Ardyce Taylor is a Digital Marketing Expert who has won awards and is known for pushing the boundaries and producing outcomes. She serves as the Director of Performance Marketing at Filevine.
In this episode of RevOps 500, Ardyce discusses how, in the beginning, she had to spend some time carefully reviewing and evaluating data. She would also debunk myths and answer questions related to RevOps.
Myth: Activities related to Return on Investment and Brand Awareness are not the same.
There is a common misconception that brand awareness marketing campaigns and ROI-driven activities are distinct. It is now possible to monitor how different marketing initiatives, such as events and trade exhibits, affect the bottom line because of technological developments.
Marketers can efficiently calculate the return on investment of brand awareness activities, such as trade exhibitions and events, using the appropriate tools and tracking systems.
How would Ardyce approach tracking traditionally challenging metrics like trade shows?
Trade show ROI can be monitored in a few different ways. The first step is figuring out how much the event will cost. This will serve as your starting point expense for calculating the return on investment.
The next step is to decide which activities you wish to monitor. Common KPIs include booth visits, leads produced, demo requests, and sit-down meetings. Your overall trade show goals will determine exactly what metrics you use.
Numerous platforms are available to assist you in monitoring these metrics. You can still manually track your progress without access to a complex platform. You may, for instance, keep track of how many people you speak with at the trade show.
The secret is to go into the trade show with certain goals in mind and monitor your progress towards them. It doesn't matter if you use an advanced platform or a straightforward notebook—the key is to collect information that will allow you to calculate the event's return on investment.
Which part of the whole tech stack keeps her up at night?
The handling of data gets harder as businesses get bigger. Keeping data consistent and clean is challenging because of the huge quantity and variety of data coming from different sources. In mid-to-large organizations, where data silos and diverse platforms hinder data integration and analysis, this problem is especially significant.
To ensure that data-driven decisions are built on accurate and trustworthy information, dedicated resources are frequently needed to manage and maintain data quality.
Organizations should always work to improve the quality of their data, even if absolute data cleanliness could be hard to achieve. Accurate and trustworthy data must be available for decision-making. Efficient interpretation and use of data require experience, while data holds significant value.
Data-driven decision-making needs to maintain a balance between the power of information and practical experience. Businesses that manage their data well are better able to make intelligent choices, optimize operations, and accomplish their strategic goals.
Which techniques or methods does she use to keep her data clean?
The importance of data cleaning increases with the volume of data we gather. Manual cleaning is doable for small datasets, but it is not practicable for huge datasets. This requires the employment of specific equipment and knowledge.
Along with their corresponding languages, programs like R and SQL can assist in locating and resolving data conflicts. However, to understand the data and put corrective actions in place, these instruments need human intervention.
Even though AI can automate some data cleaning tasks, it still needs human supervision throughout training and improvement. Complex data pattern identification and analysis are still areas that require humans.
Where does she see the future of RevOps?
She is optimistic about the increasing reliability of data, pointing to patterns in global warming and cancer forecasting as examples.
In the future, she sees enormous volumes of data being gathered daily and combined into a single AI-driven business model, enabling previously unthinkable levels of insight and decision-making.
Although data is currently separated, which limits its value, she thinks technology will close this gap. She envisions an AI-powered system that uses data analysis and sourcing from several sources to optimize resource allocation, accelerate business growth, and streamline operations.
Adopting an AI-driven business model can significantly improve the productivity, creativity, and overall performance of the company. Her perspective emphasizes how data and artificial intelligence (AI) have the power to change company operations in the future.
It will take some time to get used to using AI in decision-making. AI models will be managed and observed by humans for a considerable time. She does not, however, think that AI will ultimately replace human decision-making. Instead, AI will be an effective tool for enhancing and improving human abilities.
What made her interested in RevOps and data?
Her fascination with statistics and narrative originated from a life-changing encounter in junior college, during which she learned about biological modeling. She was captivated by this idea since it demonstrated how maths could be used to both predict and explain natural events.
She pursued a degree in maths but ultimately realized that marketing was her profession, allowing her to merge her love of numbers with narrative. Currently, she uses statistics to tell the stories of marketing and money rather than researching animal populations.
Using data to inform decisions is essential today, but experience and judgment should still be valued equally. Organizations must place a high priority on having clean data and the ability to understand it correctly. The growing involvement of AI in this industry necessitates human guidance and oversight. Future data-driven decisions are most likely to combine human understanding with the power of data in a more integrated way.
Listen to the complete episode here.
