Do you know why customers stop buying from you and choose competitors instead? We bet that you focus on customer acquisition and business development, usually diminishing the importance of retaining existing customers. Just like most companies do. But no customer should ever feel forgotten. That’s the rule no retailer should ever compromise.
Harvard Business School found that reducing customer churn by 5% may increase company profit by up to 95%. Moreover, nurturing loyal customers is six to seven times cheaper than acquiring a new audience. Simply put, keeping existing customers helps you increase brand loyalty and improve company’s reputation.
But how can companies control customer retention?
What is customer churn analysis?
Customer churn analysis in retail involves examining your customer retention data to identify problematic areas. It requires gathering and analyzing information on customer interactions and satisfaction with your store. Collecting and analyzing this data can be intensive. But why even bother trying to predict customer churn?
The importance of churn rate analysis in retail
Retail customer churn analysis helps identify trends in customer loyalty, enabling you to address emerging issues before they escalate and strengthen relationships with high-value clients to boost their customer lifetime value (CLV). Retail customer churn prediction also allows you to segment customers based on their likelihood of leaving, which can guide you to take preventive actions, such as:
- Optimizing inventory to prevent dissatisfaction
- Launching engagement campaigns
- Offering exclusive deals or pricing
Losing customers soon after acquiring them can significantly impact your ROI and inhibit future growth, especially in the face of rising inflation and living costs. Additionally, customer churn prediction for retail businesses is crucial for evaluating the effectiveness of your marketing efforts. For example, a sudden increase in churn following a change in your email campaign might suggest the campaign was poorly received. However, it’s important to consider that many factors can influence churn rates.
Given the volume and speed of data acquisition, manual customer churn analysis in retail is virtually impossible. Therefore, AI and ML technologies are essential tools for performing effective customer churn analysis.
Improving customer retention with machine learning
While many companies accumulate vast amounts of data, only a few analyze it effectively. Manual analysis is impractical and, even if feasible, is prone to human error and unable to detect complex patterns. Using machine learning for churn prediction, however, can overcome these limitations, making churn prediction more accurate and efficient.
No matter what size or operational model is your business, you have to keep customers satisfied all the time, knowing their needs and foreseeing wants. You may know customers who already left your business, but it’s a tough challenge to identify customers planning to leave soon. The best way to tackle this problem is to analyze clients that don’t buy from you anymore.
Machine learning algorithms and applied statistics methods can help build a business intelligence reporting system that allows revealing clients at churn risk. Using the ML power to historical data, you’ll make it work to predict future churn as accurately as possible. The deployment model will show you valuable figures daily.
Given that customer churn analysis is an essential part of complex customer relation management, you need to integrate it with your overall marketing plan. When you know churn probability for each client, you can apply an actionable strategy for their retention and restructure marketing activities accordingly:
- develop customer loyalty campaigns;
- focus on valuable customers and let go jumpers;
- use more flexible marketing and sales campaigns;
- change products to cover customer needs.
Clothes stores might even consider new assortment based not only on the fashion demand forecasting that defines upcoming trends but based on consumer demand forecasting.
The most common reasons for customer attrition may even surprise you. They include such factors as the company’s service level, pricing terms, delivery policy, competitors’ strategies, economic climate, seasonality, and general industry trends. rs as the company’s service level, pricing terms, delivery policy, competitors’ strategies, economic climate, seasonality, and general industry trends.
1. Using more flexible campaigns
While it’s impossible to know everything about every customer and personalize every interaction, AI can help you tailor your marketing campaigns to a broader audience effectively:
- Segmentation and personalization: Machine learning models can segment your customers and deliver personalized offers based on their risk of churn.
- Predictive analytics: ML models can forecast which customers are likely to churn, enabling timely preventive actions. Cloud-based predictive analytics can further reduce response times and provide real-time data.
- Optimization: ML algorithms can dynamically adjust campaign elements, such as email subject lines, ad placements, or offer details, to enhance effectiveness.
- Sentiment analysis: AI can analyze text and visual data from sources like customer reviews, social media posts, support tickets, and even memes to gauge sentiment and adjust your strategies accordingly.
Changing your marketing strategy isn’t the only way to reduce customer churn and improve retention. Often, updating or enhancing your products is necessary to meet evolving customer expectations and address issues that may drive customers away.
2. Changing products to cover customer needs
Businesses should adjust to their customers’ needs, but this can be challenging in retail, especially with thousands of products that can’t be directly influenced. Here’s how machine learning (ML) can help:
- Personalized Product Recommendations: AI analyzes customer preferences and behaviors to suggest products that better meet their specific needs, potentially filling gaps in their current product usage.
- Dynamic Product Bundling: AI algorithms create personalized bundles based on purchase history and preferences, offering comprehensive solutions tailored to customer needs.
- Product Usage Analysis: For connected products, AI examines usage data to identify how customers use products, guiding improvements or new feature development.
While flexibility with products is crucial for improving customer retention and reducing churn, there’s another effective technique companies use.
3. Developing customer loyalty programs
According to Accenture, over 90% of companies had loyalty programs in 2016, and for good reason. Antavo’s Global Customer Report found that 90% of companies using loyalty programs reported a positive ROI, with average returns of 4.8 times. Here are a few ways of how artificial intelligence services can enhance your loyalty program:
- Dynamic pricing: ML algorithms adjust reward values in real time based on inventory, demand, and customer value.
