Data-Driven Marketing Strategies for Cirque du Soleil in Barcelona
Data sources promote ticket sale Cirque Soleil
Structure:
Structured: Utilize past ticket sales data for insights into customer preferences such as popular seat sections, peak booking moments and demographics (income, gender, age…), help segment audience aiding in pricing targeted promotions and marketing campaigns.
Semi structured: Analyze past bcn shows reviews & ratings on Google, TripAdvisor, and social media to identify strengths and weaknesses. Address negative feedback and highlight positive aspects in marketing.
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Not structured: Analyze customer reviews, feedback, and online forums to understand cust satisfaction, preferences, and issues. Use sentiment analysis to inform marketing straegies and identify areas for improvement.
Statics: (not structured)
History: Analyze past shows data in BCN for attendance patterns, ticket sales trends, and demographics. Use these insights to schedule marketing campaigns & promotions strategically, due to identifying peak seaons & jigh demand. For instance, if summer months show spikes in sales, focus resources on promotions then.
Product infromation: details about the show such as its name, unique identifier, category (e.g., acrobatics, theater, music), and high-quality photographs or promotional materials. This information helps in crafting targeted marketing campaigns.
Dynamic: (not structured)
Bevaioural: provides insights into how people engage with Cirque du Soleil across channels. By monitoring website visits, email activity, social media interactions, and ad engagement, you understand audience preferences and intent. Analyzing this data allows segmentation for personalized marketing messages.
For instance, if someone has shown interest in Barcelona shows, targeted ad campaigns for upcoming performances or exclusive tickets can be deployed to drive conversionsTransactional: involves analyzing past ticket purchases for Cirque du Soleil shows in Barcelona. This includes details like purchase dates, ticket types (e.g., standard, VIP), and quantities bought. Examining history helps pinpoint repeat customers, high-value segments, and ticket purchasing trends. Additionally, gathering information from customer invoices, such as billing details and ticket prices, provides insights into demographics, preferred payment methods, and spending behaviors.
Collected data:
1st party (from our company): Utilize CRM data to segment customers based on their previous interactions with Cirque du Soleil, i(ticket purchases and engagement with marketing campaigns). This aids in identifying loyal customers and re-engaging inactive ones. Implement email marketing campaigns with targeted promotions and exclusive offers, segmented by customer preferences and behavior, to enhance conversion rates.
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2nd party (company working with you): Collaborate with local businesses, travel agencies, or tourism boards to access their audience data. . This facilitates targeting potential attendees interested in cultural events like Cirque du Soleil.
3rd party: Analyze Amazon purchase data through appropriate partnerships to identify potential customers who have bought related products, such as acrobatics equipment or family entertainment items.
Origin:
Self reported data: Self-reported data is directly provided by customers through surveys, registration forms, or feedback mechanisms. It offers insights into preferences, intentions, and show experiences. For example, post-show surveys gather feedback on performance quality and venue experience, aiding future
improvements. Additionally, registration forms provide demographic details, contact preferences and reasons for attendance, enhancing targeted marketing messages.Observed: Observed data is acquired through the monitoring of customer interactions and behaviors, such as visits to the Cirque du Soleil website and ticket purchases. Through the analysis of website analytics, it’s possible to monitor user behavior, including which pages visitors frequent most, how they navigate the site, and their interactions with ticketing and booking processes. This data is invaluable for optimizing the website layout, enhancing user experience, and improving conversion rates to boost ticket sales. Additionally, attendance records from previous Cirque du Soleil shows in Barcelona offer valuable insights into attendance patterns, repeat attendance rates, and peak demand periods. These insights enable the forecasting of future ticket sales and the customization of promotional strategies to better target audience preferences.
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Inferred: Inferred data involves analyzing and interpreting existing data to extract meaningful insights and predictions. For example, assigning a user profile and targeting advertisements based on browsing patterns is a common application. By monitoring user interactions on websites and online platforms, such as clicks, page views, and time spent, it’s possible to infer audience interests, preferences, and behaviors. This data facilitates the creation of personalized marketing campaigns tailored to specific audience segments. For instance, if a user frequently browses pages related to performing arts or entertainment events in Barcelona, they might be classified as interested in similar shows like Cirque du Soleil. Leveraging this inferred data, you can target advertisements, promotional offers, and show recommendations to effectively increase ticket sales among relevant audiences.
DIAGRAM
Workflow Marketing Automation
A workflow: A visual depiction outlining a series of steps or tasks necessary for completing a specific process or project. It showcases the sequence of actions, decisions, and dependencies needed to accomplish a desired result.
Benefits of utilizing workflows:
Convert leads into potential customers: Workflows enable database segmentation based on user characteristics and behaviors, leveraging information from contact emails and forms. Well-defined form fields are crucial for gathering necessary lead information.+ Personalized emails tailored to specific interests increase the likelihood of converting leads into sales. A clear content strategy aids in this effort.
Personalize content and scheduling: Tailor email frequency and content based on lead progression in the sales funnel. + Control the timing and type of content sent to each customer segment, optimizing engagement based on preferences.
