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The Next Generation Of French Web Analysts

The Next Generation Of French Web Analysts

The role and purpose of Web Analysts has evolved over the last few years, and now there are a number of different types of candidate profile across the French marketplace. Whilst, traditionally, Web Analysts focused on Data pulled from websites before using their findings to make business recommendations on how to improve the site and streamline user experience.  However, as, digital channels, including apps, social media and mobile devices have multiplied, the amount of Data available to gather insights from has increased dramatically. Web Analytics has become Digital Analytics as a result of the need to quantify and better understand customer behaviour regardless of the channel or device used.  Across the world’s leading technology hubs, the role of the Digital Analyst is no longer to just relay insights from a company’s website, but to analyse different Data sources, work with complex technologies and tell stories with their findings. We’re now seeing the same evolution take place across the French market.  Today's Web Analysts  Throughout the era of digital measurement and optimisation tools, the use of AB tests and MVT tests has allowed Web Analysts to trial different online solutions for their enterprises. Nevertheless, until recently, these have remained centred on only one channel; the website. Over recent years, however, new categories of Analytics have now emerged, all of which need to be viewed as equally important:  In-store Analytics: The measurement of physical store Data, a real-world equivalent of web analytics. Mobile Analytics: The analysis of users’ traffic and behaviour on mobile sites and applications. Social Analytics: The analysis of Data from social networks such as Facebook, Instagram or Twitter.  As a result of this diversification, businesses are now not only looking for technical Web Analysts who can work with Google Analytics or Adobe Analytics and implement tags with GTM or DTM. There is now an appetite to go further and deeper with their analysis and Web Analysts who can use tools such as Big Query/ SQL, R or Python are high in-demand. A candidate with ‘Data Web’ vision, a strong knowledge of Data and KPIs in different business models, stands out amongst ever-increasing competition.  Furthermore, as Web Analysts use a lot of Data, particularly personal Data, a strong knowledge of GDPR and the legal implications of their work are also incredibly beneficial.  In other words, Web Analysts are becoming more versatile. No longer siloed to their own space, Web Analysts should have experience of collaborating with marketing and technical teams, as well as to top management and senior stakeholders.  Tomorrow's Web Analytics With this progression of Analytics tools and skillsets, Digital Analysts are now playing a more important role in businesses than ever before.  As they continue to present new ways of interpreting and visualising Data, their impact on the bottom line is being felt more significantly than ever.   As a result, Web Analysts are now open to significantly more professional opportunities. Specifically, if they have a strong technical skillset and a business mindset, they can move into a Digital Business Analyst or Data Scientist position. This means that the best candidates are in incredibly high-demand and businesses need to be sure of what skillset they need before beginning a recruitment process.  For example, a company recently going through a big change in tools migration, such as moving from Adobe to GA, would be in need of a strong technical Web Analyst who can implement those tools. A business that is further down the line with their capabilities, on the other hand, may be looking for a candidate with a real business vision, in additional to an analytical skillset, who can make informed business recommendations. Whilst the French market may be in transition, we’re already seeing these changes take place in other regions. In the UK, there is a large amount of conversation around ‘Digital Intelligence’, and Web Analysts are now beginning to be viewed as important as Data Scientists within many leading organisations, partially because these roles are overlapping more and more. In fact, the lack of appreciation for Web Analysts in France is a point of contention for many candidates, something that was discussed frequently at this year’s MeasureCamp Paris.  Businesses who are looking to hire, and retain, Web Analysts need to be aware of this mindset. Candidates often share their apprehensions around the lack of training offered within their companies, as well as concerns about investment in their area. As Web Analysts continue to upskill, enterprises need to make sure they continue to offer growth, opportunity and a good working environment, particularly if they are seeking domestic talent.  Whether you are looking to expend your Web Analytics function or take the next step in your career, we can help. Take a look at our latest opportunities or get in touch with one of our expect consultants to find out more. 

