Introduction to AI in Business Blog Series: Unveiling the Future

Introduction to AI in Business Blog Series: Unveiling the Future

Author: Antti Pohjolainen, Codento

 

Foreword

In today’s dynamic business landscape, the integration of Artificial Intelligence (AI) has emerged as a transformative force, reshaping the way industries operate and paving the way for innovation. Companies of all sizes are implementing AI-based solutions.

AI is not just a technological leap; it’s a strategic asset, revolutionizing how businesses function, make decisions, and serve their customers.

In discussions and workshops with our customers, we have identified close to 250 different use cases for a wide range of industries. 

 

Our AI in Business Blog Series

In addition to publishing our AI.cast on-demand video production, we summarize our key learnings and insights in the “AI in Business” blog series.

This blog series will delve into the multifaceted role AI plays in reshaping business operations, customer relations, and overall software intelligence. In the following blog posts, each post has a specific viewpoint concentrating on a business need. Each perspective contains examples and customer references of innovative ways to implement AI.

In the next part – Customer Foresight – we’ll discuss how AI will provide businesses with better customer understanding based on their buying behavior, better use of various customer data, and analyzing customer feedback.

In part three – Smart Operations – we’ll look at examples of benefits customers have gained by implementing AI into their operations, including smart scheduling and supply chain optimization.

In part four – Software Intelligence – we’ll concentrate on using AI in software development.

Implementing AI to solve your business needs could provide better decision-making capabilities, increase operational efficiency, improve customer experiences, and help mitigate risks.

The potential of AI in business is vast, and these blog posts aim to illuminate the path toward leveraging AI for enhanced business growth, efficiency, and customer satisfaction. Join us in unlocking the true potential of AI in the business world.

Stay tuned for our next installment: “Customer Foresight” – Unveiling the Power of Predictive Analytics in Understanding Customer Behavior.!

 

 

About the author: Antti  “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2020. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Central & Eastern Europe. Apo has been working in different sales roles longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. Apo received his MBA from the University of Northampton. His final business research study dealt with Multi-Cloud. Apo has frequently lectured about AI in Business at the Haaga-Helia University of Applied Sciences.  

 

 

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100 Customer Conversations Shaped Our New AI and Apps Service Offering 

100 Customer Conversations Shaped Our New AI and Apps Service Offering 

 

Author: Anthony Gyursanszky, CEO, Codento

 

Foreword

A few months back, in a manufacturing industry event: Codento  just finished our keynote together with Google and our people started mingling among the audience. Our target was to agree on a follow-up discussions about how to utilize Artificial Intelligence (AI) and modern applications for their business.

The outcome of that mingling session was staggering. 50% of the people we talked with wanted to continue the dialogue with us after the event. The hit rate was not 10%, not 15%, but 50%. 

We knew before already that AI will change everything, but with this, our  confidence climbed to another level . Not because we believed in this, but because we realized that so many others did, too.

AI will change the way we serve customers and manufacture things, the way we diagnose and treat illnesses, the way we travel and commute, and the way we learn. AI is everywhere, and not surprisingly, it is also the most common topic that gets executives excited and interested in talking. 

AI does not solve the use cases without application innovations. Applications  integrate the algorithms to an existing operating environment, they provide required user interfaces, and  they handle the orchestration in a more complex setup.

 

We address your industry- and role-specific needs with AI and application innovations 

We at Codento have been working with AI and Apps for several years now. Some years back, we also sharpened our strategy to be the partner of choice in Finland for Google Cloud Platform-based solutions in the AI and applications innovation space. 

During the past six months, we have been on a mission to workshop with as many organizations as possible about their needs and aspirations for AI and Apps. This mission has led us to more than a hundred discussions with dozens and dozens of people from the manufacturing industry to retail and healthcare to public services.

Based on these dialogues, we concluded that it is time for Codento to move from generic technology talks to more specific messages that speak the language of our customers. 