Time-saving automation:
– Streamline customer acquisition and nurturing processes, freeing up team members to focus on other tasks.
– Clearly defined roles and tasks, along with improved alignment between marketing and sales teams, enhance efficiency and effectiveness throughout the sales process.
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Prepare a workflow mkt aut for El Corte Ingles, with the following scenario: Objectives: – Increase online sales of two types of users: store customers who already have ECI card and store customers who do not. + Increase loyalty of both targets through their registration on the platform. – Test how discount coupons work in both modalities. Available info: Previous purchases of users with ECI card
Question= diamond, Action = square, Waiting time = on the lines
What is marketing automotion?
Marketing automation: the process of utilising technology to streamline marketing efforts and make them more effective. It uses a single automation platform to manage every aspect of every campaign from the same place, including but not limited to email and social media marketing, mobile messaging, and managing ads.
Through tracking and analysis of visitor behaviour, the best marketing automation software enables teams to create 1-to-1 cross-channel journeys that lead to a unified experience for the customer. This improves the efficiency of a sales funnel, quickly turning a broad base of leads into happy customers by using a combination of tactics.
benefits of marketing automation are that it can help businesses to:
1. Free up time to focus on strategy
2. Get a rich, detailed picture of the behaviour of potential customers
3. Customise follow-up
4. Deploy wider marketing campaigns
5. Prioritise leads
6. Improve marketing ROI
7. Predict future investment more accurately
ATRAPALO CASE STUDY:
Problem: Company has gathered a big user database that signup to the Atrapalo Newsletters
– A big part of them are inactive
– Inactivate after 3 months: cost of sending and brand reputation as sender (spammer)
Objectives: Change activation process to avoid losing potential customers + Develop digital media activity to recover some of the inactive users.
Distribution and states:
KO = unsubscribed. We can’t send anymore emails to them. OK = user that opened an email Sleep = user that is more than 3 months that hasn’t opened an email. Pending = user that didn’t confirm double opting. Receives emails but if they don’t click they will become sleep
Sleep users have similar purchasing levels than OK users.
Improve the conversion from “pending users” to “ok users”:
Transform the confirmation email in a cycle of welcome emails with offers, benefits and reminders
If the user doesn’t confirm in 1 week, do reminders through other channels: pop-up in website, user private area, app and display advertising, and Facebook page Result: Increased the conversion to OK in 50%
Propose something to avoid users to go to the status “sleep”: Change process: if user doesn’t click in 3 months’ time, they will go to “pending”, decreasing the frequency of emails (30% less sleep users in the first 2 months)
Propose something to reactivate “sleep users”:
Send a reactivation email offering the option to manage their preferences to receive less amount of emails per week.
Send emails with special promotions (coupons) to the ones who didn’t open the first emai.l + Retargeting banners on the website to incentivate reactivation
Social media ad to followers to motivate reactivation Result: reactivation of 10% of the sleep users
How would you reactivate users that are potential customers? Automatic reactivation of the sleep users that did any purchase in the Atrapalo site AND of sleep users who navigated on the Atrapalo site. Result: reactivation of 20% of sleep users that are potential customers
Sales Data AnalysisImagine you are in a company and suddenly your boss asks you to analyze an excel. explain to me the process that you will follow in order to analyze this data:
1. Understand the excel Content:understand what the Excel file contains. Is it sales data, customer information, financial records, etc.? This will give us an idea of the kind of analysis we can perform.
2. Define Questions to be answered: Once content iunderstood, define questions we want to answer through the analysis. EX: if it’s sales data, questions could include: What are the top-selling products? Which regions generate the most revenue? Are there any trends over time?
3. Data Quality Check: Assess and analyse the quality of the data before analysis is crucial. This involves checking for gaps, inconsistencies, missing values, duplicate entries, and any other data anomalies. It is important to determine if any cleaning is required before analysis.
4. Select Analysis Tools: Choose & employ appropriate tools relative to the complexity of the analysis and the available resources. This could range from simple Excel functions and pivot tables to more advanced statistical software like Python or R.
5. Exploratory Data Analysis:Involves initially delving into the data using descriptive statistics, visual representations, and summaries. This approach aids in uncovering insights within the dataset, highlighting patterns, outliers, and correlations between variables.
6. Graphical Analysis: Generating visual representations like bar charts, line graphs, scatter plots, histograms, etc., to depict data visually. These visualizations facilitate the interpretation of trends and patterns within the data, enhancing effective communication of findings.
7. Advanced Analysis (if needed): Depending on questions previously determined, we might need to perform more advanced analysis techniques such as regression analysis, time series forecasting, clustering, or machine learning algorithms.
