Unlocking Future Value: How AI Predicts a Customer’s Lifetime Worth

CLV (Customer Lifetime Value) is one of the most important marketing metrics. It measures how much business a brand can expect from its customers. This allows the brand to make decisions such as how much to spend on customer acquisition, or how to provide differentiated products or services to high CLV customers, or how to create retention strategies for the high CLV segment, or how to increase the lifetime value of customers.

How is CLV calculated?

The simple calculation uses how much a customer spends on each transaction, how many transactions does a customer make in a year and what is the lifespan of that customer in years.

CLV =     Average order value (amount spent per transaction) X

Purchase Frequency (Number of times a customer buys in a year) X

Average Customer Lifespan (How many years does a customer continue the relationship)

For example, if a customer spends $50 on an average on each purchase, and shops every other month i.e. 6 times a year and stays with a brand for 3 years. Then, the CLV is $50 X 6 X 3, which is $900. 

The more complex calculation sums the profit from each customer (revenue in each year minus direct cost associated with that customer) discounted over time to calculate present value, for the period of the business relationship.

Historical CLV looks at past data to make judgements. Predictive CLV leverages past data to predict future value of CLV.

What is the difference between CLV and LTV?

While the terms CLV (Customer Lifetime Value) and LTV (Lifetime Value) are often used interchangeably, CLV is a metric of the value of an individual customer whereas LTV is more of an aggregate of all customers. CLV focuses on individual customers and segments and allows brands to make differentiated decisions, whereas LTV is an indicator of overall business profitability and growth.

How can AI models help in predicting CLV?

There are several AI models that can predict the CLV of a customer based on historical data of customer’s transactions and engagement with the brand.

A linear regression model can predict the value of the dependent variable CLV based on the value of independent variables such as average transaction, frequency of purchase, time since last purchase, number of returns, demographic variables such as age, gender, location. A logistic regression model on the other hand can give a binary outcome such as decide whether a customer is high value or low value, based on the model training data.

A brand can also use survival analysis to predict how long a customer will stay with the brand, by predicting the time of churn. This can go as an input to CLV prediction.

A decision tree uses a flowchart like structure and uses historical data to predict outcomes. Each node in the tree represents a test on an attribute, each brand represents outcome, and each leaf represents a label or the final decision. For example, a fashion e-commerce company may have average spending purchase more than $50 as the root node and purchase frequency more than 10 in a year as branches, finally leading to high CLV of $1200 if the frequency is more than 10 or low CLV of $500 if the frequency is less than 10. Further, another node for average spending less than $50 could be whether the customer is signed up for the loyalty program. The response yes or no will determine whether the CLV falls under medium or low.

Similarly, Gradient Boosting Machines (GBM) can be built using several decision trees to incrementally improve the prediction of CLV, where each new tree works on reducing residual error from the previous tree. GBM takes parameters like number of trees, depth (number of decisions each tree can make) and learning rate (how much each tree tries to correct errors of previous tree)

CLV estimation can also be framed as a clustering problem where the customers are clustered into low, medium or high CLV customers, using a model such as K-means clustering. This allows the brand to identify distinct groups and have separate marketing strategies for each.

Neural networks can be a powerful method to predict CLV identifying complex patterns and data relationships. The input layer will take in parameters that could impact the CLV. This could be demographics, purchase frequency, average order value, product categories, engagement scores etc. The hidden layers will detect complex patterns, such as the relationship between product categories, spending habits, and engagement level, and then compute an outcome and pass it on to the next layer. The output layer will give CLV as a single value. Over time, the model will adjust the weights of each of the features to give more accurate prediction.

Support Vector Machines (SVM) are another popular method to classify customers based on CLV. An SVM works by mapping data points in space and finding a surface or hyperplane that best fits the data points, based on historical data. Imagine plotting a customer Sara’s data point in a multi-dimensional space where each dimension is a feature (age, income, purchase frequency, preferred categories) The precise position of Sara’s data point relative to the surface will determine her predicted CLV.