Thus, we are thrilled to introduce our new service portfolio, shaped by those extensive conversations with various organizations’ business, operations, development, and technology experts.

Tailored precisely to address your industry and role-specific requirements, we now promise you more transparent customer foresight, smarter operations, and increased software intelligence – all built on a future-proof, next-generation foundation on Google Cloud. 

These four solution areas will form the pillars of Codento’s future business. Here we go.

 

AI and Apps for Customer Foresight

As we engaged with sales, marketing and customer services officers we learned that the majority is stuck with limited visibility of customer understanding and of the impact their decisions and actions have on their bottom line. AI and Apps can change all this.

For example, with almost three out of four online shoppers expecting brands to understand their unique needs, the time of flying blind on marketing, sales, and customer service is over.

Codento’s Customer Foresight offering is your key to thriving in tomorrow’s markets.  

  • Use data and Google’s innovative tech, trained on the world’s most enormous public datasets, to find the right opportunities, spot customers’ needs, discover new markets, and boost sales with more intelligent marketing. 
  • Exceed your customers’ expectations by elevating your retention game with great experiences based on new technology. Keep customers returning by foreseeing their desires and giving them what they want when and how they want it – even before they realize their needs themselves. 
  • Optimize Your Profits with precise data-driven decisions based on discovering your customers’ value with Google’s ready templates for calculating Customer Lifetime Value. With that, you can focus on the best customers, make products that sell, and set prices that work. 

 

AI and Apps for Smart Operations 

BCG has stated that 89% of industrial companies plan to implement AI in their production networks. As we have been discussing with the operations, logistics and supply chain directors, we have seen this to be true – the appetite is there.

Our renewed Smart Operations offering is your path to operational excellence and increased resilience. You should not leave this potential untapped in your organization. 

  • By smart scheduling your operations, we will help streamline your factory, logistics, projects, and supply chain operations. With the help of Google’s extensive AI tools for manufacturing and logistics operations, you can deliver on time, within budget, and with superior efficiency. 
  • Minimize risks related to disruptions, protect your reputation, and save resources, thereby boosting employee and customer satisfaction while cutting costs.  
  • Stay one step ahead with the power of AI, transparent data, and analytics. Smart Operations keeps you in the know, enabling you to foresee and tackle disruptions before they even happen. 

 

AI and Apps for Software Intelligence 

For the product development executives of software companies, Codento offers tools and resources for unleashing innovation. The time to start benefiting from AI in software development is now. 

Gartner predicts that 15% of new applications will be automatically generated by AI in the year 2027 – that is, without any interaction with a human. As a whopping 70% of the world’s generative AI startups already rely on Google Cloud’s AI capabilities, we want to help your development organization do the same. 

  • Codento’s support for building an AI-driven software strategy will help you confidently chart your journey. You can rely on Google’s strong product vision and our expertise in harnessing the platform’s AI potential. 
  • Supercharge your software development and accelerate your market entry with cutting-edge AI-powered development tools. With Codento’s experts, your teams can embrace state-of-the-art DevOps capabilities and Google’s cloud-native application architecture. 
  • When your resources fall short, you can scale efficiently by complementing your development capacity with our AI and app experts. Whether it’s Minimum Viable Products, rapid scaling, or continuous operations, we’ve got your back. 

 

Nextgen Foundation to enable AI and Apps

While the business teams are moving ahead with AI and App  initiatives related to Customer Foresight, Smart Operations, and Software Intelligence   IT functions are often bound to legacy IT and data  architectures and application portfolios. This creates pressure for the IT departments to keep up with the pace.

All the above-mentioned comes down to having the proper foundation to build on, i.e., preparing your business for the innovations that AI and application technologies can bring. Moving to a modern cloud platform will allow you to harness the potential of AI and modern applications, but it is also a cost-cutting endeavor.BCG has studied companies that are forerunners in digital and concluded that they can save up to 30% on their IT costs when moving applications and infrastructure to the cloud. 

