Harnessing the Power of Data Science in Sales & Marketing

Building on the ideas presented in our previous article, I delve deeper into the subject to provide a more comprehensive understanding.

Target Audience?

• Beginners of data science who want to shift into the sales and marketing domain.
• Who wants to implement new ideas in sales and marketing domains

Data science can be used in various sales and marketing applications, such as:

1. Customer segmentation: using demographics, behavior, and purchase history to divide customers into groups for targeted marketing
2. Predictive modeling: using past customer data to predict future sales and inform marketing strategies
3. Personalization: using data to create personalized experiences for customers, such as product or content recommendations
4. Marketing attribution: using data to track the effectiveness of different marketing channels and campaigns
5. Lead scoring: using data to prioritize information and identify the most promising sales prospects.
6. Customer lifetime value prediction: using historical data of customer behavior and demographics to predict how much a customer will spend in the future with a company
7. A/B testing: using data to test different versions of marketing campaigns and website designs to see which performs better.

A few scopes and examples are discussed below.

1. Prospect Modeling — Predictive Modeling

Problem:

Let’s assume nearly X million users have registered with the customer e-commerce platform; however, they have never made a single purchase. The marketing team has a challenge identifying which of these million user bases are the perfect candidates for better targeting to increase activations.

Solution:

Based on the browsing patterns of the new and existing users, we developed a machine learning approach that builds a look-alike model and predicts which prospects among the list are highly likely to convert.

Approach :

1. Identify the traits of existing High-Value customers by going back in time and exploring their features before users convert into customers.
2. Typically, High-Value customers tend to convert with specific days of activations.
3. Observe the browse patterns for the first six days of incoming prospects and futurize their behavior

Prospect modeling can also be combined with other techniques, such as market segmentation, customer lifetime value prediction, and marketing attribution, to understand a business’s potential customer base better.

2. Churn Analytics — Customer Segmentation

Churn analytics in data science analyzes customer behavior and predicts which customers are likely to cancel or stop using a company’s products or services. This can be a valuable tool for businesses, allowing them to address customer issues and retain valuable customers proactively.

Problem:

Identifying the customers who are at risk of churning out/unsubscribing/ leaving the product

Solution:

Build unified metrics that showcase the health of customer experience and proactively identify accounts/products at risk of losing them

Approach:

1. Data Collection: Gathering data on customer demographics, behavior, and purchase history.
2. Data Cleaning: Cleaning and preparing the data for analysis by removing any missing or irrelevant data.
3. Exploratory Data Analysis (EDA): Analyzing the data to identify patterns and trends in customer behavior.
4. Churn prediction: Building a predictive model using machine learning techniques such as logistic regression, decision trees, or neural networks to predict which customers are likely to churn.
5. Model Evaluation: Evaluate the predictive model’s performance by measuring its accuracy, precision, and recall.
6. Model deployment: Deploy the model in production to predict real-time churn.
7. Churn prevention: Taking action based on the predictions to prevent customer churn, such as launching retention campaigns, providing incentives to stay, or addressing specific customer issues.
8. Post-deployment monitoring: Continuously monitoring the model’s performance and retraining the model if necessary.

Exclusive Tips:

1. As a starting step, categorize the high-level levers/KPIs segments like Demographics, Voice of customer, Engagement
2. Drill down into these categories and give a weightage to these levers
3. List down the possible data sources and techniques ( lift charts, IGs) to be used
4. Build a user-friendly score that is easily understandable to the sales team for rightly targeting the users

3. Account Potential Forecasting

Account potential forecasting uses data to predict the potential revenue a business can generate from a specific customer or group of customers (accounts). It is typically used by sales and marketing teams to prioritize their efforts and allocate resources effectively.

Overview of Account Potential Forecasting

The potential account value is a range based on the model output and can be calculated at any time.

The Potential Account Model does not account for absolute New Logo capture

Account potential would be the sum of the cumulative values from new product sales, renewal effects, and expense value.

4. Marketing/Channel Attribution — Using

Determine the best path for increasing user conversion for specific campaigns or platforms.

1. Last-click attribution: credits the last marketing touchpoint before conversion with the entire value of the transformation.
2. First-click attribution: credits the first touchpoint with the entire value of the conversion.
3. Linear attribution: assigns equal credit to each touchpoint in the conversion path.
4. Time decay attribution: assigns more credit to touchpoints that occurred closer in time to the conversion.
5. Position-based attribution: assigns a higher percentage of credit to the first and last touchpoints in the conversion path.
6. Algorithmic attribution: uses machine learning techniques to determine the optimal credit allocation to different touchpoints in the customer journey.
7. Attribution with data visualization: using data visualization techniques like heatmap and path analysis to identify the most critical touchpoints in the customer journey.
8. Multi-touch attribution uses statistical models to assign credit to all the touchpoints in the customer journey.

Conclusion

In conclusion, data science has revolutionized how companies approach sales and marketing. By leveraging data-driven insights, businesses can now make more informed decisions, target their audience more effectively, and measure the effectiveness of their campaigns. However, it is essential to note that data science is not a magic solution but a powerful tool that must be used in conjunction with other strategies and business acumen. As companies continue to collect and analyze more data, it will be crucial for them to stay up-to-date with the latest technologies and best practices in data science. By embracing data science, companies can gain a competitive edge and drive growth in the highly competitive world of sales and marketing.

Watch this space for more additions to the list of topics. Feel free to shoot me any questions in the comments below or connect with me on LinkedIn.

Thanks for reading!…

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