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15 Top Tips to Deliver the Ultimate Customer Experience – Mazaru and Unify in Partnership

We’re excited to announce our partnership with Unify Communications

Mazaru will form part of Unify’s solution set – adding our conversation design, tone of voice and soft skills expertise, to Unify’s next generation contact centre technology solutions. Or in other words, we’ll be helping Unify’s customers get more from their hosted technology set-ups, and wisely reducing customer contact, increasing sales and increasing satisfaction in the process. 

To give you an insight into Unify’s thinking, here are 15 Top Tips To Deliver the Ultimate Customer Experience, from Russ Attwood – Unify’s Founder and Business Development Director:

1.     Put your customers’ needs first

Look, your customers are demanding. They don’t want to wait around to talk to you, and they certainly don’t expect to be limited on how they can contact you. They want flexibility and omnichannel availability. It’s up to you to meet those needs. 

Your customers need to be able to choose a preferred communication channel – and switch between channels – at will. Your agents must therefore be able to pivot channels, seamlessly.

 2.     Context is Key

Don’t make your customers reiterate their problems. They don’t want to keep re-hashing their issues/asking the same questions. Think about the quality of experience and ensure your agents have full interaction history – giving them the context they need to respond to situations in the most empathetic and effective way.

 3.     Make Your Agents’ Lives Easier

Typically, agents are wasting 14% of their time looking for information about the customer they’re serving. Often, this is because the on-premise contact centre solution for the voice element requires individual, disconnected user interfaces for each of the additional channels. Nightmare, right? 

When their tools are disconnected, agents, customers and contact centre managers all suffer. It’s inefficient and hard to find the context needed for interactions.

 4.     Enable a Unified Experience

How can you integrate all of your critical tools? Automatic call distributor (ACD), computer telephony integration (CTI), and interactive voice response (IVR) should all be available in one solution. 

Make them natively connect to your CRM system, helpdesk tickets, call script generators and other systems. Your satisfaction scores will stay low unless you can empower your agents and enable the right processes through your technology.

 5.     Track What’s Happening

We get it. It’s hard to track interactions and agent performance. Your contact centre is collecting massive amounts of data every time you engage with a customer. Accessing a unified view of what’s happening is tough, but ultimately essential if you want to decrease attrition rates, improve agent performance, influence business outcomes and create positive customer experiences. 

Think about integrating your technology to enable near-time data refresh, real-time reporting and historical data. Seek out solutions that can offer you incredibly accurate information around real-time queue events and agent status.

 6.     Share the Wealth

Share the contact centre data you collect with your other tools (like those used to track case duration). Generate a wider view of areas for improvement, agent growth and operational efficiency by running reports showing your contact centre data alongside other important data.

 7.     Quality is Key

Typically, the contact centre is the main point of contact with your organisation for your customers. That’s why it’s so critical to monitor and improve your agent performance. It’s easy for us to say, but how do you actually do this? It’s nearly impossible using spreadsheets or legacy platforms.

 8.     Capture the Agent & Customer Experience Across the Contact Centre

Supervisors need to monitor, evaluate, and improve the quality of the customer experience by documenting customer interactions and accurately evaluating agent activity. Ensuring agents adhere to internal policies and procedures to deliver the best possible experiences is essential.

Integrating quality management (or Workforce Optimisation/WFO) into your contact centre solution can help you to unlock insights into customer responses to automated support and agent-assisted services. You’re free to make informed process changes, train your agents more effectively and deliver real-time feedback.

 9.     Don’t Fear Automation!

Automating the tedious processes wherever possible leaves you free to spend less time on routine tasks and more time delivering the better customer experience. 

Tools like an intelligent routing system can send your customers to the right agents equipped with the right resources. Implementing intelligent routing could improve first-call resolution rates, reduce the number of times customers are transferred and decrease the time both agents and customers spend on the phone to resolve the issue.

10.  Can You Use Chatbots?

Increase the efficiency of interactions without tying up your agents. Handing initial discussions to an automated bot could help your business to collect and pass along information about customers and issues to a live agent. Getting a bot to help with simple tasks like resetting passwords would help your agents spend their time and energy where it’s needed most.

