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In today's content-rich world, consumers are overwhelmed by the choice available to them on TV, Video on Demand, etc. Initially this is exciting for the new consumer, but it rapidly becomes an irritating experience as, each time, they click through channel-after-channel on the EPG or wander through the VOD library.

The result - people stick to watching what they already know, and after a while begin to question whether they are getting value for their subscription money. In this situation customer retention becomes a problem, and raising ARPU similarly challenging. The fact is consumers cannot select what they don't know - so 'Search' is the wrong approach to a constantly moving EPG or large VOD library. Intelligent, personalized 'Recommendations' solves this problem, thereby improving retention and raising ARPU.

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 Constantly monitored and updated to match consumer's exact mood.
Ageing process to maintain relevance.
Creates a consumer's Unique Learned Vector containing explicit preferences, implicit preferences, mood values, likes, dislikes, etc

Essential Taste Broadening

To improve customer retention and grow ARPU, it is essential to expose customers to a wider range of content than they would normally discover for themselves.

Where the customer explicitly declares a wide range of tastes this is relatively simple to achieve, but in most cases (especially during the critical early phase of the relationship with the recommendation system) the customer is cautious/lazy and will declare a narrow range of tastes or even none. For instance, he will often start by declaring only gender, age and perhaps one taste area. In response, instead of recommending a default Top 10 list based on collaborative filters (which leaves the customer repeatedly stuck with the same recommendations until he trusts the system enough to start giving feedback), ThinkAnalytics system generates a fresh and diverse list of programs every time, using feature-based sub-genres linked to the declared profile. In this way, different programs are selected from the live EPG, including 'Long Tail' surprises or one-offs to intrigue the customer, and as a result he is encouraged to divulge more and progress his relationship through this crucial start-up phase. Without this sophisticated approach, even customers declaring a wide range of tastes risk receiving recommendations which are no more than a 'reminder service', whereas ThinkAnalytics ensures the customer is regularly offered broadened content.

Seeding the Application using Local Data

It is critical that a customer's first experience is a good one. We therefore do not use data based on other companies or countries because all markets and TV providers are different in terms of content and culture. The whole point about 1-2-1 personalization is to provide a localized, differentiated customer experience from day one, otherwise customer's will stop using the service. You need a Dutch system for Holland, a French system for France, etc, all differentiated to your customer base. Because ThinkAnalytics technology trains itself, the seed can either be created from a set of sub-genre template associations that your Marketing Department has defined, or ThinkAnalytics can automatically generate associations from focus group data that you have collected. Either way, this seed is rapidly enhanced by customers providing real feedback ratings.

Automatic Classification of your Content

All your content is automatically classified by the system. This means:

  • No ongoing reliance on external manual classification/editorial coverage services
  • No ongoing fees for external manual classification services
  • Syndicated programs can be automatically classified in a consistent way
  • One-off programs are easily classified
  • TV series can be classified by episode where appropriate
  • You retain editorial control of the automated classifications.

Business Rules control marketing bias

This ensures the system can balance strong consumer centric objectives with those of the business's marketing objectives.

Keeping Recommendations Fresh

Many successful retail recommendation systems are often criticized for having too long a memory which causes it to make out-of-date suggestions. ThinkAnalytics avoids offering suggestions based on old data by applying ageing factors. In addition, it understands time-of-day, weekday v weekend, and seasonal variations; and being a real time system, it responds instantly to customer feedback and to the constantly evolving content.

Who owns the data?

The data collected is hugely valuable as it reflects not just genre preferences but individual program likes/dislikes. This ongoing data and derived intelligence is owned and controlled by you, available only to you, stored wherever you choose and accessible in any way you wish. You therefore control your ability to differentiate yourself - a key requirement in the commercial world.

Open Application

ThinkAnalytics' technology uses a range of open, industry standard algorithms with which any commercial analytics person will be familiar. Coupled with an open, component based architecture, this ensures you are:

  • in control of the future direction of your recommendations application
  • able to select alternate analytic algorithms when necessary (thereby avoiding the limitations of a one-size fits all, black-box solution)
  • able to extend the use of important consumer learnings to any part of your organization such as marketing, retentions, package configuration, acquisition, channel marketing, etc
  • able to ensure the application meets your corporate style requirements and fits the local cultural preferences of your country and your customer base
  • able to integrate the application into all your customer touch-points.

Focus on: Content Discovery

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