Leveraging Data Efficiency Through AI

Data has always been crucial for marketing technology (martech) applications. After all, without data, what do you have? With AI’s ability to unlock the true potential of your existing data silos, you can optimize operations, improve efficiency and enhance metrics in surprising ways.

Distinguishing First- And Third-Party Data

When talking about data, it’s important to distinguish between first-party data and third-party data. Third-party data is often aggregated from a variety of sources, while first-party data is collected directly from users interacting with an app or website.

First-party data is typically preferred over third-party data. Jeff Ragovin, CEO of Fyllo | Semasio, noted that first-party data “enhances the accuracy and reliability of the information, allowing for better-informed decision making in marketing efforts.”

Ticketmaster, as an example, integrated Pushly into their existing processes to utilize first-party data (purchase history, venue location, etc.) and deliver targeted web push notifications to new and existing users. Within six months, Pushly was able to use data relevant to the customer to help Ticketmaster increase their business and gain new customers.

Furthermore, a Hospitality Net article stresses the value of utilizing first-party data with AI to provide a more personal touch when using apps to book hotel rooms for repeat visitors. Considering customers on average spend only 10 seconds reading brand emails, it’s imperative to use a personal touch however possible to grab their attention.

First-party data will be available to companies in ways they never thought of before thanks to AI. This may look like separate metrics being combined intelligently to give an all-new approach to how you look at your data with customizable dashboards.

The user experience can also be enhanced. It’s one thing for a customer to opt-in to a survey that is taken hours or days after the fact to give their opinion on your apps; to be able to get impressions in a more real-time fashion can be a game changer.

The Impact Of Data Silos

Now that we understand the difference between first- and third-party data, where do data silos come into the picture? By themselves, data silos can be inefficient. There are several problems silos can cause, from increased costs to a lack of visibility into important business metrics. Data silos might have been useful when being set up in the first place, but they need to evolve to function better in a modern work environment. If data isn’t available to those who need it most or if data is too hard to find, there will be a lot of time wasted in getting the information that’s needed.

Ideally, data should be stitched together with first-party datasets. Let’s imagine your company has both a customer relationship management (CRM) platform (such as Salesforce, Dynamics 365 or HubSpot) and a help desk management (HDM) platform (like JIRA, ZenDesk or Zoho Desk). AI could combine a customer’s name and job title from Salesforce with customer support data from Zendesk and give suggestions on how likely they are to renew their annual contracts.

AI and data silos can make for quite the team. For example, your app might have a data silo for potential clients and a data silo for your salespeople. With some fine-tuning, AI could match salespeople to the type of potential clients they’d have a better chance of converting into a sale. It’s worth thinking about how your different data sets are organized before coming to an AI-enhanced solution.

AI can unlock the potential for data silos to do more than ever before, whether it’s analyzing existing data or presenting it to users in a fresh way.

In order to take the right steps toward an effective solution for using AI with their data silos in a meaningful way, companies should first consider how their business objectives will best align with their strategy. It’s always important to keep the big picture in mind. Furthermore, it’s worth making sure your existing data has a solid, unified foundation that can be improved with AI. Having both good data and great organization makes for a sterling starting point!

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This article was written by Forbes and originally published here.

 

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