Can packaged customer data platforms predict CLV?

The two commonly used platforms Salesforce and Adobe both have capabilities of CLV prediction through their in-built AI platforms, Einstein and Sensei respectively. Einstein aggregates data from various sources such as sales, service and marketing cloud and then using prediction builder and defining CLV as a target variable, it can forecast future value. Adobe Sensei can help identify which customer attributes correlate strongly with higher CLV and then create precise and dynamic customer segments through Audience manager.

What is the future of CLV prediction?

The future of CLV prediction will be characterized by more sophisticated models, more comprehensive data integration including unstructured data like social media reviews, real-time data processing, increasing the accuracy of the predictions. This will play a broader role in personalization at scale, helping with micro-segmentation which is creating highly specific small segments, and driving predictive personalization, by predicting what a customer needs at any given point of time and recommending just that.

Several companies are using CLV predictions today. Amazon and Netflix use CLV models to tailor recommendations. Starbucks uses CLV for its rewards program. However, there is still untapped potential in CLV prediction. With research suggesting that a 5% increase in retention can boost profitability by 75%, it's a worthwhile pursuit. 

(Article originally published at: https://www.linkedin.com/pulse/unlocking-future-value-how-ai-predicts-customers-lifetime-neha-verma-gzzhc/ )

Understanding the magic behind AI-powered personalized recommendation engines

Sometimes godsend, sometimes annoying, product recommendations are a part of our day-to-day digital lives. Let’s take a look at behind-the-scenes of AI powered product recommendation engines, and understand the tools, the steps and the algorithms required to make them work.

Why personalized product recommendations?

The future of marketing is personalized, and it is here to stay. Amazon generates 35% of its revenue from recommendation engines. Netflix generates 80% of its user activity through personalized recommendations. Personalization helps brands increase marketing ROI from 10% to 30%, in addition to AOV (Average Order Value) and conversion rate, among other key metrics. 

How does a personalized recommendation engine really work?

A personalized recommendation engine collects and processes customer, product and interaction data, and runs different AI models on it to generate recommendations. It leverages user profile information, browsing and purchase history, add to cart and wish list history, ratings and reviews by the user, returns, search queries, frequency of purchase etc., in combination with product data such as features, price and category. A good model also factors in time of the day, device, context of the customer’s visit, location, and products they are currently viewing. The effectiveness of the engine depends on the quality and variety of data that is fed into it – The more relevant and well-rounded the data, the more accurate and personalized the recommendations are.

What tools and systems does it require?

It requires a multi-layer architecture and a combination of tools across data collection, storage, AI modeling and deployment:

Data Collection: User, interaction, and product data are collected and stored in a database management system such as MySQL or MongoDB. User behavior data from website and mobile app is gathered through analytics platforms like Adobe or Google analytics.

Data Storage: Collected data is stored in cloud platforms like Azure, AWS, or GCP, which also provide computing power.

Data Processing: Tools like Apache Spark, Flink, Hadoop clean and transform the collected data into a format that can be utilized by the recommendation algorithms.

AI Modeling: Machine learning libraries such as scikit-learn, TensorFlow, PyTorch, Surprise, or LightFM are used to build, test and deploy recommendation models.

Serving recommendations: Server side language like Python and a user interface are used to serve recommendations via website, mobile app, email, etc.

Feedback: User feedback on the recommendations is collected to refine the system over time, through user behaviour analytics data, user feedback data or data from A/B tests.

What are the steps involved in building a recommendation engine?

