Tomorrow delivered Today
In these customer centric times what better than a way to improve your customer service. In these P&L heydays what better than a way to reduce your cost. A chatbot is a great way to do both. Increasing customer satisfaction while at the same time reducing the cost of serving your customers. Please note this applies as much to ‘real’ external customers as to internal customers. Too good to be true? Let’s examine the technological developments, some real life experiences, perspectives on how to implement chatbots, and their bright future potential.
People above 30 years old will remember Clippy the office assistant Microsoft included in Office for Windows from 1997 till 2003. Any user would try to silence Clippy as soon as possible as it was of absolutely no value. It would never come up with an answer for the problems one was experiencing.
The days of Clippy are over and since a few years chatbots are becoming a very useful, always available and highly scalable tool to support users in getting what they need quickly and instantaneously.
The biggest progress comes from AI and its ability to understand natural language. The user no longer has to describe his or her problem in exactly the same wording as the tool makers were expecting. AI is able to interpret the natural language pretty accurately and identify the intent of the user. Once the question is properly classified (“where can I find the print button….” e.g.) the chatbot feeds back the right answer from a knowledge base or executes an action (“buy this product…”).
The world’s organizations are discovering the benefits of chatbots fast. Research&Markets.com reports ‘the chatbot market size is projected to grow from USD 2.6 billion in 2019 to USD 9.4 billion by 2024, at a CAGR of 29.7% during the forecast period’. Gartner says Artificial Intelligence (AI) will be a mainstream customer experience investment in the next couple of years. 47% of organizations will use chatbots for customer care and 40% will deploy virtual assistants.
There is a lot of magic talk about self learning chatbots. Don’t be fooled, with today’s technology creating and improving a chatbot still requires a significant amount of manual work:
Although getting the chatbot conversations right is definitely possible today, it is not a given. Forrester predicts that even in 2020 four out of five chatbot-based customer interactions will continue to flunk the Turing Test. Spiceworks provides an overview of the most common errors.
A solid implementation methodology, thorough testing, reasonable expectations, picking the right conversation cases, and an experienced partner are amongst the key success factors for implementing meaningful chatbot conversations.
Advancing technology will make things easier. For example, improving chatbot conversations is increasingly supported by strong analytical tools and automatic improvement suggestions. Also, technology is now enabling the automatic creation of new intents by scraping websites or uploading (somewhat structured) pdfs. This is e.g. part of Google Cloud’s Contact Center AI product. More and more chatbot vendors are including predefined workflows and connectors to make it easier to integrate with backend systems for the execution of identified tasks. These kinds of features reduce chatbot implementation and maintenance effort.
Biggest advancement however is today’s fast increasing maturity of voice technology. Where traditional chatbots require users to type to interact with them, the new wave of chatbots (or perhaps better, Voicebots) are able to interact by voice. Most mobile phones already have an assistant like Siri or Google Assistant installed, and more and more households have Alexa or Google Home (25% of US households in 2019). While American English still performs best it is quickly followed by mainstream European and Chinese languages.
Humans are very comfortable and skilled in using voice to interact with each other. Now computers can do this too. Today many organizations have implemented e-commerce, webcare and chatbots to make customer interaction easier but still operate large call centers with agents talking to customers. This is expensive for organizations and often little appreciated by customers (wait time and frequent hand-overs). Voice technology promises to speed things up for customers as they can be served immediately anytime of day no matter the call volumes, to lower costs for organizations and to augment quality of work for agents as voicebots are likely to deal with routine tasks first. So get it right, and the future is bright!
Google Cloud’s Contact Center AI is now generally available. That is fantastic news for everyone in customer service.
The power of AI made available to improve the performance of customer service . Promising to improve at the same time customer satisfaction (getting the answer you are looking for faster and easier) and reduce costs (automate some of the more mundane customer service tasks). Especially highly impactfull when combined with voice technology.
For more details please see https://cloud.google.com/blog/products/ai-machine-learning/contact-center-ai-now-ga
For companies that weren’t born in the digital age transforming into a data-driven organization is a hell of a job. It requires the right technology, the right data, the right agile culture, the right business vision, the right transformation, the right AI methodology and the right AI & Analytics team. History confirms the complexity of this kind of challenge. It took about 30 years to move from steam driven factories to modern day electric engine factories. Most steam engine based companies did not survive this transformation. Let’s eat the elephant in pieces. In this blog we zoom in on one aspect, getting the AI & Analytics team right.
The AI & Analytics domain is evolving quickly. It now encompasses technical topics like extreme data, building data pipelines, creating algorithms, training AI models and contextual understanding. Business topics are essential, like improving performance with specific use cases, building your data-driven organization, creating agility and embedding AI & Analytics in all of your business processes. On top of this we find social topics like compliancy, fairness and ethics have become condition sine qua non in this field.
Inevitably therefore the right AI & analytics team is a multidisciplinary team. Yes, even a kind of multiskilled dream team in our vision. A team consisting of very different, complementary roles & capabilities, with open minded people. Sounds terribly logical but what does this team actually look like and how to make it thrive?
Prime objective of any AI & Analytics team is to create business value through predictions and insights. As technological possibilities are relatively new to mankind, no surprise that many organizations still need to find out what the best possible business outcome exactly looks like. And how to create this best. This is where subject matter experts, business managers, AI business consultants, AI experts and data engineers need to meet in a creative process.