11.  What About AI?

By incorporating AI, you could arm your agents with real-time insights to help them make smarter decisions. Listen to conversations between agents and customers and “whisper” suggestions to agents based on keywords noticed.

12.  Make It Simple to Scale

Virtualized contact centres can achieve a 92% saving versus a premise-based model, according to a report from Datamonitor. Whether you need to scale up for seasonality or down because you are driving customers toward self-service options, ensure your needs are supported. Help your workforce to work anywhere, anytime, and offer 24/7/365 service cost-effectively, by moving to a cloud-based solution.

13.  Empower Your Agents to Collaborate

Encourage your agents to work together, even when based in different locations, by enabling a distributed, virtual workforce in the cloud.  Deliver exceptional customer experience with a cohesive team.

14.  Deliver on Your Vision

While your people and processes are undeniably an essential part of the formula, the technology you use to enable them is critical to your ability to deliver a great customer experience. By giving your agents the right tools, you can keep them productive and efficient, resulting in higher retention rates and business growth.

15.  Keep Things Simple

Ultimately, your customer experience needs to be simple. The world’s most passionate, customer-focused brands achieve better interactions, deeper insights and more meaningful outcomes with cloud contact centre solutions.

Connect and Inspire using your Tone of Voice

Watch our Tone Maven, Janina Heron’s TED talk on how to connect and inspire using your tone of voice. 

Janina leads the ‘empowering front line teams’ part of our Service Communication Programmes. She really knows her stuff when it comes to the use of tone of voice and language in everyday customer service interactions, and has achieved some fantastic results and good outcomes for many of our customers.

In this talk, Janina shares tips on how to improve your tone, and why it’s so important.

As part of our Service Communication Programmes, we give tone of voice training to advisors, coaches, trainers and managers, and develop quality scorecards, to help embed skills and improve customer satisfaction scores and deliver real business results.

How to write a good customer service letter

Can customer service letters help you to reduce contact, support your move to digital, increase sales and customer satisfaction scores?

Yes they can.

If you want to know exactly how, head over to Call Centre Helper
Their article “How to write a good customer service letter – with examples”, has tips from our own Fran Fish. 

Call Centre Helper’s guide also includes Mazaru’s four-part (Clear, Credible, Answered, Tone) approach, backed up by consumer research and some real-world examples. 

If you’d like to get better results from your letters and emails, drop me a line or send me a letter – katie@mazaru.com

Mazaru is a wise monkey

You’ll find a carved panel of the three wise monkeys above the door of the Tshogu temple in Nikko, Japan. The carving is one of the oldest representations, dating back to the 17th century and was erected in honour of Shogun Tokugawa Ieyasu.

On the panel, the monkeys are sitting in a row – the first covering its ears, the second its mouth and the third its eyes. But no matter the order of the monkeys, they’ve come to embody the proverb “See no Evil, Hear no Evil, Speak no Evil”. 

Purists amongst you have spotted that their original Japanese names are Mizaru, Kikazaru and Iwazaru. But outside of Japan, you’ll see the monkeys’ names  given as Mizaru (See no Evil), Mikazaru (Hear no Evil) and Mazaru (Speak no Evil). 

This western version, Mazaru, is a little easier to pronounce – we hope you’ll allow us a little creative licence.

Legend tells us that the monkeys were sent by the gods as observers and messengers.

We’re now Mazaru. We’ve been sent by the gods to help you Speak no Evil.

soh is now Mazaru

With all the evolving we’ve done over the last twenty years, we thought it was time our brand evolved too – so from 25th April 2018, we’re Mazaru (Muh-zah-roo).

But, how have you evolved, you may ask. We’re now a multi-faceted agency, helping global brands to get full potential from something they already have – people and technology. We exist to rid the world of the evil communication and we’ve adapted to do it in many ways:

  • Creating tone of voice strategy and actionable guidelines
  • Making your marketing tone of voice work for your contact centre
  • Using design, words and tone to reduce calls and emails and improve quality and satisfaction
  • Making. AI. Sound. Human.
  • Getting web-chatty with live chat, even if your Agents are talking to 5 people at once!
  • How and where to use SMS messages, and how to write them so people take notice
  • Auditing, monitoring and fixing automated phone experiences
  • Empowering teams through quizzes and e-learning
  • Training people to speak and write better (and drive the right customer behaviours)

Why Mazaru?
Of the three wise monkeys, Mazaru speaks no evil. That’s something we can help you do, every day. And being adaptable problem solvers, collaborators and purveyors of communication wisdom, we already share a lot of traits with our monkey namesake.

