:

• Even in situations of change/uncertainty, predictable patterns can be identified and applied to current situations.

• Aggregates data among multiple entry and exit points.

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Keys to success in

harnessing Big Data within

the Contact Center?

• From a technology perspective:

• Ability to process petabytes and exabytes of data • Use of integrated platforms to share data across

applications.

• From a Key Performance Indicator / Measurement of Success Perspective:

• Organizations ability to define what constitutes success.

• What is really important for you to know about your customers.

• More specifically, big customer data makes it incumbent on organizations to:

• Fully understand the value of the data they are capturing.

• Adopt a mindset to trust what their most valuable data tells them.

• Develop fundamentally new strategies for organizing and leveraging that data.

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Big Data for Cross Channel

Success

By visualizing customer data, patterns of behavior can be formed and conclusions drawn, and this is where the real value of Big Data starts to become apparent. Now organizations can introduce a wide variety of data to develop:

• Deep customer insights:

• Not just profiles, but insights into attitudes, behavioral habits, browse and visit patterns, engagement indices and response scores.

• Cross-channel personas:

• Brands can use deep segmentation (the greater the volume and variety of data, the deeper the potential segmentation) to create rich marketing personas that can be deployed across channels and are continuously enriched by near real-time behavioral data.

• Multifaceted measurement:

• Going beyond traditional key performance indicators, aggregated and trend data can be used to develop performance metrics across any number of categories of dynamic customer behavior, including customer

engagement, channel preferences, cross-channel response attributions, etc., informing everything from merchandise allocations to complex marketing budgeting.

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• There is no going back to the days of favorite channel marketing. Customers are not

thinking channels – they are thinking

convenience -- and convenience means any platform, any channel, at anytime.

• The very same technologies driving these customer changes are creating a Big Data trail that -- when properly collected and synthesized -- delivers invaluable insights not simply as to who your customers are, but the attitudes, motivations, habits, etc. upon which more robust engagement can be built.

• Most of what constitutes Big Data has an increasingly short shelf life; its relevance to the brand is driven either by rapidly

changing customer behavior or the brand’s incorporation of that data into larger

engagement strategies. Marketing Big Data is of value only to the extent that it is organized, useful and accessible to those who most depend on it.

Big Data Insights

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