10 Examples of Data Science in Marketing

Provided by Wojciech Prażuch, Jun 9, 2021 • 17 min read

Data has never been more accessible or essential to running a business. An increasing number of sophisticated sources, from social networks to web databases, provide big data on an unprecedented scale.

Data scientists, who process and translate data, have emerged, enabling business owners to use this new, valuable intelligence to inform their marketing strategies.

Understanding the value that data science can add to your business is now incredibly important. For that reason, we have for you 10 examples of using data science in marketing that will help you to understand its potential.

What is Data Science?

Data science is an interdisciplinary study of large volumes of data using modern tools. It aims to provide a holistic, thorough, and refined look into raw data. It enables your business to focus straight in on those insights that will directly influence how your business works, help you make practical predictions for the future, and enable you to make effective marketing decisions.

New trends in the field of data science reflect the growing possibilities for businesses, be it in the realms of customer service, product development, or customer value.

Find out how can Data Science be used in marketing and read on the examples of its application below.

1. Channel Optimization

Over the years, the key insights that businesses collect about their customers have been primarily their age, location, and gender. These details give businesses and their marketers very little idea of who their client is and what their client wants.

Data science, in the form of tools such as affinity (or market basket) analysis, can paint a much more accurate picture of the kind of person the business is looking to appeal to and where best to market to them.

Connections can be made, through a detailed analysis of the customer’s social media interaction, that will form a particular story or pathway. This pathway will highlight any missed opportunities on YouTube, Instagram, Pinterest, or any other channel that is popular with your ideal client, where advertising and content would be most effective.

2. Customer Segmentation

Recognizing the different needs of your customers is essential to a productive marketing plan. No two customers are exactly the same, but their pain points, desires, and aspirations can be grouped in helpful ways to inform marketing strategies and drive conversions.

Customers can be segmented based on attributes such as their location, their historic purchase patterns, and how they have navigated through your website. Data scientists can use specific machine learning algorithms to determine both the potential value of each ideal customer group and which products are most likely to appeal to them. This can then inform your content strategy, your channel optimization, and your advanced lead targeting.

3. Lead Targeting and Advanced Lead Scoring

Reaching out to the right potential customers at the exact right time is widely recognized to be the most challenging part of the digital marketing process. Data science and machine learning systems can make customer analytics a lot easier.

Through extensive analysis of your collected marketing data alongside insights from data libraries, data scientists can predict which offers or products will be most attractive to different customers and demographics at different times.

The potential value of each lead and each potential customer can be scored, depending on factors such as the behaviors of similar customers in the past, their word choice when interacting with you, and the characteristics of the customer segment they fall into.

The process will save you from wasting part of your marketing budget on guesswork and trial and error.

The effectiveness of each predictive algorithm will then go on to inform future marketing decisions, including any new products or services you choose to create. Implementing lead targeting with a machine learning model will turn this process into a streamlined end-to-end solution that is constantly refining and improving itself.

4. Real-Time Interaction and Analytics

There are benefits to using data collected over time to inform your marketing decisions, but the delay in gathering this data can put businesses on the back foot.

Real-time analytics enable businesses to measure and process customer behavior as it happens, providing meaningful, actionable insights at what could be a critical time for customer conversion. Real-time analytics also allow for a faster response time when your focus market fluctuates, saving you money and wasted marketing in the long run.

Two key ways in which real-time analytics can be used in marketing are:

• sending out targeted offers and incentives to appropriate customers when they are in-store or on your website;
• and, using customer behavior to understand when and why sales are lost or made.

5. Content Strategy

Devising an effective content marketing strategy to pull in new leads can sometimes feel like a shot in the dark.

Without data and analytics to support your findings it can feel almost impossible to pinpoint what it is exactly that your customers are enjoying, even if you receive a high response and conversion rate from your content.

This is where data science steps in. Though testing is still necessary to truly understand the quality of your content, methods such as serial testing allow you to do so in the most effective, least time-exhaustive way, using an unsupervised machine learning algorithm.

Serial testing can help you drill down on details as small as word choice and color. Techniques such as time-series forecasting can then help you predict when these creative choices will be most effective across all platforms, allowing you to put fully optimized content in front of exactly the right people at exactly the right time.

6. Sentiment Analysis

Creating a positive reputation for your business or brand is vital.

Your customer’s initial reaction when they find your social media channels or website can go a long way to shaping how positively they view you, even before they have experienced your service. This reaction is often shaped by reviews or responses left by others.

A key way to ensure you have control over your reputation is to tap into the emotions of your clients through sentiment analysis. Though this can be done manually, machine learning algorithms greatly increase the speed and effectiveness of this data analysis.

Specific values (negative, neutral, or positive) can be assigned to individual words, to give each social media post a score based on the reactions in the comments section. This same theory can be applied to email correspondence, Google reviews, and even to phone conversations using speech-to-text analytics.

This can help you single out the products, services, or social media marketing efforts that elicit the desired reactions from your pool of potential customers, and to determine where any breakdowns in customer service are occurring.

7. Maintaining Customer Loyalty

Maintaining customer loyalty and building a better lifetime value for your average customer is potentially a more profitable application of your marketing budget than solely acquiring brand-new customers.

Data science and machine learning models can help businesses identify three things that may help improve customer loyalty:

• the next best action or the next best offer for each customer as they interact with your website or product;
• how a customer might react in a specific circumstance;
• and what the problem is if a client doesn’t return.

Once these three things have been uncovered, you can solve many problems that may be making customers reluctant to return, set up automatic recommendations for clients who have already worked with you, and predict the best set of actions should a specific interaction be realized.

Thanks to the availability of their data, marketing to a customer who has already enjoyed their experience with you is easier and cheaper than marketing to a lead that is ‘cold.’

8. Predictive Analytics

Predictive analytics bring together machine learning models (and sometimes artificial intelligence in general) to predict what might happen in certain situations that affect either your business or your customer.

With more Internet of Things (IoT) devices than ever, there has never been so much data on which to base these predictions, and so, by extension, the predictions that are possible with the right system in place have never been more accurate.

Predictive analytics can help businesses do the following:

• Target customers with a higher potential lifetime value and/or lower churn rate
• Successfully distribute content to the right audiences
• Determine the effectiveness of digital advertising campaigns before their release
• Effectively cross- or up-sell products.

9. Recommendation Engines

Recommendation engines work off the back of a successful predictive analytics system.

There are two types of recommendation engine:

• collaborative filtering
• and content-based filtering.

A recommendation engine based on collaborative filtering will suggest products to customers based on the purchase habits of other customers, irrespective of the product type. A recommendation engine based on content-based filtering is more aware of product type and description and recommends products that are similar to one another.

Both types of recommendation engine have their downsides. Collaborative will potentially recommend products that do not suit the exact needs of the customer at that time, and content-based filters will recommend very similar products rather than up-selling.

Most businesses, therefore, use a mix of the two, recommending products based on type, description, and past popularity with similar customers.

10. Marketing Budget Optimization

The overall aim of data science is to ensure that every penny of your business’s marketing budget is spent in a useful way that maximizes profits.

By optimizing who you market specific products to, and when, your business can avoid spending money on marketing strategies that are not driving results.

All of the examples above can help achieve this, allowing you to put together a sharp, comprehensive, and often automated marketing plan that covers everything from identifying your customer base to determining how the weather might affect the sale of one specific product.