  • Future-proof your architecture and operations with Google’s secure, compliant, and cost-efficient cloud platform that will scale to whatever comes next. Whether you choose a single cloud strategy or embrace multi-cloud environments, Codento has got you covered. 
  • You can unleash the power and amplify the value of your data through real-time availability, sustainable management, and AI readiness. With Machine Learning Ops (MLOps), we streamline your organization’s scaling of AI usage. 
  • We can also help modernize your dated application portfolio with cloud-native applications designed for scale, elasticity, resiliency, and flexibility. 

 

Sharpened messages wing Codento’s entry to the Nordic market 

With these four solution areas, we aim to discover the solutions to your business challenges quickly and efficiently. We break the barriers between business and technology with our offerings that speak the language of the target person. We are dedicated to consistently delivering solutions that meet your needs and learn and become even more efficient over time.  

Simultaneously, we eagerly plan to launch Codento’s services and solutions to the Nordic market. Our goal is to guarantee that our customers across the Nordics can seize the endless benefits of Google’s cutting-edge AI and application technologies without missing a beat.

About the author:

Anthony Gyursanszky, CEO, joined Codento in late 2019 with more than 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. Hehas also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. His experience covers business management, product management, product development, software business, SaaS business, process management, and software development outsourcing. Anthony is also a certified Cloud Digital Leader.

 

Contact us for more information on our services:

 

Video Blog: Demonstrating Customer Lifetime Value

Video Blog: Demonstrating Customer Lifetime Value

 

Contact us for more information:

 

Customer Lifetime Value Modeling as a Win-Win for Both the Vendor and the Customer

Customer Lifetime Value Modeling as a Win-Win for Both the Vendor and the Customer

 

Author: Janne Flinck, Codento

Introduction to Customer Lifetime Value

Customer analytics is not about squeezing out every penny from a customer, nor should it be about short-term thinking and actions. Customer analytics should seek to maximize the full value of every customer relationship. This metric of “full value” is called the lifetime value (LTV) of a customer. 

Obviously a business should look at how valuable customers have been in the past, but purely extrapolating that value into the future might not be the most accurate metric.

The more valuable a customer is likely to be to a business, the more that business should invest in that relationship. One should think about customer lifetime value as a win-win situation for the business and the customer. The higher a customer’s LTV is to your business, the more likely your business should be to address their needs.

A so-called Pareto principle is often used here, which states that 20% of your customers represent 80% of your sales. What if you could identify these customers, not just in the past but in the future as well? Predicting LTV is a way of identifying those customers in a data centric manner.

 

Business Strategy and LTV

There are some more or less “standard” ways of calculating LTV that I will touch upon in this article a little later. These out-of-the-box calculation methods can be good but more importantly, they provide good examples to start with.

What I mean by this is that determining the factors that are included in calculating LTV is something that a business leader will have to consider and weigh in on. LTV should be something that will set the direction for your business as LTV is also about business strategy, meaning that it will not be the same for every business and it might even change over time  for the same business.

If your business strategy is about sustainability, then the LTV should include some factors that measure it. Perhaps a customer has more strategic value to your business if they buy the more sustainable version of your product. This is not a set-and-forget metric either, the metric should be revisited over time to see if it reflects your business strategy and goals.

The LTV is also important because other major metrics and decision thresholds can be derived from it. For example, the LTV is naturally an upper limit on the spending to acquire a customer, and the sum of the LTVs for all of the customers of a brand, known as the customer equity, is a major metric for business valuations.

 

Methods of Calculating LTV

At their core, LTV models can be used to answer these types of questions about customers:

  • How many transactions will the customer make in a given future time window?
  • How much value will the customer generate in a given future time window?
  • Is the customer in danger of becoming permanently inactive?