Broadly, a brand needs to do the following to build and deploy a recommendation engine:

• Collect, clean and process data about users, products and interactions.
• Choose features or attributes to make the recommendations.
• Choose a recommendation algorithm. It could be a collaborative filtering, content-based filtering, hybrid approach or deep learning algorithms.
• Train the selected model feeding the collected data into the model and adjusting its parameters to tweak its performance.
• Evaluate the performance of the recommendation model using appropriate metrics such as F1 score which measures precision (how many predicted positives were actual positives) and recall (how many positives were correctly captured) and accuracy (total number of correct predictions divided by total number of predictions)
• Test and implement the recommendation system on the e-commerce site or app.
• Monitor the performance of the recommendation system and retrain the model with new data, tweak the model's parameters, or switch to a different model to optimize it.

Which recommendation algorithms are typically used?

Collaborative filtering is a popular recommendation algorithm. It works on the idea that if two people tend to agree on one thing, they are likely to agree on others. There are two types of collaborative filtering – user based and item based. In user based collaborative filtering, if users A and B tend to buy similar items, then if user A buys a new item, the same item will be recommended to user B. In item based collaborative filtering, if users tend to buy items X and Y together, then if a user buys X, the system recommends Y to that user.

Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback.

Hybrid models combine collaborative and content-based filtering to leverage the strengths of both methods. While this method is more effective, it is also more complex and computationally expensive.

There are also deep learning models like Restricted Boltzmann Machines, Autoencoders, which use complex patterns to generate recommendations. They can be used only if the brand has the resources to implement such models.

Each method has its pros and cons in terms of effectiveness, complexity and requirements.

How do you know if it’s working?

Ultimately, the recommendation engine should drive up key business metrics and have positive ROI. The ROI of the recommendation engine is calculated by comparing the increased revenue or another relevant metric against the costs associated with implementing and maintaining the system. The business metrics include revenue, conversion rate, AOV, customer engagement metrics such as time spent and clickthrough rates, customer satisfaction, retention and loyalty scores.

Go deeper into collaborative filtering machine learning (ML) models

Collaborative filtering uses either model-based ML or memory-based ML or a hybrid approach.

Model-based machine learning involves building a mathematical model to make predictions. The model is trained on existing data and then used to predict outcomes for unseen data. A collaborative filtering model learns patterns in user-item interactions and then uses these patterns to predict future interactions. Matrix Factorization is an example of model based machine learning. This method breaks down a large matrix into a product of several smaller matrices to represent only the most important parts. We can then use these matrices to predict the rating a user might give to a product, by looking up corresponding features of the user and the product in our two matrices and multiplying them together.

Memory-based methods make predictions based on their memory of the training data, without building an explicit model. A common memory-based method is user-user or item-item similarity. For example, to predict a user's interest in a product, we might find other users who have similar product preference patterns and use their preferences for the product to make a prediction for the user in question. K-Nearest Neighbors (KNN) is an example of memory based method. KNN predicts the label of a given data point based on the labels of its nearest neighbors. For a product recommendation, KNN will first find the users that are most similar to the user who needs recommendation. Then, it will look for products that those similar users commonly like and recommend them to this user.

Finally, with all the advances in AI, we will see product recommendations becoming more contextual, relevant, real-time and multi-modal. With the increasing demand for transparency, we may see explainable AI for recommendations. There may also be integration of conversational AI allowing users to interact with recommendation systems through speech, text or visual content. As we navigate the future of AI-powered product recommendations, while we don’t know exactly what the future will entail, yet we can be assured that boundaries will be pushed and new possibilities will unfold, revolutionizing the customer experience.

(Article originally published at: https://www.linkedin.com/pulse/understanding-magic-behind-ai-powered-personalized-engines-neha-verma-u727c )

Cart abandonment: Using AI to win the battle against unfinished purchases

Online cart abandonment rates remain as high as 70% on desktop and 80% on mobile. An abandoned cart happens when a customer adds products to their online shopping cart but leaves the site without completing the purchase. This is a huge unrealized revenue opportunity in e-commerce. Even a small percentage of recovery can lead to a significant revenue lift. Let’s understand how AI can help tackle the challenge of abandoned shopping carts. By using AI technologies, brands can identify the reasons behind cart abandonment and deploy strategies to encourage customers to complete their purchases.