How AI Will Revolutionise CRM

How AI Will Revolutionise CRM

If we can be sure of anything in today’s business climate, it is that new trends will emerge and disrupt, new technologies will continue to be developed and attract hype, and companies will be left to navigate a landscape of opportunity and uncertainty. Customer Relationship Management is an upright concept or strategy to solidify relations with customers whilst at the same time reducing cost and enhancing productivity and profitability in business. CRM systems provide a well-defined platform for all business units to interact with their customers and fulfil all their needs and demands in order to build long-term relationships. Every business unit has an emphasis on developing long-term relationships with customers in order to nurture their stability in today’s blooming market. Customer’s expectations are now not only limited to get best products and services, they also need a face-to-face business in which they want to receive exactly what they demand and in a quick time. New Look CRM CRM is vital for the success of any organisation that seeks to continuously build relationships and manage countless interactions with customers. Now CRM systems bring together customer Data from a multitude of different sources, delivering it to all customer-facing employees to provide a complete picture of each customer across all department Today, there is a ton of available information across many devices and platforms. Companies need a way to integrate this “Big Data” into their intelligent CRM that can produce predictive results. The Value of AI Artificial Intelligence (AI) CRM systems built on Machine Learning algorithms now have the ability to learn from past experience or historical Data. Having these insights at the disposal of any customer-facing employee (sales, support, marketing, etc.) empowers a business to build deeper relationships with its customers. As a result, integrating AI and Machine Learning with CRM can deliver more predictive and personalised customer information in all areas of your business. By predicting customer behaviour, companies can take personalised actions to avoid the use of invasive advertising and to provide material of real interest to each prospect. There is no question personalising communications to customers has become critical. Today’s buyers demand more than a “spray-and-pray” email blast. A recent McKinsey study found that personalisation can lift sales by 10% or more. The analysis also showed that by personalising just 20% of email content, open rates increased more than 40& on average. Reply rates also increased a whopping 112%. As a CRM stores all the information in one centralised place, this makes it a lot easier to analyse your performance as a whole. This helps businesses build a relationship with their customers that, in turn, creates loyalty and customer retention. Since customer loyalty and revenue are both qualities that affect a company's revenue, a strong CRM have a direct result in increased profits for a business.  Those that use Big Data & Analytics effectively show productivity rates and profitability that are higher than competitors and those that put Data at the centre of their marketing efforts improve their ROI by 15-20%.  AI and CRM AI is becoming an ever-present theme across a variety of industries, from healthcare and retail to software development and finance. CRM vendors are no different; over the past year, numerous CRM vendors have introduced AI components into their product offerings. AI will develop in parallel with user interactions using various touch points within CRM and evolve continuously to deliver more intelligent and personalised actions. Learning critical patterns will also enable AI-infused CRM to automate certain actions, decrease the manual work required, and empower sales and marketing professionals to work more efficiently and effectively. The inefficient processes that hinder CRM will no longer be acceptable, and AI-powered automation will play a much bigger role in streamlining workflows. The rise of AI presents businesses with a wide array of unique benefits and opportunities. It can empower them to provide better, more relevant experiences to their customers and forge bonds with them in a way that was simply not possible before.  It’s estimated that 85% of businesses will start implementing AI solutions for their CRM by 2020. It seems inevitable that with further advancements, AI will move from a novelty tool to a must-have feature and dire necessity of every business. If you’re looking for to build a team of CRM experts, or to take the next step in your career, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

