By Swati Sahai
What’s better than acquiring one new customer?
The answer isn’t acquiring two new customers. It’s actually retaining an existing one.
Much of the marketing world is still focused on customer acquisition, but to improve customer retention will yield far better ROI and cost about 5-25X less than customer acquisition.
In this post, I take an in-depth look at why customer retention matters and the ten powerful ways in which customer journey analytics can help you immediately improve customer retention.
What is Customer Retention?
Customer retention is the set of actions that companies take to stop customers from leaving and to retain and grow as many as possible into loyal customers. Customer retention starts with the first customer interaction and continues throughout the customer’s entire relationship with your organization.
Why Customer Retention Matters
As per Gartner, a 5% increase in retention can increase profits by 25% – 125%. Yet companies struggle with customer retention or seem to underestimate its revenue impact. Most ecommerce businesses retain fewer than 20% of their customers. The average app only retains 10% of its users after 30 days, per mobile intelligence firm Quettra.
If you are unable to retain your customers after you’ve just made a huge cash outlay to acquire them, they will only be a net-negative for you, unable to pay back the customer acquisition cost (CAC).
Essentially, you are only spending money to lose more money. This is why customer retention matters to growth and profitability and why it’s critical to focus on strategies to improve customer retention.
Though acquisition seems more attractive because acquisition campaigns yield faster, more measurable results than customer retention campaigns, it is important to take a long-term view and remember that your company’s future revenue and profitability will depend largely on retaining your existing customers.
My Customer Retention ≠ Your Customer Retention
The Timescale Conundrum
Retention can often be a confusing concept, nebulous and shifting in meaning from industry to industry. It would be easy to define and measure if we relied on the customer to indicate they are no longer a customer.
Sure, that’s easy in subscription-based industries such as SaaS or telecom or insurance. But for most businesses, retention activity is passive in the sense that the customer does not inform you that she is leaving. As a result, you have to deduce customer retention based on activity within a certain period of time.
Richard Boire, of Boire Filler Group, lays out this difference in a compelling fashion. For a grocery retailer, he says, 70% of active customers exhibit repeat purchase activity within a two-week period.
For credit card issuers, 70% of active customers repeat activity within a two-month period, while that number goes up to two years for a tire manufacturer. When it comes to the automobile industry, that number is higher still—as much as 5 years.
Many companies, in such circumstances, identify shorter-term behavior that can be used as a proxy for customer retention. Getting customers engaged in shorter-term activities often bodes well for long-term customer retention.
Is Retention About Churn or Loyalty?
Another source of confusion is that in some industries retention is synonymous with churn, while in others it goes hand-in-hand with loyalty.
In subscription-based industries such as telecom or Software as Service (SaaS), retention is about preservation and preventing the loss, rather than focusing on the growth of the customer. Retention campaigns are focused on indications that the customer is about to leave and how to make her stay.
In retail and eCommerce, the focus of retention is on creating engaged customers that return to shop with you again. Retention (or Retention Marketing) is about campaigns that increase the likelihood of a customer purchasing again as well as increasing the profitability with each repeat purchase.
The point is, customer retention is a nuanced concept and there isn’t a one-size-fits-all strategy to improve customer retention that can be applied in broad strokes across all industries.
How to Measure Customer Retention
Retention rate measures the percentage of your customers retained during any given period of time. It is the opposite of ‘customer churn rate’ which measures how many customers leave during a specific time period.
[Hand-picked related content: How to reduce churn using customer journey analytics]
Applying this formula, if a business starts a month with 500 customers and loses 40 but gains 80 customers, at the end of the month they have 540 customers. The retention rate for the month is (540 – 80)/500 = 92%.
Is your retention rate high or low? There isn’t one right answer and it depends on a company’s particular context—industry, business and revenue model. The key is to benchmark yourself against competitors and similar companies but most importantly against your own past performance.
10 Steps to Improve Customer Retention with Journey Analytics
Journey analytics can quickly focus attention on the biggest opportunities to improve customer retention by answering critical questions such as:
- Which customer service interactions result in poor customer experiences and sub-par retention?
- Are there sub-segments with retention issues that can be targeted for quick resolution with high revenue impact?
- Which customer segments are most likely to churn?
- How much do service outages or technical product issues increase churn risk?
