Tag Archives: retention

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

Where Is My Broadband Insurance Bundle?

As I continue to counsel insurers about master data, they all agree immediately that it is something they need to get their hands around fast.  If you ask participants in a workshop at any carrier; no matter if life, p&c, health or excess, they all raise their hands when I ask, “Do you have broadband bundle at home for internet, voice and TV as well as wireless voice and data?”, followed by “Would you want your company to be the insurance version of this?”

Buying insurance like broadband

Buying insurance like broadband

Now let me be clear; while communication service providers offer very sophisticated bundles, they are also still grappling with a comprehensive view of a client across all services (data, voice, text, residential, business, international, TV, mobile, etc.) each of their touch points (website, call center, local store).  They are also miles away of including any sort of meaningful network data (jitter, dropped calls, failed call setups, etc.)

Similarly, my insurance investigations typically touch most of the frontline consumer (business and personal) contact points including agencies, marketing (incl. CEM & VOC) and the service center.  On all these we typically see a significant lack of productivity given that policy, billing, payments and claims systems are service line specific, while supporting functions from developing leads and underwriting to claims adjucation often handle more than one type of claim.

This lack of performance is worsened even more by the fact that campaigns have sub-optimal campaign response and conversion rates.  As touchpoint-enabling CRM applications also suffer from a lack of complete or consistent contact preference information, interactions may violate local privacy regulations. In addition, service centers may capture leads only to log them into a black box AS400 policy system to disappear.

Here again we often hear that the fix could just happen by scrubbing data before it goes into the data warehouse.  However, the data typically does not sync back to the source systems so any interaction with a client via chat, phone or face-to-face will not have real time, accurate information to execute a flawless transaction.

On the insurance IT side we also see enormous overhead; from scrubbing every database from source via staging to the analytical reporting environment every month or quarter to one-off clean up projects for the next acquired book-of-business.  For a mid-sized, regional carrier (ca. $6B net premiums written) we find an average of $13.1 million in annual benefits from a central customer hub.  This figure results in a ROI of between 600-900% depending on requirement complexity, distribution model, IT infrastructure and service lines.  This number includes some baseline revenue improvements, productivity gains and cost avoidance as well as reduction.

On the health insurance side, my clients have complained about regional data sources contributing incomplete (often driven by local process & law) and incorrect data (name, address, etc.) to untrusted reports from membership, claims and sales data warehouses.  This makes budgeting of such items like medical advice lines staffed  by nurses, sales compensation planning and even identifying high-risk members (now driven by the Affordable Care Act) a true mission impossible, which makes the life of the pricing teams challenging.

Over in the life insurers category, whole and universal life plans now encounter a situation where high value clients first faced lower than expected yields due to the low interest rate environment on top of front-loaded fees as well as the front loading of the cost of the term component.  Now, as bonds are forecast to decrease in value in the near future, publicly traded carriers will likely be forced to sell bonds before maturity to make good on term life commitments and whole life minimum yield commitments to keep policies in force.

This means that insurers need a full profile of clients as they experience life changes like a move, loss of job, a promotion or birth.   Such changes require the proper mitigation strategy, which can be employed to protect a baseline of coverage in order to maintain or improve the premium.  This can range from splitting term from whole life to using managed investment portfolio yields to temporarily pad premium shortfalls.

Overall, without a true, timely and complete picture of a client and his/her personal and professional relationships over time and what strategies were presented, considered appealing and ultimately put in force, how will margins improve?  Surely, social media data can help here but it should be a second step after mastering what is available in-house already.  What are some of your experiences how carriers have tried to collect and use core customer data?

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.  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 warrantee or representation of success, either express or implied, is made.
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Posted in B2B, Big Data, Business Impact / Benefits, Business/IT Collaboration, CIO, Customer Acquisition & Retention, Customer Services, Customers, Data Governance, Data Privacy, Data Quality, Data Warehousing, Enterprise Data Management, Governance, Risk and Compliance, Healthcare, Master Data Management, Vertical | Tagged , , , , , , , , | Leave a comment

How Is Database Archiving Different From Backup?

The utilization of backup vs. archiving software for databases is often confused in many organizations.  Customers often use backup for the purposes of archiving and vice versa.  A survey conducted by Symantec Software recently indicates that 70% of enterprises are misusing backup, recovery, and archiving practices. The survey shows that 70% of the enterprises use their backup software to implement legal holds and 25% preserve the entire backup set indefinitely. Also, the survey respondents said 45% of their backup storage is due to legal holds.   Additionally, nearly half of the enterprises surveyed are improperly using their backup and recovery software for archiving.

So what are the differences between the two types of solutions? What should each be used for and how are they complementary?

(more…)

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Posted in Application Retirement, Data Governance, Database Archiving, Governance, Risk and Compliance, Operational Efficiency | Tagged , , , , , , | 9 Comments