The list above is just for starters. We’re evolving, so if you have an idea, or have found a problem that needs fixing, get in touch with the team – hello@mazaru.com.

#SpeakNoEvil

10 things they don’t tell you about AI

Do you want to know the inside track on the state of Artifical Intelligence in the customer management industry today? Here are a few home truths that will help you navigate the hype surrounding AI and make some decisions that will put you on the right track.

1. You’ve been using elements of it for years

That’s right. You may remember Fuzzy Logic in washing machines in the late 1990s. That was a form of AI. Chase Bank was using it even before that for reading cheques. Anyone who has used speech analytics tools has been using AI.

There is a very broad definition of AI being applied now which vendors are taking advantage of. We’ve been using ‘traditional’ algorithms to make predictions for years and many application features are simply being rebadged as ‘AI-driven’. For example, logic engines and decision trees have supported knowledgebase systems for many years and now they are called AI. The latest AI developments really centre around the use of artificial neural networks. Again, these are not new but found little commercial interest until Google started developing the self-driving car. These models can be trained with the large real-world data sets that have now become available rather than having to be told how to work based on a hard-coded formula that defines the relationship between an input and an output. 

Hence the term Machine Learning. Today you may flag a customer as a high churn risk simply if they are out of contract and offer them a promotion. With ML, you can look for more complex patterns in many data points simultaneously to identify hidden risks and relationships. For example, people who have complained twice in the last 6 months and have called about their bill 3 times this year are 80% more likely to churn on average. Therefore, the real question to ask of any vendor is “how do you use machine learning to enhance the performance of your solution?”.

2. It’s still not that intelligent, really

Self-driving cars are clearly at the cutting edge of the AI we see today. While we’re fully expecting these to be on our roads in the next few years, they are still lacking some key capabilities of our brains and the learning process has been a long one. Apparently, Google cars have driven for over 300,000 hours on public roads so far – but still not passed the driving test. My daughter has driven for around 30 hours and is taking her test in about a month. Ok, I’m stretching the comparison but it’s also important to realise that AI is good at very specific tasks and there isn’t one AI ‘brain’ in any of our systems. Specific learning algorithms are applied to specialised tasks such as interpreting the image from one camera or monitoring environmental data. A central management system, which may have further learning algorithms as well as traditional logic, then reviews the data it is getting before making decisions. If you want to know more without going too deep watch the talks by Yann LeCun, Director of AI Research at Facebook (LeCun, 2017).

In our contact centre applications, learning algorithms play similar specialist roles – doing one thing, hopefully really well – before delivering an output that can be actioned by a human or the next step in the system. However, while the average contact centre application doesn’t need 300,000 hours of data, train the algorithm with incomplete, skewed or insufficient data and you will end up with the common problem of garbage in and garbage out.

3. Many chatbots contain very little AI

Not every chatbot application is created equally but there is a common theme to most solutions available today. Understanding what the customer wants to do is called ‘establishing the Intent’. Typically, this will be done using a cognitive application that feeds the text into a natural language processing system which responds with a code that represents the Intent.

At that point a specific script is executed which processes the request. It may have steps to gather more information from the customer, access a database or knowledgebase and then provide a response. That script is built by the business team who know the process and needs to be very robust to handle as many as possible of the scenarios customers create. It will never be complete before launching and needs ongoing work to tune the process.

Humans complicate the tuning as we are good at going off script and using historical context to infer meaning. The new Lidl wine advisor on Facebook Messenger is an example of a chatbot that can only ‘remember’ what you asked for in your last request. Try to ask it to ‘show me red wines from Australia’ and then ‘show me some from New Zealand’ and it fails.