- Is acquisition targeting the wrong customers, which can lead to a lower retention rate?
The 10 steps below explain in detail how enterprises can find opportunities to improve customer retention, including examples of how journey analytics can help .
1. Take a Journey-Based View Instead of a Last-Click View
By mapping the customer journey you are able to analyze the complete experience from end-to-end in the eyes of your customer. Different customers will have different experiences and you will visually be able to see each one, the various touchpoints encountered and the actions taken from there.
Due to the limitations of traditional analytics tools, most organizations typically focus on the last event that occurred before the customer churned, incorrectly assuming that it is a reliable indicator of churn and therefore retention.
But experiences accumulate over time. As in personal relationships, trust, familiarity or resentment in customer relationships build up over years. Customers can have experiences that make them feel neglected or indifferent long before they end their relationship with your business. To discover the root causes of churn, you need to look at the complete customer journey or you will likely reach the wrong conclusions. Conversely, not understanding what makes your loyal customers feel valued is also not helpful.
Identify High-Impact CX Issues that Lead to Churn with Journey Analytics
The CX team at a leading telco uses Pointillist to understand the drivers of churn, so they can identify and prioritize CX initiatives that will have the largest impact on reducing churn.
First, the team uses Pointillist to analyze CX issues across all support channels to identify the most common issues customers encounter that could subsequently lead to churn. Through the analysis, they find that billing and tv issues are by far the most prevalent.
However, in the next step of the analysis they discover that while the highest volume of customers are experiencing billing and tv issues, internet-related issues are actually driving the largest number of account closures and are responsible for the greatest revenue loss. They also determine that customers who experience internet issues are most likely to churn, and at the highest velocity.
Armed with this new information, the CX team uses Pointillist to isolate two specific internet related issues that are having the highest impact on churn, and prioritize an initiative to address them.
They anticipate that the initiative will reduce their overall churn rate by roughly 2.0%, saving the firm an estimated $1.8 million in lost revenue over the first 12 months after implementing the solution.
2. Unify Customer Data to Create a Single View of Your Customer
Data residing in silos is the biggest barrier to understanding your customers. In most organizations, data is not organized around customers but around different business units and channels, making the analysis impossible. This leads to ineffective offers, since they are disconnected from an individual customer’s experience.
Dollar Shave Club – Retaining Customers by Understanding Customers
Dollar Shave Club was perhaps best known for its viral branding powers—who can forget it’s startup video? That is, up until Unilever dropped a cool $1 billion to acquire the company.
What makes Dollar Shave Club so valuable?
- Strong focus on retention
- Active subscriber base of over 3 million
- Deep understanding of customers
The company has always invested heavily in using technology to improve customer retention. They integrate their in-house CRM system, customer support platform and data analytics to build a powerful and rich understanding of their customers.
“We don’t respond to situations, we respond to people,” is Dollar Shave Club’s driving philosophy. They now have over 3 million subscribers who are not just happy customers but are compelling brand advocates.
To achieve the single view of your customer necessary to understand and enhance their experience, you need to eliminate the silos that are holding your data hostage by integrating cross-channel data. This enables marketing and customer experience teams to gain a complete, unified view of each customer throughout their journey.
A single view of your customer can be achieved in two steps:
A. Data Integration
The first step is to integrate customer data available to you from different touchpoints. This data will typically reside in data warehouses, point-of-sale systems, email marketing platforms, marketing automation systems, call center management systems etc.
Customer journey analytics software can integrate data rapidly and easily without first requiring schema setup, customer identity matching or fixed field mappings for different event types. More advanced customer journey analytics platforms may have built-in ETL capabilities that allow you to extract data from your system in the format that is easiest for you to use. This way, you can do this within days, as compared to taking weeks and months using traditional approaches.
B. Customer Identity Matching
The most crucial step in unifying customer data is to bring together the separate pieces of data that have been collected on an individual customer by recognizing that they actually refer to the same customer.
This is the process of customer identity matching.
An advanced identity matching approach will compare the values in customer identifiers (such as email address, loyalty card number, cookie ID, etc.) collected in every event (purchase, web interaction, store visit, etc.). It will look to match individual customer identifiers on the fly, rather than requiring all relationships to be defined in advance. This helps build a robust customer identity quickly by joining data associated with a specific person across channels, data sources and time.