Using Psychology To Enhance Your Web Analytics

Using Psychology To Enhance Your Web Analytics

Web Analytics have long been used to help companies understand their customers’ online behaviour, extracting and interrogating an abundance of information; from time spent on pages to bounce rates and conversion rates. Having provided a lot of insight as to what customer are doing online, these techniques have been less useful for understanding why they do it. This is where psychology comes in. As the why of Web Analytics becomes more and more important, with companies always looking ways to edge out the competition, there are more links to psychological principles than you might expect.  Of course, traditional Web Analytics and metrics remain very important. However, what psychology can do is help us speculate as to why customers may be behaving the way they do and, by doing so, allow businesses to make more informed changes to their websites, or conduct more conscious testing.  Without directly asking we will never know the real reason behind customer’s actions, but we can use a number of established psychological constructs to make well informed assumptions. We can then work this backwards and use these constructs to make changes to our sites that will fall in line with these assumptions in order to convert more customers.  Familiarity People tend to favour that which they are familiar with, whether it be items of clothing that match their preferred style or holidays like ones they have been on before. A customer visiting a page to find a series of unfamiliar products is more likely to leave without making a purchase. This is why personalisation is important; it gets rid of unnecessary information and leaves the user with products they are more likely to want.  By working backwards, businesses can personalise their sites to each individual customer. If you’ve ever bought an item of clothing from an online shop only to be shown a number of similar items the next time you log on, you’ll know what I’m talking about. The thinking is that, as these items are more familiar to you, you’ll be more likely to either purchase them, or remain on the site to purchase something else.  Social Proof  Research into social proof has shown psychologists that the more people who reinforce a certain concept, the more likely it is that other individuals will perceive it as correct. This heuristic is used widely by companies like Just Eat and Deliveroo who allow customers to leave comments about their restaurants and give them a star rating. It is much more likely that conversion rate will be higher on restaurants with better reviews as the rating allows the customer to make a quick judgments on its quality.  Scarcity This is a cognitive bias where humans put more value on things that are scarce over those that are in abundance. If one site is showing a product with no indication to the quantity left but another company is showing a similar product where there are only three left, the customer is more likely to convert on the second site. Where an item is nearly unavailable, this suggests a number of things; it is more valuable and it is desired by more people (social proof) etc. Companies like Amazon and Asos use this technique by showing visitors when an item is low in stock or even showing how few are left, giving them an edge on conversion over their competitors.   Web Analysts and CRO professionals should take note of user psychology and start to implement it in their day to day practice. In fact, some might be already without even knowing the fundamentals of the psychology behind these techniques. Applying the above techniques and testing these ideas could produce a boost in conversion that simple changes to user experience, like changing the position of the checkout basket, aren’t providing.  A number of businesses are now looking for Analysts who can explain why customers are behaving in a certain way and tell a story with the Data, rather than just explaining what was found. Finding someone with this deeper understanding of user psychology has therefore an integral part to many hiring processes. By looking into this area, candidates are likely to increase their chances of securing the role they want.  Whether you’re looking to expand your Web Analytics function or want to take the next step in your career, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

