Tag Archives: CSP

Data: The Unsung Hero (or Villain) of every Communications Service Provider

The faceless hero of CSPs: Data

The faceless hero of CSPs: Data

Analyzing current business trends helps illustrate how difficult and complex the Communication Service Provider business environment has become. CSPs face many challenges. Clients expect high quality, affordable content that can move between devices with minimum advertising or privacy concerns. To illustrate this phenomenon, here are a few recent examples:

  • Apple is working with Comcast/NBC Universal on a new converged offering
  • Vodafone purchased the Spanish cable operator, Ono, having to quickly separate the wireless customers from the cable ones and cross-sell existing products
  • Net neutrality has been scuttled in the US and upheld in the EU so now a US CSP can give preferential bandwidth to content providers, generating higher margins
  • Microsoft’s Xbox community collects terabytes of data every day making effective use, storage and disposal based on local data retention regulation a challenge
  • Expensive 4G LTE infrastructure investment by operators such as Reliance is bringing streaming content to tens of millions of new consumers

To quickly capitalize on “new” (often old, but unknown) data sources, there has to be a common understanding of:

  • Where the data is
  • What state it is in
  • What it means
  • What volume and attributes are required to accommodate a one-off project vs. a recurring one

When a multitude of departments request data for analytical projects with their one-off, IT-unsanctioned on-premise or cloud applications, how will you go about it? The average European operator has between 400 and 1,500 (known) applications. Imagine what the unknown count is.

A European operator with 20-30 million subscribers incurs an average of $3 million per month due to unpaid invoices. This often results from incorrect or incomplete contact information. Imagine how much you would have to add for lost productivity efforts, including gathering, re-formatting, enriching, checking and sending  invoices. And this does not even account for late invoice payments or extended incorrect credit terms.

Think about all the wrong long-term conclusions that are being drawn from this wrong data. This single data problem creates indirect cost in excess of three times the initial, direct impact of unpaid invoices.

Want to fix your data and overcome the accelerating cost of change? Involve your marketing, CEM, strategy, finance and sales leaders to help them understand data’s impact on the bottom line.

Disclaimer: Recommendations and illustrations contained in this post are estimates only and are based entirely upon information provided by the prospective customer and on our observations and benchmarks. While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warranty or representation of success, either express or implied, is made.

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Posted in Business Impact / Benefits, Business/IT Collaboration, Data Governance, Data Integration, Data Quality, Operational Efficiency | Tagged , , , , | Comments Off

Understand Customer Intentions To Manage The Experience

I recently had a lengthy conversation with a business executive of a European telco.  His biggest concern was to not only understand the motivations and related characteristics of consumers but to accomplish this insight much faster than before.  Given available resources and current priorities this is something unattainable for many operators.

Unlike a few years ago – remember the time before iPad – his organization today is awash with data points from millions of devices, hundreds of device types and many applications.

What will he do next?

What will he do next?

One way for him to understand consumer motivation; and therefore intentions, is to get a better view of a user’s network and all related interactions and transactions.  This includes his family household, friends and business network (also a type of household).  The purpose of householding is to capture social and commercial relationships in a grouping of individuals (or businesses or both mixed together) in order to identify patterns (context), which can be exploited to better serve a customer a new individual product or bundle upsell, to push relevant apps, audio and video content.

Let’s add another layer of complexity by understanding not only who a subscriber is, who he knows and how often he interacts with these contacts and the services he has access to via one or more devices but also where he physically is at the moment he interacts.  You may also combine this with customer service and (summarized) network performance data to understand who is high-value, high-overhead and/or high in customer experience.  Most importantly, you will also be able to assess who will do what next and why.

Some of you may be thinking “Oh gosh, the next NSA program in the making”.   Well, it may sound like it but the reality is that this data is out there today, available and interpretable if cleaned up, structured and linked and served in real time.  Not only do data quality, ETL, analytical and master data systems provide the data backbone for this reality but process-based systems dealing with the systematic real-time engagement of consumers are the tool to make it actionable.  If you add some sort of privacy rules using database or application-level masking technologies, most of us would feel more comfortable about this proposition.

This may feel like a massive project but as many things in IT life; it depends on how you scope it.  I am a big fan of incremental mastering of increasingly more attributes of certain customer segments, business units, geographies, where lessons learnt can be replicated over and over to scale.  Moreover, I am a big fan of figuring out what you are trying to achieve before even attempting to tackle it.

The beauty behind a “small” data backbone – more about “small data” in a future post – is that if a certain concept does not pan out in terms of effort or result, you have just wasted a small pile of cash instead of the $2 million for a complete throw-away.  For example: if you initially decided that the central lynch pin in your household hub & spoke is the person, who owns the most contracts with you rather than the person who pays the bills every month or who has the largest average monthly bill, moving to an alternative perspective does not impact all services, all departments and all clients.  Nevertheless, the role of each user in the network must be defined over time to achieve context, i.e. who is a contract signee, who is a payer, who is a user, who is an influencer, who is an employer, etc.

Why is this important to a business? It is because without the knowledge of who consumes, who pays for and who influences the purchase/change of a service/product, how can one create the right offers and target them to the right individual.

However, in order to make this initial call about household definition and scope or look at the options available and sensible, you have to look at social and cultural conventions, what you are trying to accomplish commercially and your current data set’s ability to achieve anything without a massive enrichment program.  A couple of years ago, at a Middle Eastern operator, it was very clear that the local patriarchal society dictated that the center of this hub and spoke model was the oldest, non-retired male in the household, as all contracts down to children of cousins would typically run under his name.  The goal was to capture extended family relationships more accurately and completely in order to create and sell new family-type bundles for greater market penetration and maximize usage given new bandwidth capacity.

As a parallel track aside from further rollout to other departments, customer segments and geos, you may also want to start thinking like another European operator I engaged a couple of years ago.  They were trying to outsource some data validation and enrichment to their subscribers, which allowed for a more accurate and timely capture of changes, often life-style changes (moves, marriages, new job).  The operator could then offer new bundles and roaming upsells. As a side effect, it also created a sense of empowerment and engagement in the client base.

I see bits and pieces of some of this being used when I switch on my home communication systems running broadband signal through my X-Box or set-top box into my TV using Netflix and Hulu and gaming.  Moreover, a US cable operator actively promotes a “moving” package to help make sure you do not miss a single minute of entertainment when relocating.

Every time now I switch on my TV, I get content suggested to me.  If telecommunication services would now be a bit more competitive in the US (an odd thing to say in every respect) and prices would come down to European levels, I would actually take advantage of the offer.  And then there is the log-on pop up asking me to subscribe (or throubleshoot) a channel I have already subscribed to.  Wonder who or what automated process switched that flag.

Ultimately, there cannot be a good customer experience without understanding customer intentions.  I would love to hear stories from other practitioners on what they have seen in such respect

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Posted in Business Impact / Benefits, Complex Event Processing, Customer Acquisition & Retention, Customer Services, Customers, Data Integration, Data Quality, Master Data Management, Profiling, Real-Time, Telecommunications, Vertical | Tagged , , , , , , , , , | Leave a comment