The commoditization of the concierge service

Digital assitants

Yesterday the American Express Black personal concierge service, tomorrow Siri, M and Cortana.

There’s been a lot of talk about the moves by Apple, Facebook and Google to enhance their “digital assistant” services’ AI and capabilities. In the latest news, Facebook is testing its M AI (a part of its Messenger app) with the aid of a team of humans, who currently are doing much of the back-end work to meet user requests as they train the AI and find out what capabilities their users actually want. If you’re a beta tester, you can use Facebook Messenger (through M) to make a reservation at a restaurant, order flowers, plan a route, and other such tasks through the chat app, just like a concierge service might do.

Facebook Messenger with M is part of a broader trend that’s been developing for years – the proliferation of “walled garden” social platforms that want users to spend a greater and greater amount of time on their site or app or consuming content mediated or accessed through that site or app.

Making that happen means removing reasons for people to leave the network or app to access other services or get information. Thus, Uber for Messenger (order a car right from the app, just as if you were texting). Users’ conversations and social interactions will move closer to being seamlessly and naturally and integrated with their brand interactions.

A better Ask Jeeves or a digital American Express Black?

The challenge with all these digital assistants will be scalability. Facebook’s M still only serves a small audience because many requests cannot yet be effectively answered by the algorithm.

For example, some beta testers used the app to get M to cancel their internet service for them by calling their provider. The app couldn’t call the provider itself, so a human had to do that. That’s the kind of service that you could get from a concierge service, but we’re a long way from having an algorithm that can actually perform such tasks. So the progress of these services will depend on other providers making their services similarly electronic and integrated – if it can’t be done online, a digital assistant will have trouble with it.

But let’s talk about what can be done with messaging and with digital assistants in the consumer realm – one of the more straightforward use cases, since e-commerce and online research are both already so well integrated into consumers’ lives. Some considerations include:

Algorithm-aligned SEO

“SEO” that’s adapted to the method each app will use to find the answer for you. If users are getting fully automated answers, this means understanding the methods Facebook, Apple and Google’s virtual assistants are using to answer these questions and ensuring that your relevant content shows up in those contexts.

In many cases, this may integrate with existing search engines – but the results will be mediated by this additional automated layer. If they’re tapping Yelp for reviews, your brand should be on Yelp and positioned effectively. Or, since Siri initially used Bing, it might mean optimizing for mobile on Bing in particular.

Not only that, but your content will need to be in a format that enables the assistant to respond in a useful and attributable manner either in a plain text response (“The best Italian Restaurant is <your brand>”) or by linking to your mobile-friendly, quickly loading and relevant site.

Integrated offerings

Where possible, partnering with social networks like WeChat or Facebook to gain added functionality can enable a custom experience for your brand. For example, a customer could text their local department store to ask about their men’s jeans, provide their size, and get a text back with images and integrated tap-to-buy buttons to make the process easy.

Conversational marketing

Taking a step back from the “digital assistant” format to the messaging apps they live within, it seems equally clear that conversation-based marketing and sales through these apps is going to become more prominent as people spend more time there and want to interact with brands more seamlessly.

The growth of conversation-based interactions as a primary way to learn about products will mean an additional blurring of the lines between content marketing, customer support and sales. As customers learn to expect that they can send a text for information or to order products, companies will have to be prepared to have conversations with their customers that feel natural and to provide support through similar methods.

These conversations will build on top of your existing content. It’s no longer enough to generate great static content that sits on your website, or even great “push” social posts. If the resources are available to make it possible, direct customer interactions will only grow in relevance.

Will digital assistants take over?

We’re still in the early stages. But there’s certainly untapped potential.

Image used under a CC-0 license from https://www.pexels.com/photo/hands-coffee-smartphone-technology-4831/.

Treating data with respect

respect data
The New York Times ran an interesting piece on the opportunities and risks of data science yesterday. One mention was that IBM, which has been working to integrate its Watson big-data “artificial intelligence” into healthcare, has started to offer Watson Paths, a software program that allows doctors to see “the underlying evidence and inference paths Watson took in making a recommendation.” Along similar lines, it discussed the case for integrating a human element into big data-driven decisions in order to double-check the numbers and try to understand the processes used.

The volume of content online is often such that we have to use data analysis tools to find timely answers to any number of questions – who are the influential figures in your market? Which consumers matter? How should I tailor my product and my messaging to speak to these people? There are tools from Sysomos to Brandwatch, Klout to Traackr, Twitonomy to Google Analytics, and more – all to help you drive your marketing and PR decisions.

