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Four ways AI will shake up service in 2018

24th Jan 2018
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As artificial intelligence (AI) continues to grow, new technologies and applications will naturally emerge. AI cannot be ignored. 

If you’re interested in gaining a competitive advantage in the retail ecommerce space, then it’s essential to understand the AI trends that will influence business in 2018. Here’s what you can expect:

Trend #1: AI will personalise pricing

Online shopping on Amazon, for example, can often be a price hunt and peck. Items that sit in a shopping cart for even short lengths of time, often experience price fluctuations. Amazon and its respective vendors are learning not only how to price items to match demand and seasonality (think: last-minute holiday shoppers), but also match specific buyer personas and behaviours.

Knowing when to target a shopper with the price point that will ultimately lead them to purchase is invaluable. Ecommerce business owners can set minimum and maximum price point parameters, but AI will automate the rest. Prices and discounted prices will no longer be aimed at a business’ entire consumer population. They will instead uniquely target the right person at the right time with the right price. Not only will the price be personalised to each shopper, but it will also determine the profit margins that each retailer desires to reach, so you can be achieving the same sales level with higher profit margins.

AI-powered platforms can enable ecommerce professionals to analyse large amounts of consumer data in order to offer personalised incentives to each visitor that enters their website. Backed by the science of behavioural economics, they can create unique shopping offers based on each visitor’s shopping habits, personal preferences and the retailer’s KPI’s.

Trend #2: AI will collect omnichannel data and deliver actionable insights

As AI develops, its ability to collect and utilise data from multiple consumer channels continues to improve. Data from both digital and in-store shopping experiences can be collated and analysed to provide direct insights to consumer habits, interests and purchase intent.

Here are a few examples of these channels and how they can benefit shoppers and ecommerce business executives alike:

  • Digital customer journeys. Business intelligence (BI) measures the customer’s online journey and is significantly enhanced by AI. BI comprises of the strategies and technologies used by ecommerce companies for analysing business data. BI powered with AI can provide past, current and future forecasts of business operations, with data points such as page visits, clicks, and time spent on specific sites. Once collected, AI can utilise this data to predict personalised buying habits and a consumer’s likeliness to purchase.
  • Customer service interactions. Interactions between consumers and customer service professionals are highly powerful. This is the company’s opportunity to directly speak with customers to find out what they want, what their challenges are, and what solutions will work best. Measuring success via surveys or Net Promoter Scoring (NPS) are highly efficient ways for AI data systems to help business leaders get a more solid grasp on the bigger picture.
  • Chatbots and virtual assistants. AI is already capable of immediately capturing consumer pain points and frequently asked questions through chatbot and virtual assistant platforms. Not to mention, AI-powered chatbots are on-call 24/7, allowing ecommerce websites to answer customer inquiries no matter what the time of day.

Trend #3: Expect superior service from chatbots

Most shoppers who have used a website’s chatbot know there is a great deal of opportunity for these digital customer service representatives to improve. We predict that 2018 is the year chatbot technology will get it right.

Once implemented, chatbots are easy and inexpensive to maintain. By automating data measurement and analysis, chatbot technology uses predictive analytics and survey tools to learn more about a given business’ audience.

The key to chatbot improvement in 2018 is incorporating Natural Language Processing (NLP) in the technology in order to adapt to better serve customers.

Facebook Messenger Platform’s lead project manager, Kemal El Moujahid, says: “The promise of chatbots is personalisation at scale.” Whether chatbot or human, he explains: “The most important thing for humans is for their expectations to be managed.”

As long as chatbots are managing expectations by providing solutions, they will continue to seamlessly adapt into the customer service fold.

Trend #4: Conversational interfaces will replace point-and-click analysis

Point-and-click analysis is the traditional way most ecommerce professionals have historically interpreted website audience traffic. Customer journeys, site-pathing and click-through-rates do provide useful data points such as:

  • Audience interest.
  • Audience engagement.
  • An understanding of which digital creative assets, price points, and messaging work best.

In 2018, these metrics will not go away, but they will be enhanced by conversational analytics. Far more personal than clicks, chatbot conversations empower AI analysis to get directly to the heart of a consumer’s needs.

Conversational analytics move beyond point-and-click methods to examine real-time conversational interactions. Consumer segmentation becomes automated. AI assesses when a user is most engaged with a chatbot, what their last conversation point or question was, and can more seamlessly adapt messaging that is most useful to that user.

This information will, without a doubt, dictate and speed up brand-wide improvements in a far more consumer-centric manner.

We can’t wait to see how these AI trends will position ecommerce growth for even greater success in 2018.


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By PaulineAshenden
29th Jan 2018 10:21

These are all really strong advantages that AI brings to CX. However, it is important to remember that consumers won’t always want to interact with a chatbot - there will be times that they want to communicate with a human. AI can help here too, providing agents with real-time conversational analysis through NLP that enables them to better understand and respond to queries.

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