AI systems can monitor customer behavior in real time and trigger a reminder if a customer adds a product to a cart but does not check out in the next few minutes. For example, Wendy adds a pair of shoes to her cart, but then stops browsing. AI can trigger an email to Wendy encouraging her to complete her purchase by giving an added incentive such as free shipping or a limited period discount.

Based on the customer’s past interactions, preferences, and items in the cart, AI can personalize the email reminder’s content. This includes product recommendations, personalized messages, or specific discounts tailored to the customer. The message can leverage scarcity techniques such as low stock, impending price increases, or expiring discounts. For example, Carol abandons a cart with an organic cotton sundress. AI can then send a reminder re-emphasizing the benefits of her chosen ecofriendly product and may even suggest an organic jute bag, offered at a discount to go with the dress. Generative AI can dynamically create email content that may resonate with the customer by crafting unique subject lines, compelling body text, and personalized product recommendations based on the user's browsing and purchase history.

AI can also help determine the best time to re-engage a customer and to send a reminder to urge her to complete her purchase. For instance, Diana is usually browses in the evening. However, while browsing, she abandoned a cart in the morning. But AI would determine and send a reminder to her in the evening when she is typically more active and may have a higher chance of completing the purchase.

AI can not only determine the time, but also the best channel to re-engage the customer. This could be via email, SMS, push notifications, or through social media ads. For example, Elle may receive a push notification urging her to complete the checkout of her sunflower tote bag before it runs out of stock.

AI can also be leveraged for feedback collection, initiating a feedback loop if a customer repeatedly abandons cart. It may send an email for feedback on their shopping experience, with questions geared towards uncovering barriers they face at checkout.

AI can be used to prevent abandoned carts in the first place. Using predictive analytics, AI can forecast the likelihood of cart abandonment by analyzing customer behavior patterns and engagement levels throughout the shopping process. This allows businesses to proactively intervene with targeted actions to reduce abandonment rates. AI can also help optimize and simplify the checkout process by analyzing data from numerous customer interactions to identify bottlenecks or pain points in the checkout process. AI can also dynamically offer promotions or discounts specifically targeted at customers who are at risk of abandoning their carts. This can be based on the customer's past behavior, the value of the cart, or other contextual data. Using AI, websites can detect when a user is about to leave the page and present them with a last-minute offer or a reminder to encourage them to complete the purchase. This could be through a popup or through AI-powered chatbots or virtual assistants who can ask if the customer needs help, offer assistance with questions, or provide incentives to complete the purchase.

AI-based trigger mechanisms for abandoned cart require integration of real-time data monitoring, behavioral analysis, and automated actions to re-engage customers who have left items in their shopping carts. Here are the steps involved:

1. Data Monitoring

At the core of the trigger mechanism is continuous monitoring of user actions on a website or application. AI systems track various activities such as items added to or removed from a cart, page navigation patterns, time spent on pages, inactivity periods etc. This data is collected in real-time, allowing the system to react promptly to user behaviors indicative of potential cart abandonment.

2. Behavioral Analysis

Using machine learning algorithms, the AI analyzes the collected data to identify patterns that typically lead to cart abandonment. This might include recognizing that a cart has been inactive for a preset amount of time, identifying exit patterns, such as moving the cursor towards the close button or switching tabs or analyzing the customer’s history of cart abandonment. The AI uses this analysis to predict when a cart is likely to be abandoned based on current and past user behaviors

3. Setting Trigger Conditions

Based on the behavioral analysis, specific conditions are set as triggers for the abandoned cart notifications. These conditions are predefined actions or inactions that, when detected, initiate an automated response. Examples of trigger conditions could be: A user has added items to the cart but hasn’t proceeded to checkout within 20 minutes or a user has visited the checkout page but left the site without completing the purchase.