8. Transitioning to PowerBI Analysis: Transitioning to PowerBI Analysis involves importing clean and analyzed data into Power BI. This robust tool enables the creation of interactive dashboards that surpass static Excel reports in visual appeal and user-friendliness. With Power BI, you can construct dashboards equipped with slicers, filters, and drill-down features, empowering users to explore data from various perspectives and extract deeper insights. The true essence of Power BI lies in its capability to seamlessly connect to Excel data sources, automatically updating dashboards whenever the source file is refreshed. This ensures that your reports consistently reflect the latest data, keeping your marketing insights relevant and actionable. After defining the initial version of the analysis, we can explore options for automating the process. This could involve setting up scheduled updates for the data source, creating scripts for automated analysis using tools like Python or R, or utilizing features within PowerBI for automated data refresh and report generation.Top of FormBottom of Form
9. Results Presentation:Conclude your analysis by presenting your findings and insights using reports and dashboards within Power BI. These resources allow for effective communication of results, which can be shared with colleagues or superiors to convey your conclusions clearly and comprehensively.Top of FormBottom of Form
10. Continuous Improvement and updating: Analysis isn’t a singular task but rather an ongoing process. It’s essential to consistently review and refine our analysis in response to new data, evolving business requirements, and input from stakeholders. This iterative method guarantees that our analysis stays pertinent and insightful as circumstances change over time. Set up automated data refreshes within Power BI to ensure that the analysis remains up-to-date with the latest sales data. Test the automated update process to verify its accuracy and reliability. Document the procedures for maintaining and updating the analysis over time.
Each aspect of the sales data analysis serves a distinct purpose in understanding performance drivers, identifying opportunities, and informing strategic decision-making. Together, these analyses provide a comprehensive view of the company’s sales dynamics and empower stakeholders to make data-driven decisions to drive growth and profitability.
According to Internet:
Governmental: Utilize data on entertainment sector trends, demographics, and types of shows provided by governmental entities such as the National Institute of Statistics (INE) or the Department of Culture of the Generalitat de Catalunya.
Social Media:
Facebook: Analyze Cirque du Soleil fan groups.
Instagram: Examine followers of theater/circus accounts.
TikTok: Observe channels focused on this type of events.
Related advertisements.
Websites:
Gather data from clients of event/entertainment/activity agencies.
Analyze data from hotel websites.
Review data from El Corte Inglés or other major retailers’ websites.
According to Origin:
1st Party:
Utilize website traffic data obtained through analytics tools like Google Analytics.
Analyze conversion funnel data, including conversion rates per funnel stage.
Collect emails, phone numbers, etc., from individuals who have visited or registered on the website, gathered through registration forms.
2nd Party:
Obtain data from clients of event/entertainment/activity agencies through collaboration agreements or strategic alliances.
3rd Party:
Analyze Amazon information on buyers of related products or searches, acquired through third-party data analytics services.
According to Type:
Structured:
Utilize sales history, including detailed data on past transactions such as purchase dates, ticket types sold, prices, etc.
Semi-structured:
Analyze data from theater newsletters and cultural association bulletins, which may include information on promoted brands, featured events, and contacts for potential collaborations.
Unstructured:
Examine user comments on social media, user-generated content, which can be analyzed using natural language processing techniques to extract information on opinions, preferences, and trends.
According to Categories:
Static:
Use sales history to identify purchasing trends and patterns.
Dynamic:
Analyze engagement on social media and the website to measure user interaction with promotional content and adjust strategies in real-time.
With this detailed information, you can design specific marketing strategies using each data source, such as identifying relevant audience segments, personalizing promotional messages, establishing collaborations with relevant brands, and improving the performance of promotional campaigns.
Sales data analysed benefits:
Sales evolution during the years:the company can assess overall performance trends. Understanding fluctuations in sales and margins helps identify areas of strength and weakness, enabling strategic decision-making to improve performance.
Margin Evolution: offers insights into the company’s profitability trends. Clarifying incomplete representation ensures accurate interpretation of the data. Helps identify factors influencing profit margins.
Sales and margin per product: Comparing them across different product types highlights variations in performance. Noting trends underscores the importance of understanding product-level dynamics. Analyzing changes over time provides insights into market demand, product competitiveness, and potential shifts in consumer preferences.
Sales and margin per customer: Examining them per customer segment reveals customer specific patterns, the importance of customer segmentation and targeted marketing strategies. Understanding customer profitability helps prioritize resources and tailor services to maximize overall profitability.
Sales and margin by zone: Analyzing by geographic zone helps identify regional performance trends, underscores regional disparities in sales and profitability, informs strategic decisions regarding market expansion, resource allocation, and targeted sales efforts.
Product seasonality: Recognizing seasonal fluctuations in sales and margins provides insights into demand patterns, anticipate seasonal trends and adjust inventory, marketing, and pricing strategies accordingly, optimizes resource allocation and revenue forecasting.
Sales per seller and margin: Evaluating them highlights individual performance contributions. Identifying top-performing sellers and areas for improvement informs sales team management strategies. Recognizing overall sales and margin alignment validates the accuracy of the analysis and ensures accountability within the sales team.
Each aspect of the sales data analysis serves a distinct purpose in understanding performance drivers, identifying opportunities, and informing strategic decision-making. Together, these analyses provide a comprehensive view of the company’s sales dynamics and empower stakeholders to make data-driven decisions to drive growth and profitability.