- Fraud detection: AI identifies unusual patterns to prevent abuse of loyalty programs.
- Program optimization: AI continuously analyzes program performance and suggests improvements.
With AI and ML on your side, you can create a flexible loyalty program to reduce retail customer churn and keep as many customers as possible.
But do you need to keep all the customers at any cost?
4. Keeping only active clients in focus
According to Bond Brand Loyalty, an average customer in the US is a part of 16.7 loyalty programs while only being active in 7.4 of them. While there are always clients who switch between you and your competitors, retail companies should offer a differentiated client experience and tailor their loyalty rewards individually.
There are always migrating clients, and a certain level of customer churn is quite reasonable. One of your main tasks is to differentiate customers who belong to this group. Defining figures that describe this client outflow and observing it is the key to successful customer relationship management.
Even if there were no jumper-customers, the natural attrition would always exist. For example, children’s products cease to be relevant when the child grows up. The client can switch segments — from an economy to a premium sector — or move to another area, region, or even country.
Global trends also affect churn. When electronic books substituted paper ones, bookstores experienced terrible losses of customers.
We suggest creating the criteria for a clear understanding of which customer is considered to be lost. Plus, you should understand the customer value: always define the ratio between the price of customer acquisition and the profitability they bring or will bring to the company. And if talking about churn, you should always distinguish between the customer turnover and the cash one.
But how exactly can you set up reliable churn algorithms?
Customer churn prediction model and machine learning in retail analytics
During the churn analysis, it’s vital to conduct an assessment of the acceptable churn level. It will allow you to adjust your customer churn modeling according to the company’s current conditions. Different customer segments vary in behavior, so one or several specific customer groups form the most significant turnover.
Now, let’s define critical factors for a successful churn analysis.
Data
The prerequisite to building an actionable machine learning churn model is that you collect and analyze customer data. Here are the characteristics of the data relevant to create an ML algorithm:
- Quality
Your data should correspond to reality. For example, if your sales assistants don’t validate customer questionnaires, the data collected is most likely useless.
- Variety
You need every piece of information: social data, financial data, data on transactions, purchases, and preferences.
- Amount
The more, the better.
- Relevance
Only up-to-date insights are valuable.
- Origin and format
The format of data is also precious — text, photos, videos, and so on. The higher the variety of relevant data used to build the model, the more accurate is its prediction.
You may find inconsistencies and anomalies during the initial data review — it’s possible to eliminate them with each iteration, validating and selecting the most relevant pieces.
Prediction window
The forecast period is called the prediction window. You should adjust this parameter along during the process of model validation. One of the most compelling cases is to create a stepped scheme that allows making predictions for several periods simultaneously.
Relevance
Every model becomes outdated over some time — internal and external conditions change, company goals grow. You should update and calibrate the model frequently upon the results you get.
Results interpretation
The ML-powered model aims to tell you what is the probability of this or that client leaving your business within a specified period. Considering clients based on the profitability they bring will allow managing churn risk with the help of targeted marketing strategies — either loyalty campaigns or assortment changes. The acceptable churn rate is defined by industry peculiarities in general and company specifications in particular. Thus, for example, the 75% churn rate of a client is quite risky for one company, while another will accept the level of 85%. The same company may also have different numbers in various locations.
Modern churn detection is already impressive but what are the improvements we can all look forward to in the future?
The future of customer churn analysis in retail
There are several promising technologies that could see use for predictive churn analysis and prevention:
- Augmented reality data utilization. As AR shopping experiences become more common, analyzing customer engagement with virtual products could provide new insights into potential engagement with real-world ones.
- Environmental factor correlation. AI systems might incorporate external data like local economic indicators, weather patterns, or social trends to predict churn risk more accurately.
- Emotional analysis. AI-powered systems could analyze customer voice patterns as well as body and facial movements during in-store interactions to detect frustration or dissatisfaction.
- Neurological response analysis. As wearable technology advances, retailers might use brainwave or other neurological data to gauge genuine customer satisfaction.
- Quantum computing. The speed at which modern computers process data is already impressive, but quantum computing could further enhance this ability to detect subtle hints of dissatisfaction across millions of customers in the blink of an eye.
These are the biggest trends we see in 2024, but we remain open to being pleasantly surprised by unexpected developments. What won’t surprise us is unpredicted customer churn — because we stay ahead of the curve.
Stop customer churn before it even starts
Knowing the probability of customer churn lets you create targeted loyalty campaigns aimed at the segment with the highest chances of attrition. Churn analysis provides valuable insights on the risk and level of outflow (both client and money) as well as the ability to manage these factors.
At Intellias, we help provide churn assessment in a tight cooperation with you. We provide a detailed report on key factors that make customers leave. We conduct the assessment based on both industry and company peculiarities, defining segments at the highest churn risk. Our experts validate data, eliminate anomalies, and help customers remove errors in data collection and storage processes with the help of retail software design services.
If you fail to nurture existing customers, don’t know how to analyze retail data, or want to perform customer churn prediction with machine learning, contact Intellias. Our retail software development experts will make your business profit with ML-based models and powereful eCommerce supply chain solutions that will help you engage customers and optimize ROI.
