It is exciting to finally start using advanced data analytics as part of your product’s growth strategy with a huge investment, expecting fast profits. However, not many companies successfully implement data and analytics to drive growth beyond the hiring and spending of resources.
Studies, such as the Bain and Co. study
on advanced analytics, were based on real-world applications of analytics surveyed from 334 executives, of whom two-thirds said that their companies invested heavily in Big Data. Half of them expected a significant or transformational return from these investments. Sadly, despite these high expectations, the survey found that “30% of these executives said they lack a clear strategy for embedding data and analytics in their companies”
This tells us clearly that something is lacking in the process of implementation of data analytics. Why is your analytics failing to produce revenue from the product? Are your customer fall-through rates higher than your acquisition? Are your customers unable to find value with your product’s services beyond the first usage? Retention analytics is an interesting and valuable use of product analytics data that can address such concerns that eventually lead companies to failures.
Why should you take customer retention analytics seriously?
cannot be overlooked in today’s rapidly competitive business environment. This is especially true when considering the time and revenue required by product teams to gain customers versus retain them.
Businesses are most certainly bound to suffer an irrecoverable loss when existing customers are not retained. The lost pool of customers takes away future business chances and nullifies or minimizes the investments in gaining the first business from them. Still, only a meager 18%
of companies focus on customer retention when compared to customer acquisition.
Here are 3 main reasons for you to take customer retention seriously:
Acquiring a New Customer is 5 Times More Costly
Even though it’s exciting to watch the climbing number of new users on your product analytics application
, we tend to forget how little it pays if they are not staying with you in the end. If companies knew that getting a new customer to make the first purchase is five times more expensive
than maintaining an existing customer’s account, customer retention would become the single biggest driving force for product teams.
Every dollar you spend in making a new sales campaign, the number of adverts that are displayed across apps, and the emails, posts, and calls that go out on a weekly basis, are all counted toward the cost of acquisition. It then seems equally important to aim every marketing and sales effort at accommodating the existing customer base’s needs on top of the new ones.
The Risk of Losing Customers to Competitor Services
Sometimes customers could be leaving due to the lack of attentiveness from a brand rather than a loss of interest in their service as a whole. If they are going for your competitor because you could not step in at the right moment to rectify something that could have been easily fixed, the loss can be terrible. All the effort and money you spend in getting the customer attracted becomes money down the drain. This means you essentially made business for your competitor after spending resources that convinced users to like your service.
Understanding what makes your business different is also what makes customers stick with you. The uniqueness of a product helps it gain an upper hand over the competition if you market it the right way. Simultaneously, you should also monitor competing businesses for a feature or perk that is attracting users to their service. A simple implementation or change in handling a customer query could convince a customer on the fence to stay with you.
Retention Improves the Customer Lifetime Value (CLV)
In this graph, retention analytics is used to measure user loyalty by comparing user count against a certain period of active days. Here, sample data is taken for the time periods 30 days and 7 days, respectively.
The Customer Lifetime Value (CLV) is the projected value of a customer’s relationship with a business over the period of the average customer life cycle. CLV helps businesses analyze how much a customer’s purchases can be worth with a lifetime of loyalty
. It’s a common understanding that a customer who keeps purchasing from you is a greater asset to the business than a one-time buyer. It also cuts down the cost of building a new process with every new customer that’s onboarded.
Increasing customer retention rates by a small percentage can see profits from anywhere between 25% to 95%
The possibility of selling to an existing customer is also exponentially more than getting a new customer to make a purchase. The trust and comfort that existing users have experienced from your brand will motivate them to stay longer than a new customer who is more willing to jump ship easily.
How to Use Cohort Retention Analytics to Sell More
are a set of users who exhibit particular, shared behaviors while using your product or those who take part in a common event. Analytics based on cohorts’ behavior is more effective in segregating users into segments than the traditional ‘one size fits all’ approach.
