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║║ ╚╝║║ ║║║╚═╝║║╚══╗╚╝║║╚╝║║ ║║║╔╗╚╝║║╚══╗ ║╚═╝║║╚═╝║║║ ║║ ║║║╚══╗║║ ╚╝╚╝║║╚╝
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The domain Marketing & Retail Analytics mainly focuses on the marketing and promotional efforts of retailers & brands and the overall science behind the shoppers.
The Analytics part helps brands and retailers identify and target their audience, measure the effectiveness of the promotional activities and understand their customers better. It also helps to study customer behaviour, preferences and trends by using various data sources such as customer surveys, online activity, point-of-sale (POS) data etc.
This helps retailers to tailor their strategies according to customer needs and maximize their revenue. It also helps brand managers to understand how their products are doing in the market, where they need to improve, and how they can better connect with customers.
The insights that we tend to gain from this analysis help to identify opportunities and develop strategies for growth in sales and profits. It also helps to identify and target the right customers and optimize promotional campaigns.
This Retailer is an established Brick & Mortar retailer that frequently conducts marketing campaigns for its diverse product range.
Here, we are analyzing it’s promotional activities particularly their discount marketing process using the power of machine learning. The captured data is being provided to address this promotional activity as well as to research on aspects of cutomers’ behavior and their purchase patterns.
Promotional techniques should always be used to incentivize customers and communicate what they can accomplish when they buy a product. Anyone can come up with a deal, but how effective is the deal at retaining loyal customers?
Discount marketing and coupon usage have been shown to increase sales for new customers, but often result in lower conversion rates for repeat customers. This can lead to decreased loyalty, even among repeat users of a product or service. With extensive knowledge of a single user's purchasing behavior (called "loyalty metrics"), companies can use this information to better understand the preferences of their consumers and construct targeted marketing campaigns to retain current customers and attract new ones.
This data is used to answer questions pertaining to the store's audience, customers' consumer behavior, brand awareness, sales performance, conversion rates and more. The end result is a solution to their client's marketing goals that will provide concrete evidence of success in order to build trust in their decisions.
Our approach to this project can be divided into five key stages, each vital for the development of an effective and reliable model.
Our first step is to familiarize ourselves with the data. This includes understanding the nature of the dataset, the variables we are working with, and how they interact with one another. By doing so, we can ascertain what preprocessing might be necessary and which models may be suitable.
Next, we focus on detecting and handling anomalies within the data. Any irregularities, data mismatch, or unusual observations can lead the data and analysis loose it's meaning and sense. And that can lead to poor model performance. We identify these anomalies and decide how best to deal with them.
In this stage, we prepare the data for modeling. The process of data preparation may include data cleaning, data transformation, and data encoding. This might involve dealing with missing values, transforming categorical variables, and normalizing the data for better performance with our chosen models.
This stage involves creating our predictive or descriptive model. We select an appropriate algorithm, train the model on our data, tune parameters for optimal performance, and validate the model using a holdout dataset. This step is iterative and may be repeated as necessary to achieve satisfactory results.
The final step is to evaluate our model. We use a variety of metrics such as accuracy, precision, recall, F1 score, AUC-ROC, and others depending on the specifics of our project and model. This allows us to understand the robustness and reliability of our model and informs any necessary revisions in the preceding stages.
By following this approach, we aim to ensure the robustness and validity of our model, fostering reliable predictions and insights.