Positioning Up in the Journey to Enhanced Customer Analytics

Senior Content Marketing Manager, Communications Sector, IBM Analytics

In my recent post at AnalyzingMedia entitled Driving Insights & Relevance in the World of the Connected Consumer, we outlined the disruptive forces that are necessitating changes to Media & Entertainment revenue and industry models and driving the need for big data analytics.

Today, I’d like to explore where organizations typically sit in the customer analytics journey and start looking at realistic solution strategies that keep you moving down the path. Let’s get started by defining the steps along the maturity curve toward delivering smarter customer experiences that add lasting business value:

  1. Integrated Information: Capture and consolidate data about consumers across touch points for insights and decisions.
  2. Predictive Insights: Understand consumer purchase behavior, preferences, motivations and interactions.
  3. Precision Marketing: Optimize messages and offers to deliver targeted communications across relevant channels.
  4. Personalized Interactions: Deliver personalized recommendations tailored to each consumer and sync messages across channels.
  5. Continuous Dialogue: Engage consumers in an ongoing dialogue with the right message at the right time and place.

We typically see that M&E organizations (including the attendees at the recent NAB show) tend to agree that they lie somewhere between steps 1 & 2 in an area we call “pre-personalization.” This means that they are generally aware of the various consumer touch point data sources that they want to access, and now they are working to “marry” them to their customer purchase behavior in order to keep driving down the path.

And how exactly are they doing that?

Let’s consider the strategy that IBM has developed to help move M&E companies along the analytics maturity path outlined above and increase the value added with each step taken toward multi-platform audience insight analytics:

  1. Shared Data: Consolidate and standardize data about audiences, content, campaigns, markets, platforms, advertising and windows to provide an integrated and trusted source for all business functions.
  2. Multi-Platform BI: Develop business intelligence capabilities that leverage insights from linear, digital and social to create a multi-platform view of audience and consumption.
  3. BI-Enhanced Core Business Functions: Integration with core business function systems business intelligence into core business functions through development of BI-enabled core processes and adoption of data governance and master data management principles.
  4. Predictive Analytics: Develop predictive analytics and scenario modeling capabilities to improve demand and inventory forecasting and optimize distribution.

As you consider this strategy and line it up to your current state, it becomes clear that as leading M&E companies improve audience insights, an enterprise-wide transformation starts to occur:

They advance from siloed to shared data. This includes, but it not limited to, marketing, ad sales, distribution and programming data all of which can be “combined” to drive more accurate insights.

They evolve from a discrete platform where different points of engagement (i.g.: Internet, TV and mobile) remain disconnected to a multi-platform approach where the different points work together in a consistent and intelligent manner.

They grow from ad-hoc analysis to enriched business intelligence as they finally assemble the talent and technology needed to look at all of their data as opposed to small parts at any given time.

Finally, they transition from reactive and predictive decision making as they can now understand their customers beyond a basic concept or general segment. They can clearly “see” them, and therefore “serve” them, as individuals. This creates a “win-win” scenario for companies and consumers.

Next, let’s examine two key areas that build an information foundation and enable media enterprise customer centricity.

The first area is called “Customer Information Innovation.”

This is where IBM enables a 360 degree view of the customer and establishes the information platform for advanced analytics. By bringing disparate data sources together to create a single view of each customer, we start to see increased customer satisfaction and decreased customer churn.  To discover if this is a good fit, bring your VPs of Data, BI and or Audience Research together (or their equivalent) and ask them questions such as:

  • What type and how many customer data sources do we have?
  • Do we have a common BI platform? If not, why not?
  • How complex is our customer or audience reporting processes?
  • Are you able to analyze customer behaviors across linear and digital platforms?
  • Are the ad sales teams satisfied with the granularity of audience information?

The second area is known as “Customer & Market Insight.” 

This is where IBM helps M&E organizations reveal deep insight into the behavior of their connected customers to enhance the value of media products and services. When enterprise data is brought together with unstructured data for real-time predictive and social analytics, we see benefits along the content delivery value chain such as a deep understanding of audience sentiment, the ability to anticipate customer behavior and offer real time incentives to accelerate offer acceptance.  Like we did above, bring your VPs of Marketing, Analytics, Digital Media, Customer Care and or IT Business Operations together and discuss the following:

  • Do you believe social conversations contain valuable insights about our content and services?
  • Is our marketing spend a big portion of our costs?  If so, is it being used effectively? If not, why not?
  • Do we use segmentation, targeting, and response modeling in our campaigns?
  • Do we leverage audience behavioral profiling to understand demand for our content, optimize resources, or drive personalization?

These questions are meant both as a measurement of where you reside in the journey and as a way of identifying a “do-able” starting point as you take the plunge into the world of big data analytics. That starting point, if you haven’t already identified it, is the single most important clue as to the nature of the exciting evolution that now lies before you.

In my next post, we’ll dig into these two key areas of customer centricity to understand how they directly apply to different types of M&E companies, dive into the specific use cases and carefully consider a few different case studies.

In the meantime, watch the video below as I describe more about why and how organizations are embarking on this journey, and please feel free to submit in the comments below the questions you’ve been using to challenge your internal teams as you journey together down the path towards generating actionable customer analytics.