When you are predicting LTV, there are two distinct problems which require different data and modeling strategies:

  • Predict the future value for existing customers
  • Predict the future value for new customers

Many companies predict LTV only by looking at the total monetary amount of sales, without using context. For example, a customer who makes one big order might be less valuable than another customer who buys multiple times, but in smaller amounts.

LTV modeling can help you better understand the buying profile of your customers and help you value your business more accurately. By modeling LTV,  an organization can prioritize their actions by:

  • Decide how much to invest in advertising
  • Decide which customers to target with advertising
  • Plan how to move customers from one segment to another
  • Plan pricing strategies
  • Decide which customers to dedicate more resources to

LTV models are used to quantify the value of a customer and estimate the impact of actions that a business might take. Let us take a look at two example scenarios for LTV calculation.

Non-contractual businesses and contractual businesses are two common ways of approaching LTV for two different types of businesses or products. Other types include multi-tier products, cross-selling of products or ad-supported products among others.

 

Non-contractual Business

One of the most basic ways of calculating LTV is by looking at your historical figures of purchases and customer interactions and calculating the number of transactions per customer and the average value of a transaction.

Then by using the data available, you need to build a model that is able to calculate the probability of purchase in a future time window per customer. Once you have the following three metrics, you can get the LTV by multiplying them:

LTV = Number of transactions x Value of transactions x Probability of purchase

There are some gotchas in this way of modeling the problem. First of all, as discussed earlier, what is value? Is it revenue or profit or quantity sold? Does a certain feature of a product increase the value of a transaction? 

The value should be something that adheres to your business strategy and discourages short-term profit seeking and instead fosters long-term customer relationships.

Second, as mentioned earlier, predicting LTV for new customers will require different methods as they do not have a historical record of transactions.

 

Contractual Business

For a contractual business with a subscription model, the LTV calculation will be different as a customer is locked into buying from you for the time of the contract. Also, you can directly observe churn, since the customers who churn won’t re-subscribe. For example, a magazine with a monthly subscription or a streaming service etc. 

For such products, one can calculate the LTV by the expected number of months for which the customer will re-subscribe.

LTV = Survival rate x Value of subscription x Discount rate

The survival rate by month would be the proportion of customers that maintain their subscription. This can be estimated from the data by customer segment using, for example, survival analysis. The value of a subscription could be revenue minus cost of providing the service and minus customer acquisition cost.

Again, your business has to decide what is considered value. Then the discount rate is there because the subscription lasts into the future.

 

Actions and Measures

So you now have an LTV metric that decision makers in your organization are happy with. Now what? Do you just slap it on a dashboard? Do you recalculate the metric once a month and show the evolution of this metric on a dashboard?

Is LTV just another metric that the data analysis team provides to stakeholders and expects them to somehow use it to “drive business results”? Those are fine ideas but they don’t drive action by themselves. 

LTV metric can be used in multiple ways. For example, in marketing one can design treatments by segments and run experiments to see what kind of treatments maximize LTV instead of short-term profit.

The multiplication of probability to react favorably to a designed treatment with LTV is the expected reward. That reward minus the treatment cost gives us the expected business value. Thus, one gets the expected business value of each treatment and can choose the one with the best effect for each customer or customer segment.

Doing this calculation for our entire customer base will give a list of customers for whom to provide a specific treatment that maximizes LTV given our marketing budget. LTV can also be used to move customers from one segment to another.

For pricing, one could estimate how different segments of customers react to different pricing strategies and use price to affect the LTV trajectory of their customer base towards a more optimal LTV. For example, if using dynamic pricing algorithms, the LTV can be taken into account in the reward function.

Internal teams should track KPIs that will have an effect on the LTV calculation over which they have control. For example, in a non-contractual context, the product team can be measured on how well they increase the average number of transactions, or in a contractual context, the number of months that a typical customer stays subscribed.

The support team can be measured on the way that they provide customer service to reduce customer churn. The product development team can be measured on how well they increase the value per transaction by reducing costs or by adding features. The marketing team can be measured on the effectiveness of treatments to customer segments to increase the probability of purchase. 