L’Internalisation Des Achats Media En France

La data est aujourd’hui au cœur de toutes les stratégies digitales et media et les entreprises expriment de plus en plus la volonté de reprendre la main sur leurs investissements media et digitaux Analytics. Nous remarquons donc une tendance d’internalisation où les sociétés souhaitent intégrer les expertises des agences au sein de leurs équipes. Obstacle ou réelle opportunité pour les agences media et pour les candidats?  C’est déjà une réalité en Angleterre et aux Etats-Unis depuis plusieurs années où 80% des sociétés ont internalisé tout ou partie de leur conseil et leur achat d’espace publicitaire (autrement dit, une partie de leur marketing et de leur expertise sur le conseil et les médias).  A contrario, en France, nous sommes légèrement plus en retard. Air France a été précurseur de cette internalisation et cela leur a permis d’économiser 20% dans leurs achats d’espace. Néanmoins, il est impossible pour toutes les entreprises d’internaliser 100% de leurs activités publicitaires digitales, car un appui externe leur sera toujours nécessaire - les agences médias ont pour but de proposer les outils et technos à la pointe du marché et c’est un métier à part entière.  COMMENT FONCTIONNE L’INTERNALISATION?  L’internalisation ne pourra se faire que de manière progressive, car c’est un processus long et complexe (on parle même de plusieurs années lors de ce type de changement/restructuration qui impact directement les organisations en interne), qui mobilisera très certainement plusieurs business unit. Ce qui permet aux agences médias d’appréhender et de proposer en amont des solutions compatibles avec les besoins clients.  L’idée est d’accompagner et de former les équipes du client final pour les aider à internaliser un ou plusieurs leviers digitaux (composantes essentielles des stratégies digitale pour développer la présence de leurs clients sur la toile). Cela a débuté avec le SEA (Paid media) et ensuite tout le programmatique a suivi comme l’achat d’espaces publicitaires, le display et les réseaux sociaux. Cette équipe travaillerait directement en régie pour auditer les campagnes et accompagner sur du conseil et tout le long de la transformation digitale.   QUEL EST L’IMPACT SUR LES AGENCES ET SUR LES CANDIDATS? En France, les agences media baignent dans le digital depuis longtemps et leur expérience et expertise est extrêmement forte. Les sociétés auront toujours besoins de l’intervention de spécialistes. En effet, ce n’est pas le métier de la société mais celui de l’agence spécialisée que de se tenir en veille régulièrement et d’investir dans de nouvelles technos ( et les tester encore et encore). Autrement dit, ce métier demande autant de l’investissement en terme de temps que de montée en compétences régulière en très peu de temps.  Dans ce cadre, l’impact que les agences pourraient avoir sur la transformation digitale des sociétés avec lesquelles elles collaborent pourrait être très large et c’est la possibilité pour les agences média de peser d’avantage dans la stratégie digitale globale des annonceurs. Cette situation est donc d’avantage une opportunité à saisir par les agences plutôt qu’un obstacle à proprement parler. Côté candidats, de nouvelles opportunités d’évolution s’ouvrent. L’évolution de leurs expertises est impressionnante, que ce soit dans l’UX, la data ou l’Analytics, cela leur permet d’apporter de nouvelles solutions tant en interne en tant que consultant ou directement chez le client. Les postes qui en découlent seront principalement des postes de Media Trader, Traffic Manager Display, Expert SEA, Digital Data Analyst, Social Analytics, etc.  Dans cette logique de créer de la valeur et de se réinventer chaque jour, les agences offrent de plus en plus un accompagnement personnalisé ce qui leur permet de se différencier de la concurrence. Si vous êtes à la recherche de profils Analytics ou un candidat curieux d’en savoir plus sur les évolutions de carrière qui s’offrent à lui, vous pouvez me contacter par email.

