Customer Activation Curve
Versão em português aqui.
In chemistry, a reaction does not happen out of nowhere. It has to meet certain criteria and, most importantly, it needs energy. That energy is called activation energy and the energy curve throughout the reaction is called an activation curve. On this curve, you plot the energy needed on each phase of the reaction and it usually looks something like the figure above, where the x-axis is the path of the reaction. So in order to get the products you want, you have to add energy to the system until the reaction begins to happen on its own.
Therefore, the activation energy is the most important characteristic of a certain reaction; it determines when the reaction will occur and how much energy — in the form of heat, electricity, or other — you will need to change what you have into what you want. The “height” of the curve and its length are characteristics that have to be taken in consideration when planning for a certain reaction.
There are, of course, ways to facilitate the reaction using elements called catalysts. What they do is diminish the height and/or length of the curve so the reaction happens quicker and/or with less energy, therefore increasing efficiency and allowing more reactions to take place with a certain amount of energy. Moreover, the catalyst itself is not consumed by the reaction, so you can theoretically use it repeatedly without any loss on that end.
What does that have to do with customers?
Let us ask a simple question with a very hard answer: why do you buy a certain product or service? It can be because you have a need the product fulfills, because the marketing is good, because it resembles something you already know and that makes it easier to use and cheaper, or even because the price is low. The bottom line is, you buy the product because you believe the product and its sales process match what you expect or doesn’t tire you to the point of quitting the buying process. Actually, the probability is, you have more than one reason to buy something, especially if you have several products, each performing the same function but in a different way. How do you choose between a product or service and another? If you are a company, the question of how and why people buy your products must have a clear answer.
So, analyzing from a company’s point of view, you have on one end of the graph above your future customers and product (reactants) and on the other end the complete transaction or simply a client (product). Your job as a manager of said company is to make the reactants turn into products at an efficient rate, faster and with fewer resources. However, in order for that to happen, you must go through the Customer Activation Curve, where it takes energy from the customer to convert into a client or completed transaction. See what I’m getting at?
How do I use the Customer Activation Curve?
First, an understanding of the concept of energy used by the customer to purchase a product or service is needed. What I define as energy in this situation is the amount of actions, understanding, familiarity, and money a customer needs in order to go from knowing your product and what it does to buying it from your company. It entails different aspects, from user experience and the sales process to strategic pricing, but can be used to better understand the aggregate effects and conditions under which someone chooses to buy something. We all know, as I have stated before, you do not choose an Android phone over an iPhone simply because of the price. There are pro and cons of each option and also where you buy it. Do you choose Best Buy, Walmart, or Amazon? Usually the answer is “it depends”. It depends on how high the activation energy is on each of the stores and products.
Your objective as a company using the Customer Activation Curve is to either reduce the activation energy, decrease the time for the “reaction” or simply map out your curve. By any means, before you perform any actions to alter the layout of your curve, you should map it out. I do not think I need to remind you, but remember to always use real data and effective metrics for measuring the different aspects that compose the customer’s energy and your own processes inside the company. Do not use projections or gut feeling as this will skew your curve and can even hurt your company.
To begin the process of mapping out your curve the first step should be to identify the different energy levels where your customers start. This is not the same as customer segments, but more like the technology adoption curve below. As a general rule as you go left in that curve the initial energy for your client decreases, and therefore the activation energy increases. You can map out this initial state talking to your salespeople and trying to identify the different customers they talk to according to certain profiles. It should be understood that this identification will create different curves for different profiles, as conservative customers will behave very differently from innovative ones and someone who is used to using a competitor’s product will have a different approach to buying than one who is buying into the sector for the first time.
Once the customer’s profile is chosen, a scale is needed to evaluate how to position and measure the energy levels in a graph. One way to tackle this problem is to divide a scale from 1 to 10 (totally arbitrary) in levels that resemble the profiles. At this point you can once again use your salespeople to understand which are the “hardest” customers to sell to and what profile they fit into. An example where the profiles are the same as in the technology adoption curve is:
· Innovators: 9–10.
· Early Adopters: 7–8.
· Early Majority: 5–6.
· Late Majority: 3–4.
· Laggards: 1–2.
This is just a baseline but it is one of the most important decisions in building your Customer Activation Curve. The starting energy level can skew your perception of your activation energy. For example, if you choose a large scale for plotting the initial levels of energy but measure the energy needed on a small scale it will distort and make it seem like your activation energy is less than it actually is. In order to solve this you can either use the rule of thumb that a customer will always spend energy to acquire a product or contract a service, or use a better method for measuring initial energy. The method I recommend for plotting it is to measure the energy the process takes and then calculate proportions for each customer profile, for example: the total energy in the process is 100%, so a profile that resembles the Early Majority would start at 60–70%, the laggards, however, would start at 10–20%. But how do you measure the total energy in the process?
Going back to the concept of energy, the total energy in the process is how many actions are needed to buy something as well as the other intangible aspects as motivation and availability of information. Therefore, in order to measure how much the process of buying something demands from the customers, you should start counting how many steps (and how long they take) someone must go through in order to complete the transaction. Remember the transaction is not limited to when money exchange hands, but has its end when the product is in possession of the client or the service has been performed. For each step, you should attribute a level of energy, it can be 1 unit or 5, but remember this will impact the whole process, as it is this scale that will create the visual representation of the graph and impact your analysis. Furthermore, different actions in the process have to be attributed different energy. It is not the same to click a button on a website and to have your call transferred from one person to another. Be thorough and analytic with this process, each step, each click or person on the phone impacts the energy level a customer needs in order to buy your product.
Finally, once you have mapped out the energy in the process it is time to determine what is the customer’s energy level in the end. Doing this is quite simple, compared to the other parts, and involves a simple question: what are the odds of that customer profile buying your product again? There are different techniques to measure that, but what is important is this: the percentage of customers of that profile who repurchase, decreased by 50%, should be increased, or decreased, form the initial energy.
So, for example, if 70% of your customers that fit a certain profile repurchase, the final energy level should be 1.2 times the initial energy level for that customer profile (Initial Energy*(1+(70%-50%)).
Conclusions
The first note I have to make is that this is a work in progress. Even while discussing this article with people around me I have developed several improvements to the model that are more fit for a book on the subject than a Medium post. I have also skipped talking about catalysts and inhibitors in the process, but there are different aspects for the purchase process like expectations, availability of information, and the motivational profile during the purchase that can become either catalysts, accelerating the process by either reducing the energy (or perception of energy) required or the time taken, or inhibitors, with the opposite effect.
Secondly, the process can become a very arbitrary one and difficult to use at first. The concept of energy needs to be better discussed and presented in a way that is easily understood and measured. Also, the amount of information needed is somewhat large as you need relevant amounts of data on each type of customer as well as during the sales process. However, the Customer Activation Curve can be of great help in defining sales, marketing, and operational strategies in order to become a more efficient and agile company.
Summing up, the Customer Activation Curve is a tool in development that aims to transform companies into more efficient versions of itself by mapping out the buying process from the viewpoint of the customer and defining what are the different effects on the probability of purchase form a group of customers an action may have. As a tool in development, any help is welcome and encouraged. If you decide to try to use it, please contact me with results, doubts or advice on LinkedIn or e-mail.
Thanks to Lílian Duarte and Pedro Lopes for reading drafts and suggesting edits.