Companies that have created and are using a single customer view are using it to develop deep segmentation and employ it to track and analyze customer behavior across multiple channels.
3. Use Behavioral Segmentation to Improve Customer Retention
A large majority of companies still segment their customers only by who they are, by product type, or by region. As a result, when retention offers are made, they are almost identical and largely undifferentiated. It’s no wonder they typically have low success rates.
A lot more is needed to enable true differentiation. The answer lies in behavioral segmentation, which allows businesses to divide customers into groups according to their knowledge of, attitude towards, use of, or response to a product, service or brand. Behavioral segmentation goes a long way in improving customer retention by segmenting customers based on how they interact with products and services.
[Hand-picked related content: 10 Powerful Behavioral Segmentation Methods to Understand Your Customers]
Behavioral segmentation can find customer groups with desired characteristics, like a positive response to an offer. Use behavioral segmentation to create meaningful clusters, which when deployed using the right technology platform, can deliver true differentiated treatment.
4. Identify Targets for Customer Retention Campaigns
Although behavioral segmentation certainly adds to a company’s understanding of its customers, it does not automatically deliver differentiation. The next step is to identify the right targets, in other words those who are at the highest risk of churn, so that you can proactively reach out to them.
Companies are using customer journey analytics to improve their ability to identify at-risk customers in order to improve their customer retention rate. By gaining a data-driven understanding of customer preferences and the best ways to reduce friction in particular situations, you can more easily identify and prioritize opportunities for improvement.
Using predictive analytics and machine learning algorithms, journey analytics can help identify which customers are most likely to churn, thus providing you with valuable data to turn the situation around. You will be able to assess the probability of churn risk well before a customer churns, instead of waiting until it’s too late.
With these probabilities in hand, customer treatment and retention decisions can be made more accurately, creating offers that are more likely to be accepted and targeting those customers at greatest risk.
Companies that deploy sophisticated analytics to find their most valuable and at-risk customers and use this data to improve decision making at every point along the customer journey are able to focus retention efforts and uncover new paths to increased revenue.
5. Use Artificial Intelligence-enabled Analytics to Discover Retention Opportunities
Retention really soars when a company remembers a customer and treats them with attention, respect and consideration throughout their unique customer journey.
But, mining insights across billions of unique customer journeys using traditional analytics methods and tools is a laborious and slow process, which tends to confine it’s usage to a small set of pre-defined problems.
AI-enabled customer journey analytics provides you with the power to sift through a much larger and more complex data space and thereby uncover opportunities to improve customer retention in places you didn’t even realize you should look. As a result, you can spend your time prioritizing these insights instead of hammering away at the underlying data.
AI-enabled customer journey analytics will search across every single relationship in the data (without expressly being told to look for it). It can predict the likelihood of future behaviors with high accuracy, while simultaneously finding the drivers and inhibitors of customer retention.
Using Customer Journey Analytics to Identify Friction Points that Lead to Soft Churn
A leading retail bank uses the Pointillist Customer Journey Analytics platform to learn that usability issues with their mobile app are indicators of soft churn.
The CX team at a retail bank wants to understand the root causes of soft churn, i.e. when an account remains open, but activity severely drops.
Using Pointillist’s predictive analytics and machine learning algorithms, the team is able to visualize high-impact journeys that lead to decreases in savings account deposits, reduction in app activity and stoppages in auto payments.
By analyzing mobile app usage, the CX team discovers that in-app check deposits and bill payment problems are the main contributors to soft churn.
The bank uses the data to justify a redesign of their app experience to improve usability, increase account engagement, and ultimately reduce churn risk.
6. Make Differentiated and Targeted Offers
Actively engaged customers will naturally make more frequent purchases, spend more in each transaction and will be more likely to remain loyal customers. Making differentiated and targeted product offers is the best way to keep customers engaged and keep them coming back for more.
The single customer view and real-time customer behavior data provided by customer journey analytics platforms are powerful ingredients for creating highly personalized and targeted recommendations.
For example, a marketer could use location-based data to send an offer when a potential customer is near a store. Rather than sending a generic promotional offer, by using a customer journey analytics platform you can send a special deal featuring the pair of shoes the customer abandoned in their cart a week ago or perhaps a complementary product suggestion which they liked on Facebook recently.