Some chatbots that handle more conversational requests such as FAQs are being built without scripts and learn from analysing lots of previous live chat interactions – if this data is available. Some vendors target agent assistance in a chatbot as they don’t believe the technology is capable of delivering effective customer interactions, yet. It’s therefore important to think about what you want to achieve and what data you need before selecting an approach. 

4. The basic AI tools you need may be available online at little cost

In the last 3 years IBM Watson, Microsoft, Amazon and Google have all released extensive toolsets for cognition (i.e. speech and text analytics), chatbots and machine learning within their cloud platforms. There are others which are less well known but equally capable such as Floydhub. The best part is that they are free or amazingly cheap to get started with and typically have extensive online guides and support communities.

Building your first machine learning algorithm or simple bot is something you can do almost overnight. Of course, you will need the right team (see more below) to build something in-house that is robust enough to deploy and it is less likely to be something that can be managed easily by the business rather than IT. The management and reporting framework is a critical component that needs to be created to ensure tuning is effective and the applications can evolve with customer needs. An in-house build may not fit with your strategy but if it does then it’s worth exploring the benefits.

5. There isn’t one answer and the answer may not always be AI

AI is powerful and there are many questions it could be used to address – most of which are not clear to us yet. But there are also many AI solutions. As I’ve mentioned, lots of data science techniques are now being classified as AI and there are many variations of machine learning.

Machine learning is a computationally expensive approach which involves an element of discovery to determine if a solution exists that improves on traditional models. For example, could machine learning predict Lifetime Value or Churn better than current approaches? If it can deliver a 10% improvement, then it could be worth it, but what will it cost in time and effort to achieve?

So, what are the elements that affect the discovery process? The size and quality of the dataset are key factors. Then, the initial training parameters and size of training steps for the algorithm are important. But critically, different algorithms work better on different types of data. For example, Convolutional Neural Network models work best with image processing and Recurrent Neural Networks are good for understanding context in language. The amount of data needed for training and evaluation will vary by type of model used and how you train it. I won’t go on, but you get the point – machine learning algorithms can do some tasks very well and much better than we could ever expect from a traditional computing approach. Knowing if you are applying machine learning to the right task is a business problem as much as a data science problem.

6. AI won’t magically make sense of all your data

As anyone who has been through a system migration when implementing a CRM platform will testify, the quality of the data you extract can be very poor. Spelling mistakes, missing fields, data in different formats and so on. Basic cleansing always takes much longer than ever expected.

Your data may also be skewed. If you are looking at time series data as you would in a forecasting model, poor service levels will drive more repeat contacts rather than genuine new contacts from different customers. You may also have less understanding about the needs of customers who interact with you less frequently perhaps. The data could also contain human biases – there are many articles on that (Tobias Baer, 2017) (Giannandrea, 2017) (Devlin, 2017). Machines that learn can handle missing data reasonably well as long as the data that exists is representative. You need to spend time validating that this is the case.

Also, it is rare that you will be accessing only one system. The data needed may come from different systems, may not be stored digitally or may be in people’s heads – especially when dealing with customer process exceptions. It may not even exist and so you may need to create it, if you don’t track a particular metric, for example. Data creation, extraction and consolidation is time consuming.

Chatbot vendors may just ask for your live chat transcriptions but that is often just the start of weeks of data manipulation behind the scenes to make it fit for use in training and testing. When starting with machine learning, it is therefore wise to consider data quality very early. You may not able to solve the biggest issues until you have gathered more reliable data.

7. You need to think about RPA if you are going to optimise the AI you deploy

You will often hear the terms Robotic Process Automation and Artificial Intelligence talked about in the same context – is that just the marketeers and conference organisers getting more bang for their buck? Well no, it makes sense to think about RPA with AI. RPA provides the glue that allows the business to automate repetitive aspects of customer processes. Often these are the tasks queued to back-office teams, but RPA can also be used to improve performance at the desktop by reducing rekeying of data between systems.

For example, a current client is looking to implement a chatbot to take a request from a customer so they can issue a replacement loyalty card. While it is possible to use an existing web API to link into the CRM system to capture or validate customer details, the request to send the new card is issued through a standalone system. In a live agent chat scenario this is inconvenient but not an issue as the agent can initiate the request manually. What you don’t want to be doing with a chatbot is providing a self-service process that does not complete the transaction but instead queues the work for a back-office team – bad process ! This is where RPA can help.