This process is typically driven by the AI business consultant, sometimes known as the analytics translator, making the different domains collaborate. A design thinking based approach delivers the best results in our experience. Combining AI technology, available data with products and services, business processes, business models and human resources. The next step is to validate the concept and create its first value by building and shipping a Minimal Viable Product.
The data product needs to be fair, ethical and compliant with internal procedures and legislation like GDPR. This is where the data privacy and security expert comes in. Advising and monitoring the team, and working with the data privacy officer, the security officer and the ethical board.
For the largest part of their time an AI & Analytics team is preparing, cleaning and analyzing the dataset to create the data product. The more mature organizations have implemented supporting tools (like e.g. Google’s AI hub, Data catalog and Kubeflow), organized their data and data governance, and made it as easy as possible to find and access data in a controlled manner. All this greatly reduces the time spent on preparational tasks.
AI & Analytics team roles
Still in the AI & Analytics team it is advised to have specialists on board to execute these tasks, the data engineers. They excel at transforming, shaping, filtering, moving and connecting the data. Leaving space for the complex and unique statistical methods to the data scientists and the AI model training to the AI experts.
Making the data product available at scale to targeted end-users and/or customers requires yet another set of capabilities in the team. On the technical side we need a solution architect to ensure the solution fits with the organization’s IT landscape and delivers the intended performance. A SW programmer is needed to integrate the data product with the input data and to integrate the outcome with the applications used by the end-users and/or customers. To deliver a dataproduct that meets today’s ease of use standards a UX-designer is onboarded.
Managing the success of your data product by clearly communicating with stakeholders and if needed providing training to stakeholders is an essential task. This is the task of the AI business consultant or a specialized success manager.
The right environment is needed to make your AI & Analytics team thrive. Sponsoring, mandate, subject matter expertise, business support, interpretation of findings, IT support, changed business decisions as a result of insights or predictions are key items typically situated outside of the team.
Successful organizations create the right environment. In all the right environments you will find the following. At the highest level a Board that provides the AI & Analytics function with guidance, support and mandate. On executional level a product owner who prioritizes to get the most important features of the data product right. Subject matter expertise is available to inject specific knowledge. The IT department supports the AI & Analytics team by providing data, tooling, IT support and integration into the existing IT landscape. You have implemented the agile way-of-working as the default standard. An innovation friendly culture, digitally skilled employees and most decision making low in the organization?…. check!
All of the above enables your AI & Analytics team to really thrive. They are also the building blocks which you will find in any data-driven organization.
Let me introduce you…Digital Sundai
October 1st, 2019 is the day Digital Sundai opened for business. Based out of Amsterdam this new company strives to create superior performing organizations through digital and AI.
Our believes
Our contribution to your business
Digital Sundai offers AI solutions and consultancy to improve your business. Call us when:
What we bring to the table
Digital Sundai brings AI solutions and accelerators, methodologies, and digital & AI expertise. Our own or partner AI solutions & accelerators increase speed and de-risk the implementation of AI use case. Digital Sundai is committed to expanding the offered AI solutions and accelerators.
Born in the digital age Digital Sundai is designed as a modern consultancy. We use digital & AI as much for our own operation as for our customers. Key difference with traditional consultancies is the focus on our network. We believe that by applying a clear, rigid and agile methodology we are able to integrate the best experts and partners in our projects, regardless whether they are employed by Digital Sundai or not. A typical project is staffed by Digital Sundai employees, first ring resources and partners, and flexible resources from digital platforms like UPWORK. Our skills are digital business, AI & analytics and data engineering on the Google Cloud platform.
B2B-sales is traditionally focused on human interactions with limited support from digitization. However, digitization can boost the effectiveness and efficiency of your sales team. Besides ensuring the ability to target the right customers at the right time, digitization can help provide additional insights, support deal qualification and enable your team to hunt the long tail of opportunities efficiently.
Case: Selling the long tail opportunities through AI
In some businesses the smaller sales opportunities require almost as much sales effort as the bigger ones. For sound economic reasons sales teams tend to spend little time on these long tail opportunities. At the same time these opportunities could typically add 10-15% additional revenue when pursued.
What if digitization with AI could support sales teams in closing these long tail deals in an efficient way? Have a look at the following client case. With this client we worked to jointly develop a data product in order to predict (long tail) opportunities for their organization’s sales teams all over the world.
Here is how we did it. We used existing data to get empirical performance insights into what the most popular products were and what products were frequently sold together. We collected relevant market data, both from the client’s internal sources and external sources. We then created algorithms to detect specific issues in the marketplace and matched these with our client’s product catalog. These led to new sales opportunities and ultimately to increased product sales.
Technically we packaged these results by connecting the data sources to a data product. The resulting sales opportunities were published using an API that connected with the CRM system. The sales people are using this CRM system to manage their deals and pipeline. Quite pragmatically the automatically detected opportunities and their deal proposals were just added to the salesperson’s pipeline. Through this, sales people can benefit from the sales opportunities without having to spend a lot of time detecting the lead and devising a proposal. Now the long tail can be targeted.
Next to sales people benefitting the Product Development team can leverage the data on sales opportunities as well to help make their roadmap decisions.
The data appeared to be relevant for new opportunities, forecasting and financial modelling. The API and Cloud Platform enabled the build of a re-usable data-product within the company.
To conclude, using digitization, AI and data to make the sales process more efficient and therefore being able to address a wider range of opportunities is a great example of the benefits of digitizing sales. Especially in a profession like B2B sales we expect digital and AI to augment and complement people to their benefit.
On October 1st Digital Sundai has opened it’s doors!