After all, you get what you measure for. 

 

A Word on Data

LTV models generally aim to predict customer behavior as a function of observed customer features. This means that it is important to collect data about interactions, treatments and behaviors. 

Purchasing behavior is driven by fundamental factors such as valuation of a product or service compared with competing products or services. These factors may or may not be directly measurable but gathering information about competitor prices and actions can be crucial when analyzing customer behavior.

Other important data is created by the interaction between a customer and a brand. These properties characterize the overall customer experience, including customer satisfaction and loyalty scores.

The most important category of data is observed behavioral data. This can be in the form of purchase events, website visits, browsing history, and email clicks. This data often captures interactions with individual products or campaigns at specific points in time. From purchases one can quantify metrics like frequency or recency of purchases. 

Behavioral data carry the most important signals needed for modeling as customer behavior is at the core of our modeling practice for predicting LTV.

The data described above should also be augmented with additional features from your businesses side of the equation, such as catalog data, seasonality, prices, discounts, and store specific information.

 

Prerequisites for Implementing LTV

Thus far in this article we have discussed why LTV is important, we have shown some examples of how to calculate it and then discussed shortly how to make it actionable. Here are some questions that need to be answered before implementing an LTV calculation method:

  • Do we know who our customers are?
  • What is the best measure of value?
  • How to incorporate business strategy into the calculation?
  • Is the product a contractual or non-contractual product?

If you can answer these questions then you can start to implement your first actionable version of LTV.

See a demo here.

 

 

About the author: Janne Flinck is an AI & Data Lead at Codento. Janne joined Codento from Accenture 2022 with extensive experience in Google Cloud Platform, Data Science, and Data Engineering. His interests are in creating and architecting data-intensive applications and tooling. Janne has three professional certifications and one associate certification in Google Cloud and a Master’s Degree in Economics.

 

Please contact us for more information on how to utilize machine learning to optimize your customers’ LTV.

Leading through Digital Turmoil

Leading through Digital Turmoil

Author: Anthony Gyursanszky, CEO, Codento

 

Foreword

Few decades back during my early university years I bacame familiar with Pascal coding and Michael Porter’s competitive strategy. “Select telecommunication courses next – it is the future”,  I was told. So I did, and the telecommunications disruption indeed accelerated my first career years.

The telecom disruption layed up the foundation for an even greater change we are now facing enabled by cloud capabilities, data technologoes, artificial intelligence and modern software. We see companies not only selecting between Porter’s lowest cost, differentation, or focus strategies, but with the help of digital disruption, the leaders utilize them all simultaneously.

Here at Codento we are in a mission to help various organization to succeed through digital turmoil, understand their current capabilities, envision their future business and technical environment, craft the most rational steps of transformation towards digital leadership, and support them throughout this process with advise and capability acceleration. In this process, we work closely with leading cloud technology enablers, like Google Cloud.

In this article, I will open up the journey towards digital leadership based on our experiences and available global studies.

 

What we mean by digital transformation now?

Blair Franklin, Contributing Writer, Google Cloud recently published a blogpost

Why the meaning of “digital transformation” is evolving. Google interviewed more than 2,100 global tech and business leaders around the question: “What does digital transformation mean to you?”

Five years ago the dominant view was “lift-and-shift” your IT infrastructure to the public cloud. Most organizations have now proceedded with this, mostly to seek for cost saving, but very little transformative business value has been visible to their own customers.

Today, the meaning of “digital transformation “has expanded according to Google Cloud survey. 72% consider it as much more than “lift-and-shift”. The survey claims that there are now two new attributes:

  1. Optimizing processes and becoming more operationally agile (47%). This in my opinion,  provides a foundation for both cost and differentiation strategy.
  2. Improving customer experience through technology (40%). This, in my opinion, boosts both focus and differentiation strategy.