4. Automated Actions

Once a trigger condition is met, the AI initiates an automated action aimed at re-engaging the customer, such as sending a personalized email or SMS, displaying a pop-up message to complete the purchase, or a push notification to the user’s device if they have an associated app installed.

5. Personalization

AI systems use customer data (previous purchases, browsing history, demographics) to tailor the message or offer in the reminder. Personalization can involve recommending similar products that might interest the customer, offering discounts on items the customer seems hesitant to purchase or tailoring the messaging tone and content to match the user’s profile.

6. Optimization and Learning

AI systems continuously learn from each interaction. They analyze the outcomes of triggered actions (e.g., whether the customer returned to complete the purchase) to refine the prediction models, adjust the trigger conditions, and improve the personalization of messages. This feedback loop helps in optimizing the abandoned cart recovery strategy over time, making it more effective.

Some of the commonly used AI models for predicting cart abandonment are:

Classification Models - These models predict whether a customer will abandon a cart based on past behavior and other attributes. Common approaches include logistic regression, decision trees, and random forests.

Clustering Models - Techniques like K-means or hierarchical clustering are used to segment customers based on their shopping behavior, which can help in tailoring interventions aimed at reducing cart abandonment.

Sequential Pattern Mining - This technique identifies common sequences or paths that lead to cart abandonment, helping marketers to intervene at the right moments.

Neural Networks - More complex AI models like deep learning are used to predict cart abandonment by analyzing vast amounts of data and identifying non-linear relationships that simpler models might miss.

Here is a quick word on how packages such as Salesforce and Adobe handle cart abandonment. In Salesforce ecosystem, Salesforce commerce cloud captures data on customer interactions and behaviors. It leverages Einstein AI for predicting abandonment and personalizing interactions. Then, Salesforce Marketing cloud is used to engage customers to complete their purchase. In Adobe ecosystem, the Adobe real time customer data Platform tracks cart additions and abandonments in real time. Adobe Sensei analyzes customer data and behavior patterns to predict abandonment. When cart abandonment is detected, personalized reminders are sent through Adobe Journey Orchestration or Adobe Campaign. Adobe Target is used to personalize web and mobile experience for cart recovery messaging.

Apart from these, there are several e-commerce platforms such as Shopify, WooCommerce, and Magento that have built-in features or plugins/add-ons specifically designed to handle cart abandonment. CRM systems can also track customer interactions and data to facilitate more personalized recovery strategies.

By leveraging AI and data-driven insights, e-commerce businesses can significantly increase the chances of recovering abandoned carts and turning potential sales into actual revenue. Timely and personalized efforts can drive recovery upto 20%. AI can help push that percentage even higher.

(Article originally published at: https://www.linkedin.com/pulse/cart-abandonment-using-ai-win-battle-against-unfinished-neha-verma-bvaqc/  )

How AI is transforming discount dilemmas into sales success

Discounting is a timeless marketing strategy. If done well, it can give just the nudge needed to turn hesitant decision makers into buyers, reward the right customers, and boost sales. If done poorly, it devalues the product or service, incentivizes the wrong customers and throws money away.

So, how does AI help brands make better discounting decisions? There are essentially 4 types of decisions:

1.       Who to give discounts to?

2.       When to give discounts?

3.       Which products to give discounts on?

4.       How much discount to give?

Let’s unpack how AI helps with each of these decisions, using the example of a fictional fast fashion brand called Sizzle.

Who to give discounts to?

AI models can help determine which customers are most likely to buy when offered a discount. This is typically done through propensity modeling, in which based on customer’s past behaviour, a propensity score is calculated. The higher the score, the higher the chances of a customer responding positively to a discount. For example, AI model may determine Sizzle’s fashion forward customer segment to be responsive to discount on new arrivals. Customers in this segment may have features like – they regularly buy latest arrivals, follow trends and engage with the brand on social media.