Some of these give you access to the raw data they work with. For example, if you pull social content from Sysomos, you’ll have the chance to review all the posts yourself to ensure that they’re vaguely on track (though you’ll still miss whatever’s in the database). Others don’t. If I search for influencers on Traackr, I don’t really know what goes into their database and their algorithms.

The old axiom of Garbage In, Garbage Out still applies
So when working with tools, brands and agencies need to think about what level of granularity they’re going to reach with the data, and to what extent they’re going to trust the tools to get the right answer on their own.

Many are basically black-boxes – and there’s a lot of inaccuracy in there. If you’ve ever tried automated social media sentiment analysis, you’ll see this in action. Where “I hate when I miss my [favorite brand] coffee in the morning” gets tracked as “Negative” for [favorite brand]. People are complicated, and it’s hard to measure the right things without knowing what’s being measured.

Transparency helps, when possible. Perhaps there should be more Watson Paths-style aids for marketers that show users how their tools are coming to their conclusions. It’s much easier to test different outcomes online on a micro scale than it is to make healthcare decisions. Yet broad campaign strategies and approaches still have a lot of momentum and funding invested in them, so in 21st century PR and marketing, we need to ensure we’re working with good data, not just any data.


Image is from the author’s collection.

How do you want to fail?

Don't feel blue! Hopefully most of your failures won't be this bad.

Failing fast is a common mantra in tech and digital media – moving through a lot of wrong approaches in order to learn the lessons and get the experience needed to work through to the right approach. In particular, it’s great advice not to be afraid to plunge into ambitious projects and to deal with the issues that come up without being fazed.

However, it’s not enough just to fail. Failing is an art in itself.

Let’s say you’re working PR for a consumer tech company that uses online documentation as the first line of support for customers struggling to install and use a new product. But your product has gone through a lot of reworking, and your online support is far out of date.

So the question is – is the brand experience worse for:

  • A customer who goes to the website and gets no help at all?
  • A customer who goes to the website and gets the wrong answer to their problem?

Partially, the answer depends on in what way each support document is wrong – perhaps one has a button whose name has changed and another mis-addresses a critical functionality issue – but customers’ experiences with each of them reflect on your brand in different ways.

Or, if you’re coordinating with the engineers reworking your product as they try to update its software, there are similar questions to face: if you reset all customers’ software to fix an problem that only some customers are facing, will that be worse than letting the pre-existing issue continue?

No one’s perfect. One way or another, you’re going to fail some customers. How do you want to fail them?

Image is “IMG_1624” by Neal Jennings, available under a CC BY-NC-SA 2.0 license. ©2014

Learning to Notice ideas

I know these aren't Fate dice, but they'll do.

Before I talk about ideas, I have a short story about games. Bear with me, if you will.

Every weekend, I spend a few hours playing a roleplaying game called Fate. It’s a fantasy setting (dragons, pirates, gnomes, all sorts of similar excellent critters) and the characters we play are – well, not heroes exactly, but people trying to shape a world that’s much bigger than they are.

One of us is a world-class swordswoman, another silver-tongued, others wield the primal forces of the world. Me, I’m good at a skill called Notice. All it does is let you notice things. But what that means (in this particular game) is that it lets you define the scene. Roll well, and you can describe a detail that might not have been there before, a detail that you can turn to your advantage.

So we’re facing off with some palace guards in an open southern courtyard. I look around for something I can turn to my advantage. Roll Notice – and I have the opportunity to add to the scene the detail that there’s a big awning providing some shade from the sun over half the courtyard. We knock it down, and suddenly half the guards are tangled up in cloth, making things easier for our merry crew.

What am I getting at with all this?

For clients, I often take the data they’ve collected (let’s say, social media data, or customer metrics) and making recommendations based on it. That’s the scene set before you. Here’s the numbers you have – what do you do now? And that’s a great place to work from (many folks don’t even collect metrics, so if they don’t, you may want to work on selling that in first).

But there are many things that aren’t in the numbers. Or at least, not in those numbers. Those are easy – they’re in front of you and you know they relate. But you don’t want to get so focused on the scene that’s been set before you that you forget to search for the detail you could add to the scene. What isn’t there that – if it were – would be fantastic for you? Then, can you get it to be there somehow?

It’s not a new idea – the idea of thinking about a problem sideways, or from another angle. But I think it’s still worth asking: What can you add to the scene?

Image is “Dice five” by @Doug88888, available under a CC BY-NC-SA 2.0 license. ©2008