The golden age of business, when clients once found stayed loyal to your brand for good, is irrevocably over. Customers of today are rather fastidious and picky. They don’t want to suffer inconveniences or setbacks of any kind and know where they can get a more favorable offer or better quality of service. The more so as markets of the early third millennium are becoming increasingly globalized and digital technologies provide easy access to them. Recognizing the extreme volatility of current consumer preferences and allegiances, businesses focus their marketing efforts on customer retention. They realize only too well the universal commercial truth that it is easier to keep the old clientele than find a new one. To map out a successful retention policy, businesses should focus their efforts on customer churn prediction.
Customer churn explained and gauged
This phenomenon passes under several names – customer churn, attrition, turnover, defection. Yet, whatever its moniker might be, it doesn’t change the unsavory event behind it: clients abandoning a brand and ceasing cooperation with it. In which case, they stop being clients, in fact.
Customer churn comes in different guises: a client closes an account, cancels a subscription, opts for non-renewing a contract, or departs elsewhere to shop or employ services of a kind. But one way or the other, you are left without a customer, which tells negatively upon your revenues (especially when this development becomes a large-scale tendency).
The simplest metric to calculate attrition rate is to count the number of customers who defected within a certain period (typically, a month, a financial quarter, or a year). However, the figure received in such a way may not be exhibitive, since the same number of defectors can have a different meaning for a startup and a well-established business with a long history of presence on the market. That is why, to arrive at a more reliable index, companies quantify the percentage of lost customers, dividing the number of defectors by the total number of customers a company reports at the beginning or by the end of a definite period.
What attrition rate is acceptable and when it is a cause for concern? There is a one-size-fits-all number because it varies across industries – from around 6% of SaaS companies to 25% of banks and credit card organizations. Moreover, while measuring attrition, it is necessary to consider business cycle duration in different fields. For instance, the clientele of furniture shops or optics manufacturers can make purchases once in a while, but their long absence doesn’t mean that they have churned.
As soon as you have calculated the churn rate, you should learn why customers choose to defect.
Top 5 reasons for customer churn and possible ways to minimize it
There can be dozens of personal causes why clients defect – from abandoning old habits and finding new interests to changes in income level. These are hard to predict and remedy. But most of the reasons conditioned by client-vendor relations are pretty common.
1. Customers’ expectations aren’t met
Your product may operate flawlessly, but the customer evidently hoped for another outcome. It just can’t help them solve their problem, they don’t know how to use it properly, or may have misunderstood its functionalities at the pre-purchase stage.
Solution: The best recipe here is a well-planned pre-emptive policy to improve onboarding procedures. It should focus on customer activation instead of acquisition. As you initiate communication with them, you must make sure your product is a perfect fit for their case. Otherwise, you will be engaging the wrong customers who will eventually churn.
2. Customers think you can’t handle issues
Your product may have deficiencies and clients are ready to put up with them as soon as you fix them. But when you fail to address issues, you earn the reputation of an unreliable partner who should be rather shunned.
Solution: Such situations must be envisaged at the development phase, with bug-fixing activities thoroughly mapped out. Moreover, you must not only deal with problems on short notice but also keep clients in the know of regular updates and improvement efforts you undertake to provide a satisfactory UX.
3. Customers think your rivals do it better
Once consumers find out your competitors’ product excels yours in some aspects (primarily, price or quality – or, God forbid, both), you are in for trouble. By word of mouth, this news will spread like wildfire, and you are likely to lose a whole market share and bite the dust, trailing after your adversaries.
Solution: Keep your eyes skinned for the latest trends in your domain and the products of your competitors. You should realize your fortes, emphasize the uniqueness of your product’s features, and leverage a flexible pricing strategy to stand out in your niche.
4. Your product has lost value for customers
Businesses always try to channel their finances into essential investments only, which they consider as painkillers, and curtail non-essential expenditures as vitamins that are generally useful but can be done without. If your product crossed into the latter category, it is bound to appear on the list of items to be pruned in order to stay lean.
Solution: Emphasize the painkiller nature of your product and do your utmost to make customers invested (both financially and workflow-wise) in it. One of the ways to achieve it is through extending long-term contracts to clients. And don’t forget to constantly monitor customer satisfaction to react promptly if it plummets.
