How Can AI Improve Sales And Support?

I often get asked this about artificial intelligence (AI) and similar questions about customer support. The complete answer is found in a previous article of mine. This approach offers the best return. However, some are reluctant to make the commitment needed here.

There is, however, an alternative: You must pilot test the technology before expanding it throughout your business. Consider the following attributes you should harness in your project:

AI Needs Data

The first step is to identify your data points. Even though most CRMs fail to provide data-driven insights, they will hold the most usable data. Data from phone communication is another good source. Using ASR, commonly known as speech-to-text technology, makes this data available and purposed for natural language processing and natural language understanding. Because a large portion of business communications happens over the phone, it is significant when leveraging AI for sales or support.

However, your project’s success is not driven by data volume. If it was, Google or AWS would offer the best performing AI-powered tools. They don’t because a significant driver of success is data quality, combined with the training needed to create a model specific to a vertical. High-quality data allows an algorithm to identify underlying trends and weed out the noise.

By using historical and real-time data for training and adapting your models, your project will deliver better results than with the generic tools. The challenge is in standardizing unstructured data. You must pre-process everything and connect the flows to a common data lake.

Of course, there are two ways to go about this. You can put together your own data science team or partner with an AI startup.

Hidden Sales Opportunities

In pilot testing your product, you must identify the hidden opportunities that are revealed by processing additional data from social media, messages, emails and transaction history. This will help you divert customers and potentials to the most knowledgeable employee. This offers many benefits. Onboarding time and acquisition costs are lowered when a potential customer is paired with your most suitable employee. When existing customers are paired with the employee best equipped to address their inquiry, customer engagement can increase. Over time, customer service may improve, and your customer experience (CX) can be enhanced. Satisfied customers may result in more sales and increased customer lifetime value (CLV).

Deep learning algorithms can reveal these hidden opportunities and lay the path to achieving optimal outcomes. These algorithms offer high accuracy in lead scoring, customer scoring, employee scoring, skill-based routing and intelligent churn rate prediction.

Intelligent Lead Scoring

Lead scoring has always proved difficult for sales professionals. However, correctly distributing those sales leads separates the best sales managers from the rest.

Traditional lead scoring involves attributing values to qualified leads based on their perceived interest in a product or service. But when calculated manually, it is imprecise and often highly subjective. Vast quantities of data need to be analyzed to determine how hot a lead is. The required data processing power here is beyond manual limits and requires an AI assistant to do the heavy lifting. Machine learning (ML) can work out this customer behavior.

Combining lead scoring with employee scoring can also improve your sales process. However, analyzing the key performance indicators (KPIs) of sales reps in real time is next to impossible. Like lead scoring, it requires the analysis of a vast amount of data, which is only possible with tools powered by ML. An ML algorithm runs through customer data and establishes trends — and recognizes patterns in order to create a prediction model.

Make The Best Offer

Sales, particularly for businesses offering low-volume/high-margin products, is highly consultative and requires tailoring your solution to each client. This is time-consuming, and if these leads are unlikely to purchase, lots of time and money is wasted. By establishing which leads are the most likely to purchase, you can focus your sales team’s time on the best prospects.

This process also requires prescriptive analytics, which can suggest how to take advantage of an opportunity — for instance, choosing between upselling and cross-selling to a customer and the implications of each. With access to real-time data, prescriptive analytics can improve prediction accuracy and provide better options.

Along the same lines, with access to quality data, ML and prescriptive analytics can detect customer behavior that indicates a high probability of a customer leaving for a competitor and suggest how to successfully reengage them.

Choose Your Path

According to Gartner’s 2019 study (paywall), there are over 1,000 vendors worldwide offering very simple platforms using modified open-source components. As it points out, there’s a huge difference between offering a turnkey AI feature and an AI feature that requires professional services to make it work. As such, when it comes to selecting your AI solution, you have three main choices:

• Self Service: With this option, you should focus on selecting a solution or software development kit that is as flexible as possible — because deep customization will not be an option for you.

• Enterprise Offerings: This option focuses on platforms of higher complexity and sophistication than self-service vendors. This comes with the cost of professional services that help clients customize their solution.

• Managed Offerings: These solutions are similar to enterprise offerings, but the projects are further updated and trained by the vendor. Vendors are also responsible for customization capabilities and improvements.

No matter which route you choose, you should aim to create or purchase a scalable platform that enables your business to implement data-driven decision-making wherever possible.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

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