Suppose that you’re the proud owner of a clothing store. You stock anything from the latest trending teen fashion to smart business suits. Surely sending one promotional message to all customers for the business suits won’t provide the biggest return on investment.
Rather, you decide to use k-Means clustering to segment your customer-base by age, gender, and shopping habits.
You’ll then create a marketing campaign to more effectively promote your stock to the right customer audience.
You’ll benefit from a more tailored and focused promotional messages that are posed to yield greater returns.
Sentiment analysis is a technique whereby we analyse some text and gauge whether that text has a positive or negative intent.
Sentiment analysis is extremely valuable for businesses to assess how their customers are receiving their products. Are your customers happy with their purchase? Do they recommend it to their friends?
By analysing text from social media platforms and even customer reviews, your business can determine the overall sentiment of the customers towards your business. This will uncover valuable insight on your marketing strategy. For example, you might opt to double-down on a well received product line or decide to discontinue the one that is gathering negative sentiment. You can also take the required steps to change or update your offerings to better suit your customers.
Predictive analytics is a big one and one that is extremely versatile and dynamic. Depending on the business and industry, there are different use-cases for predictive analytics.
In short, predictive analytics is the technique of using historic data to predict some future event through machine learning and statistics.
Some examples might include forecasting customer behaviour, forecasting trends, and also sales by seasonality.
Additionally, some other highly effective use-cases also include Customer Lifetime Value (CLV), churn prediction, and fraud detection.
Consider this.
You own an online shop. Your current landing page is a red background with blue buttons.
You’ve grown tired of this layout and based on your sentiment analysis, your customers seem to not like this as well.
You decide to change the buttons from blue to white.
Was that change a god one? Or did it make it worse?
A/B testing helps us answer this very question. A/B testing is a rather simple but highly effective technique to measuring the impact that a change makes. For example, in this case, we can use A/B testing to determine whether the new layout change improved the customers’ sentiment score or whether it resulted in more sales when compared to its previous version.
A/B testing is critical to optimising marketing since it can also help us measure which marketing campaign yielded the better results.
It’s no secret that nowadays, everyone and their grandmother is on at least one social media platform.
Also, we all have that one family member or friend that overshares on social media.
Subconsciously, every single one of us is guilty off letting others know what we enjoy and what we don’t.
Businesses use this information to gain a deeper understanding on their customers. Social network analysis is the technique where social structures and patterns are examined to determine their relationships within a network.
In the context of promotion, social network analysis can track the spread of information, uncover hidden relationships, and also provide additional information on other interests which your target audience might have.
Perhaps this might be the most controversial aspect to exploiting data for marketing, but its definitely happening all around us.
Another approach to social network analysis is to identify key customers (i.e., ‘influencers’) who are likely to share your product with their network and convert as much paying customers as possible. The ultimate goal here is to make your product ‘go viral’.
As its name implies, a recommender system is a system that recommends stuff.
It analyses user behaviour and preferences to suggest the right content to the right audience. The benefits here are two-fold: 1) tailored marketing campaigns, 2) increased brand engagement. More targeted and focused marketing campaigns.
The typical examples here include Amazon’s ‘other people also bought this with that’ or the famous Netflix ‘because you watched’.
Suppose a real-life example of a small online store selling eco-friendly products.
Below is a summarised step-by-step guide to powerful and effective marketing.
- customer segmentation to identify focus groups based on purchase history, demographics, and their browsing behaviour. This resulted in more personalised marketing content that resonated with each segment and thus yielding higher engagement.
- sentiment analysis to analyse their customer reviews and get an idea about their opinions on social media . This meant that they were able to address concerns and customer feedback to improve their brand image.
- predictive analytics to forecast their customer needs and preferences. This resulted in a more targeted marketing campaign and also reduced the churn rates of their customers.
- A/B testing enabled them to fine-tune the ad-designs, offer structures, and messaging style to drastically improve their click-through rates.
- social network analysis was used to identify influential key customers to promote their products and help with the brand image and exposure.
Data science is an interesting avenue that provides a plethora of opportunities when it comes to marketing. Leveraging the power of data enables us to create more focused and ultimately, more effective, marketing campaigns that resonate with your target audience.
