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Nothing can put more emphasis on customer retention as this quote by Brian Balfour. It is very important to keep track of where your customer mindset is at, with respect to your brand. Customer retention is not only a measure of how effective are your company’s solutions for your target customers, but also have a huge impact on the revenue of the company.
The current business scenario is highly uncertain with customers constantly on a lookout for better and reasonable alternatives, thus, resulting in higher defection. In order to ensure that the company is on the right track with its targeting and messaging, companies need to predict the likelihood of a customer staying with the brand.
This is where customer retention predicting techniques come in:
To put it simply, companies need to assure themselves of a loyal customer base by making sure that customer feels positively about their experience with the brand. Predicting the possibility of customer becoming inactive with the brand provides companies the time to modify strategies and plans to keep the customers from defecting or losing interest. A ballpark knowledge of the traits that customers show when they are about to leave is a prudential approach to reducing customer turnover.
Losing even a small number of customers has a huge impact on the revenue generation capabilities of the business. Not only does it lose out on potential sales, it is indicative of poor market performance. It’s better to find out what percentage of customers are bound to become inactive in order to get to know the reasons behind their lost interest. This acts as market knowledge that helps in making informed decisions and altering company products to make it more appealing for target customers.
It is common knowledge that it is easier to retain existing customers than bearing the cost of targeting and acquiring new ones. New customers have to be informed about the brand using marketing and promotional efforts that require a relatively higher resource investment as against convincing current customers to stick with their current brand choice. Predicting customer retention provides an analysis of the subset of customers that are likely to abandon the brand and this provides sufficient time for brands to come up with ideas to fix current customer issues.
Customers that are likely to churn help you refine your business plans and strategies. This improves the company’s functioning, making it more consumer focused. This prevents current customers from leaving the brand as well as allows the company to direct its attention towards maintaining and expanding market share. The flaws from a customer point of view are major drawbacks and so, can lead to loss in market share. Predicting customer retention allows the company to eliminate the uncertainty associated with a sudden loss of customer base.
Churn for customers defines those customers who have decided to leave the company. This is a major problem for companies as it means a loss of revenue as well as the fact that the company is doing something wrong with its customer service and there is scope for improvement.
The first step is to define a specific time frame after which a customer is considered as lost. This time period should be carefully decided based on the type of product developed by the brand and its frequency of purchase by an average customer. Customer base must be carefully segmented based on a rigidly defined criteria that results in a meaningful differentiation between various customer types Thereafter, different customer timelines must be established based on historical data and current market trends.
Study historical data of the previous customers who are no longer active. Create a comprehensive customer profile that highlights the traits and characteristics of inactive customers. This data will help you study your current customers with respect to the same characteristics. These characteristics can be anything from buying behaviour to customer feedbacks.. Anything that helps the company narrow down on customer behaviour is useful.
Use qualitative and quantitative prediction models to forecast the customer retention based on current customer behaviour. The historical profiles are used as input to predict how your current customers will interact with the brand based on the traits they currently exhibit.
There are multiple statistical models that can efficiently predict retention. One such model is the Probit regression
Probit regression is a binary classification model that estimates the probability of a certain characteristic falling into one of two categories and analyse it with respect to certain variables that influence the outcomes.For example: In this case we are estimating the probability of a customer staying active or becoming inactive based on a number of characteristics such as customer experience, product choices, personalization etc.Probit regression is a prediction based model that identifies a customer’s future behaviour towards the company. Customer behaviour is an insightful characteristic that can be very complex to predict accurately, however, using a data model such as probit regression can help in establishing certain correlation.
A pre-requisite to using this model is deciding your independent variables. These are a list of aspects carefully selected and are likely to have major impact on the resulting category. The company management and stakeholders may come across multiple aspects but it is important that these are shortlisted. Not each aspect will have an equal impact. Customer experience, product design, demographics, suitability and promotional and marketing are some areas which can influence customer approach towards the company.
Data collection is the next step. Now that you have selected the independent variables, define them clearly and start your data collection process for you to input it in your Probit regression model. Make sure you data is accurate and thorough to make your predictions precise. For example: If you chose promotional effort as an indicator on which your customer retention depends, ask your current and past customers NPS® questions to assess how likely are they to recommend your product to a friend. This will help you give a quantitative input to your regression model. Probit regression will likely predict the impact of promotional efforts on retention as: higher the rating of a customer on the NPS® question, the more likely they are to stick with their current brand.
Move ahead with conducting your research. Input the data collected of different variables and separately analyse the affect each variable will have on the likelihood of your customer coming back in the future. Use diagrammatic presentation to chart data in order to increase your understanding of the impact that each variable has on retention.
Finally, evaluate and act on your results. Knowing how each of your independent variables can influence your company’s retention rates, come up with effective plans to prevent customers from leaving. Conduct follow-ups and feedbacks to gain perspective into why certain customers are unhappy with their present experience and how you can fix it. This will achieve two things: make your present customers happy and prevent any hurdles due to the same flaws in the future.Logistic regression is an alternative method that can be used to predict variable relationship and predict customer retention. In addition techniques such Alkaike Information criterion, data variance and ROC curves (Receiver operating characteristic) can be used to predict quality of data models, determine variable impact and assess accuracy of the prediction made.
Focus on collecting data from a sample that is well suited for your study and has balanced representation from all your customer segments. You do not want your data input to be biased towards one segment while turning a blind eye to another. Make data roadmaps to systematically follow certain pre-defined steps and prevent losing track.
In addition, assess resource and personnel requirements. You’ll be needing enough resources to gather first hand or secondary information, as the need be. Research professionals who can analyse the prediction results correctly also need to present at the right time. This requires resource availability and a feasibility test to ensure that the company has what it takes to make good of the study.
Why are you interested in predicting customer retention or churn rates? Companies may target to achieve a certain turnover by the end of the year or may seek to introduce a new product or service. Either of these activities need the presence of a loyal customer base that knows what the brand i and what kind of solutions they offer.
Set an ultimate goal that you seek to achieve as an end result of following up on customer retention prediction and taking actions to make sure customers stay. Frame time bound objectives that provide a step wise approach to achieving that goal.
You may be familiar with how to conduct a machine based prediction, but an analytics professional is better equipped with the relevant knowledge base to help you find the answers you are looking for. They may even suggest a better approach to making predictions using data analysis and statistical tools. Regression represents only a small part of the variety of options out there for companies to use. Make sure your company doesn’t miss out on extracting maximum output using the optimum business tool.
While making predictions all about numbers and accuracy, avoid underestimating the value of qualitative information. Open ended comments and feedback form a major chunk of the market intelligence that can be used for the company’s advantage. Use textual analysis and summary tools to derive key insights from what the customers have to say about your brand. Use word cloud techniques to create a holistic picture of indicative key takeaways from customer opinions.
A company’s customer base is large enough to make it a cumbersome task to formulate a single standard action to retain customers. Each customer is different in what they’re looking for and so should be treated differently. This may require some extra work for finding similarities and differences between groups, but ultimately acting and planning for separate segments becomes a lot easier with distinctions in place. Viewing customers as separate target groups allows companies to study, formulate strategies, monitor progress and follow up in an efficient and impactful manner.