Grouping customers on discount sensitivity can also be done through classification algorithms. The model can classify the customers as “likely to respond to a discount” and “unlikely to respond to a discount” and focus on the customer segment that is likely to respond.

Another approach is to predict the LTV (Lifetime value) of customers based on their transaction data and offer selective discounts to high LTV customers to drive up loyalty and engagement. For example, the AI model may identify offer discount to a customer Alisha who has high predicted LTV based on her behavior – She shops twice a month on Sizzle on an average, buys across product categories, has low return rate and leaves positive ratings on products.

Customers who are likely to buy a product based on their similarity to other users or other products they bought in the past represent another category of customers who can be targeted for discounts. By predicting which product a customer is likely to buy using techniques like collaborative filtering or content based filtering, discounts can be targeted towards these products at the optimal time. For example, customers who buy Boho chic floral maxi dress also tend to buy strappy block heels. By offering discount on block heels when a customer buys the maxi dress will help drive sales of both products. 

If a brand wants to attract new customers or enter a new market, it can leverage AI to determine which new segments to target or which geographical region to enter.  Similarly, while launching a new product, AI can determine which customer segments are likely to be interested in that product, and then offer discounts to those customers on the new product. For example, while launching an eco-friendly line of clothing, the AI model may determine that female customers in the age group of 25 to 36 who typically buy casual wear and accessories, are active on social media, and follow sustainable fashion influencers may be most interested in this product line.

Inactive customers is another segment where AI can help offer the right discount prevent customer churn. Let’s say the model determines Betsy to be a customer likely to lapse, based on her last purchase being older than 3 months, low engagement with brand and low open rate on marketing emails. The model also determines that Betsy being a value shopper will buy products she likes if available on a discount. So, the brand can offer a discount on her next purchase while highlighting products she may like.

When to give discounts

AI models can help predict the time of a customer’s next purchase, and brands may offer discounts around that time to nudge the sale. For instance, a survival analysis may determine that Sizzle’s customer Dina typically makes a purchase every 30 days, and it's been 25 days since her last purchase. So, there is a high probability that she will make a purchase in the next 5 days. So, Sizzle may offer a discount around that time to increase the likelihood or value of the sale.

Time series forecasting is another method to determine the timing of discounts by predicting trends and patterns over time, such as seasonal fluctuations or peak shopping periods. For example, Sizzle’s AI model may predict that summer dresses that have consistently sold well during the spring and summer seasons may see an unexpected dip in the coming spring season. This may be due to factors such as shift in fashion trends, a downturn or customer’s spending behavior where they may want to spend less in spring after heavy spending in winter. This insight will allow Sizzle to discount this product to push sales.

Brands can predict periods of high or low spending using AI using linear regression models using past purchase history and time the discounts. They can time the discounts with high spending periods to push sales even further or time them during low spending periods to stimulate sales.

Brands can also deploy reinforcement learning techniques to determine best times for discounts through trial and error. In this type of machine learning method, an agent learns to make decisions by taking actions and receiving rewards (increase in sales) for correct actions and penalties for incorrect actions.

Which product to give discount on

Association Rule Learning can identify associations between products and by offering a discounted price on that bundle, it may stimulate higher sales of one or both products. For example, Sizzle’s satin marble dress offered as a bundle with sequined silver clutch may drive higher sales.

Using collaborative filtering, if customers like a certain product, then by predicting which products they may be interested in and offering discounts on those products may drive higher sales. For instance, the model may determine that customers who like strappy crepe dress also tend to like flared jumpsuit, and therefore, by offering discount on the flared jumpsuit, Sizzle can drive additional sales.

If a predictive analytics model predicts that a customer is likely to purchase a certain product, offering a discount on that product may increase conversion rates.

By using Natural Language Processing in Sentiment Analysis through customer reviews and social media posts, a brand can identify products that are currently popular or trending. Offering discounts on these products can capitalize on their current popularity.

