3 Examples Of Using Data Science To Focus Brand Strategy
Updated: Apr 14
Data science is a powerful tool that can be used to fuel better business decision-making. It can help businesses streamline operations, accounting and also brand positioning by providing key insights that drive action.
In this article, we explore a few cases where businesses are leveraging data science to focus their brand strategy.
Machine Learning and Artificial Intelligence adoption have helped plenty of organizations strengthen their brands.
The company I work for provides a cloud-based built-in ML tool that has helped companies gather historical consumer information and translate it into comprehensive datasets for more precise client insights.
By applying predictive analytics techniques, these organizations were able to customize marketing campaigns to their intended audience, which decreased their churn rate and increased cross-sell and upsell, and of course, improved clients perceptions and engagement with their brands.
In my experience, data science rarely helps to narrow down a brand strategy. It is difficult, as often the board’s mind is really hard to change even when the data shows something that definitely should be changed.
However, I had an interesting situation once, where the data actually helped a lot and changed the direction of main activities, but they were still within the strategy of a company I was working with.
I worked with a big culinary service, which shared many recipes on their website that described how you can make a dish using one or two of their branded products.
When we looked at the users’ behavior and the best ways to communicate with them, we ended up analyzing many different data sets and their users’ paths.
While trying out various different approaches, we checked how their users behaved when it came to printing the recipes. To my team’s surprise, the scale of printing was huge.
The most surprising fact we found, was that the majority of all recipe printing happened on professional printers used in offices rather than at home, between 4 and 5 pm. After 5 pm, the printing rate was decreasing dramatically.
When we combined this insight with the target group research, we realized that the website was mostly used as a source of free printed recipes and shopping lists that were printed out at work and taken home by our users.
Realizing that helped us optimize the advertising campaigns so that they were appearing mostly during the time when our users were most active (4-5 pm).
Then, as a next step, we created and implemented a mobile app with online shopping lists so our users did not have to use their printers anymore and still could take the lists with them – on their phones.
Mithil Gupta, Grad. Student in Analytics and Marketing . Partnered with ZainCo in implementing this project for their Atlanta Clients.
We wanted a consistent content strategy, that was of high quality to our audience, relevant and frequent (We wanted to target at least one blog and 2-3 posts on Twitter and Instagram).
Following the Lean methodology, we wanted to keep the team size as well as expenditure to the minimum while increasing content turnaround at the same time.
We needed an automated way to help us find the topics and words that would help us achieve this. This is where we discovered that Machine Learning could help.
We decided on the top Hashtags to follow in the different categories we wanted to write content in. For each category, we extracted the latest posts from Twitter and Instagram. Then, using NLP techniques like – Topic Modelling and Bag-of-Words Analytics, we derived the topics being discussed as well as the top words that were being used in posts that had maximum engagement.
This project is currently in the Beta test phase for our client and we see this giving process giving automated templates to the content team to expand upon, helping reduce time and produce more dynamic content.
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