5 Trends in customer journey analytics

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5 Trends in customer journey analytics

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Build a comprehensive 360 customer profile

• Creating a truly comprehensive profile of a
  customer requires tapping into multiple data
  sources and touchpoints. These include internal,
  external and syndicated sources. A large portion
  of a profile may come via unstructured data,
  especially from sources such as social media
  data, sensor data, surveys and more. An increase
  in information sources results in a larger data
  pipeline and puts greater emphasis on data
  quality. The right technologies, people and
  partners to qualify, process and help understand
  what the data reveals is essential to
  understanding the 360-degree view of the customer
  to drive impactful business decisions.

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Big Data, Big Models Big Compute expands the
scope of analytics

• The use of analytics is exploding thanks to
  better technology cost-effective big data
  storage, more powerful analytics services and
  access to better algorithms. Organizations now
  have the ability to drive better business
  outcomes by leveraging multiple analytics
  techniques. While most companies view
  prescriptive analytics as the most advanced of
  all analytics techniques, predictive and
  diagnostic type of analytics are also powerful in
  their own right. Depending on the business model,
  predictive analytics is used for scenarios like
  setting prices or evaluating inventory. Companies
  who are looking to understand the why and how of
  a business challenge can employ the diagnostic
  approach.

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Customer Intelligence is at the forefront of
Digital Transformation

• Digital Transformation across industries requires
  organizations to deliver personalized, relevant
  content on a 247 basis to enhance customer
  experience every single time across all
  touch-points. This means that Customer
  Intelligence has to move beyond analytical
  solutions created from structured data sources to
  incorporate techniques that can derive insights
  from unstructured and semi-structured data. The
  necessity to derive comprehensive customer
  insights has accelerated with companies coming up
  with digital only business models (Ex Uber,
  AirBnB, etc.) and also traditional brick mortar
  firms embracing digital in a big way. Thanks to
  open source software, analytics as a service,
  cloud-based infrastructures, organizations have
  all the tools at their disposal to re-imagine all
  aspects of Customer Intelligence, viz.
  Segmentation Targeting, Acquisition,
  Engagement, Growth Retention.

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Edge analytics required for personalization

• A complete view of the customer is possible via
  more data from more places. Again, diversity in
  data sources represents both a significant
  challenge and opportunity for businesses seeking
  to become more digital and closer to customers.
  Segmented analytics in specific areas at the
  edge of a business such as mobile interactions
  or IoT sensors is going to become more important
  when it comes to understanding customer behavior
  and deliver a better customer experience. For
  example, automobile manufacturers will use
  in-vehicle sensor data to alert owners to
  schedule maintenance or when a warranty is about
  to expire. For a CPG company, sensor data inside
  a processing plant can be used take a machine
  offline before it breaks down, causing supply
  chain delays. Becoming better at reading edge
  data on this individualized level has obvious
  benefits for companies in any sector.

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Customer journey mapping as the foundation for
marketing analytics

• The question of who drives analytics in an
  organization, CFO, CMO or CIO, is debatable. It
  depends on the business, and there is no right
  answer. C-level buy-in for analytics is now a
  given, and the need to understand data is acute.
  While its an interdisciplinary endeavor, theres
  no question that marketing is at the forefront
  when it comes to driving analytics in an
  organization. The desire for brand and customer
  insights is compelling companies to invest more
  seriously in analytics that map the customer
  journey more completely from initial
  interactions at the point of brand awareness and
  perception to more structured engagements within
  the buying experience and measuring loyalty of
  existing customers.