It was mid-July 2017 when I got an email from a sales colleague about a potential chatbot opportunity with a short window to respond. The prospective customer was the UK branch of supermarket chain Lidl. The Lidl digital marketing director and his team had already looked at a variety of vendors and were ready to make a selection, but they were still open to listening to us. It was a long shot, we thought, but we still wanted to give it our best effort.

Fast forward 5 months, and we’re seeing tweets like this from the Daily Mail and The Sun:

Wow, what had happened? Well, a fantastic team effort is what had happened.

Winning Team Lidl Over

Lidl saw value in:

– our expertise around all things Customer Experience

– our unique approach to Natural Language Understanding with support for many languages

– our “cloud clout” and the resulting time-to-market

– our design-once-deploy-anywhere framework that allows taking one bot and deploying it on channels like FB Messenger, SMS, Amazon Echo, IVR, etc. with minimal additional effort

– our experience in building and hosting enterprise-grade, transactional customer self-service solutions that integrate into the larger marketing and contact center ecosystem of a multi-national corporation, including the ability to expand into eCommerce

From our Press Release: https://www.aspect.com/company/news/press-releases/lidl-uk-launches-ai-wine-chatbot-to-uk-customers-with-aspect-software

How does a conversation with Margot look like? Have a look here:

When implementing the application, we decided to work with one of our long-time application development partners, 2-steps-ahead, who has been building voicebots on our IVR and chatbot platform, Aspect CXP™, for over a decade. Internally, we had all of the right people in place and ready to take on this project: Aspect Professional Services provided the project management and architectural support, R&D provided support by our on-staff linguists. With the combined forces of our team and 2-steps-ahead we were ready to embark on the buildout of the solution.

A Dream Customer

The project kicked off with a workshop in Lidl’s Wimbledon England HQ at the end of August 2017. The Lidl digital marketing team came with a thorough understanding of how a chatbot experience should look and even a high-level concept of the functionality it should provide.

One of the main drivers for this project was the realization that, while Lidl was successful with their wine selection, winning awards and increasing sales, they didn’t have anyone in their 800+ stores or their contact center that knew much – if anything – about wines. The knowledge was sitting with their full-time on-staff sommelier! The Lidl team wasn’t interested in sharing that knowledge through static content on a website like an FAQ; instead, they wanted to create a much more interactive and engaging way through which they could impart their wine knowledge on their customers.  Artificial Intelligence allowed them to accomplish that by creating a chatbot accessible via Facebook Messenger that could answer questions from customers directly, on the spot. They clearly understood the power of a Conversational UI (CUI) for an engaging customer experience!

A name for the chatbot was quickly found by the Lidl team: Margot – chosen to remind the wine connoisseur of the French wine-making region (Margaux), while generally being a popular first name in the UK.

What Can You Do, Margot?

On a high level, Margot is trained to help customers with 4 different types of inquiries, as the chart shows. In addition, it can direct users to the existing Store Finder, which will pop up as a webview within the Facebook Messenger app.

One of the key decisions we made together with the Lidl team was to put Margot on the main Facebook page of Lidl UK: www.facebook.com/LidlUK. Previous experiences have shown that if you create a separate page just for a chatbot, discovery and retention suffer severely. By adding her to Lidl’s main Facebook presence we were sure that Margot had her home in the heart of the social Lidl experience online.

Lidl UK has several full-time staff members that help with answering questions from social networks. Margot thus “collaborates” with live staff in the sense that they are monitoring the incoming messages for mentions of the desire to speak to customer service, and the user always has the option to involve live staff when needed.

From a CUI design perspective, we decided to go with a mix of natural language dialog and menu-based flows. The initial menus introduce the scope of the solution and provide basic guidance; from our 15+ years of experience of building these kinds of applications, we know that people can be overwhelmed if they know they’re chatting with a computer and feel they can’t interact with it like they would with a human when presented with an open-ended question such as “how can I help you” – they simply expectsome guidance in the form of menus. Over time, as these chatbot experiences establish themselves across brands, these hesitations should go away.

In the middle of the project, we came up with the idea to teach Margot to understand emojis. What if people asked her about ? or ?? We didn’t want to pass on the opportunity to add that element of fun to the mix.

Generally speaking, the Margot persona was designed to be light-hearted and informal; a true wine enthusiast but definitely not snooty, someone who loves food and good company. Margot is truly conversational in her style, answering questions as such as “which red wines from Chile under £6 do you sell”, “what goes well with grilled salmon”, or “how do I get that stain out of my tablecloth??” She has additional features, such as an interactive wine quiz to encourage further brand engagement and to interact with customers in a fun, intuitive, and light-hearted manner.

From a linguistic perspective, Margot was a welcome challenge for our linguists. Not only was Margot designed to allow for plain English inquiries such as “I’m making roast beef for my friends tonight, what should I get with that”, or questions including multiple items of information as in “looking for a red wine from Italy under 6 pounds”, or those staccato communicators that send a single word such as “rose”; but Margot was also to benefit from the food ontologies built into Aspect NLU, the natural language understanding engine embedded in Aspect CXP™, so that she could easily subsume inquiries about ziti, farfalle, macaroni, spaghetti, tagliatelle, rigatoni, vermicelli, fettucine, pappardelle, penne, fusilli, … under pasta, or roquefort, gorgonzola, stilton under blue cheese, or … you get the idea. With the built-in ontology, Margot can now easily help with thousands of food items, offering over different 200 individual recommendations.

