The Three Pillars of A Winning Data-Driven Customer Retention Strategy: Data [Part 1]
It’s not easy to craft a winning customer retention strategy unless you know what makes customers tick and can act on that information, in real time, and at scale. Surely, you can track any given customer’s purchases, be they individuals, B2B or enterprise sales. If only it were as easy as pulling up last year’s purchases and sending an “it’s time to renew” email, or sending an offer to this year’s customers before their subscription is up.
Customers aren’t just numbers and spreadsheets, they are people with beliefs, feelings, and ideas. Each of them has had a unique experience with your brand. So, the more you tap into the data, the more personal and relatable your service or product should become. Quite an equation!
While the Digital Experience Team at Exadel can translate your business objectives regarding customer retention into a digital solution, the best outcomes rely on having the right data, and the right insights. But, what is it that you need to analyze and how do you build digital solutions that maximize customer retention outcomes at scale?
Over the course of two decades, we’ve learned that there is more to a website, an algorithm or intuitive design, which is this: the importance of a data-driven customer retention strategy. Because of our experience and expertise in helping clients with customer retention data, we’re going to share our insights in a series of articles.
Today, we’re starting with data — good data is as good as gold, of course — so we’ll start by walking you through several topics that we hope get you thinking differently about your data and how you can better harness it to drive retention.
Data Accuracy Makes All the Difference in an Effective Customer Retention Strategy
Data accuracy and fidelity is especially valuable when it comes to evaluating your data sets for activating customer retention campaigns and digital experiences. Data accuracy builds the foundation for you to work on and go back to when alterations are needed.
Data Accuracy & Customer Retention
Let’s admit it, no one’s data is “perfect”. Data accuracy is impacted by the nature of large enterprises, disparate systems, and technical debt. Additionally, business and technical realities of what happens to enterprise data over time — acquisitions, divestitures, product roadmaps, lines of business, regions and territories — have an impact on data accuracy as well.
While no organization can be perfect, establishing a baseline of data hygiene and a customer retention focus as key imperatives can help guide your roadmap.
One of our clients was stuck in analysis paralysis over the gaps in their data when it came to activating it for future campaigns and digital experiences.
How did we break through with something that was sure to make an impact on their customer retention strategy?
We looked at the last round of bad retention outcomes (cancellations, downgrades, deep discounts) and asked ourselves: “How could better data have helped us save the situation?” Looking for themes among the gaps instantly broke the paralysis. In this case, the overarching theme was modeling renewal outcomes into groups and noticing that bad outcomes shared a few things in common, and translating those things into both KPI’s for ongoing measurement, as well as Audiences; so each of the Audiences, in the future, would receive differentiated experiences in revamping the approaches for their next best action, next best offers.
Data Fidelity & Customer Retention
Data Fidelity means having the details and timing “just right”. For example, if your team is telling you they can’t form audiences by geography, sales stage, product line, or household — then focusing on details should help (see harvesting and other strategies below).
If your customer retention data is siloed, batched, or has a freshness date that expired too long ago, you could be missing out on opportunities. In some industries, hours and seconds could make or break the bottom line – timing is everything! Not only does Data Fidelity serve your data retention strategies, but it’s also baked into customer journeys, from “happy path” and “intervene to save” sequences.
A client we worked with was so focused on sending the right audience the right renewal offer, that they missed a critical subscription downturn. A multitude of cancellations were due to sending offers after a recent system outage, others were from customers with recent calls to customer support with an open (and aging) support ticket.
Remember, with Data Fidelity, details and timing matter! Data Fidelity helps you provide experiences without the risk of being “tone deaf” or missing important cues in the customer journey.
Building Complete Pictures Through Data Harvesting
Data Harvesting is a crucial part of a digital experience strategy that involves enriching datasets and their associated metadata from first-party and third-party data sources. The best companies focus on both when it comes to customer retention analytics.
In a nutshell, you need a well-organized data harvesting program to fill in gaps over time in your customer profiles and audiences, and build deeper insights. As the image below implies, you pull a set of data based on your data accuracy and data fidelity gaps (discussed above).
Data harvesting to fill in gaps in your data is especially important before you label customer behaviors or synthesize customer sentiment in order to build reliable, high-confidence audiences to activate with digital marketing campaigns and differentiated experiences.
Customer Behavior Analysis
Collecting and analyzing data on customer behavior, such as the items shown below, allows businesses to understand their preferences and habits. A customer retention strategy is incomplete without customer behavior insights. The data can be used to personalize marketing, improve products or services, and anticipate customer needs, ultimately leading to higher retention rates.
Website Analytics
Social Media Monitoring
Email Marketing
A/B Testing
Personalization Algorithms
Location-Based Data
Feedback and Sentiment Analysis
Harvesting customer feedback and sentiment data from sources like reviews, comments, user generated content, surveys, and social media helps understand customer satisfaction and pain points. By analyzing these data, businesses can address issues promptly, enhance customer experience, and fine-tune their offerings to keep customers engaged and loyal.
Order above all, they say. Unless you have categories and supporting data behind them, it will be difficult to accurately analyze your customer profiles.
Third Party Data
The topic of third party data might warrant its own article. If you’re familiar with the Adobe Experience Platform, Adobe Audience Manager, and the variety of Adobe tools for experience personalization, you may have already implemented one or more third-party data solutions. Many companies find they significantly up-level their results by including well-matched third party data as part of their customer retention data set.
Some examples of how the most successful brands use third party data to help customer retention:
- A high-end retailer might use third-party data to identify segments of customers who are at risk of churning based on factors such as changes in income, recent life events, or shifting interests. With this data, they can design highly targeted retention campaigns, such as personalized discounts or loyalty rewards, to re-engage these at-risk customers and improve retention rates.