Suppose that you’re the proud owner of a clothing store. You stock anything from the latest trending teen fashion to smart business suits. Surely sending one promotional message to all customers for the business suits won’t provide the biggest return on investment.
Rather, you decide to use k-Means clustering to segment your customer-base by age, gender, and shopping habits.
You’ll then create a marketing campaign to more effectively promote your stock to the right customer audience.
You’ll benefit from a more tailored and focused promotional messages that are posed to yield greater returns.
Sentiment analysis is a technique whereby we analyse some text and gauge whether that text has a positive or negative intent.
Sentiment analysis is extremely valuable for businesses to assess how their customers are receiving their products. Are your customers happy with their purchase? Do they recommend it to their friends?
By analysing text from social media platforms and even customer reviews, your business can determine the overall sentiment of the customers towards your business. This will uncover valuable insight on your marketing strategy. For example, you might opt to double-down on a well received product line or decide to discontinue the one that is gathering negative sentiment. You can also take the required steps to change or update your offerings to better suit your customers.
Predictive analytics is a big one and one that is extremely versatile and dynamic. Depending on the business and industry, there are different use-cases for predictive analytics.
In short, predictive analytics is the technique of using historic data to predict some future event through machine learning and statistics.
Some examples might include forecasting customer behaviour, forecasting trends, and also sales by seasonality.
Additionally, some other highly effective use-cases also include Customer Lifetime Value (CLV), churn prediction, and fraud detection.
Consider this.
You own an online shop. Your current landing page is a red background with blue buttons.
You’ve grown tired of this layout and based on your sentiment analysis, your customers seem to not like this as well.
You decide to change the buttons from blue to white.
Was that change a god one? Or did it make it worse?
A/B testing helps us answer this very question. A/B testing is a rather simple but highly effective technique to measuring the impact that a change makes. For example, in this case, we can use A/B testing to determine whether the new layout change improved the customers’ sentiment score or whether it resulted in more sales when compared to its previous version.
A/B testing is critical to optimising marketing since it can also help us measure which marketing campaign yielded the better results.
It’s no secret that nowadays, everyone and their grandmother is on at least one social media platform.
Also, we all have that one family member or friend that overshares on social media.
Subconsciously, every single one of us is guilty off letting others know what we enjoy and what we don’t.
Businesses use this information to gain a deeper understanding on their customers. Social network analysis is the technique where social structures and patterns are examined to determine their relationships within a network.
In the context of promotion, social network analysis can track the spread of information, uncover hidden relationships, and also provide additional information on other interests which your target audience might have.
Perhaps this might be the most controversial aspect to exploiting data for marketing, but its definitely happening all around us.
Another approach to social network analysis is to identify key customers (i.e., ‘influencers’) who are likely to share your product with their network and convert as much paying customers as possible. The ultimate goal here is to make your product ‘go viral’.
As its name implies, a recommender system is a system that recommends stuff.
It analyses user behaviour and preferences to suggest the right content to the right audience. The benefits here are two-fold: 1) tailored marketing campaigns, 2) increased brand engagement. More targeted and focused marketing campaigns.
The typical examples here include Amazon’s ‘other people also bought this with that’ or the famous Netflix ‘because you watched’.
Suppose a real-life example of a small online store selling eco-friendly products.
Below is a summarised step-by-step guide to powerful and effective marketing.
- customer segmentation to identify focus groups based on purchase history, demographics, and their browsing behaviour. This resulted in more personalised marketing content that resonated with each segment and thus yielding higher engagement.
- sentiment analysis to analyse their customer reviews and get an idea about their opinions on social media . This meant that they were able to address concerns and customer feedback to improve their brand image.
- predictive analytics to forecast their customer needs and preferences. This resulted in a more targeted marketing campaign and also reduced the churn rates of their customers.
- A/B testing enabled them to fine-tune the ad-designs, offer structures, and messaging style to drastically improve their click-through rates.
- social network analysis was used to identify influential key customers to promote their products and help with the brand image and exposure.
Data science is an interesting avenue that provides a plethora of opportunities when it comes to marketing. Leveraging the power of data enables us to create more focused and ultimately, more effective, marketing campaigns that resonate with your target audience.