How do executives prioritize and decide what actions to take on customer experience (CX) investment options – in the time of COVID-19 or at any other time? Many CEOs increasingly ask “where's the beef?” (some of you may remember the Wendy’s commercials from the 1980’s). The “beef” or “meat” they seek is your ability to clearly and confidently demonstrate that your recommended CX investments will lead to measurable business benefits like reduced customer churn, lower cost operations, improved customer loyalty, repeat visits, greater spend and higher revenue and profits. Linking CX program and operational changes to improved organizational KPIs is an effective way to get funding for ongoing and future customer experience investments. Although I only refer to customer experiences in this post, the same principles apply to employee experience and employee engagement investments.
Why do CEOs feel there is often insufficient justification for (additional) investment in CX programs and their recommended operational changes to improve the customer experience? As Chris Argyris makes clear in his book Flawed Advice, sometimes justification for recommended business decisions is based on limited information that is not backed-up with sound business analysis using appropriate data. It seems so obvious that better customer experiences will create greater customer loyalty and business success, but how can we demonstrate this linkage in specific and compelling ways that drives confident decisions on actionable CX investment opportunities?
What Leads to Poor Decisions and Outcomes?
This decision-making challenge is not limited to just businesses. Most people probably consider their physical and financial health to be of utmost importance. However, we often compromise desired health and financial success even when we rely on the advice of highly trained, knowledgeable and dedicated professionals in these industries. For example, the most reliable and effective medical treatment available relies on a framework called evidence-based medicine. There is a substantial amount of medical research that provides clear guidance on how health care professionals should treat certain conditions to have the best possible patient outcomes. Despite this information being widely available, some medical doctors decide to provide a "different" course of treatment than what evidence-based medicine would suggest. The risk of a poor health outcome for you goes up substantially when your health care provider acts on the belief that their judgments and decisions are superior to evidence-based medical best practices.
A similar thing happens frequently in diverse business industries. Seasoned executives and functional leaders bring their extensive experience to solving complex strategic and operational problems to better serve their customers' needs, become more competitive and achieve greater business success. Even when relevant evidence-based information is available, it is often not presented or used as a significant input into decision making. A more common situation exists where actionable information that could help in deciding the most appropriate actions does not exist, or it is not clear where the data are and what to do with them to help drive optimal decisions. It is lamented by many business professionals that they and their entire organization is awash in data, but it is unclear how to structure and analyze the data to help drive decisions that will propel their organization to achieving business success. But it does not have to be that way.
Pivot to Evidence-Based CX Investment Decisions
Most organizations would be well served to adopt a rigorous, evidence-based approach when making major business decisions, including CX investments. Just like with the health care example, following a disciplined method using relevant and objective information is the most cost-effective and timely way to attaining better business outcomes.
There are at least two types of evidence-based inputs available to organizational leaders to help guide them in solving complex business problems and in creating business success: (1) industry best practices, often derived from relevant industry case studies and research, and (2) rigorous analysis of company-specific data that is used to verify and update presumed cause and effect relationships that describe the actual drivers of success within that specific organization. Ideally, these two types of evidence-based approaches work together. Type 1 evidence can be very helpful in guiding the creation of “hypotheses” about key drivers of business success within a specific company that can be tested and refined using a Type 2 approach.
Type 1 evidence-based guidance for executives (industry case studies and research) has been used for decades (if not centuries) with a high degree of success. However, there are several limitations to relying solely on this type of information in the modern business environment:
- The guidance is often somewhat generic in that conclusions and implied recommendations are based on analyses that did not use any data from your own company, or your data is mixed in with many other companies within your industry.
- The guidance provided is usually somewhat dated given that it relied on events and data that often happened a year or more ago.
- Since nearly all companies have or can get access to the same industry information, it no longer provides much competitive advantage.
The Business Performance Key Driver Model
Type 2 evidence-based guidance for executives (company specific analyses using their own data) is where the greatest value is that leads to a sustainable competitive advantage. Company specific information includes relevant data about its own customers, employees, structure, processes, operations, finances and sometimes competitors. These data are used to create quantitative “cause and effect” models identifying the key drivers of overall business success. Although extensive organizational data and sophisticated statistical methods are used to create the models, there are graphical approaches to easily communicate the meaning and implications of the models.
It is important to note that a cause and effect model (i.e., causal model, or more generically a key driver model) may sound theoretical and squishy, but it is not. Recent advances in causal model design and technical analytic approaches using a wide variety of readily available business data allow for strong conclusions to be made about which business “intervention” (e.g., a specific customer experience impacting operational change) will influence one or multiple business outcomes to improve, and by how much.
In future blogs I will expand on the topic of causal / key driver models and the practical steps you can take to leverage them in prioritizing your CX investments and estimating the ROI of your overall CX program.
Would love to hear your thoughts on this and other topics of interest. You can comment on this post, or you can reach me via email at email@example.com, on LinkedIn at https://www.linkedin.com/in/randylaw/, or at www.analyticsandinsightsmatter.com.