Clearly, the benefits of RPA need to be assessed relative to the option of building a dedicated interface. However, as AI evolves it is going to provide more of the ‘decision making’ glue between automated processes tasks, with only exceptions being handled by humans. Typically, vendors focus on RPA or AI solutions so you will need to raise your own thinking up to the business process or, better still, customer journey level, in order to evaluate where RPA and AI can jointly streamline your business.

8. You will need to add new skills and processes to your business to manage AI

If you choose to go down the DIY route and use the tools available online to build your own AI applications, then you should clearly already understand the technical capabilities needed. You don’t just need great Data Scientists. Real world AI problems need a team. This team may often consist of business process experts, product owners, hardware engineers, programmers, cognitive engineers and a commercial lead focused on the outcome.

Unfortunately, beyond designing chatbot scripts, most business analysts who have come up through the contact centre with increasing proficiency in Excel just don’t have the mathematical skills needed. I’ve therefore seen many businesses building up their Business Intelligence teams with very expensive Data Scientists who are detached from the operational world. That’s why a team is essential. This combines the business knowledge of the Analysts and Commercial lead with the specialist skills of the Data Architects and Scientists.

When it comes to developing solutions, adopting Scrum principles (often called ‘Agile’) is almost essential, whether it’s for building a chatbot or developing a predictive up-selling model. This is because the development of machine learning models requires a highly cyclical approach and regular feedback from experts in the business to ensure the solution is evolving in the right direction. Scrum requires a different mindset from the business and a highly empowered team. 

Finally, an operation that uses machine learning models needs to be continuously evolving the model as the environment changes. If you introduce new products or contact channels, how does that affect what you can up-sell to customers or affect the accuracy of your call forecasting, for example? The models need retraining and constant monitoring to evaluate effectiveness. Someone must take on the responsibility for the decisions that the algorithm makes, and own the metrics that track performance – a critical new role.

This sounds like a lot of business change but this is part of every technology implementation. Unfortunately, it’s probably the area where most projects apply the least attention.

9. It might not revolutionise your business today

You’ve read this far, and you are probably thinking, “why would I even bother right now?” McKinsey estimate that investing in the right AI technology will give benefits that outweigh the costs by up to 10 times (McKinsey Global Institute, 2017). But if you don’t already have a digital customer transformation strategy in place then deploying a chatbot is probably not your next step. Work out your strategy first and think about where AI could play a part – you may look for help with that.

You are not missing the boat. No vendor has a magic piece of technology that is going to reduce 50% of your costs in one go. At present you can buy packaged chatbot solutions or you can invest in Business Intelligence tools (or managed services) powered by machine learning algorithms. These tools allow you to explore your data in new ways and identify patterns that were previously hard to see. But the question has to be asked before you start on this path – “what are we trying to achieve?”

New applications are being developed that will embed the technology at key points of the interaction with customers. For example, making smarter decisions on routing of contact, next best actions for sales or to improve agent quality and performance monitoring. Salesforce.com has embedded tools called Einstein into its platform and is claiming its clients see reduced attrition and increased sales conversion rates as a result. Amazon.com also has a lead on AI in service given the expertise it has gained through its own operations. I predict that the Amazon Connect CRM platform is ‘one to watch’ in the next 12 months as much of the extensive AI capability now offered through its web services platform could be surfaced in Connect (Amazon Web Services, 2017).

10. AI is changing fast. Instability is opportunity not just a threat

According to government statistics, a new AI company has been launched in the UK almost every week over the last 3 years and we are small compared to the advances being made in China and the US. The amount of private and public funding available continues to rise and so the rate at which research moves into commercial applications is continuing to speed up.

A new technique called Generative Adversarial Networks (GANs) was proposed by academics only in 2014. It has been highlighted as “the most interesting idea in the last 10 years in machine learning” by Yann LeCun, Facebook’s AI Research Director. GANs involve two neural networks competing against each other to help the system self-learn with the intention of being able to mimic rather than classify patterns that most networks do today. General applications are seen in areas such as art and music where the network could compose a piece of music in the style of Mozart or paint a Van Gogh.

