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Hacking Growth in Marketing with Data Science

Few years back, I have avidly watched every episode of Mad Men. I have excitedly embraced the show as they tried to tell a story about marketers in the ’60s. From Mad Men, I always think that marketing is a creative domain. However, in this day and age of computer and digital marketing, brands are collecting an extensive amount of data at various stages of the customer journey. Marketers start trying to address this customer data, ranging from demographic to behavioral insights. Data science helps us leverage this data into actionable insights that results in a greater return on investment.
Data science methods like clustering, regression, or even machine learning algorithms have broaden marketing from a creative domain to also include a scientific one. In these last few years, data science have proved that their approach can uncover product and customer insights at scale in an unprecedented way. To do this, marketers better understand what data science can and cannot do, as well some of the methods and few examples of how marketing teams use a data science approach.

What Is Data Science, and What Does a Data Scientist Do?

Data science is a blend of programming and statistics principles with the goal to discover hidden patterns from the raw data. Although a data scientist does need programming ability, it is simply an instrument to derive insights and run statistical analyses, not to develop software or manage infrastructure like a software engineer would. Data scientist make models using statistical methods or machine learning algorithm to predict the future.

Beside programming and statistics, data scientist uses domain expertise to unlock key insights. A marketing expert can generate answers to problems such as: Who are your most promising customers? What other choices do your costumers have? What do people think about your product? What other products do your customers want to buy? By embracing data, a marketing team can mitigate risks and target customers in ways that are personalized and cost-effective.

Examples of Data Science Approaches in Marketing

Market Basket Analysis

As a marketer, most likely you will be in a situation where you are spending a considerable budget on marketing. The campaigns might be getting a lot of engagement, but somehow your return on investment is below expectations. Through data gathered from your website and social media pages, a data scientist can understand the customer base’s demographics. This understanding goes beyond age, geographic location, and gender. A simple market basket analysis, where we do data mining to discover co-occurrence between customers, will give you details about what else this customer is likely purchase. While the market basket analysis has been used for years by retailers, in modern age, it gives you insights beyond people who buy milk are also likely to buy cereals. It might give you a less intuitive, but equally actionable insight such as dog lovers are also likely to be house interior enthusiasts and are likely to watch fitness videos. This allows you to market where your customer base is present, while still exposing you to a new audience, increasing your visibility without additional effort on marketing.

Customer Segmentation

We can define segmentation as clustering customers into groups of people with similar characteristics or behaviors. Every marketer knows that different audiences respond to different story. This is where our data science toolkit comes in. Clustering customer segments together when you only have a few input variables is easy. However, the task becomes much more challenging as the number of variables grows. Data scientists use a machine learning method called clustering to figure out where the segments really are. Clustering algorithms are unsupervised, meaning the algorithm figures out what variables are similar to each other without input from the user. These clustering algorithms strive for the most mutually exclusive, collectively exhaustive segments. When used correctly, this approach optimizes clusters in the most efficient way possible. It does not segment where segments are not needed and does not miss segments necessary for a targeted campaign. Not only are points within clusters similar to each other, the clusters themselves are dissimilar, meaning marketers can then make personalized campaigns or offers that each segment will respond to, rather than a generic campaign with low ROI.

Full Funnel Optimization

Marketing campaigns have traditionally focused on awareness, acquisition, and activation. Through the use of data science, marketers get all the way down to revenue, retention, and referral. A business can predict the lifetime value of new customers through the use of several machine learning methodologies. Rather than just focusing on first conversion, marketers use data science insights to help businesses target longer customer relationships. Data scientists can give you the insights to drive your marketing strategy to target the customers that make the most sense for your business. Suddenly, your marketing team is directly impacting your revenue growth. Machine learning can predict churn probability, helping you develop a strategy to target customers who are likely to stop engaging with your brand in near future. Your marketing team is now working on retention. This works all the way down to referrals. Data science can help you determine which customers are evangelists for your brand through analyses on the quality of content, brand affinity, and brand engagement. You can then target them to make their referral process simpler and more effective.

Insights and Experimentation

Equally important as predicting these segments is understanding “why.” Data scientists look for causal relationships that marketers can then reverse engineer into an effective campaign. For example, let us hypothesize that clicks and conversion rate are positively correlated. The data scientist can test if that is true with a regression analysis, then the marketer, in partnership with the data scientist, can come up with experiments to test which campaigns produce more clicks, therefore a higher conversion rate.
An effective marketing strategy must always attempt to be ahead of the curve. This requires some level of risk, but with data science toolkit, that risk is minimized. In the business world, we often get caught up in the “best practice” rhetoric, when in reality, every business we have seen that grew massively has done so by taking risks. The best practices of sixty years ago were risks at the time. Progression happens through experimentation, and data science equips you to perform a large volume of micro-experiments that together give you immense insight without making drastic or sudden changes, therefore mitigating any risks when trying something new.

Taking Advantage of The Insights Gathered as a Marketer

Channel Optimization

Knowing where your greatest conversions are from, you can choose what channels to use to bring your product to market. Data science can help you automate this process and make sure you are always getting the greatest possible ROI.

Customer Persona

Marketing and data science have something in common in their approach: making hypotheses, then validating or invalidating them. Data scientist can help you test your hypotheses to understand who your customers are. Once you have a solid understanding of who your true customer persona is, the data will show you deeper insights about what content they are likely to respond to.

Customer Targeting

A marketer should know that a product will have several customer personas, not just one. The problem is, how do we know which one will generate the highest return? Data science can track which costumers are the best leads, or even develop a model to predict lifetime value (LTV) or any other KPI that your business needs, then you can focus your effort on those leads.

Sentiment Analysis

Any marketer knows that the most important trait a marketer should possess is empathy. Sentiment analysis allows you to collect data at scale to help you empathize with the customer. It allows you to monitor their reactions and beliefs towards the information they receive and gives you feedback on content and how people are engaging with your campaign.

Product Development

Data science will help you match the right product with your customer. By looking at insights given to you by customer persona data you can perform various clustering analyses to see what else they are likely to buy and what price they are likely to buy it at. These insights let you know exactly what your customer is looking for both from your current collection and give you data to develop new products they might be interested in.

Real-Time Data Insights

Data science also gives you the power to communicate with your customers quickly based on real-time data. For example, a marketer may want to target customers who have a bad experience. Data science allows you to find customers who fit the mold and market to them immediately. This helps marketers improve their customers’ experience by further personalizing content.

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