The current scenario is highly digitalised and increasingly competitive. In this environment, customer retention has become a priority for organisations. Moreover, it has been proven that keeping an existing customer is up to seven times less costly than acquiring a new one. Therefore, it is vital for a company to develop customer retention strategies based on solid and efficient data in order to understand the customer’s buying behaviour and guide the strategy to optimise retention.
If you want to discover some of the strategies based on data analysis to retain customers effectively, this is the article for you!
4 strategies based on data analytics to retain customers
Customer Segmentation Analysis
This approach allows organisations to group their customers into different segments based on common characteristics, such as buying behaviours, preferences, geographic location, among others. By segmenting their customers, companies can tailor their offers and communications to meet the needs of each group.
Customer segmentation can be done in multiple ways, but in case you don’t know where to start, we bring you 2 options to analyse your customers’ behaviour:
RFM segmentation: analysis focused on the frequency of purchase and spending to position customers and establish the appropriate management strategy, prioritising those with the highest value. In addition, customers with less activity are identified and precise campaigns are carried out for each one according to their consumption, carrying out more personalised and effective communications.
Customer lifecycle: through this analysis, knowledge and anticipation of the movements of the customer cycle is achieved in order to achieve an excellent experience with the brand and increase their level of satisfaction. In addition, it allows commercial actions to be directed with greater criteria, content and anticipation of customer needs.
Three-dimensional customer segmentation (proprietary methodology)
At Cognodata we have our own segmentation in which we define 3 fundamental axes to outline the segments and be able to offer campaigns that are more targeted to the needs and preferences of the client.
1. We analyse the client on 3 axes
Through the development of a scoring algorithm based on similarities, we classify customers in the following way:
- Household type: we determine the situation of customers in relation to spending and frequency at the brand. This analysis can be done through surveys.
- Potential (pocket share): on this axis, we study how the spending and frequency of customers is distributed in order to calculate their potential. In this way, cut-off rules are established to analyse the consumption of each share of wallet (SOW) of each income level and the general SOW of all customers is estimated.
- Basket analysis (income level): to determine the income level, the socio-economic level index of the customer’s home country is used. A price gap catalogue is created for each product against the cheapest product in each sub-category. After this, the shopping basket is analysed and the spending rate of the customers is identified.
2. Analysis of opportunities according to customer, product and channel
Based on the previous analysis, the opportunities are analysed by action in order to subsequently define the strategy to be followed for each customer, product and channel. For example:
- Main shops: by carrying out strategies in shops where certain segments are very relevant, consumption in those segments can increase by 5%.
- Tastes and preferences: by implementing strategies based on customers’ tastes. You can increase a percentage of customers for each segment depending on how we respect the market.
- Top 10 products: by strategising on top products for each segment, you can increase consumption of these products by 10% among customers in the same group.
Predictive analytics uses data, algorithms and machine learning techniques to predict future behaviour and trends based on historical data.
After studying the customer’s profile, is able to reveal what they want, when they want it, how they want it and how it is easiest to reach them in order to convert. Algorithms work to identify satisfied and dissatisfied customers, allowing the creation of strategic marketing campaigns tailored to each need.
Therefore, this type of analysis can be useful to anticipate customer needs and offer proactive solutions, which will improve customer satisfaction and strengthen the long-term relationship with the brand.
Sentiment data analysis allows you to understand how customers feel about a brand, product or service. It allows you to use that information to improve the customer experience and foster loyalty. In addition, it helps to identify problems or concerns that lead customers to abandon the brand.
The first step in sentiment data analysis is to collect relevant data. This can include customer comments on social media, online reviews, satisfaction surveys and other communication channels.
It is important to use text mining and natural language processing techniques to extract meaningful information from this unstructured data. So you can understand customers’ emotions and opinions on social media, emails, product reviews and other channels.
Data analytics strategies to retain customers should be an integral part of any company’s marketing strategy. As the world becomes increasingly digital, companies that fail to adopt these techniques risk being left behind. By adopting a data-driven approach to customer retention, businesses can ensure they stay ahead of the competition and continue to thrive in the changing business landscape.
Want to learn more?
At Cognodata we have over 20 years of experience in strategic customer management using machine learning and artificial intelligence techniques. Do not hesitate to contact us if you would like us to show you a demo with some of our use cases in the retail sector.