Applications of NLQ in Marketing

Applications of NLQ in Marketing

Natural Language Querying (NLQ) is one of the buzzwords in corporates around the globe. The consistent effort to erode the barriers to data interaction for non-technical users, many of whom are in important leadership positions, has led to the rise of NLQ-based solutions in the market. 

Natural Language Querying can be explained as a human-to-machine input in completely natural phrases or terms, ones that do not necessitate the user to use any kind of special characters, code, syntax, logic, etc. Natural language queries can be conducted via voice command as well as text. 

NLQ is a subset of NLP (Natural Language Processing), which is defined by Statista as ‘a branch of artificial intelligence (AI) that helps computers understand, interpret and manipulate human language’. 

NATURAL LANGUAGE QUERYING - A GAME-CHANGER 

 

 

NLQ holds the potential to completely revolutionize the way the marketing department works, primarily by infusing more ‘science’ in the hybrid of ‘science + arts’ that is marketing. The amount of customer data pouring in is incessant, and the parameters to classify and view such data are also dynamic. In such an exploratory landscape, where the ‘WHAT’ you need to find is ever-changing, NLQ, through simple conversational querying mechanisms, can help find immediate answers to important questions and assess marketing ROI.

So how can NLQ help marketers? Here are some of the ways: 

MEASURING CAMPAIGN PERFORMANCE

 

 

NLQ can help marketers understand performance and fine-tune campaigns better by letting them know which of their ad campaigns are performing and on which parameters, for example. Large companies run multiple ad campaigns at the same time, even conducting A/B tests, and it is difficult to track the success/failure of each ad campaign. With NLQ tools like Phrazor, users can simply ask for a hands-on comparison of the various ad campaigns and receive insights in language, where they can further explore, compare, and measure through dynamic click-and-dig drill-down capabilities. 

Again, the important bit to note here is that NLQ will help you explore ever-changing data and parameters. The NLQ process remains the same - query in simple language (no-code no-syntax), and receive an instant response/insight in simple, understandable language.

SIFTING THROUGH WEB ANALYTICS

 

 

It is very easy for CMOs (themselves) to obtain granular insights around website performance and its various metrics like Click-through rate, impressions, sessions count, bounce rate, etc. In fact, Phrazor has ready-made templates available for the same. The templates use demographic information to make data cuts and produce relevant data-driven insights for each cut, with additional flexibility to customize the report and collaborate through commenting. 

Very few tools - Phrazor being one of them - have a unique ability to provide context-based reasoning for the numbers that appear on the screen. Phrazor’s algorithm has been designed to grasp the ‘Why’ question and provide an answer to it. Once they have the root-cause figured out, planning the next step becomes so much easier for marketers.  

IMPROVING LEAD GENERATION 

 

 

Improved lead generation can be chalked to a fairly simple function of knowing whom to target. From there, if your product or service induces buyer interest, there’s no reason why it shouldn’t sell. Through Natural Language Querying, marketing managers can easily question the segmentation information, like geographic segmentation, and demographic segmentation (age, gender, income, occupation, and education level of the target audience). 

Whether NLQ can help in behavioral and psychographic analysis depends on a number of other factors, having the right data being the primary one.

CREATING EFFECTIVE CONTENT 

 

 

Impactful content creation is another derivative of successful data analysis through NLQ. Once you know the content that piques your audience’s interest, you can go ahead and create more of the same and publish it across platforms in various distribution formats. Better content creation will eventually connect itself to better CTR and conversion rates.

Much of this is possible within Google Analytics too, by conducting a content marketing performance audit with the help of various reports that Google Analytics offers, like Landing Page reports for insights on content categories and topics, and Site Search report to advise on what to write next, by letting you know where users devote their attention to while on your site.

However, the ease and speed of time-to-insights and the independence that NLQ tools help give to marketing managers to take charge, ask questions, and extract actionable insights is not afforded by Google Analytics. The barrier of entry in learning Google Analytics for these kinds of insights is simply too high.

NLQ FOR DATA ANALYSTS

 

 

Data analysts often get embroiled in daily delivery, thus not being able to work on more sophisticated, strategic tasks, ones that would be a better fit for the rich skillset they bring to the table. By using NLQ to put the power of ad-hoc analysis into the hands of marketing managers, they would be relieved from the duties of ad-hoc requests from management, tiring email communications, and a final delivery that may yield further rework.  

Data analysts can certainly help themselves and their company by evaluating the maturity of modern-day NLQ tools and explaining to management the monetary and non-monetary value implications of such an investment. 

If you are a business user or a data analyst wanting to explore NLQ through our double-patented tool Phrazor, we invite you to a conversation with our solutions consultants.

 


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Written By

Akanksha Ramchandani

Akanksha Ramchandani has worked in IT industry as Developer, Project Lead, Strategy Lead, and Solutions Consultant for over a decade. She understands the requirement and problems of the clients and delivers a tailor-made solution. Currently, she is looking to help fortune 500 companies in changing the way they look at data and automating their reporting by using vPhrase's AI product.

Akanksha Ramchandani has worked in IT industry as Developer, Project Lead, Strategy Lead, and Solutions Consultant for over a decade. She understands the requirement and problems of the clients and delivers a tailor-made solution. Currently, she is looking to help fortune 500 companies in changing the way they look at data and automating their reporting by using vPhrase's AI product.

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