Latest Updates: evidence-based service RSS

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  Mark Angel 10:30:21 am on January 8, 2010 | 1 | # |
  Tags: evidence-based service, predictive analytics, process

  Predictive Analytics Part 1: Optimize Your Decisions

  How exactly do you go about optimizing your business decisions? Can you determine, for example, whether it’s more advantageous to cross-sell or upsell a customer during a service interaction?

  The success of your decision depends on a variety of factors – the persona, or profile of the customer, the issue at hand and the mood of that customer at the time are a few of the critical factors. Since we live in a world of information overload, you very likely have mounds of information relating to these various factors stored somewhere in your service organization.

  Look at the persona of your customer. A single trait of this persona can help you answer the question above, to effectively identify if a cross-sell or upsell offer will be accepted.

  You must treat information like this as evidence and leverage it systematically in making your decisions. Essentially, your success means predicting the outcome based on historical trends. This is where predictive analytics is a key.

  Predictive analytics is based on techniques for symbolic regression, which creates mathematical models used to synthesize even petabytes of data into intelligible answers. They help you better understand true variables driving success. These formulas work by comparing past examples of success and failure, and then using them to predict future outcomes. Models like these can be deployed and run by business analysts, not just statisticians with PhDs.

  Such models have been successfully applied to a range of real-world scenarios:

  • Adjusting fuel mixtures for optimal engine efficiencies
  • Understanding the set of environmental and genetic attributes that correlate with breast cancer survival
  • Adjusting oxygenation levels of premature babies to increase their rate of survival

  They enable scientists and doctors to efficiently and effectively understand and capture the value from available information and use it as evidence to optimize outcomes. If predictive analytics is applied to fields like chemistry and medicine, then it can certainly be applied to customer service. In my next post, we’ll explore exactly how you can apply it effectively in your service environments.
   
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  Vikas 12:23:33 pm on December 2, 2009 | 0 | # |
  Tags: customer loyalty, customer service, customer value, evidence-based service, predictive analytics, service experience

  What Are Your Customers Worth?

  I’m a frequent business traveler who’s likely to rack up $30,000 in airline, car rental and hotel expenses each year. And, I take vacation with my family twice per year, which offers a potential value of $15,000 annually. That means over the next three years my value as a customer is approximately $150,000. I’d say I’m pretty valuable.

  So, while I’m navigating a travel vendor’s site (one that I use frequently) – looking at available fares and the cost of car rentals and hotels in the area – does that vendor know my value as a customer? It does not seem so. Do they offer me a service experience that’s tailored to my needs, based on my preferences or previous buying patterns? Nope. My experience is likely similar to all other customers, irrespective of my potential value.

  I’d love for this vendor to offer me SMS updates informing me of flight delays. When my flights are delayed, it’d be great if one of the vendor’s reps called me before I called them and offered to book an alternate route. These simple examples of a personalized service experience amount to what I refer to as delivering “WOW” through customer service. Consistently delivering this WOW-worthy service would certainly earn my loyalty as longtime customer.

  Speaking of loyalty – and by inference, the travel vendor’s marketing programs – I receive an email once a week offering me a deal for a trip that, without fail, I have no interest in taking. In fact, I have never clicked on any of their offers. My unresponsive behavior should serve as a clue. It offers ample evidence that cookie cutter offers are not working on me.

  The travel vendor should use this type of evidence, or predictive analytics, to make me a more relevant offer. For example, say I have been travelling to a particular city every three weeks for the last three months. It’s natural to assume that I’m going to continue this travel pattern in the future. So, why not use the evidence to offer me a package deal for that city?

  Few businesses truly understand just how much a particular customer is worth! And even if they are able to recognize different customer values, they fail to tailor the experience to suit the diverse needs of these customers. When it comes to courting customers, this one-size-fits-all just strategy doesn’t cut it anymore. It’s time we being asking the right questions, gathering customer data and applying the evidence intelligently. In doing this, we’ll be able to better quantify the value of our customers, offer personalized service experiences and launch more informed, effective marketing campaigns.
   

• Vikas 09:51:54 am on November 10, 2009 | 0 | # |
  Tags: customer experience management, customer relationship management, customer service, evidence-based service, social crm, Vikas Nehru

  Hype or Hyper-Measurement?

  Recently, Seth Godin posted on how we are moving into an era of “hyper-measurement.” As he explains, a journalist being fired because his or her web content isn’t drawing a lot of traffic is not going to be a newsroom novelty for much longer. In a world where, more and more, success can be measured in quantifiable metrics, why wouldn’t a publisher base business decisions on newly available evidence? It just makes sense.

  The customer service industry is no different. As my fellow blogger, Kate Leggett, points out, to truly maximize the value of their contact centers, customer service mangers must take an experimental approach towards service processes.

  For every end-to-end service process they design and deploy, managers must define a testing period during which they play around with different aspects of it and measure results against business objectives. These metrics can then be used to determine the relative effectiveness of the process – what worked and what didn’t – and update the service process, or eliminate it entirely. To ensuring ongoing optimization, no process should ever be viewed as final. Rather, managers must consider each and every service process as a prototype and resign themselves to always retesting and tweaking them.

  Delivering “evidence-based service” in this manner will help consumer-facing companies break bad habits, discover new tricks and cope with the constant shifts going on in their marketplace.

  For instance, a business introduces a new tool that captures and analyzes customer interactions in various community forums – essentially, pooling the collective input of the crowd at given time. The customer service manager applies that insight in real time to change, reprioritize and improve information presented to a customer on the self-help portion of a website or educate agents on a shift in incoming questions (depending on the level of service the company provides). Over time, they begin to see patterns in terms of what information is just chatter and what type of information is important to meeting their business objectives and their customers’ brand expectations. And, the way they use the tool – and incorporate the customer voice – becomes a highly sophisticated and beneficial to their service processes.

  The “hyper-measurement” trend, as Mr. Godin puts it, shouldn’t be ignored. It should be embraced. It offers a way for service departments, as well as publishers, to be more precise and pragmatic with their decision-making – to remain in control and one step ahead of the customer.<

   

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