Appstrail Technology

Consider that you have set up a new Artificial Intelligence (AI) powered voice support assistant for your business, to answer rudimentary and simple questions like, “Where is my order?” or “When can I expect my order?” It seems simple enough and you would hope their response to sound something like this:

“Ravi, thanks for being a platinum member. I see you would like to know where your order is. Don’t worry, I have your order number and I will share an update shortly.”

This message sounds perfect, short and considerate. It acknowledges your customer and his membership, addresses his concern, and assures him of a quick solution. For appropriate response, companies need to ensure that data is accurate, updated, accessible, and complete. Otherwise, a response could very well sound like:

“Thank you for reaching out to us. Tell us your name, email, and order number, and we will get back to you.”

Businesses today are generating and transferring enormous amounts of data from and across multiple touchpoints from their stores, websites, apps, and technology systems. Yet, that data is not managed properly or analyzed to draw actionable insights. Hence, organizations struggle to enhance customer service and lower costs.

After all, a firm’s AI strategy is only as efficient as its data strategy. In fact, in a study conducted by Salesforce, 87% of analytics and IT leaders felt that enormous advancements in AI have made data management a top priority. However, in the same study, only 57% were confident of the accuracy, quality and entirety of their data.

The simple truth is that AI will significantly improve how customer relationships are managed but it will require data to be efficient. It will also require businesses to be connected and deliver real-time customer insight. So, let’s look at the ways in which businesses can lay the groundwork for AI to deliver amazing customer experience:

Remove Duplicate or Irrelevant Observations

Data duplication occurs when data is combined from various sources and identical entries are created. Irrelevant observations take place when data isn’t suitable for the problem you are analyzing. By eliminating such data, AI can deliver better responses through analysis that is more efficient, accurate, and useful.

Resolve Structural Errors

When data is mislabeled, does not follow a consistent format, filled with mistakes or has different headings for the same thing like ‘N/A’ and ‘Not Applicable, data will get tangled. Data entries must be consistent for AI systems to deliver accurate and complete analysis.

Filter Unwanted Outliers

In most cases, these are one-off observations occurring due to incorrect data entry. As such, they need to be excluded. However, there are instances where these outliers can substantiate the theory you are working on. But in any case, only an analysis can establish its validity.

Manage Missing Data

Incomplete or missing data is a frequent challenge, reducing AI’s predictive analysis. Here are a few ways to tackle that:

• Remove observations that have missing values (You will lose information, though)
• Based on other observations, input missing values (You will lose data integrity, though)
• Re-evaluate how data is employed to navigate the missing values effectively

At a time when AI is transforming customer relationship management, quality and strategic management of data will be the true catalyst. So, businesses must ensure that AI receives accurate and complete data to deliver excellent customer experience.

Continuing the Journey with Appstrail: Empowering Your AI Strategy with Salesforce Data Cloud

You’ve grasped the significance of accurate and accessible data in driving an effective AI strategy. Now, envision elevating your business further with Salesforce Data Cloud, a transformative solution that aligns perfectly with your AI ambitions.

Salesforce Data Cloud: Unleashing Data Potential

In the dynamic landscape of AI-driven customer engagement, Salesforce Data Cloud emerges as a game-changer. It extends beyond traditional data management, offering a unified platform where businesses can seamlessly integrate diverse data sources, enrich customer profiles, and gain real-time insights.