In conclusion, we have now moved from “lift-and-shift” era to a “digital leader” era.

 

Why would one consider becoming a digital leader?

Boston Consulting Group and Google Cloud explored the benefits of putting effort on becoming “a digital leader” in Keys of Scaling Digital Value 2022 study. According to the study, about 30% of organizations were categorized as digital leaders. 

And what is truly interesting, digital leaders tend to outperform their peers: They bring 2x more solutions to scale and with scaling they deliver significantly better financial results (3x higher returns on investments, 15-20% faster revenue growth and simlar size of cost savings)

The study points out several characteristics of a digital leader, but one with the highest correlation is related how they utilize software in the cloud:  digital leaders deploy cloud-native solutions (64% vs. 3% of laggers) with modern modular architecture (94% vs. 21% laggers).

Cloud native means a concept of building and running applications to take advantage of the distributed computing offered by the cloud. Cloud native applications, on the other hand, are designed to utilize the scale, elasticity, resiliency, and flexibility of the cloud.

The opposite to this are legacy applications which have been designed to on-premises environments, bound to certain technologies, integrations, and even specific operating system and database versions.

 

How to to become a digital leader?

First, It is obvious that the journey towards digital leadership requires strong vision, determination, and investments as there are two essential reasons why the progress might be stalled:

  • According to a Mckinsey survey a lack of strategic clarity cause transformations to lose momentum or stall at the pilot stage.
  • Boston Consulting Group research found that only 40% of all companies manage to create an integrated transformation strategy. 

Second, Boston Consulting Group and Google Cloud “Keys of Scaling Digital Value 2022” study further pinpoints a more novel approach for digital leadership as a prerequisite for success. The study shows that the digital leaders:

  • Are organized around product-led platform teams (83% leaders vs. 25% laggers)
  • Staff cross-functional lighthouse teams (88% leaders vs. 23% laggers)
  • Establish a digital “control tower” (59% leaders vs. 4% laggers)

Third, as observed by us also here at Codento, most companies have structured their organizations and defined roles and process during the initial IT era into silos as they initially started to automate their manual processes with IT technologies  and applications. They added IT organizations next to their existing functions while keeping business and R&D functions separate.

All these three key functions have had their own mostly independent views of data, applications and cloud adoption, but while cloud enables and also requires seemless utilization of these capabilities ”as one”, companies need to rethink the way they organize themselves in a cloud-native way.

Without legacy investments this would obviously be a much easier process as “digital native” organizations, like Spotify, have showcased. Digital natives tend to design their operations ”free of silos” around cloud native application development and utilizing advanced cloud capabilities like unified data storage, processing and artificial intelligence.

Digital native organizations are flatter, nimbler, and roles are more flexible with broader accountability ss suggested by DevOps and Site Reliability Engineering models. Quite remarkable results follow successful adoption. DORA’s, 2021 Accelerate: State of DevOps Report reveals that peak performers in this area are 1.8 times more likely to report better business outcomes.

 

Yes, I want to jump to a digital leadr train. How to get started?

In summary, digital leaders are more successful than their peers and it is difficult to argument not to join that movement.

Digital leaders do not only consider digital transformation as an infrastructure cloudification initiative, but seek competitive egde by optimizing processes and improving customer experience. To become a digital leader requires a clear vision, support by top management and new structures enabled by cloud native applications accelerated by integrated data and artificial intelligence. 

We here at Codento are specialized in enabling our customers to become digital leaders with a three-phase-value discovery approach to crystallize your:

  1. Why? Assess where you are ar the moment and what is needed to flourish in the future business environment.
  2. What? Choose your strategic elements and target capabilities in order to succeed.
  3. How? Build and implement your transformation and execution journeys based on previous phases.

We help our clients not only throughout the entire thinking and implementation process, but also with specific improvement initiatives as needed.

