Cartesian Data Science is an industry leader when it comes to providing data science solutions for corporations. They specializes in advanced analytics and machine learning in a marketing context.
In this project, I completed a blog post to educate their readers on different ways data science can improve marketing. It’s a very interesting topic since it touches on some front-line data science techniques and tools.
The blog post covers 12 ways to use data science to improve marketing, as well as a soft call-to-action at the end of the article.
Main Uses of Data Science in Marketing
In the last blog post, I discussed 10 ways that data science can help improve company sales. Some of these topics included improving lead scoring, optimizing customer engagement, and increasing customer purchase frequency. If you haven’t read that article, you can read more here.
In this article, I will cover a different function of data science—improving marketing. Achieving the best marketing results isn’t just about how much a company can spend; more importantly, it’s about spending that marketing budget effectively.
Here, I will share 12 ways that data science can help with your marketing. To help you better understand the content, I divided this article into two parts: 1) Insight & Engagement and 2) Cost Optimization, Planning, & Return on Investment (ROI).
Insight & Engagement: Using Data Science to Focus Your Marketing Efforts
#1 Identify Key Marketing Factors
The first way that companies can use data science to improve marketing is through the testing and learning process. The goal of this is to measure the impact of a specific marketing factor, and it is often done with design of experiments (DOE).
DOE allows companies to collect and analyze data, changing more than one factor at a time. The main idea in DOE is to design an experiment that includes a control group. Companies then apply different treatments to the experiment and observe their effects by comparing the experimental group to the control group. With the findings of the experiments, companies can gain insight into what affects their marketing. Therefore, they can strategically adjust their marketing to achieve the best return.
#2 Improve Customer Satisfaction
Data science can also help identify the drivers of customer satisfaction. Customer satisfaction is often measured in Customer Satisfaction Score (CSAT), Net Promoter Score (NPS), or Customer Effort Score (CES). Companies can improve their customer satisfaction by applying a regression analysis to find out the root cause. The results can help companies identify the different drivers of customer satisfaction and focus on the key areas.
#3 Voice of Customers (VoC)
The term “Voice of Customers” describes understanding customers’ needs, preferences, and expectations. During market research, understanding VoC can help companies improve their customers’ experiences in different stages of their journeys. The method that companies commonly use to improve VoC is neural language processing (NLP). NLP applies different algorithms to unstructured data and interprets them into valuable insights. This process includes data cleaning, extracting keywords, and data analysis. The results can help companies understand customer feedback from emails, social media platforms, and website comments on a much larger scale.
One common method of quantifying VoC is through data profiling. Data profiling is a process that summarizes data and identifies positive and negative variables. As a final deliverable, it scores each variable so that companies can understand which variables are closely related to VoC.
#4 More Efficient Market Segmentation
Market segmentation is the next category of customer insight and engagement. It divides the entire market into smaller groups based on criteria such as household income, age, purchase behavior, and interest. Market segmentation can help companies meet their customers’ needs more effectively.
This is often done through k-means clustering. Here’s how it works: after deciding on the number of segments (k), the algorithm runs iterations until it minimizes the total variance of data points inside each segment. By optimizing segmentation, companies can specify their marketing to each type of customer, therefore achieving the best outcome.
#5 Optimize Contact and Engagement
The next opportunity to improve marketing is through contact optimization (also called engagement optimization). Companies often apply this to outbound telemarketing by optimizing when and to whom they make outbound phone calls. They achieve this through machine learning. After training, testing, and validating a machine learning algorithm, it can help identify the best time to reach out to a customer and what to offer over the phone.
#6 Personalizing Customers’ Experiences
Data science also allows companies to personalize their user experiences for every individual. This is commonly done through recommender systems.
There are two common ways to achieve this goal. Item-based collaborative filtering predicts items that are similar to what customers have consumed in the past. User-based collaborative filtering, on the other hand, predicts items that customers might enjoy based on other similar users in the database.
#7 Increase Customer Lifetime Value
Accurately calculating customer lifetime value (CLV) can largely affect the strategy of a marketing campaign. Companies use CLV to quantify the lifetime value of each customer based on where they are in their journeys.
Why is this important? If a company is offering steep discounts on a product at a loss, then that means that they are losing money in the short term. However, if calculations show that each newly acquired customer will result in more future purchases and a higher lifetime value, it’s okay to strategically sacrifice short-term earnings to gain a longer-term advantage.
Cost & Planning: Using Data Science to Maximize ROI
#8 Applying Models to Marketing Data
Marketing data can help companies create more powerful and effective campaigns. This is especially true when it comes to online marketing. Data science can help analyze the cost efficiency of a marketing campaign on an aggregate and a granular level. Some common metrics include cost per click, cost per acquisition, and conversion rate. Measuring and understanding the performances of marketing campaigns on different levels can help companies optimize their targeting and achieve a higher ROI.
#9 Marketing Mix Modelling
Companies often ask, “How can we allocate our marketing budget to achieve the maximum results?” The answer is marketing mix modelling (MMM). They often do this through linear regression. The results explain how much each marketing input contributes to the sales, and companies can then optimize their marketing efforts based on the different outputs.
In a digital environment, companies can also achieve budget allocation optimization through attribution modelling. This helps companies attribute credits to different touchpoints that the customers make before they make buying decisions. Common methods for this practice include the First Touch model, the U-shaped model, the Shapley value model, and the Markov chain model.
#10 Improve Store Site Selection
If you are planning to open a new location for your business but are not sure what the best location is, data science can also help in the store site selection process.
First, examine your current store performances across all locations. Which stores are performing above average? Which ones are performing below average? Then, put different store site attributions together and create a model to describe or predict performance. Finally, test this model at new locations and use your predictions to make better decisions.
#11 Pricing Optimization
When companies decide to increase pricing, it is seldom the best decision to increase prices across the board.
This is because not all customers and products react the same way to price increases. Customers for certain products and services are far more price-sensitive than others. For them, even the slightest increase in price will cause them to flee. However, there are also customers who are much less sensitive to price changes, and sometimes they won’t even notice that the price has increased.
In some cases, new customers who walk into a store use certain items as a benchmark for the whole business, so it’s also important to understand this pricing factor.
To reach a price increase target, it’s essential to understand how customers will react to a change. Data science can help companies identify different price elasticities among customers and distribute the increase more efficiently.
#12 Dynamic Sales Forecasting
Though forecasting sales can be as easy as creating a spreadsheet and clicking a few buttons, dynamic forecasting can take the analysis to a new level and provide companies with much richer information.
Let’s say that you’re an apparel company and that your spring line is going into stores as we speak. In this case, dynamic sales forecasting looks at the trends in sales from day one. Within days, you’ll be able to predict which parts of your line will outperform, so you can go ahead and place orders. Similarly, dynamic sales forecasting can tell you which parts will underperform, so you can begin discounting items or moving them to stores where they might perform better. Dynamic sales forecasting can optimize companies’ results by giving them an intelligent and active view of how things are going in the marketplace.
There you have it: 12 ways that data science can help you customize your marketing efforts. We have covered useful practices such as market segmentation, contact optimization, and different recommender systems. They can all help you gain deeper insights into your current marketing data. Data science can also help you optimize marketing data, determine store site selection, and strategically increase prices. This will effectively optimize your marketing dollars and ultimately lead to a higher ROI.
Data science has the ability to revolutionize your business’s operation. It can lead to higher foot traffic, greater retention, more revenue, and happier customers.
Do you want to find out how data science can improve your marketing? Simply give us a call at 1-877-709-5264 or send us a message (https://cartesiandatasciences.com/contact-us).