- A financial services firm might integrate third-party data about customers’ hobbies, interests, and lifestyle changes; the customer can personalize and update content and recommendations. This can include tailored product recommendations, content, and promotions that align with each customer’s evolving preferences and needs, thereby enhancing the overall customer experience and increasing loyalty.
Predictive Analytics Can Drive Better Customer Retention
Predictive analytics is a data analysis technique that uses AI solutions to interpret historical and current data to forecast future events or trends. It lets you make informed decisions, optimize processes, and achieve a multiple goals by providing insights into what is likely to happen in the future.
In the case of digital experience, predictive analytics for customer retention can help you offer the right experiences, to the right customers, at the right time, leading to optimal results – most notably in revenue and margin.
Predictive analytics can guide your customer retention strategy and:
- improve campaign performance
- allocate resources where they make the most impact
- enhance customer experience
- reduce risks of cancellations, downgrades, discounts/margin erosion
Top 10 Ways Predictive Analytics Improves Customer Retention
Predictive analytics can significantly impact your customer retention strategy by using historical data and analyzing patterns to predict which customers are at risk of churning so you can implement strategies to keep them engaged.
| Data Use Case or Retention Relevance | The Essence of this Data | Examples and Actionable Steps |
| Customer Segmentation |
Predictive analytics segments customers based on their behavior and characteristics. Some are obvious, like grouping customers by revenue, margin, product, shopping history, age, location. Some are less obvious, like grouping customers by behaviors and engagement score, where engagement includes support, satisfaction and brand advocacy. | Companies who mix and match demographic segments with behavior segments automatically start with a number of unique audiences (combinations) they can use to provide differentiated, relevant experiences. As an actionable next step, think about how many combinations might be relevant for your particular customer retention strategy. Some companies have dozens, or even hundreds. |
| Churn Prediction | By analyzing data, such as customer activity, support interactions, and feedback, predictive analytics identifies customers showing signs of potential churn. | A services company might notice that customers who experience issues in their first three months are more likely to stop using services and silently opt out at their annual renewal. They can then prioritize a follow-up interaction / intervention for those customers and present them with compelling reasons to re-engage. |
| Personalized Recommendations | Predictive models, specifically those which are aligned with customer retention analytics, provides personalized product or content recommendations. | Streaming services like Netflix use predictive algorithms to suggest content that a viewer is likely to enjoy based on their past viewing behavior. This keeps customers engaged and less likely to cancel their subscriptions. Whatever business you’re in, the more you can harness data to personalize recommendations, the less risk you have for customer churn. |
| Price Sensitive Customers | Some businesses, like airlines and hotels, use predictive analytics to adjust pricing dynamically. Customers get used to this and knowing which customers are most price sensitive can help you avoid unnecessary discounts. | A company with a complex pricing strategy creates audiences which reflect a variety of inputs and calculated metrics, including past discounting, margin, sales team input, competitor research. Having a “bargain hunter” audience might be relevant for your business, and if it’s not, perhaps one that takes into account other pricing inputs, such as customer lifetime value, product demand elasticity, and market segmentation. |
| Feedback Analysis | Predictive analytics analyzes feedback and survey responses to predict customer satisfaction and identify areas where improvement is needed. | An online service provider captures in-application feature ratings (like a thumbs up / thumbs down). Feedback trends serve as a signal for when a customer might benefit from hearing from the sales or product team on future roadmap, features under development, or planned enhancements. |
| Customer Lifetime Value |
Predictive analytics estimates the lifetime value of a customer. | An online subscription service can predict how much revenue a single customer can generate over time. By identifying high-value customers, businesses can offer them special incentives, or premium experiences to keep them loyal and engaged. |
| Anticipating Customer Needs | Predictive analytics forecasts what a customer might need in the future. | Sometimes the most under-utilized data sources are those from fully engaged customers, those without much retention risk at all. Building audiences around the most engaged customers and mapping out the customer journey, can result in customer data that substantiates requests for the sales and product teams to consider. |
| Proactive Customer Support |
By analyzing customer data, predictive analytics identifies potential issues before they become critical. | An e-commerce platform monitors user behavior and identifies a pattern where customers frequently abandon their shopping carts at a specific step in the checkout process. Predictive analytics flags this issue as a potential point of frustration for customers. The marketing team then collaborates with the user experience (UX) team to optimize the checkout flow, thereby addressing the bottleneck. By proactively improving the digital experience, they reduce cart abandonment rates, enhance customer satisfaction, and ultimately contribute to customer retention. |
| Retention Offers | Predictive analytics identifies customers who are at risk of leaving and why. | A subscription-based streaming service can use predictive models to identify users who have reduced their viewing frequency. They can then send targeted offers or content recommendations to re-engage those users, improving retention rates. |
| Optimized Communication | Predictive analytics helps businesses determine the most effective communication channels and timing for different customer segments. | A healthcare provider uses patient data to discover that certain patients prefer SMS for appointment reminders and health tips. They adapt their communication strategy accordingly, sending SMS messages at convenient times. This improves patient engagement, reduces missed appointments, and enhances satisfaction, showcasing the value of data-driven communication optimization in healthcare. |
It’s important to note that while data harvesting can provide valuable insights and enhance digital marketing and user experiences, it must be conducted ethically and in compliance with relevant data privacy regulations to protect users’ privacy and rights.
Lots of data? You bet! It’s the tool you can use to learn about your customers over time and really offer something they will appreciate or can’t say no to. Refining your customer retention strategy with each insight is the approach each customer-facing organization should be working towards — continuously.
Data-Driven Customer Retention Strategy — Come for More!
The second part of this article series focuses on the wealth of data gathered through loyalty programs and customer profiles, as well as how to use those insights to improve customer retention. The final installment examines the use of customer data in corporate storytelling.
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