7. Build Effective Loyalty Programs
Customer loyalty programs are proven to have a powerful impact on customer retention. As per recent research by the International Institute for Analytics (illustration below), customer retention is the top priority and objective of most customer loyalty programs.
According to a recent study by The Temkin Group, loyal customers are 5 times more likely to repurchase, 5 times more likely to forgive mistakes, 7 times as likely to try new offerings, and 4 times as likely to refer other customers. Loyalty programs usually take the form of memberships that confer rewards, such as airline frequent flyer programs.
By using data integration, a single customer view and the behavioral segmentation techniques described above, you can model and track customer behavior to design and execute cutting-edge loyalty programs. In order to stand out in today’s market where consumers are already flooded with many loyalty offers, differentiate your loyalty program by offering unique and personalized rewards and not just “free stuff” that has no intrinsic value in the life of your customers.
One idea that is beginning to take hold now is that of “reciprocal” loyalty or pre-purchase loyalty where the intent is to offer thanks for a small step on the part of the customer before the purchase, in order to generate a sense of loyalty and encourage the purchase. For instance, discover a way to give thanks to a customer who has tweeted positive things about your brand or contributed positively in an online forum.
Neiman Marcus Grows InCircle
Neiman Marcus has done an excellent job retaining customers shopping on any budget with its InCircle loyalty program. Through its tiered program, it provides a level of personalized customer experience that sets Neiman Marcus apart from other fashion brands.
By mining all the customer data available, the company has used it for creating sophisticated individualized recommendations and next-best actions that has helped grow the loyalty program significantly.
In turn, the data generated by the loyalty program has produced plenty of upside in the form of increased lifetime value and improved retention.
8. Determine Product Fit for New Customers
By conducting an in-depth analysis of the most impactful customer journeys and combining it with relevant product information, journey analytics can identify the needs of new customers, so your company can match new customers and prospects with the product options that are best for them.
Let’s say an insurance provider could use online customer data, call center notes, insurance claims and other behavioral data to predict what kind of insurance policy would be the right fit for a particular new customer. A tailored recommendation be provided at the very start of the customer relationship. By taking this analytics-driven step, the customer is far less likely to experience any issues and the company is much more likely to retain them.
9. Track Customer Satisfaction Along the Entire Journey
A happy customer is much more likely to stay than an unhappy customer, right?
In order to improve retention rates, it is important to track customer satisfaction rates with metrics such as Customer Satisfaction (CSAT), Net Promoter Score (NPS), First Call Resolution (FCR), Customer Effort Score (CES) and others at important points along the customer journey.
Low scores at a ‘moment of truth’ can direct product marketing and customer experience teams to focus their efforts on fixing problematic processes or underperforming features in order to retain important but unhappy customers.
10. Put Real-time Data in the Hands of Employees
Your customer experience, marketing, and product teams are ultimately the ones who will launch process improvements that improve customer retention. So, provide them with insights generated by journey analytics and real-time customer experience data, as well as fresh customer feedback.
There is no replacement for human touch and speaking directly with customers allows companies to get a better handle on what real customers want instead of thinking in terms of averages. New technologies that help you better understand your customer’s journey should not be considered a replacement for a human touch, but an invaluable aid to dig deeper and understand your customers better and provide the context to improve the way you interact with them.
Wine.com – Combining Digital Experience with Human Touch
Leading online wine retailer wine.com has put a lot of stock in analyzing end-to-end customer journeys. They have invested in recommendation algorithms that make personalized product suggestions that are very well received by their base of loyal customers.
Their convenient online experience for selection and shipping is also rated highly by customers. Overall, a well-oiled digital machine. Still, a key service differentiator for wine.com has been the way they encourage personal interaction of their customers with sommeliers.
Customers learn more about wine and explore a broad selection with the help of the sommeliers, ultimately resulting in higher retention, loyalty and increased Customer Lifetime Value (CLV).
Putting it All Together
By building customer understanding, establishing targeted and personalized product offers and selecting the right retention audience, customer journey analytics can improve your understanding of customer retention and make better decisions about how to improve it.
To improve retention, every customer interaction should be differentiated, based on what has worked in the past and what is predicted to work well in the future. More importantly, it should be tailored to each individual customer and their unique customer journey.