How Data Scientists Lead Marketing Efforts for Increased ROI
When you first meet data scientist Jasmine Joseph, you will learn that she is passionate about data and the potential for data to influence the success of a marketing campaign. Understanding exactly what her team of marketing data scientists at Franklin Madison does is a little harder to grasp.
What Is a Data Scientist in Marketing?
Marketing data scientists are similar to data scientists in any other industry. Their jobs include analyzing internal and external datasets to help organizations better understand their target customers, including their likeliness to respond to offers.
Data science is often misunderstood or confused with data analytics, which not all data scientists handle. Typically, analysts look to the past to summarize data that has been collected and look at patterns, while a data scientist looks to predict the future. They answer questions such as:
- Who are the most likely customers to interact with your offers?
- How do people react to your brand and marketing?
- What other products do your consumers want to purchase?
- How can we eliminate waste and increase marketing ROI?
What Does a Data Scientist Do?
To best help a company understand their target consumer and how to get the most out of marketing campaigns, data scientists work with information that is collected and organized to:
- Build predictive models using regression, machine learning, and other advanced statistical methods
- Analyze campaigns
- Create a strategy for the best marketing tactics
- Provide selling points for specific products and clients
- Ensure customer data is protected and properly stored
The level of analytics and deduction a data scientist uses goes beyond the average marketer’s ability to look at Google Analytics and determine how a marketing campaign is performing. Analytics shows you the basics of who visits a website and from where, but data scientists use demographic and habit-based information about consumers to determine:
- How they will react to various marketing channels
- What type of marketing performs best for each segment
- What additional offers they may appreciate
- How to best personalize marketing content
A Day in the Life of a Franklin Madison Data Scientist
As Franklin Madison’s VP of Analytics, Jasmine Joseph, wakes up every morning with a fresh cup of coffee, ready to work with her team to improve marketing campaigns.
The first thing Jasmine does every morning after opening her computer is refresh the daily-updated report to review how clients’ campaigns are performing. The first thirty to sixty days of a campaign are the most crucial times for reviewing results to determine whether it is performing below or above expectations.
Every campaign brings in results that prompt questions like:
- What segments aren’t performing as well as projected?
- What happened to make the campaign perform below or above expectations?
- Were there file makeup changes?
- Did the state have a large natural disaster affecting responses?
- Did the file include demographic changes?
- Can we make any immediate changes to improve the response?
Questions like these allow the data team to predict what the membership response rate will be once it reaches 100% and judge the response curve.
If the data scientists look at the daily performance report and determine that everything is performing as usual or that no new results have come in, the wait for more results begins.
How Does a Data Team Work Together to Maximize ROI?
While the Franklin Madison modeling team is made of multiple individual data scientists who work on building models for individual campaigns, the whole team works together to ensure campaigns are constantly being studied. This team utilizes over 50 years of proprietary data, plus the data they are receiving from current campaigns each day, to improve results and better understand consumers.
Once the data modeling team sets a plan for the following year, they can determine file sizes and performance predictions. Since so much changes from when the budget is set to the end of the year, all changes must be factored into the projections. Models must be reviewed at the beginning of each year and for all new campaigns to ask updating questions like:
- When was the last time the model was updated?
- Does the model still have the predictive power to be used again, or do we need to incorporate multiple models or rebuild?
- Does the model need to be refreshed and more historical campaigns consolidated?
Once an individual data scientist creates a model, the team convenes for a model governance review, where the model is validated prior to implementation. This rigorous process includes reviewing whether the variables are stable enough or if two models need to be combined to make them stronger for maximum results. Decisions are made as a team in these meetings.
Technology Used by the Data Modeling Team
The data modeling team at Franklin Madison utilizes different types of technology to ensure the accuracy of data and the privacy and security of all consumers.
A large part of protecting consumers’ privacy starts with storing data in data warehouses, which is always stored by a third-party and never enters Franklin Madison directly.
Other technology used by the team includes:
- Integrated SQL developer for data analysis, reporting, and quality control
- Integrated SAS for seamless data flow for model development, analytics, and production
- Open-Source Platforms (R and Python) to enhance modeling procedures and advanced analytics
- Integrated Tableau in Acxiom environment for daily reporting, visualizations, and interaction analysis
For everyone who isn’t a tech whiz, this information may seem confusing, but it all blends together with a team of bright minds to create increased performance results for insurance marketing campaigns.
Marketing Budget Optimization
Marketing budgets tend to be strict, and companies want to get the most for their money. To maximize the ROI of campaigns, marketers must find efficiency in their budget. Data scientists can help with this.
Data scientists analyze a marketing team’s spending and acquisition data by building models that can help them understand how the money is being spent and how this can be optimized. These models can help marketers distribute budgets across locations, channels, mediums, and campaigns so they are better able to optimize for key performance metrics.
While plans can be put in place for budgets without spending models, the data scientist is able to get more specific and project for the future more accurately by looking at the data they have in front of them.
Identifying and Marketing to the Right Audience
If you are sending all the right content via all the right channels to the wrong audience, your marketing budget will be blown quickly with little to no return.
Data scientists can look at all the past data and analyze it while looking at future projections to understand demographics, buying behavior, locations, and other specific details that can get the highest reaction and response out of a campaign.
This may even include analyzing what channels your consumers prefer for shopping and communication by using a time series model to compare and identify the kinds of lift seen in various channels.
With all this information, data scientists are able to work with marketing teams to build customer personas and accurately profile prospects before a campaign is built.
Strategy and Testing
Data scientists typically work hand-in-hand with the marketing team to determine creative and strategy changes that need to be made according to the analytics. They also identify what tests need to be done to get more answers. The more tests done, the better, as long as they are chosen based on data. Otherwise, testing blindly can waste your budget.
Depending on the company, a data scientist may also work with the sales team to provide selling points for specific products and clients which can translate into sales material as well as marketing content that converts.
Why Does Data Matter for Marketing?
For everything that data scientists do to help campaigns, the data collection itself matters too. “80% of consumers are more likely to purchase a product from a brand that provides a personalized experience.” This experience includes all aspects of the customer journey from initial marketing content to the sign-up process and beyond.
Additionally, data improves brand loyalty when used properly. Integrate.io reports that “70% of consumers say how well a brand understands their individual needs influences their loyalty for that brand.” When a financial institution has a long list of consumers but isn’t sending them personalized marketing, it is much easier for that consumer to leave and find a new organization with which to do business. However, personalized marketing experiences paired with products the consumers actually want can increase the lifetime value of any one consumer.
Choose the Right Data Partner
Franklin Madison understands the importance of consumer privacy, which is why we don’t house any consumer information ourselves. We work with data security partners that follow all federal communication and privacy standards and help keep your consumers’ data secure.
Our team of data scientists, combined with Franklin Madison’s 50+ years of experience in the industry, bring the knowledge, tools, and experience needed to help generate increased loyalty and incremental revenue for your financial institution.