Online objectives
LVT (Lifetime Value): LTV measures the total value that a customer brings throughout their relationship with the brand. It is key to understanding profitability and justifying marketing expenses, since a high LTV indicates that customers repeat purchases and generate sustained income. Comparing LTV with CAC will allow us to evaluate whether the acquisition cost is offset by the value generated.
Data Collection: It will be calculated using the average purchases per customer, the average order value (AOV) and the estimated duration of the relationship with the customer, obtained from the sales system and CRM.
Responsible: The Finance Analyst will calculate the LTV to guide loyalty strategies.
Customer Retention: Customer retention is crucial because maintaining existing customers is more profitable than acquiring new ones. A high retention rate indicates that the brand is offering a satisfactory experience and building loyalty among its buyers. Improving this KPI helps maximize the long-term value of each customer, reducing acquisition costs and increasing recurring revenue.
Data Collection: The percentage of customers who make repeat purchases over a given period will be calculated using the sales and CRM system.
Responsible: The Marketing Analyst will be responsible for collecting and analyzing retention data, while the Project Leader will monitor results and adjust strategies to improve loyalty.
Engagement: Engagement measures the level of interaction and connection of customers with the brand, whether on social media, email, or on the web. It is key to assessing the effectiveness of marketing campaigns, customer loyalty, and brand perception. High engagement indicates that the audience is interested and engaged, which can translate into higher sales and long-term loyalty.
Data Collection: Data will be collected on social media interactions (likes, comments, shares), email open and click rates, and website visits, using digital marketing and social media analytics tools.
Responsible: The Digital Marketing Analyst will be responsible for collecting and analyzing engagement data.
Customer Satisfaction: Customer satisfaction is critical to brand success, as satisfied customers are more likely to make repeat purchases, recommend the brand, and generate a positive image. Measuring satisfaction allows you to identify areas for improvement in products, services, and the shopping experience, optimizing the customer relationship and fostering loyalty.
Data Collection: Satisfaction data will be collected through post-purchase surveys, online reviews, and customer service scores using survey and CRM platforms.
Responsible: The Customer Service Manager and the Marketing Analyst will be responsible for managing the surveys, analyzing the results, and proposing improvement actions.
Marketing Campaign Conversion: Measuring the conversion of marketing campaigns is crucial to assess the effectiveness of advertising actions and understand how well campaigns are generating sales or desired actions (such as registrations or downloads). This metric allows you to optimize return on investment (ROI), adjust strategies, and allocate budget to the most profitable campaigns.
Data Collection: Clicks, interactions, and conversions (sales, registrations) will be measured for each campaign using marketing analytics tools (Google Analytics, email marketing platforms, social media).
Responsible: The Digital Marketing Analyst will be responsible for data collection and analysis.
Purchase Frequency: Purchase frequency is key to understanding how often customers return to the store, indicating their loyalty and satisfaction with the brand. This KPI helps identify the most loyal customers, predict demand, and develop loyalty strategies, such as personalized offers or rewards programs, that can increase profitability.
Data Collection: The average number of purchases per customer in a given period will be calculated using data from the sales system or CRM.
Responsible: The Finance Analyst and the CRM Manager will be responsible for collecting and analyzing the data.
Fashion Trends: Fashion trends are essential to staying competitive in a rapidly changing market. Measuring trends allows the brand to adapt to consumer preferences, anticipate what will be popular and adjust collections accordingly. This ensures that the brand offers relevant and appealing products, maximising sales and customer loyalty.
Data Collection: Data will be collected from sales of specific products, trend research on social media, fashion platforms and customer feedback.
