Unveiling the Power of Marketing Analytics in Data Science

In my previous post, we covered Marketing Analytics, and its use and touched upon basic elements of Marketing. Now, we take a step forward and see how it lies in the realm of data science. Let’s start with the key components of Marketing Analytics:

Key Components of Marketing Analytics:

· Data Collection and Integration: No one can disagree on the importance of data in this era. Gathering diverse data types allows marketers to create a comprehensive profile of their customers. Some of the datasets that you will come across are:

o Demographic data (age, gender, location, etc.) provides basic information.

o Website traffic (websites visited, products viewed, time spent, etc.) offers insights into preferences and interests.

o Social Media helps in understanding the different customer personas.

o Sales data (purchase history, transaction details) gives a holistic view of customer interactions and buying patterns.

Data quality and completeness play a big role here. After performing data sanity checks, integrating and organizing these datasets is a major aid in analysis.

· Customer Segmentation: Through clustering techniques based on similarities, customers can be categorized into segments. This enables personalized marketing strategies tailored to specific customer groups, boosting engagement and conversion rates. The segment varies based on the target audience and growth opportunities. For example,

· Modeling: No, not the models on the ramp but the ones you code. Modeling plays a key role in forecasting future trends, understanding customer behavior, and attributing conversions or sales to specific marketing touchpoints.

Predictive Modeling involves using statistical algorithms and machine learning techniques to forecast future outcomes or behaviors based on historical data patterns. It serves various purposes when considered in the context of marketing analytics like

o Anticipating Customer Behavior

o Customer Lifetime Value Prediction

o Production recommendations

and many more.

Attribution Modelling aids in determining the most effective marketing channels or touchpoints that contribute to conversions or sales. It assigns credits to various marketing interactions along the customer journey. It aids the analysts in:

o Optimizing Market Budget

o Improving Campaign Performance

o Holistic View of Customer Journey

Implementing these models involves a mix of statistical analysis, machine learning algorithms, and data visualization techniques to derive actionable insights.

· Performance Measurement and Optimization: Data-driven insights empower marketers to measure the effectiveness of campaigns and marketing initiatives. By analyzing key performance indicators (KPIs), they can refine strategies continuously for better outcomes. One of the most common KPIs used to measure the effectiveness of campaigns is Return on Investment (ROI).

With the ROIs of different channels and campaigns handy, it guides decision-making based on comparative studies about where to allocate future budgets and resources. Based on these insights, further optimizations can be made using A/B Testing and experimentation for better performance and efficiency over time.

Before jumping into the modeling aspect of it, let’s take a small detour on the Sales components because all this pomp-n-show is to get one thing up — — drumrolls please — — It’s the Product Sales!

I’m not going to lecture you about the sales strategy or your elevator pitch, this one tells you what the different element of the product sales are. If you understand these concepts, trust me, you’ll thank me later while we get into the modeling aspect of it!

Broad component of sales:

· Brand Equity: It refers to the value associated with a brand name beyond its tangible attributes or products. It represents the perception and recognition of a brand’s value by consumers. For example, for brands that have been long in the market, people tend to trust them more. As a result, even with products launched in a new category, they have the edge over others given the customer’s trust they have earned and hence initial sales start to pour in.

· Sales Carryover: Sales carryover refers to the continuation of sales momentum from one period to the next. It can be the result of various factors such as seasonality, customer habits, marketing efforts and customer retention to name a few.

· Promotion: It involves various marketing activities aimed at stimulating sales and attracting customers’ attention to a product or service. Promotions play a crucial role in influencing consumer behavior, creating urgency, and driving immediate sales. They can also contribute to increased brand visibility and awareness.

Speaking of Promotions, let’s cover a few concepts that play crucial role from the modeling aspect:

o Ad-stock Theory: Adstock is a marketing concept used to describe the prolonged or lingering effects of advertising on consumer behavior over time. The theory is based on the idea that the impact of advertising on consumer memory and behavior doesn’t disappear immediately after an ad stops running but instead decays gradually.

Let me explain it to you in simple terms. When you see an ad for the first time, you do remember it, for at least a month or more. As you can see below, over time, the lagged effect of ads on consumer’s purchasing behavior:

This lagged effect varies from channel to channel.

o Diminishing returns: It refers to the concept that after a certain point, the incremental benefits or returns obtained from additional investment or effort in a marketing campaign or strategy decrease or level off.

In simple terms, once you are exposed to a promotion for a certain period, it encourages you to purchase the product however, if exposed to the same ad for a longer period it might not make you spend more on the same product.

o Cannibalization: Cannibalization, in the context of offering free samples, refers to a situation where providing free samples of one product reduces the sales or demand for another similar product within the same brand or product line.

Allow me to share an incident from my childhood around the age of 7 or 8. During my vacation stays in my hometown, I frequented a local store where the owner kindly gifted me two chocolates every Saturday whenever I greeted him with a cheerful ‘Hari-Om’. Being a mischievous kid, this routine led me to visit his store nearly 2–3 times every Saturday, resulting in accumulating enough chocolates to last an entire week. Although this hardly affected the shop owner’s profit but does give us an idea about how distributing free samples can sometimes turn into a loss-making decision.

Using targeted sampling is a better strategy when sending free samples for product promotion.

And that’s a wrap. Be ready with your coder hats on as in the next post, we’ll get our hands dirty with modeling. So, stay tuned!