Solutions

Marketing Analytics

The goal of marketing analytics is to optimize marketing strategies and tactics, improve customer engagement and loyalty, and maximize return on investment (ROI). It enables marketers to make data-driven decisions and measure the effectiveness of their marketing efforts, helping them to identify areas for improvement and make necessary adjustments to achieve better business outcomes.

Marketing analytics involves the use of data analysis tools and techniques to measure and improve the effectiveness of marketing campaigns. Some important components of marketing analytics include:

Data Collection: Collecting and organizing relevant data from various sources, such as customer demographics, purchase behavior, website traffic, social media engagement, and email marketing campaigns.

Data Analysis: Analyzing the collected data using statistical and machine learning methods to uncover patterns, correlations, and insights that can inform marketing decisions.

Segmentation: Segmenting customers into different groups based on their characteristics and behaviors, in order to tailor marketing messages and offers to their specific needs and preferences.

Predictive Modeling: Using historical data to build models that can predict future customer behavior and trends, and optimizing marketing strategies accordingly.

A/B Testing: Conducting controlled experiments to test different marketing tactics and messaging, and measuring their impact on customer behavior.

Campaign Execution: Planning, executing, and monitoring marketing campaigns, and measuring their performance using key performance indicators (KPIs) such as conversion rates, click-through rates, and return on investment (ROI)

Reporting: Presenting marketing data and insights in a clear and visually appealing way, to facilitate understanding and decision-making by marketing teams and stakeholders.

By leveraging these components, marketing analytics can help businesses make more informed and data-driven decisions, optimize their marketing strategies, and ultimately drive better results and return on investment.

Demand Forecasting

Accurate demand forecasting is crucial for eCommerce businesses to succeed in today's competitive market. At PeraLogic, we offer a comprehensive demand forecasting service that leverages the power of AI and data analytics to help eCommerce businesses make informed decisions about inventory management, sales and marketing, and supply chain optimization. By analyzing historical sales data, consumer trends, and other relevant factors, we can predict future demand with a high degree of accuracy. This allows our clients to make strategic decisions about inventory levels, production, and pricing, reducing the risk of stockouts, overstocking, and lost sales. With our demand forecasting service, eCommerce businesses can increase efficiency, reduce costs, and stay ahead of the competition.

Inventory Optimization

By analyzing historical sales data, lead times, and other relevant factors, our inventory optimization solution provides businesses with real-time insights into their inventory levels and makes accurate inventory predictions. This allows our clients to make informed decisions about stock levels, production, and fulfillment, reducing the risk of stockouts, overstocking, and lost sales. With our inventory optimization services, eCommerce businesses can increase efficiency, reduce costs, and improve profitability. Whether you're just starting out or you're an established business, we're here to help you take your inventory management to the next level.

P/O Generation

Managing purchase orders for a large set of products can be a very tedious and time consuming task. Efficient purchase order generation is essential to optimize inventory and save time for eCommerce businesses. Our purchase order generation service is designed to be both user-friendly and efficient. Our solution automates the procurement process, using real-time inventory data and supplier lead times to generate purchase orders quickly and accurately. With our purchase order generation service, eCommerce businesses can easily manage their procurement needs, reduce the time and resources needed for inventory management, and avoid costly inventory mistakes. Whether you're a small business or a large enterprise, our service is designed to save you time and streamline your procurement process. Get started today and see the benefits of a faster, more sefficient procurement process.

How Do We Do It?

When building a machine learning based sales or inventory forecast model, the important features or variables in a dataset depend on the specific business and its products. We have the in-depth industry expertise that allow us build fine tuned solutions to different kind of businesses, but here are some common examples of what should be considered:

Historical sales data: This is the most important variable for building a sales forecast model. It provides insights into past trends, patterns, and seasonality that can be used to predict future sales.

Inventory levels: Knowing the current stock levels can help predict how much product needs to be ordered and when, as well as the lead time for delivery.

Promotions and discounts: Including data on promotional activity and discounts can help model the effect of these strategies on sales.

Time-related variables: Day of the week, month, quarter, and year can all have an impact on sales, and should be included in the dataset.

External factors: External variables such as holidays, weather, economic conditions, and industry trends can also affect sales and should be considered.

Customer demographics: Information about customer demographics, such as age, gender, location, and buying behavior, can help create a more accurate forecast.

Product characteristics: Information about the product, such as price, category, and SKU, can also provide useful insights into sales patterns.

Marketing efforts: Information about marketing campaigns, such as email or social media promotions, can provide valuable insights into the effectiveness of marketing strategies.

By combining these variables, a sales or inventory forecast model can be built to provide valuable insights into future sales trends and inventory needs.