These applications are moving us towards the AI of the future where the system can create something new. They will be inventing a game like chess rather than just playing it. How far they will be able to go in mimicking the characteristics of your best sales advisor or top performing customer service representative, we will have to see.

While that is all some way off, the current challenge is ensuring you are taking the right steps on the AI journey when you select services, solutions and vendors. Has a product simply been rebadged? Is there any real AI in there? Is the AI ‘vapourware’ and are you being used as the guinea pig? Will they be around in 12 months’ time?

The answers to these questions are important of course, so enter a relationship with a supplier with your eyes wide open. There can be some great risk-reward opportunities available not to mention competitive advantage in a brave new world.

If you’d like to know more about how we can help you better understand what AI can really do for you now — and in the future — get in touch.

Advisor QA scorecards — making them work for soft skills (and your business)

American author Gretchen Rubin said: “What you do every day matters more than what you do once in a while.” Training, workshops and guides are those once in a while interventions that are a good starting point for advisors’ communication soft skills development. And, may give a short-term boost for business outcomes like customer satisfaction, sales or cost reduction.

But, what can be done every day to keep it front of mind and make sure that improvements are sustained?

We work with many clients on improving the scorecards used to assess advisor interactions — whether these be calls, emails, webchats or social media responses. Scorecards are also often the centre of that everyday all-important coaching, as well as Quality Assurance (QA) measurement and improvement efforts.

Creating scorecards to measure compliance is relatively simple — for example a yes / no for whether advisors let customers know that calls may be recorded. When it comes to assessing soft skills things may seem trickier.

Worryingly, scoring soft skills sometimes becomes a tick box exercise that can drive the wrong behaviours. When we speak to advisors, as part of our QA discovery, sometimes they’ll say they do something just to get a “tick in the QA box”. Although no harm is done if a sales promotion is hurriedly copy – pasted at the end of a webchat session to meet a “sales through service” requirement. Asking an angry caller “how are you today” because it’s required to meet the “rapport” criteria isn’t going to help an advisor, the customer or your satisfaction scores.

Soft skills that mean advisors are able to take opportunities to improve sales, reduce repeat contact to cut costs and build rapport to increase customer satisfaction may be shades of grey in what can be a very black and white world.

As such, it can be tempting to remove soft skills from scorecards altogether. However, with studies showing how important something as simple as advisor tone of voice is for customer satisfaction, for example, you’d be missing an opportunity to produce some very real business benefits.

With this in mind, here are our top 10 tips for scorecards that’ll improve advisor communication soft skills and deliver business results:

1. Develop and test your scoring criteria — be willing to try different criteria to find what works best for advisors, team leaders and your business goals. To help those scoring, and receiving feedback, criteria that have a good Inter-Rater Reliability are best. It’s also vital to confirm that criteria are driving business outcomes you want.

2. Don’t over engineer it — Just having scorecards, that are used regularly, will keep soft skills at the front of advisors’ minds and drive improvements.

3. Be specific — it’s useful to divide scorecards down into broad categories, e.g. Rapport. And, also give specific guidance — e.g. having a tone that is warm, clear and interested — so that everyone knows what helps to build rapport.

4. Support your scorers — run calibration workshops so that those doing the scoring can see how they compare and discuss, as well as feedback, problems.

5. Lead by example — scored reference interactions help scorers and advisors to know what ‘good’ looks and sounds like.

6. Accept some scoring variability — choosing criteria with good Inter Rater Reliability, providing examples and running calibration workshops all help to reduce scoring inconsistency. But, QA results from softs skills assessments just aren’t as black and white as those from compliance for example. As long as your scorecard is driving the business outcomes you want (point 2) it’s doing its job.

7. Think ahead to coaching — scorecards help team leaders and coaches find skills gaps. Work with them to create skill development activities that are aligned to criteria to make coaching and skills development more effective.

8. Don’t forget about reward — praise for what advisors have done well is just as important as advice on how to improve.

9. Have a joined-up approach — use categories and criteria that work with other parts of your improvement programme such as training, workshops and internal communications etc.