FROM HEALTHCARE TO HOSPITALITY HOW BIG DATA TRANSFORMS INDUSTRY

From Healthcare to Hospitality: How Big Data Transforms Industry

Big Data, once a looming anomaly for many businesses, has transformed. No longer a buzzword, it is essential to enterprises everywhere – from healthcare to hospitality. Whilst it’s taken about a decade to get here, the last two years are truly telling.  With the amount of Data flowing through our systems, over 2 quintillion bytes each day. Think Apple Pay at Starbucks, credit card purchase, filling out of forms, GPS, our phone Data, traffic cameras and lights for traffic control. Big data is now big business. Top Industries Using Big Data and Analytics While the most prevalent industries which come to mind are retail, entertainment, and politics. There are two which, until now, have been coming in under the radar and have seen some of the biggest changes using Data & Analytics; healthcare and hospitality. Whilst they don’t seem to go together, they do have one thing in common – the experience. Hospitality  As you plan for your next vacation, you may be debating the merits of a hotel reservation versus an AirBnB. Lodging options in the share-economy have forced traditional accommodation options to rethink their strategies. The ease of “mobile first” which allows customers to manage their bookings, stays, and travel experience through their phones is in direction opposition to the client-facing hotel industry.  There is a massive shift happening in this industry and a powerful Data Analytics tool can help create visualisations from a company’s Data. Not only can these provide insights for the future, but they also offer suggestions for strategies which can be implemented now to impact future prospects. Healthcare  One of the most telling industries being transformed by Big Data is healthcare. Access to care is not only available in-office, in-person, but now with the advent of Telemedicine, patients can get questions even more quickly. No matter the industry today, this is a buyer’s market, or in most cases, a customer’s market. And its customer satisfaction which drives the success of a business. In healthcare, it’s patient satisfaction. Patient satisfaction scores underlies everything from hospital funding to the return visits in the private sector. Like any business, the patient experience in the healthcare industry, begins with initial contact, staff responsiveness, communication by doctors and nurses, wait times, even equipment and cleanliness of facilities to name a few examples. Once all the Data and information is gathered, collected, and analysed, these healthcare professionals are able to make any necessary adjustments. As quickly as Data has grown in the last couple of years, the projections for healthcare can expect to see a high volume in the next seven years. One of the highest benefits which can add to patient experience is the database of patient’s information can be shared across healthcare organisations saving time, money, and patient stress which all leads to better treatment for the patient’s needs.  In fact, according to the International Data Corporation (IDC), healthcare Data is expected to grow faster than industries such as the media, manufacturing, or financial services. Advancements such as chatbots, virtual assistants, Big Data Analytic tools, and medical imaging have all added to the transformation.  As strong and as prevalent as many of these advances are, many organisations still struggle to find the right candidates with the right Data skill sets. Many have neither a blockchain strategy nor have plans to implement one and are falling behind. There is a next generation opportunity here to more fully transform digitally, but the right people need to be in place to make it happen. Digital transformation isn’t slowing down and is becoming more critical at a rapid rate. By making investments in your health IT, analytics tools, and people, you’ll be ready to close the digital transformation gap. If you’re interested in Big Data and Analytics with an eye toward the Life Sciences field, we may have a role for you. Check out our current vacancies or get in touch with one of our expert consultants to learn more. 

