Customer Service: Cost Reduction or Revenue Generation?

Bill Schmarzo By Bill Schmarzo November 18, 2013

Note: for those who have the book “Big Data: Understanding How Data Powers Big Business,” you should print off this blog and insert it into Chapter 10: Solution Engineering. This blog provides another example of “Solution Engineering Tomorrow’s Business Solutions.”

I’ve been involved in several exercises over the past few months with organizations looking to leverage big data to drive down their customer service and support costs. Makes logical sense. Couple the data you have about your customers’ purchases (what products they have bought, how long ago they bought them, how they might be using the products) with real-time insights from website or mobile app customer interactions. This data includes what terms they are searching on, what pages they are visiting in what order, and how long they spend on what pages.

This will allow you to better predict what support information the customer might be seeking. You’ll then be able to present recommendations to the customer that can best address their needs. We could tag the recommendations to see which ones the customers accepted, as well as the corresponding results, so that we can be continuously fine-tuning our prediction and recommendation engines. Then, if the customer does need to call the service center for more help, the service center representative would have relevant information that they could use to more quickly address the customer’s request and move on to the next call.

The business initiative in this example is leveraging big data to minimize the time that service center reps spend with a customer in order to maximize the number of support calls per call center rep. Yep, this makes logical sense.

But what if this is the wrong business initiative? What if the business initiative shouldn’t be to minimize the amount of time a service center rep spends with a customer? Instead, what if the business initiative focused on maximizing the data and insight gleaned from every individual customer interaction in order to reduce churn, improve customer lifetime value, increase likelihood to recommend and drive customer referrals and advocacy? What if our customer service organization is focused on the wrong business initiative?

Really Know What Problem You Are Trying To Solve…Really

Big data can be used against any number of business initiatives. That is both the blessing and curse of big data – deciding upon which business initiative to focus big data. Pick the wrong business initiative and you are likely to end up with a less than optimal outcome.

I think that’s the issue that many service center organizations need to wrestle with today – that instead of focusing on driving down customer service costs, maybe they should instead be focused on driving customer value and, ultimately creating more profitable, “stickier” customer relationships.

There is a large number of anecdotal data that highlights the value of a satisfied customer (or the converse, the cost of a dissatisfied customer). Here is a partial list of some of these factoids:

  • 68% of customer defection takes place because customers feel poorly treated. (Source: TARP)
  • It can cost five times more to buy new customers than retain existing ones. (Source: TARP)
  • For every customer who bothers to complain, 26 other customers remain silent. (Source: Lee Resource Inc.)
  • It takes 12 positive service incidents to make up for 1 negative incident. (Source: Lee Resource Inc.)
  • The average “wronged customer” will tell 8-l6 people about it. Over 20% will tell more than 20. (Source: Lee Resource Inc.)
  • It costs 6 times more to attract a new customer than it does to keep an old one. (Source: “Understanding Customers” by Ruby Newell-Legner )
  • A typical business hears from only about 4% of its dissatisfied customers. 96% just go away and 91% will never come back. (Source: “Understanding Customers” by Ruby Newell-Legner )

On the positive side of customer relationships, we have the following factoids:

  • Happy customers who have their problems resolved will tell 4-6 people about their positive experience. (Source: the White House Office of Consumer Affairs, Washington, DC.)
  • Customer loyalty can be worth up to 10 times as much as a single purchase. (Source: the White House Office of Consumer Affairs, Washington, DC.)
  • 7 out of 10 customers will do business with you again if you resolve the complaint in their favor. (Source: “Understanding Customers” by Ruby Newell-Legner)
  • 56%-70% of the customers who complain to you will do business with you again if you resolve their problem. If they feel you acted quickly and to their satisfaction, up to 96% will do business with you again, and they will probably refer other people to you. (Source: the White House Office of Consumer Affairs, Washington, DC.)
  • 95% of complaining customers will do business with you again if you resolve the complaint instantly. (Source: Lee Resource Inc.)
  • Reducing customer defections can boost profits by 25-85%. In 73% of cases, the organization made no attempt to persuade dissatisfied customers to stay; even though 35% said that a simple apology would have prevented them from moving to the competition. (Source: NOP)

So what would be the big data ramifications of focusing our customer service organization on reducing churn, improving customer lifetime value, increasing customers’ likelihood to recommend, and driving customer referrals and advocacy? Let’s take a look at the big data implications from the perspective of the different business functions that might benefit from customer service change of focus:

  • Product Development:  Product Development might want to capture more detailed customer data about product usage, areas of customer frustration with using the product, how often and in what situations they use the product, and what they are trying to do with the product when it doesn’t perform as expected. This might be an opportunity to gain insights on the value of different product features, which could help Product Development to prioritize their product roadmap (based upon both customer satisfaction and sales and revenue potential).
  • Marketing:  Marketing might want to leverage product usage insight in order to improve customer profiling, targeting, segmentation, and campaign effectiveness. They can also use it to identify potential complementary products and services that they should be marketing to specific product usage-based customer segments. In addition, it allows them to better understand customer levels of knowledge regarding proper product usage that may impact messaging and uncover new product usage use cases that can drive new marketing initiatives.
  • Sales:  Sales might want to understand the current level of product usage within existing customers in order to identify service and product up-sell and cross-sell opportunities. They can also use it to profile the most satisfied and dissatisfied customers to improve their prospecting effectiveness and to create real-time assessments of customers who are experiencing higher-than-normal service problems that may jeopardize current sales efforts. In addition, it can be useful in allocating sales resources against those customers with highest repeat business potential given their product usage satisfaction levels.
  • Finance:  Finance might want to quantify the cost and value ramifications of customer product usage patterns or identify, rationalize and optimize the product portfolio. They also want to understand the product usage patterns of the organization’s most profitable (and least profitable) customers, and leverage changes in customer satisfaction indices to augment their monthly and quarterly revenue and profit forecasts.
  • Customer Service:  Customer Service might want to correlate customer usage patterns and propensities with product performance problems in order to educate the customer service teams on the most likely customer service issues and known solutions. They can also utilize customer usage patterns to validate customer’s current levels of satisfaction (and likelihood to recommend) with the company and the product. In addition, they can leverage social media to get customers to promote good customer service experiences that builds customer advocacy.

Again, there are lots of opportunities to apply big data to your organization’s value creation processes. However, it is essential that you make sure you are focused on the right value creation processes or business initiatives. In fact, big data, and the insights that can be gleaned from new customer, product, and operational data, could lead some organizations to rethink their value creation processes. This is particularly true if your goals are associated with reducing churn, improving customer lifetime value, increasing customer likelihood to recommend, driving customer referrals and building advocacy.

Bill Schmarzo

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  1. What are the relationship between a customer care officer and a revenue protection officer in a utility company