Thinking that every customer must have the same service needs and product requirements is not a factual assumption. It is also not true that every customer is very different from each other and that everyone needs to be marketed to in an entirely different manner. Cohorts are a way to separate users as well as unify them under some common usage statistics so that you can create marketing strategies based on categories instead of individual conditions.
In order to start with cohort retention analytics, you need to first begin by identifying users who make up cohorts.
The first step in identifying selling opportunities with cohorts is to identify them. Cohorts differ from the usual customer segmentation based on demography and geography by mainly focusing on user behavior segmentation. Cohorts are groups of users who share common behavioral patterns, making it easy to analyze them as a set.
Users in a cohort might be those who sign up for your service in a particular week or those who saved their cards for a simpler purchase experience. You can then make separate evaluations for them based on purchase habits.
For example, if the users who newly signed up to your app are more willing to make a purchase within a week, you may want to label them as a cohort and offer them quicker deals. Or, if users who saved their card are more likely to make repeated purchases, they may be a cohort you want to target with rewards that will encourage them to make bigger purchases.
Strategizing Promotions for Cohorts
This graph analyzes cohort retention for the ‘new users’ cohort by checking the rates of activity in the subsequent days of the first session.
Based on the method by which you categorized each cohort, you can devise new marketing strategies that best serve a particular cohort. Looking at the number of active days from a cohort that made a fresh sign up, you can set a number of promotions to go out in the time span to prevent users from leaving.
With cohort retention analytics, you may find that a category of users only made purchases when offered a discount or that a category of users made purchases when new stocks arrived.
Cohorts may also respond to certain types of marketing, such as advertisements or push notifications. While most companies avoid advertising to the existing customer base, rethinking your advertising strategies to also include them, will effectively get you a bigger ROI.
Maintaining Cohorts with Loyalty Programs
Users feel valued when their loyalty is rewarded through special programs that are crafted to better suit their purchase interests. Point programs for retail businesses or discounts on add-ons for software purchases encourage customers to make greater purchases.
Cohort retention analysis is, therefore, helpful in creating cross-selling and up-selling opportunities that are more streamlined than throwing darts in the dark.
Surveying a cohort of users who, for example, completed a certain number of transactions to gather their NPS®
(Net Promoter Score®) can identify customers who are willing to refer your product to their friends and family. The score can reflect on further analysis of retention potentials and CLV.
Revealing Reasons Behind Customer Churn
Retention analytics is used to calculate churn rates or the percentage of drop in user activity over a certain time period.
If you cannot find where your customers are dropping off in the conversion funnel, there will be no progress in your marketing and growth efforts. That is why it is also essential to compare the retention rates with the churn rates.
Churn rates are crucial to validate your efforts toward customer retention, as they reveal the stages and roadblocks to user experience that result in customers cutting short their journeys. Customer churn rates are measured by calculating the number of customers who ended their relationship with a business over a period of time.
Tracking user behavior that causes attrition/churn enables user retention analytics. Businesses may want to start by relooking at past implementations and recent changes in product experience. You may want to change up a few things along the way if you cannot really point out the exact reason for churn and see the effects of these changes.
opine that customer dissatisfaction with some aspect of the product occurs at least a couple of months before a customer churns. Measuring churn on a month-on-month basis will give you a fair amount of time to work through issues if you start to see customers falling off.
Churn is also a great motivator to rethink who your target customers should be and, from there, to find out who is worth the extra nurture for a long-term business relationship.
Customer retention capabilities of product teams boost brand value among user bases that far exceed many other sales and marketing measures. Attracting new users to your product is a way to gain traction in your product’s growth, but if such acquisition is unstable over time, you have wasted all the money and effort put into acquiring a user in the first place. Instead of focusing on acquisition alone, companies need to place significant efforts toward nurturing existing customers. That way, customers turn into advocates for a product that automatically expands your user base without you having to spend an extra dime to try and convert potential customers!
Interested in learning more about product analytics? Check out more Countly articles on our blog