Smart Segmentation Azerbaijan

Analyzing customer profiles

Most business owners do not pay enough attention to understanding their customers, focusing solely on current sales figures and ensuring timely restocking to meet demand. The overall growth in the number of transactions and sales volume is viewed as a positive outcome of marketing efforts. However, businesses know little about the customers who need additional incentives to make a purchase. As a result, it may turn out that, of the entire audience attracted, only a few are ready for long-term cooperation. Under such conditions, the business is forced to constantly invest in customer acquisition but invariably loses its value in the eyes of the target audience, loses profitability, and becomes less competitive.

Creating a customer profile addresses several issues at once:

• helps build long-term relationships by stimulating customer engagement over an extended period;
• improves the return on marketing investment by creating targeted campaigns;
• drives growth in sales volume, transaction frequency, and audience reach.

Failing to recognize the need to create customer profiles can have disastrous consequences for your business, since maintaining the loyalty and interest of your existing audience costs approximately five times less than building a new one. The main challenge here is the need to collect and process large amounts of data related to the user experience. For example, the number of receipts accumulated over the years of a company’s operation can run into the billions. It is impossible to process such a volume of information effectively without specialized software.

How we create customer profiles

A customer base can be analyzed from various perspectives. By leveraging BI capabilities, we have developed a virtually universal approach that allows for flexible customization of key parameters, helping virtually any business create target audience profiles.

Cohort analysis

Group customers based on their first interaction. This could be registration, receiving a loyalty card, or clicking a link to the company’s website. For analytical purposes, profiles are created based on the year/month/week/day of the first interaction involving the purchase of a product or the receipt of a service. The optimal grouping period depends on the specifics of the product offered and the actual frequency of its purchases. Subsequently, the formed groups are evaluated based on the duration of engagement. The results of cohort analysis clearly show that the percentage of customers acquired during a specific time period was retained. Regular and significant losses of a portion of the customer base must be identified and analyzed in detail.

Cohort analysis answers the following key questions:

• how your customer base changes over time;
• where to focus your efforts for business growth: on attracting or retaining your target audience;
• how customers respond to marketing campaigns.

Cohort analysis allows you to clearly compare the number of active and lost customers, which reveals the extent of missed opportunities. In this case, it is important to understand what caused the churn and to develop a set of measures that will help maintain audience engagement in the long term. Cohort analysis also helps determine the scale of new customer growth, which is useful for evaluating the effectiveness of marketing campaigns designed for different customer segments. Such a comparison can serve as the basis for developing a future relationship-building strategy.

By creating profiles based on cohort analysis, you will be able to:

• identify opportunities for further growth;
• analyze the customer lifecycle for different cohorts;
• determine which acquisition methods and channels have proven most effective in attracting new customers.

By cohort, you can analyze average receipt value, sales volume, and examine other metrics.

RFM analysis

This involves creating customer profiles based on criteria such as recency, frequency (number of visits, orders, etc.), and purchase amount over the period under review. RFM analysis provides valuable insights that enable the personalization of marketing offers for different customer profiles:

• Offer the most favorable terms to those who shop frequently and in large quantities;
• Provide additional incentives, such as loyalty programs and various bonuses, to those who do not quite qualify as VIP customers but shop frequently or in large quantities;
• Remind those who haven’t shopped in a while of the benefits of continuing to do business with us;
• Encourage one-time buyers to become regulars by inviting them to join the loyalty program.

RFM analysis serves as the foundation for creating a customer map. It is important to focus on new customers, who—with the right efforts and sales support—can become loyal customers and generate significant revenue. The least significant segments, which are small in number and do not generate the desired level of sales for the business, can be eliminated.

Customer clustering

Cluster analysis involves grouping objects based on the similarity of a set of characteristics. In this case, a customer profile is created based on predefined parameters. Behavioral factors within individual groups show significant similarities, which makes it possible to create personalized offers that appeal to the selected target audience. The main goal of clustering is to identify the segment where the company’s ideal customers are concentrated. To do this, you need to define key characteristics for analyzing profiles. These may include purchase frequency, length of time as a customer, total amount spent, average receipt value, product range, shopping basket, response to promotions, and others.

The main challenge of creating customer profiles through clustering is the need to process large amounts of data. For example, if you identify just 9 key characteristics and create 3 groups for each one to segment customers, you’ll end up with over 19,500 clusters. Of course, developing that many personalized offers is impractical. Therefore, the program uses internal algorithms to identify similarities among customers based on a predefined list of analysis criteria.

Clustering can be performed over a specific sales period, which provides up-to-date data based on the current product range and target audience. It is not always possible to immediately identify profiles of ideal customers who exhibit the same level of activity and generate the majority of revenue. Based on certain criteria, some clusters may not meet the ideal and require a little extra effort to improve their metrics to optimal levels. Special attention must be paid to developing relationships with such customers. At the same time, cluster analysis helps identify profiles for which it is not advisable to allocate significant resources to relationship development, as this will not significantly improve sales metrics.