5. Your customer support isn’t supportive enough
Research by the US Chamber of Commerce discovered that more than two-thirds of consumers churn if they are dissatisfied with their treatment. Moreover, they are likely to tell a dozen people in their environment about their frustrating experience with a brand.
Solution: When issues crop up, the last thing customers want to face is communicating to a bot. They prefer talking to humans. It makes them feel like they matter – valuable clients and not just cogs in the sales mechanism. It is effected through the personalization of customer support when specially trained personnel in a polite and friendly manner listens to their complaints and gets them fixed. As long as clients feel you are interested in them, they are sure to stay with your brand.
It is important to realize why your customers defect, but it is even more important to foresee which of your clients are likely to churn.
Nuts and bolts of customer churn prediction model
How can you predict that a certain customer will leave you? An analytical model can help to figure it out with a high degree of probability.
First, you identify the customers who have churned within a certain period. Then you expose a pattern behind the churn. Having recognized it, you can build a predictive model that will leave you with a list of at-risk customers. Armed with this roster, you direct your customer retention efforts consisting in the pro-active engagement at them, without wasting time on happy clients who aren’t likely to defect.
Sounds simple, doesn’t it? It would if we forget about the slews of customer data that has to be processed to arrive at a churn pattern. Such variables include purchases, return visits, subscriptions/registrations, loyalty programs participations, credit card usage, experience feedback (for instance, likelihood to recommend), and other parameters (such as client demographics) that furnish solid food for making reliable conclusions.
Handling this information manually would take eons, especially at big enterprises with a thousands-strong customer base. Luckily, in the world where IT advancements reign supreme, dealing with such tasks is essentially streamlined via the employment of customer churn prediction software.
Leveraging technology to predict customer churn
The most efficient churn prediction tools rely on machine learning (ML) algorithms that parcel customers into groups with regard to their churn risk and calculate the attrition ratio for each group and every member of it. Being an expert in ML modeling, DICEUS considers its mechanisms most suitable for handling such tasks. How do they work? Let’s outline the procedure, taking as an example the strategy of customer churn prediction Python utilizes.
At the first stage, customer data sources (primarily, CRMs) are identified and relevant information is downloaded into the system. To do that in Python, you should import the necessary libraries and load all datasets from the CSV file into the program. Then, for the software to understand the collected data, the latter must be converted to a readable format (textual to numeric) consistent across the entire database.
Stage 3 consists of classifying customers according to a set of variables that encompass age, tenure, credit score, balance, number of products acquired, etc. Known as algorithm training, this phase exposes patterns and trends symptomatic of each group’s representatives.
Finally, it is the turn of regression. This kind of analysis aims to expose the correlation between the data values and the customer churn. The regression results are presented in the form of numeric values that indicate the period of time within which the given customer is likely to churn.
What software to use for customer churn prediction?
There is a simple choice for you to make when you realize that churn prediction is a sine qua non for your business: a canned or bespoke solution.
At first sight, ready-made software is the most natural option. It is always cheaper and you can start using it immediately after you have acquired it. If these arguments seem valid, there is a whole gamut of out-of-the-box churn prediction tools, the most popular ones being Adobe Target and Google AI Platform. However, the major shortcoming of any off-the-shelf software is that it is built for an average user with a standard set of requirements. So, you will have to undergo another ordeal of selecting the solution that suits your company to a tee.
When you opt for getting a custom churn prediction solution, you are in control of every aspect of it. You can get a pre-fabricated tool tailored to your unique needs by the vendor or commission a totally new one that is aligned to your vision of this product to the greatest possible extent.
We at DICEUS have sufficient expertise to develop ML churn prediction software of any complexity, based on the model and containing scope of features that will be a perfect fit for your company, whatever business it is involved in. Moreover, we extend testing, deployment, monitoring, maintenance, and consulting services to our clients, so that your CX will be exceedingly comfortable.
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Customer attrition is a natural and even inevitable process any organization experiences. To keep it to a minimum, you should be aware of its reasons and employ customer churn prediction software. Its tools will help you foresee and forestall client defection and take effective measures to burn the churn.