Increasing Customer Loyalty
Predictive analytics combined with personalized campaign automation helps companies proactively increase customer loyalty.
Increasing Customer Loyalty Depends on Many Factors
Customer loyalty is generated by all the activities and policies which a company employs to encourage existing customers to remain with the company. In a broad sense, the treatment experienced by customers at every stage of their relationship with the company – from pre-sales to post-sales to customer service – will determine the willingness of any given customer to maintain the business relationship. Regardless of the type of business, a happy and satisfied customer is obviously much more likely to remain a customer than one who is disappointed or frustrated with the experience.
Actively Increasing Customer Loyalty
In addition to the customer-initiated interactions with a company, marketers (or “retention experts”) within the company often execute campaigns aimed at encouraging a customer to conduct more business with the company or spend more on each transaction. Typically, these types of actions include sales, discounts, special offers, bonuses, rewards, VIP clubs and membership/loyalty programs.
In order to be effective, active attempts to increase customer loyalty need to approach each customer within the context of their unique behaviors, needs and preferences. Unfortunately, many marketers essentially assume that all customers (or large subsets of their customer base) will respond similarly to any given offer. Not only does this approach reduce the effectiveness of the customer loyalty enhancement efforts, but it can actually lose the company money by, for example, providing free bonuses to loyal customers who would have spent more anyway.
Using Predictive Analytics to Increase Customer Loyalty
Marketing efforts are always a numbers game: how many customers will respond to a particular marketing action, how much impact will the marketing action have on those customers who do respond and what ROI will result. Whatever the goal of a marketing action – convert free users to become paying customers, increase customer spend, bring back customers from churn, etc. – marketers constantly seek to utilize the marketing actions which will have the greatest impact on their KPIs.
By intelligently segmenting customers into small segments predicted to behave similarly in response to particular actions, the marketer will almost always realize more successful marketing campaigns, with immediate improvements in sales and retention – and, of course, customer loyalty. As a side benefit, targeting only the most relevant customers with each action can yield lower marketing costs.
The challenge in this approach is that it requires a very high standard of technical expertise among marketers and analysts, along with large amounts of time that most marketers simply don’t have. However, there is a way for marketers (even non-technical marketing people) to access the sophisticated tools and automation technology which can achieve reliable and accurate segmentation and predictive analytics.
Improve Customer Loyalty – Automatically
Automating a marketing machine aimed at increasing customer loyalty requires sophisticated software. Such an application will drive the entire cycle of planning, running, measuring and managing marketing actions. The goal is to maximize customer retention, customer loyalty and customer lifetime value by scientifically predicting the most effective marketing action to run for every customer, every time.
The first phase performed by the customer loyalty software involves slicing and dicing all available customer behavior, transaction and demographics data in order to achieve high-resolution customer micro-segmentation. Then, the marketer uses the software to plan which marketing actions to run on each “target group” (lists of periodic customers discovered by the software, such as newbies, high-spenders, about to churn, churn, back from churn) – and possibly to automatically execute the actions as well.
The software tracks the impact of each action on each micro-segment in order to learn which actions are most effective for each micro-segment. In the immediate term, the marketer will have total clarity into the effectiveness of each marketing action. Over time, the software will be able to recommend which marketing action will be most effective for each and every individual customer.
The Technology Enabler
Achieving accurate predictions of which marketing actions will be most successful for each customer is not a simple matter. In fact, successful automatic customer loyalty software needs to seamlessly combine four disparate technological capabilities:
- Dynamic and ongoing segmentation of customers into small groups (micro-segments) who will likely behave similarly in response to marketing actions
- Behavior modeling to predict how each micro-segment of customers will respond to each available marketing action
- Customer lifetime value forecasting to predict the long-term impact of marketing actions on customers (not just the immediate-term results) at every point in the customer lifecycle
- A self-learning, closed-loop action optimization methodology which can test, track and optimize how marketing actions affect micro-segments of customers
Given the complexity of performing these calculations, even when all the required source data is readily available, it is not surprising that automated customer retention is not widely available. However, by implementing such a system, marketing and retention teams will be able to reduce the guesswork involved in their efforts – and dramatically improve their performance.
Increase Loyalty with Automated Customer Loyalty Software Today!
Optimove is a Web-based (SaaS) customer loyalty software product dedicated specifically to the mission of maximizing the personalization and effectiveness of every customer marketing campaign. The product’s ground-breaking technology is the first to integrate all the necessary auto-segmentation, statistical and predictive models required to accurately calculate and predict customer behavior and customer lifetime value, along with the application framework to select target groups, interface with campaign management systems, measure marketing action results and predict the most effective future actions for each customer.
Last updated August 2016