Increasing Online Sales with NLI Data Analysis

Sometimes it’s easy to forget that NLI can be used for so much more than speech based applications. In fact, data analysis of natural language is its second biggest usage after intelligent virtual assistants.

The number of different types of free format inputs into businesses is constantly increasing from live chat and contact forms to social media. NLI is already used in many of these applications to help customers by interpreting their requests to deliver helpful, intuitive responses and reduce inbound enquiries into the contact centre. But there’s another way to use this information.

In between the hellos, the small talk and the common queries is other data that can be invaluable to a company’s bottom line.

For instance Google analytics might be able to tell you which pages a customer visits, what links they clicked on and the exact point they gave up, but the reason why they do this is lost in a search query or an online chat. Did you not have the color they wanted, the size they desired or a particular feature?

But this is just one aspect of how data analysis could be used. Skruvat, for instance, is using NLI to find gaps in its online content and to understand the additional information its customers need to make them complete the purchase online without feeling the need to contact pre-sales staff to confirm their choice.

A financial institution might want to understand the concerns of its customers after an interest rate rise or a retailer might want to gain insight into how its customers perceive a recall on their most popular product.

Whilst it would be absolutely correct to carry out focus groups in these last two types of situations, there is a vast amount of initial intelligence online provided by the very people you want to interview – your customers!! And what’s more, they’ve given it to you in their own words. Whilst your customers may not always use polite words, the sentiment provides much more insight into their initial reaction that a check box of happy, satisfied, unhappy.

However, in the past revealing even basic information fast enough to make informed business decisions has sometimes taken months to correlate the data and even then it has only been based around set keywords. When for many businesses just a small increase in online sales can translate in to substantial profit growth at the end of the year, waiting weeks or even days is just not acceptable.

Using NLI to perform this type of analysis not only enables the information to be accessed in near real time, but the way NLI intelligently interprets the meaning provides flexibility in how the information is correlated and delivered. This enables different departments to look at the information in a way that suits their needs and not be constrained by how the IT department set it up in the first place.

Next time you’re looking at website analytics and find yourself asking why something happened, if you want the real story behind your customer’s decision then you need to understand their thoughts, needs and how they perceive the information you have provided to them. Intelligently interpreting natural language is second nature to NLI, how you chose to use the information is up to you.

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