For several decades, the quality of customer service has been a particularly fundamental aspect in the relationship between a client and brand. The creation of distinct departments came along with implementing methodologies and exhaustive scorecards that allowed a constant monitoring of customer base’s perception of the quality of a brand.
This bet responds to the fact that, over time, it has become even more onerous to compete solely based on price, with profit margins becoming small and smaller as market competition becomes even more cutthroat. Therefore, excellent service leads to the search for a satisfied consumer that will result in a purely financial return.
Despite the fact that this correlation is, in fact, logical with empirical evidence to entirely corroborate its case. It has always been the weapon of choice when it comes time to put user experience improvement strategies into place since the norm was to make the most noticeable improvements with the greatest impact and wait for optimized results to come in. However, this all was done without quantifying the real return both in the long- and short-term or in layman’s terms, financial viability.
This vision is changing tremendously within the latest trends defining the customer experience, where analytics play a vital part, in both defining the customer experience and working to drive the results that lead toward a greater conversion on the part of the client.
This definition follows a methodology that we have seen previously. In contrast to more traditional conceptions, introduces key elements from the digitally exclusive experiences (primarily in e-commerce settings) and those taken from omnichannel contexts that boast high customer traceability in all touchpoints (both physical and digital), and a constant re-engineering of those processes at the client level.
About this last one, it’s important to consider that the definition of a differentiating customer experience will have a greater impact on client satisfaction in function of how it gets adapted to each user client and tailored to their concrete needs. Although in the embryonic stages of its definition or the initial experimental phases, it tends to seek different experiences for large segments of customers. One should always aim to be as close as possible to the needs of each consumer seeking a high degree of segmentation and when at all possible, landing at the personal level.
The customer experience’s impact on CLV
When it’s time to measure the client experience’s fiscal impact, we need to get our results from available information and indicators. The most critical of these satisfaction or quality indicators is Customer Lifetime Value (CLV). As the name implies, it tells us the value that each customer has for the company regarding consumption or earnings history.
This indicator has evolved over time in which, besides a five-year profitability projection based on purchase history, they’ve added other components like social value, prescriptive capabilities, or whether they undertake activities that are valuable for the brand (MGM programs, helping other users on forums, crowdsourcing…).
In line with this trend that’s less focused on the bottom line, it’s necessary that the aim of achieving a satisfactory customer experience has a direct impact on the value of the client for the company in overall terms rather than just in earnings and profitability.
The end goal will be that the firm can calculate[G1] the customer’s value impact from the different actions they take to make use of the most valuable ones, directly intervening in their points of contact, or even trying to influence consumer behavior.
As we’ll see later, everything this passes through should have every action quantified in terms of incremental customer value with the end of having the profitability of the investment in an individual customer.
When it comes to improving the consumer experience with a return concerning customer profitability, we’ll try to make an interpretation of our method of understanding how to make the greatest impact on customer value possible.
To do so, the first thing we must do is to have a well-structured and measured CLV.
We should calculate the CLV from both a monetary and a non-monetary perspective. In this way, we’ll have to establish variables that have an impact on consumer value, distinguishing between two types:
- In the monetary calculation, we should separate the variables according to the identified impact on consumer consumption: usage history, loyalty metrics, quality, contacts with the brand…
- In the non-monetary calculation, we’ll have to define those variables that have a high impact on the link or affinity that the client has with our brand. This goes off from the premise that this will end up being translated into indirect monetary value (already his or another consumer): degree of information we have available, the level of digital connection, behavioral variables.
Those indicators will substantially change based on the sector or company, but also regarding every individual user.
The next step will be to fix monetary value that we will attach to every variable. Using statistical models to show the correlation between variables, we’ll be able to see the impact that every action has on consumer value.
For this, we’ll need a history that is not always available. In the case of not having the required information for an initial quantification, we should start the calculation with whatever’s on hand to later add in the other data we end up having available (something that will modify the deliberation of previously used variables).
Once we’ve defined the CLV and incorporated values, we’ll be able to see which are the most critical points of contact to optimize different variables according to their weight on the customer value.
In contrast to traditional approximations (based on surveys measuring satisfaction), the aim is to apply a philosophy used in just about any eCommerce site but looking at a wide-ranging vision of the customer[G1] journey throughout all channels. This is how we can examine consumer behavior and determine what steps or moments have the greatest impact on the most valuable variables.
This analysis will allow for a decision-making model based on aggregated indicators at various levels. On the one hand, we must establish critical thresholds and alerts that alert problems widespread behaviors or particular moments established. On the other, an analytical model at the individual level will let us create triggers considering certain behavioral patterns that let us predict a change in consumer behavior, an opportunity for a sale, or a possible malaise on the part of the customer.
Continuous improvement will require corrective experience measures, for both the majority of customers and those requiring specialized attention.
This is the final optimization phase where we can best impulse and develop concrete strategies and actions that let us modify the client experience and obtain greater value from our customer.
To do so, we’ll look to the support from the most ubiquitous loyalty and customer management strategies in the social context. We’ll try to apply already used mechanisms within these commonly-used tactics that let us incentivize actions related to referrals and repeated usage in CLV terms (greater consumer, up-selling, cross-selling…). In this case, we’ll apply these reward systems on previously-identified behavior that creates an improved customer experience and translates into a positive impact regarding the value of the consumer.
As an example, we could give rewards in exchange for identifying all channels, or we’ll be able to give a particular benefit to any customer that we want to try a new service for the first time.
To create a customer experience model that lets us optimize their value, we should try to satisfy a few demands that are critical for the strategy’s efficacy:
Search for a greater number of points of contact: the more inputs enriching the model, the greater the customer traceability will be and the more elements we’ll have to create an optimal customer journey.
- Identifying the customer on all channels: to be properly follow all activity, we’ll need to have the information coming from all points of contact with the client. It’s vital to have create a unique identifier of an individual consumer in all channels and incentivize the customer, both on all and online (through elements like beacons).
- Real-time analytics: to execute an effective strategy, we’ll need to have information be available to us in real-time, not only to put corrective measures into place but quickly detect possible deficiencies in the model.
- Agile decisions: in line with the last point, the governing model must allow for a fast and efficient implementation of identified measures with the end goal of acting preventatively.
- Experience flexibility: if we’re incapable of adapting to each customer’s needs, a bright idea of a unique customer experience does not do much for us. The web creates spaces for us to provide personalized information or service, but today’s challenge is to extend this mentality to more conventional channels.