To get more practical perspective on this you may want to visit our live digital leader showcase library:

You can also subscribe to our newsletters, join upcoming online-events and watch our event recordings

 

About the author: Anthony Gyursanszky, CEO, joined Codento in late 2019 with more than 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. Gyursanszky has also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. Anthony’s experience covers business management, product management, product development, software business, SaaS business, process management and software development outsourcing. Anthony is also a certified Cloud Digital Leader.

 

Contact us for more information on our  Value Discovery services.

Business-driven Machine Learner with Google Cloud

Business-driven Machine Learner with Google Cloud: Multilingual Customer Feedback Classifier

Author: Jari Rinta-aho, Codento

At Codento, we have rapidly expanded our services to demanding implementations and services for data and machine learning. When discussing with our customers, the following business goals and expectations have often come to the fore:

  • Disclosure of hidden regularities in data
  • Automation of analysis
  • Minimizing human error
  • New business models and opportunities
  • Improving and safeguarding competitiveness
  • Processing of multidimensional and versatile data material

In this blog post, I will  go through the lessons from our recent customer case.

Competitive advantage from deep understanding customer feedback

A very concrete business need arose this spring for a Finnish B-to-C player: huge amounts of customer feedback data come, but how to utilize feedback intelligently in decision-making to make the right business decisions.

Codento recommended the use of machine learning

Codento’s recommendation was to take advantage of the challenging machine learning approach and Google Cloud off-the-shelf features to get the customer feedback classifier ready by the week.

The goal was to automatically classify short Customer Feedback into three baskets: Positive, Neutral, and Negative. Customer feedback was mainly short Finnish texts. However, there were also a few texts written in Swedish and English. The classifier must therefore also be able to recognize the language of the source text automatically.

Can you really expect results in a week?

At the same time, the project was tight on schedule and ambitious. There was no time to waste in the project, but in practice the results had to be obtained on the first try. Codento therefore decided to make the most of the ready-made cognitive services.

Google Cloud plays a key role

It was decided to implement the classifier by combining two ready-made tools found in the Google Cloud Platform: Translate API and Natural Language API. The purpose was to mechanically translate the texts into English and determine their tone. Because the Translate API is able to automatically detect the source language from about a hundred different languages, the tool met the requirements, at least on paper.

Were the results useful?

Random sampling and craftsmanship were used to validate the results. From the existing data, 150 texts were selected at random for the validation of the classifier. First, these texts were sorted by hand into three categories: positive, neutral, and negative. After that, the same classification was made with the tool we developed. In the end, the results of the tool and the craft were compared.

What was achieved?

The tool and the analyzer agreed on about 80% of the feedback. There was no contrary view. The validation results were pooled into a confusion matrix.

The numbers 18, 30, and 75 on the diagonal of the image confusion matrix describe the feedback in which the Validator and the tool agreed on the tone of the feedback. A total of 11 feedbacks were those in which Validator considered the tone positive but the tool neutral.

 

The most significant factor that explains the different interpretation made by the tool is the cultural relevance of the wording of the customer feedback, and when a Finn says “No complaining”, he praises.

Heard from an American, this is neutral feedback. This cultural difference alone is sufficient to explain why the largest single error group was “positive in the view of the validator, neutral in the view of the tool.” Otherwise, the error is explained by the difficulty of distinguishing between borderline cases. It is impossible to say unambiguously when slightly positive feedback will turn neutral and vice versa.

Utilizing the solution in business

The data-validated approach was well suited to solve the challenge and is an excellent starting point for understanding the nature of feedback in the future, developing further models for more detailed analysis, speeding up analysis and reducing manual work. The solution can also be applied to a wide range of similar situations and needs in other processes or industries.

The author of the article is Jari Rinta-aho, Senior Data Scientist & Consultant, Codento. Jari is a consultant and physicist interested in machine learning and mathematics, who has extensive experience in utilizing machine learning, e.g. nuclear technologies. He has also taught physics at the university and led international research projects.