Some more impressive numbers:

Time to get serious

8 weeks after our initial project meeting we were ready to get serious… Margot was ready for friendly user testing from Lidl brand advocates. Lidl announced Margot on the Lidl Community, and first testers signed up. Testing chatbots early is critical to their success, and needless to say, we quickly found areas for improvement and gaps that we had to close, whether it was missing Mexican cuisine examples in our food ontology, or the ability to parse out money amounts expressed as diversely as “£5”, “5£”, “5 quid”, “a fiver”, or even “around five-ish”. Through a QA phase of intense collaboration between the teams, we were able to squash the bugs in record time and approach deployment readiness.

When we did go live, we decided for a soft rollout, before announcing Margot to the press. However, when the press embargo was finally lifted on January 31st, the feedback we got from the media, our partners, the analysts, but also Lidl customers expressing their joy of interacting with Margot on Twitter, was overwhelming positive, getting coverage in Contact Center World, Daily Mail Online, Telegraph, Food and Wine,  and our favorite was this video from the Huffington Post, who invited a real sommelier, Janet Harrison from Cracking Wine, to “compete” against Margot. Who won?

Itching to chat with Margot yourself? Go to www.facebook.com/LidlUK and hit “Send Message”. Or jump right into the conversation by going to m.me/LidlUK.

Stay tuned for more updates on the coming to life and the adoption of Margot over the coming weeks and months. Many new features are being added as we speak, and both teams aren’t running out of ideas what else automation, artificial intelligence-style, can do to support Lidl on their journey to the top of the UK and international grocery retail space. And this journey has just begun.

Interested in finding out more about the topics in this blog or Aspect? Aspect is one of the subject matter experts that will be sharing their insights and expertise at Customer Response Summit Charleston, March 12-14, 2018. CR Summit Charleston will feature speakers from Neiman Marcus, Indeed, Capital One, T-Mobile, and more. For more information about our leading event for CX professionals, visit our event website.

The post How We Built the Wine Bot Margot for Lidl appeared first on Execs In The Know.

Many brands ask us about the differences between using live chat versus digital messaging (ie. Messenger, WeChat, in-app/in-web messaging, etc.) for customer support. That’s right, chat and messaging are NOT the same. Although chat was loved by brands in the past, we believe its final days, as it currently exists, are near. To explain why it’s important to first clearly distinguish how the two are different.

Take a look at the examples below to see the difference between a messaging interaction versus that of a chat interaction.

 The example above is one of messaging.

In this interaction, I had reached out to one of my favorite brands, Nordstrom, to get some help finding a jacket.  Here is what was great about using messaging for customer support:

– This conversation was asynchronous, meaning the agent and I could communicate without being available at the same time.

– It was extremely convenient – I received a push notification on my phone’s home screen when the agent responded (rather than staring at my phone waiting for a response).

– It was contextual, so if I ever forgot which jacket I was looking at, I could just open up the app and go directly to the link the agent had sent me. Also, if I ever reached out again, the agent would know exactly what that previous interaction looked like.

– It felt personal and human. I was able to share just how genuinely excited I was by simply adding emoji to the message. With messaging, agents and customers can share and express emotions via emoji and gifs.

My experience with chat was very different…

In the chat interaction above, I needed help from customer support regarding a slightly more complex issue than my first example. This conversation was also much more frustrating for me due to the limitations in chat. Let’s break those down:

– The conversation was synchronous (as are all chat interactions), so I had to be actively available to receive the agent’s response and vice versa.

– It was session-based – so once I closed out of the chat, the entire conversation thread was lost. I was essentially held hostage in the chat and if I were to leave, I’d have to start all over again (which, of course, happened).

– Since chat lacks context, I had to re-explain my problem to a new agent when I reached back out for more help.

– When I had trouble explaining my issues and wanted to share a screenshot of my problem, I was deflected to email (since chat is text-based).

– There was no use of emojis or gifs and the entire interaction felt impersonal and lacked the human element.

Messaging costs less than chat

If the examples above don’t convince you that messaging is a preferable way to interact with a company, let’s look at the numbers. According to a research study by Mckinsey, a single chat interaction costs a company between 3 to 5 dollars per interaction. A messaging communication is less than $1, which is up to 1/5th the cost.

Not only are messaging interactions less expensive for brands than live chat interactions, but with the proper technology in place, agents can handle as many as ten messaging conversations at one time. That’s 5x what most contact centers report for concurrent conversation handling on chat.

People prefer messaging

Regardless of the cost savings, here’s a pretty powerful fact: 9 out of 10 customers actually prefer to message a business. Why? Because messaging is a HUGE part of our daily lives. Here’s another fact: 1.4 billion people around the world send over 50 billion messages each day to communicate with one another. In addition, WhatsApp and Messenger have around 1 billion monthly active users each and WeChat around 900 million. Messaging as a conversation and interaction interface has fundamentally shifted how we communicate with one another, and it is about to shift how people communicate with your company.

To learn more about how messaging helps improve customer satisfaction, enhances employee engagement, and drives real financial results, download the latest (and totally free) Aberdeen report: Customer Messaging: Happy Customers, Productive Employees & Better Financials.

Interested in finding out more about the topics in this blog or Sparkcentral? Sparkcentral is one of the subject matter experts that will be sharing their insights and expertise at Customer Response Summit Charleston, March 12-14, 2018. CR Summit Charleston will feature speakers from Neiman Marcus, Indeed, Capital One, T-Mobile, and more. For more information about our leading event for CX professionals, visit our event website.

The post Chat vs. Messaging for Customer Support: Why Messaging Wins appeared first on Execs In The Know.