HOW PROGRAMMATIC IS REVOLUTIONISING ADVERTISING

How Programmatic Is Revolutionising Advertising

With consumerism on the rise, and a drastic shift away from traditional avenues of advertising, the use of Digital Marketing and the demand for business to become more technically ‘savvy’ is continuously increasing. The extent of different digital media channels in the advertising space, as well as the recent evolution of approaches such as Programmatic Advertising, has caused confusion as to which approach is the best for businesses to adopt and for well versed Digital Marketers to reflect on what their next career step should be.  Irrespective, Programmatic is such a buzzword within the market at present and is widely predicted to become the future of display advertising. Despite this, many have a lack of understanding as to what it actually is. Whether you are looking for a career change or to embed Programmatic into your marketing strategy, here are some considerations: Defining Programmatic  Programmatic advertising is the automated process of bidding for advertising inventory to allow for the opportunity to display a relevant advert to the desired consumer in real time.  At a basic level, parties from the ‘supply’ side of programmatic will sell an impression referred to as ‘audience ‘inventory’ through a Supply Side Platform. Facilitated by the ad exchange, such inventory is shared with advertisers who have submitted their desired audience preference through a Demand Side Platform. Within this online, automated marketplace, all advertisers will bid within the auction and the highest ‘bidder’ will then win each impression. The advertiser, typically a media agency or in house team of specialists, will begin to target users through Programmatic Ads that can be online or Out Of Home (OOH). Redefining your advertising strategy  With pre-existing modes of marketing such as, newspapers, radio, TV and, more recently, social media and paid search; it is worth considering the additional ways in which Programmatic advertising can benefit your business. Rather than utilising Data-driven ‘trial and testing’ methods to assess what will attract audiences to your site, Programmatic advertising uses a personalised approach by only targeting users who have expressed an interest in specific products or services. The automated process of identifying target users enables this to be a lot less manual than traditional modes of advertising. As a result, this will save your business time and unnecessary resources dedicated to Predictive Analysis, which will particularly benefit smaller businesses who may have a limited marketing budget.  Programmatic advertising is also not just limited to online. The development of OOH has revolutionised the power, audience reach and impact of this long-standing method of advertising, allowing it to “bring data into the physical world” on a mass scale.  As well as delivering a single ad to the right user at the best time, Programmatic advertising can enable your business to target hundreds of relevant consumers based on their online activity and location. This form of audience targeting is still incredibly new to the marketplace and is continuing to expand. By 2021, it is anticipated that Programmatic will further bridge the gap between digital and offline media by programmatically purchasing tv adverts; representing approximately one third of global ad revenue. The future of advertising careers If you are looking for a long-term career within advertising, Programmatic is a great route to gain exposure within, given that it already dominates the industry, and looks set to continue to.  Due to such high demand and the lack of quality candidates within the market, Programmatic specialists are incredibly desired and retained by employers. As such, businesses are consistently searching for more talent within their team. Once onboard, they often invest heavily in training, personal development and internal progression.  There is often a misconception that Programmatic is not scientific, however, specialists often sit in Data teams and utilise Analytics software or Data Visualisation tools daily; extracting and manipulating Data. Server-side scripting is also a huge part of the role; if an ad is not displaying on a site suitably, the Programmatic team will be required to dive into the JavaScript or HTML code to troubleshoot the issue.  So, if you are looking for a Data-led vertical of advertising, Programmatic is a great career path. However, the supply and demand side are kept very separate due to the difference in tools utilised. Transitioning between the two can be incredibly problematic, especially further into your career so, if you are looking into a specific route, make sure you are making an informed decision. If Programmatic sales, inventory analysis and yield optimisation are appealing, the publisher side could be a great route. Alternatively, if setting up and monitoring campaigns or segmenting audience Data is of interest, I would advise starting agency side. Whether you’re looking to venture into a new aspect of digital media or require specialist talent within your team, we can help. Take a look at our latest opportunities or get in touch with myself at francescaharris@harnham.com to find out more.

Web Analytics Career To Data Science

HOW WEB ANALYTICS CAN LEAD TO A CAREER IN DATA SCIENCE

The Web Analytics world is evolving. What used to require an understanding of Google Analytics, some tag management and visualisation for presentation purposes has grown into something much more. Whereas Web Analysts may have once been lone players in a Marketing team, they’re now expected to sit as part of, and feed into, an enterprise’s Insight team.  This exposure to more comprehensive forms of Data Analysis has led many Web Analysts to explore what the next step in their career could be. Namely, should they move into a Data Science position? For those who are looking to make this move, here are some considerations: Technicalities and Technologies  Digital Analytics are not excluded from the debate over what it means to be a Data Scientist, especially given that some with a Data Scientist job title may in fact be Web Analysts, and vice versa. Many Web Analysts are now working with a number of Data Science tools, including SQL, Python, and R. By using these alongside Google or Adobe Analytics, they are able to form a comprehensive view of the customer, using different types of Data, in different forms, from different sources. However, there remains a gap between the use of these tools and actually working within Data Science.  The most logical leap for a Web Analyst to make is to a Customer Insight or Digital Insight role. This type of role would still involve the analysis of online Data, but would likely be paired with building models, Predictive Analysis, reviewing customer LTV and creating a picture of customer online, offline and post-purchase behaviour to enable better targeting and retargeting. However, the knowledge gap between Web Analytics and Data Science may be more significant than one would anticipate.  Your Current Position  As a Web Analyst, you may well sit within a larger Data, Digital or Customer/Marketing Analytics department. Your exposure to these experts is one of the best assets you have available. Use the environment you are in to learn, upskill and gain hands-on experience. Knowledge of the necessary tools and languages is unlikely to be enough to lead to a move into Data Science and by getting hands-on commercial experience, you drastically increase your chances of success.  If you are able to expand on the tech that you have already used, take advantage of this. Even if this is just in a consulting capacity, your ability to demonstrate a real-world application of your knowledge makes you significantly more appealing as a candidate. Plus, your knowledge of, and approach to, Web Analytics may actually work to your advantage when it comes to assessing Data quality. Consultancies and agencies often provide the best training opportunities and are more likely to allow you the opportunities to hone new skills. If you are fortunate enough to work in an environment like this, make the most of it. Attitude Is Everything It may sound like a cliché, but Hiring Managers are on the lookout for people that they know will benefit their business and attitude plays a huge part in this. Do not underestimate the importance that is placed on cultural fit during an interview process.  Whether you are looking to make a move internally or externally, you should demonstrate your intrigue and willingness to learn. If you already have a strong record of progression within your current career, this will benefit you moving forward. When it comes to preparing, take time to dive into the world of Data Science, attend events and meet-ups, and continue to widen your remit. If you don’t have exposure to Data Science at work then you will also need to be learning SQL, Python and R at home to ensure you have a firm understanding of all the relevant technologies.  Whatever role you are looking for, the worst thing you can do is not apply. One of the most common mistakes we see is analysts not applying to an opportunity because they would need to develop in some areas once in the role. If you are able to demonstrate the above attributes many enterprises, particularly agencies and consultancies, may still be willing to take you on. And, if you’re not looking to make a move, don’t panic; Web Analytics skillsets remain highly sought-after and valuable. Whether you’re looking for a new career in Data Science or your next role in Web Analytics, we may have a job for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.  