Using inventory Management Models, AI can predict demand for different products and optimize inventory accordingly. If an excess of a particular product is predicted, discounts can help balance supply and demand.

How much discount to give

There are different AI models such as regression models, decision trees, random forest, gradient boosting models, reinforcement learning models which predict optimal discount to maximize revenue. The models balance the increase in sales with the decrease in profit per unit due to the discount. They consume data such as product type, customer demographics, time of the year, and past sales data. Then they identify the most relevant variables that impact sales and revenue such as original price, previous discount levels, time of the year, and customer purchasing behavior. Once the model is trained, then the model predicts the discount.

No customer will ever say – “I dislike getting discounts on products I like when I need them”. As AI models become more sophisticated and powerful, brands that can effectively leverage AI powered discounting will gain competitive advantage. A good place for brands to start with their AI efforts is to get their data in order. 

(Article originally published at: https://www.linkedin.com/pulse/how-ai-transforming-discount-dilemmas-sales-success-neha-verma-bacgc/ )

Ambition, burnout and beyond – A year of growth and hard lessons

In 2017, when I embarked on my great adventure with Deloitte, I was off to a great start. As I went on my journey, in 2020 after my promotion, I hit a huge speed bump in the form of burnout. This is the third part of my story where I conquer all the challenges and get my happy ending. Except that it didn’t quite turn out that way.

Following my recovery from burnout in 2021, I focused on maintaining balance and space for creativity and churned out more than 15 original ideas throughout the year, giving me immense satisfaction in the process. While the year turned out to be my most creative, I felt that the overall impact I created was quite modest.

This led me to seek answers–What can I do to create substantially more impact? What is my potential? I thought that advancing to the next level in my career would lead me onto that path. So, I started talking to my leaders and mentors for their wisdom and decided to sign up for a larger practice role. As I pushed myself to do more, taking on more complex engagements, I got nothing but support from all those around me.

Initially, the challenge was exhilarating. It seemed like I had discovered the path I had been looking for. But then, an engagement spiraled. My team and I pushed through, solving some of the complex and high-intensity problems to get to the finish line.  While delivering the project, I also kept the momentum going on all my other practice work. The toll was heavy, and I breathed a sigh of relief when the project ended.

Six weeks later, another project hit the red zone. This time, the complexity and challenges were much bigger than any of my previous engagements. The stakes were immense – our client’s reputation and our own hinged on success. With the due date set in stone, we began our race against time, which became a blur of days, nights, and unending challenges. The sense of joint ownership and responsibility we felt as a team was immense. Our leaders rolled up their sleeves and worked alongside the team to make it possible.

By the end of the project, I noticed familiar warning signs, only this time they were far greater in intensity. I was burnt out. Yes, the work had been challenging, but my personal situation added to it. With my husband based in Dubai for the past two years, I had to juggle family responsibilities, a household of two children, and demands at work all by myself. During the crucial phase of the project, my grandmother got injured. Following that, my husband’s uncle was hospitalized with critical illness. My daughter got bitten by a dog. I also had an episode of severe neck and shoulder pain. Battling these obstacles came at a huge mental cost. The exhaustion, the stress, and the guilt of being unavailable to my children made me question my professional choices.

After the project ended, things began to settle down.  I shared my experience with my peers, team members and leaders, and it was cathartic. Not only did I receive support from all around, but it also opened a healthy dialogue on how we as individuals and as a practice can better manage well-being during critical deliverables and challenging work. 

Looking back, I know that I have grown significantly as a person through this journey. I have learned that no matter what, I am the one responsible for my choices. I have also realized that a career is not about a pre-scripted happy ending. Instead, it is about the journey that offers endless possibilities of discovery and growth. Here’s to onwards and upwards! Until next time!

(Article originally published at: https://www2.deloitte.com/ui/en/blog/life-deloitte-blog/2024/unmasking-resilience-journey-through-professional-life.html)