10. Make it easy to use — design scorecards and feedback (colours, layout and graphics) that are quick and simple for advisors to understand. Why does a scorecard have to be a spreadsheet, or be called a scorecard for that matter?

If you’d like to find out more about how we can help you develop a better scorecard, drop us a line.

AI — 6 questions you should ask before you buy

We are getting increasingly scared about the pace of technology change and how that will shape the future world we inhabit. Back in 1957 it would have been much easier to predict what the world might look like in 1967 than it is for us today to predict what it will be like in 2027. A vision for 2057 would seem simply impossible to comprehend. Ray Kurzweil is a futurologist I suggest you take a look at if you want to explore predictions further!

To lay any fears to rest, the development of ‘Real AI’, where an artificial consciousness exists, and machines can modify actions and behaviour based on self-awareness of their environment, remains a challenge for future generations.  ‘Narrow AI’ is where we are today. The difference can be explained as creating a machine that can play Chess versus creating one that would choose to invent a game like Chess just because it could. 

After working for many years to cut through the hype of speech analytics, I find myself battling similar challenges with AI. Google’s AlphaGo model beats Grand Masters, and the IBM Watson team do a great job of selling a vision. Turning that into the practical, benefit-led applications of the technology for businesses today is the challenge. 

I know we see some high profile Narrow AI applications from self-driving cars to food monitoring fridges and medical imaging diagnostics. Customer operations should be considering the potential of developing chatbots or better predictive solutions for churn or resource forecasting, for example. So how do you start? 

Google, Amazon and Microsoft now provide easy, free access to the comprehensive cloud-based tools and essential processing power to develop Narrow AI applications. 

Many AI startups have taken advantage of this in recent years, and there are some established providers in the chatbot space who you can turn to for solutions. But this brings me back to my gripe with the hype. Whether it’s chatbots or predictive models, are you set up for success? Like all technology-enabled change, the same elements apply. Here are six questions that you should ask yourself, or AI partner, before investing in AI: 

1. Is there a clear business problem to solve?

2. Would the AI approach be the best to address it?

3. How does it fit into and improve the customer journey?

4. How does it change our operational capabilities, such as skills needed by frontline staff?

5. How do we manage the business change?

6. How do we measure success against the business objectives (not just the quality of the model)? 

Humanotics is working with Mazaru to help businesses build AIs that are valuable resources for organisations, customers and staff.

The six questions are part of our “AI Ready” assessment that helps service operations make better business cases for AI by choosing the best applications and thoroughly assessing the impact. If you’d like to know more about how we can help you better understand what AI can really do for you now — and in the future — get in touch.

Happy Birthday Text Messaging

The first text message (SMS) was sent on the 3rd of December 1992 by an engineer at Vodafone to a director. It simply said “Merry Christmas”. Fast forward 25 years and the humble text message along with its cousins IRC, MSN messenger and AOL messenger led to the world we have today where WhatsApp, Twitter and others now rule. Text messaging took a while to take off but as the service got cheaper, and in many places unlimited, it exploded – even inventing its own terms and quasi-language “text speak”.

The limitations of early text messaging (160 characters only – the inspiration behind the 140 character limit on Twitter until recently) had a big impact on the language we use today with the invention of new “words” to help write more in a small amount of characters. LOL, OMG and lmao are all early examples of the changes to everyday language that is still evolving even now, with new additions such as tbh, smh and yolo.

Nowadays, no doubt caused by the variety of other services in use, the text message is in decline in the UK with the 2011 peak of 172 Billion messages no longer being reached and declining by as much as 25% from 2012 to 2013, and more recently by 5% in 2016. Does this mean the text message is doomed? Not anytime soon I don’t think. It’s important to take into account that many people use SMS because it’s easy, it doesn’t need extra apps or special phones and will still work when you don’t have a data service. For this reason it’s still used by banks and retailers to inform and assist, even if it’s fallen out of favour with the younger generations.

With WhatsApp now delivering 55 billion messages a day, the text-based messaging format is definitely here to stay. I’m excited to see how it will evolve next, and how our language and communication style will evolve alongside it.