The five steps required to radically rebuild CX

As we all know, the customer experience industry has been going through a major transformation in recent years. The change resembles the transformation that marketing went through following the tech crash of 2001; it marked the beginning of a shift towards data-centric marketing and the use of customer feedback technology, such as email. Fast forward to today, we are now in the midst of a new wave of change in the world of customer experience (CX). With the advent of advanced technologies like AI, we are moving away from survey-centric CX tracking towards a more data-driven approach.

As a CX professional, it's important to understand that this latest wave of change is not something to be feared, but rather embraced. By taking the right steps, we can ensure that our companies survive and thrive in this new era of CX. Let's start with the first step:

Step 1 - Become data-rich

Let's talk about the first step: the transition from a data-poor to data-rich measurement and improvement processes. It's about coverage, and you should not be happy with yours. I hear two communities talking regularly about how happy they are with their coverage: Customer Success Managers, and CX people who rely on surveys. The former are delighted with the improved loyalty of the relatively small numbers of customers they cover. The latter often think that a 20% survey response rate is perfection. If I put them into a Venn diagram, these communities look like this:

The issue here is simple: CSMs and surveys can give you generic information about the rest of your customer base but provide nothing specific. They might tell you that 7% of your customers are really unhappy with your project management or delivery lead times, but not which 7%, beyond the accounts they already cover. There is a better way! As the people at McKinsey put it:

Survey vendors' AI solutions DO NOT address this issue

I have been stunned by two recent discussions with customers who are longtime purchasers of a specific survey software vendor's product. That vendor has managed to convince them that their addition of AI to the product solves this issue. It does not. All it does is improve the reliability of the information about, and predictions relative to, and only relative to the customers who respond to survey requests. You get no information at all about any individual customer that does not respond to your survey requests. Here is the way I think about this:

AI software that generates insights based on operational data, rather than on surveys, gives you 100% coverage of, diagnostics of, and predictions for every single customer. You can understand what is going well, what is going poorly, and predict their likelihood to renew their contracts, for example. I cannot think of any other way to get this result.

Once you have identified your IT systems that contain customer data, it is time to move to the second step.

Step 2 - Eliminate data islands

Here is an illustration of what your customer data looks like at the moment, spread across your various IT systems:

My message should be clear. You already have all the necessary pieces of the customer jigsaw puzzle. Survey data is just one such piece. But what's the overall picture? Or rather, what is the overall picture like for each and every individual customer? I would like to suggest that you simply have no idea today.?You have never put all of those puzzles together and you just don't know.

Yes, the emergence of the Chief Data Officer position in many companies means that some of these data islands are already being assembled together for some purposes, but not to predict (for example) the NPS and Annual Recurring Revenue trend for each and every customer.

Get ready to forecast the weather and predict customer climate change

Here is how I envision the result of using AI software to assemble the puzzles for your customers:

Yes indeed, the road forward will look stormy for one customer and bright and sunny for another. Every customer will be somewhat different, and it is important to distinguish between weather and climate. The sum of the individual weather trends produces a climate trend. Is your company's overall customer climate trend improving or deteriorating, and which of the puzzle pieces have the biggest impact? The pieces do not all have equal weight, and here at OCX Cognition, we have been more than a little surprised to see how much influence Salesforce data has on both the weather for individual customers and the overall company climate.

Does this jigsaw / weather / climate metaphor work for you?

Yes, I was working on a jigsaw puzzle a when I thought of this metaphor. It was kind of a revelation, and indeed what seems to be a cultural difference between Switzerland and other countries where I have lived played a role. Around here, most people do not allow themselves to look at the cover of a puzzle box after opening it and spilling the pieces on the table. Over the years I have learned to avoid looking at the box when I take one from the stack of old puzzles. Makes it more interesting, I feel.

Yes, you will have your own preconceived notions about what your weather puzzle will look like for each of your customers. You will often be wrong, and the AI software will tell you why. Let's discuss that in more detail.

Step 3 - More advanced analytics

Let's discuss the importance of more advanced analytics in the radical rebuild of Customer Experience (CX). If you're in the CX field, you know that the key challenge we face is the ability to answer many more business questions beyond just "What's the score?"

Let's go way beyond remedial

It's time to take CX analytics to the next level. Currently, we rely on tracking metrics such as NPS, CSAT, Customer Effort Scores, and more, but these metrics only give us a limited understanding of the customer experience. Think about hospital surveys, for example. They might ask about the food and the friendliness of the staff, but they rarely ask about the outcome of the patient's surgery, which is what truly matters. Such research is usually designed to produce internal performance scorecards, rather than to improve what really matters to customers.

That's why McKinsey found that only 16% of CX leaders believe that surveys effectively address the root cause of performance.

What does analytic maturity look like?

Here is how we at OCX Cognition see things. The first four circles below illustrate both traditional best practice and the basics of analytics that use operational data for all customers. The following three represent what has now become possible, though very few companies are doing it.

