Using AI, Bots, Big Data, and Analytics to Reduce Demand for Support

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If we set as an overall goal “How can we create and sustain a consistent and awesome customer experience across multiple channels & touch points?”, there are seven challenges that have vexed companies for many years. None of them is easy to tackle, and the goal might seem elusive since customer expectations continue to rise.

The power of AI, bots, Big Data, and analytics can now enable us to attack them and thereby increase sustainable revenues, realize higher margins, and sustain greater levels of customer loyalty.

Let’s consider one of these seven challenges, Reduce demand for support. Customers tell us that they would prefer not to have to bother themselves to contact your company for support, preferring that:

  • Everything was clear and correct in the first place (e.g., shipments arrived on time, discounts were calculated accurately, or links sent them where they wanted to go);
  • They could handle the issue by themselves (e.g., change their password, place an order through an app, or find out how to use the service);
  • You alert them at critical moments (e.g., when their flight is delayed, to confirm delivery, or to offer a better deal before offering to new customers).

However, despite years of efforts to “deflect” customer contacts to self-service many companies are still adding staff to handle customer issues on a 1:1 basis as they grow the top line, instead of reducing demand for support. There are five steps using AI, bots, Big Data, and analytics to tackle this first vexing challenge, especially if we frame it as “How can we increase adoption of self-service, remove the dumb things that we do, leverage contacts we want, and engage all “owners” across the company to solve root cause issues?”

Step 1 = Redefine why customers contact you as “reason codes” by capturing what they say in the opening of their phone calls (e.g., “How can I find out how much I owe?” or “Why do I keep dropping calls?”) or in their email, chat, SMS, or other channels. Instead of offering agents choices such as “Billing issue” or “Technical problem” that doesn’t tell you much, or using dropdown menus that confuse your agents, or the dreaded “Other” reason, you can lay out 30-50 reason codes in the customer’s language by analyzing recorded calls, email or chat threads, or open-ended survey fields. 

Step 2 = Calculate the costs for each of these key reason codes, not just their frequency. It’s also important to go outside of the customer support department and its budget to include “downstream costs”. Using Big Data modeling it’s now easy to collect call detail records (CDRs) or estimated handle times for the reason codes, multiplying by agent costs and the frequency to produce contact costs. You can also associate all subsequent work after the initial customer contacts as downstream costs (e.g., dispatching a technician to diagnose and fix slow Internet speeds or investigating reported billing discrepancies). It’s also best practice to assign each reason code to an “owner” whose team caused the issue the first place, not the customer support team that responds to the issue (e.g., “How can I find out how much I owe?” might be the Marketing VP or Apps development lead). Then you can engage these owners by reversing the customer support costs to them, another best practice that focuses attention!

Step 3 = Assign one of four “actions” for each reason code based on whether the issue is (a) valuable for the customer and valuable for the company (Leverage the interaction and seek more contacts like them); (b) valuable for the customer but irritating for the company (Automate the interaction using apps, web or IVR self-service, or outbound alerts); (c) irritating for the customer and for the company (Eliminate the need in the first place); or (d) irritating for the customer but valuable for the company (Simplify the issues with the goal to Eliminate them over time). Each owner will then have his or her set of reason codes to tackle with one of these actions.

Step 4 = Build actions and solutions such as (a) chat bots or “virtual agents” on your website that use AI to convert a knowledge base into a knowledge sharing system; (b) more prominently featured self-service options on the web site or offered up first in the IVR based on previous requests by the same customer (e.g., if she calls every month to obtain account balance, simply tell her when we connects in the IVR, after authentication of course!); or (c) root cause analysis using Six Sigma or other CI tools (e.g., to fix gnarly billing errors or ensuring that these actions and solutions are reducing demand and not inadvertently increasing demand).

Step 5 = Predict improvements including lower demand for Automate or Eliminate reason codes by (a) identifying the “driver” for each reason code (e.g., “How can I find out how much I owe?” is driven by the number of invoices or bills produces while “Why do I keep dropping calls?” is driven by talk time or perhaps by travel miles using GPS); (b) associating these drivers with the actions and solutions; (c) setting target demand reductions by reason code using Big Data models; and then (d) tracking and adjusting these predictions over time.

By following these five steps and using AI, bots, Big Data, and analytics you will retain more customers, contain operating costs, and increase c-sat as well as improve agent satisfaction because you will have removed “dumb contacts” or simple ones, thereby enriching their work. You will also:

  • Avoid the plague of “averages” with more precise data on a limited set of reason codes and predictions for each one;
  • Include and reduce downstream costs, not just customer support costs;
  • Engage the “owners” who are causing the issues to dig into the drivers and pursue the best action.

For more information on these challenges and the role that AI, Big Data, and analytics can play, please take a look at my first two books The Best Service is No Service1 and Your Customer Rules!2 As well as my earlier columns in CustomerThink including “How Will Analytics, AI, Big Data, and Machine Learning Replace Human Interactions?3


Notes:

1The Best Service is No Service: How to Liberate Your Customers From Customer Service, Keep Them Happy, and Control Costs Bill Price & David Jaffe (Wiley 2008). Based partly on my years as Amazon’s 1st WW VP of Customer Service, but also on “Best Service” providers around the world who have made it easier for their customers to do business with them, we proposed 7 Drivers that start with “Challenge demand for service”:

  1. “Eliminate dumb contacts”
  2. “Create engaging self-service”
  3. “Be proactive”
  4. “Make it really easy to contact your company”
  5. “Own the actions across the company”
  6. “Listen and act”
  7. “Deliver great service experiences”

2Your Customer Rules! Delivering the Me2B Experiences That Today’s Customers Demand (Wiley/Jossey-Bass 2015). Here are the 7 Customer Needs that Lead to a Winning “Me2B” Culture; each Need breaks down into a total of 39 Sub-Needs.

  1. “You know me, you remember me”
  2. “You give me choices”
  3. “You make it easy for me”
  4. “You value me”
  5. “You trust me”
  6. “You surprise me with stuff that I can’t imagine”
  7. “You help me better, you help me do more”

3https://customerthink.com/how-will-analytics-ai-big-data-and-machine-learning-replace-human-interactions/ June 15, 2017

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