By analyzing operational data covering all customers, we can answer crucial questions such as:

• Where should we focus our customer success resources?
• What is our ideal customer profile in terms of lifetime value?
• How can we reduce costs without negatively impacting customers?

That's in addition to financial predictions, and operational diagnostics, of course. The good news is that these questions are now easily answerable with the latest AI-based analytics. Of course, analytics are one thing, and decision-making is another, so let's go there.

Step 4 - Better decision-making

We can only be successful if the new tools drive us to make better decisions. Let me start by saying what I mean by “us”. In these rather unpredictable economic times, both boards and executive teams need reliable predictive data on which to base their strategic decisions. For such decisions to be effectively communicated, the data should be the same as that used by the various teams within your company, primarily meaning operational data.

Let's dig into one type of decision. Despite the healthy overall employment situation in the USA, we are seeing lots of companies making substantial layoffs. As a general statement, layoffs in large companies are being driven by rising interest rates. Companies have borrowed money at low to zero interest rates. They have also indicated certain future earnings ranges to investors that they find themselves unable to deliver without reducing costs.

Cost reductions are going to happen, whether rational, or irrational

All too often, boards and executive teams have no data that will allow them to determine which reductions will negatively impact customers and which will not. Guess what? They then say to themselves, ”Well, I was hired for my extensive experience. I will just trust my intuition at this point.” (I would love to be able to add a canned laughter sound here.) Disaster often follows; disaster for customers in particular.

With our CX thinking of the last 20 years, we have excellent past-facing data for a sample of our customers. With all due respect, no board or executive leadership team is going to use such data to plan cost reductions. We have to do better, and we can indeed do better.

Modern AI tools predict customer futures surprisingly accurately

What sets AI apart from other data analysis tools, and surveys in particular, is its ability to not only provide insight into past customer behavior, but to also predict and optimize for the future. This predictive capability is what makes AI such a game-changer for CX professionals and allows our companies to make strategic decisions that drive both growth and cost savings.

The most effective tools use operational data from existing IT systems to analyze and predict customer futures. It's relatively easy to understand which operational items impact customers and which are reasonable targets for cost reduction efforts. And since the data comes from the very systems your teams are using, and is expressed using their own metrics, communication becomes much easier.

Some corporate decisions should not really be optional. With more reliable customer-centric operational analytics, we can make them mandatory.

Step 5 - Shift to prescriptive analytics

This fifth and final step is all about the move towards prescriptive actions: requiring individuals to do specific things to improve customer retention and ARR.

The good old days?

First, consider the situation we are all used to: we understand the status of a subset of our customers based on surveys. In most cases, we only take action on the Detractors. And of course, at the level of the overall brand survey, it can be unclear why the customer is a Detractor, often leading to loss of the customer before anything is done about the situation. And that's without considering that we sometimes see the survey results quite a long time after a bad situation has arisen.

That has all changed

Now that we have radically reinvented CX via advanced analytics, we understand the exact situation of every single customer and can accurately predict the future trend for each. We also understand the precise operational reasons that are leading to this positive, negative, or neutral trend. This creates the opportunity for us to prescribe actions for specific individuals. I suppose it would be best to prescribe and automate the action-taking process for large B2C companies in particular. For smaller companies and for B2B companies that have large customers, a playbook-based approach may be best.

Next steps

First, don't be a dinosaur. CX professionals need to learn new analytic skills and adapt to the new new environment. We all need to be:

• More operationally aware.
• Better aligned with our sales, marketing, and operations teams.
• Systems-savvy, meaning a good understanding of the IT systems each team uses.
• Data-centered.
• We need to improve our advanced analytics know-how.
• We need to improve our cross-functional skills so we can communicate with each of our company's teams using their own language and metrics.

Where to start

You don't need to start with a mega-galactic study of every single operational data point in your company. If you have a recent company-level NPS survey that you feel is representative, we suggest kicking your new strategy off using Salesforce data, then build it out further from that point. Naturally, we are happy to help. Please don't decide to wait. The survey-centric approach to CX that we have all been using for the last 20 years has failed to bring the results we all promised. We have to change. Don't be a dinosaur. You know what happened to them. Take the first step now.

Notes

OCX Cognition predicts customer futures. Our breakthrough SaaS solution, Spectrum AI, lets enterprises transform what’s possible in customer experience. Reduce your customer risk, break down silos, and drive speedy action – when you can see what’s coming, you can change the outcome. Building on more that 15 years of CX-focused expertise, we’ve harnessed today’s advances in AI, elastic computing, and data science to deliver on the promise of customer-driven financial results. Learn more at?www.ocxcognition.com.

Maurice FitzGerald is a retired VP of Customer Experience for HP's $4 billion software business and was previously VP of Strategy and Customer Experience as well as Chief of Staff for HP in EMEA. He and his brother Peter, an Oxford D.Phil in Cognitive Psychology, have written three books on customer experience strategy and NPS, and a fourth book that focuses on Peter's cartoon illustrations for the first three. All are available from Amazon.

The author can be reached here on LinkedIn or [email protected]. Please let me know what you think and what sort of content you would like to see here.