How Big Data Is Impacting Logistics

How Big Data is Impacting Logistics

As Big Data can reveal patterns, trends and associations relating to human behaviour and interactions, it’s no surprise that Data & Analytics are changing the way that the supply chain sector operates today.  From informing and predicting buying trends to streamlining order processing and logistics, technological innovations are impacting the industry, boosting efficiency and improving supply chain management.  Analysing behavioural patterns Using pattern recognition systems, Artificial Intelligence is able to analyse Big Data. During this process, Artificial Intelligence defines and identifies external influences which may affect the process of operations (such as customer purchasing choices) using Machine Learning algorithms. From the Data collected, Artificial Intelligence is able to determine information or characteristics which can inform us of repetitive behaviour or predict statistically probable actions.  Consequently, organisation and planning can be undertaken with ease to improve the efficiency of the supply chain. For example, ordering a calculated amount of stock in preparation for a busy season can be made using much more accurate predictions - contributing to less over-stocking and potentially more profit. As a result, analysing behavioural patterns facilitates better management and administration, with a knock-on effect for improving processes.  Streamlining operations  Using image recognition technology, Artificial Intelligence enables quicker processes that are ideally suited for warehouses and stock control applications. Additionally, transcribing voice to text applications mean stock can be identified and processed quickly to reach its destination, reducing the human resource time required and minimising human error.  Artificial intelligence has also changed the way we use our inventory systems. Using natural language interaction, enterprises have the capability to generate reports on sales, meaning businesses can quickly identify stock concerns and replenish accordingly. Intelligence can even communicate these reports, so Data reliably reaches the next person in the supply chain, expanding capabilities for efficient operations to a level that humans physically cannot attain. It’s no surprise that when it comes to warehousing and packaging operations Artificial Intelligence can revolutionise the efficiency of current systems. With image recognition now capable of detecting which brands and logos are visible on cardboard boxes of all sizes, monitoring shelf space is now possible on a real-time basis. In turn, Artificial Intelligence is able to offer short term insights that would have previously been restricted to broad annual time frames for consumers and management alike.  Forecasting  Many companies manually undertake forecasting predictions using excel spreadsheets that are then subject to communication and data from other departments. Using this method, there’s ample room for human error as forecasting cannot be uniform across all regions in national or global companies. This can create impactful mistakes which have the potential to make predictions increasingly inaccurate.  Using intelligent stock management systems, Machine Learning algorithms can predict when stock replenishment will be required in warehouse environments. When combined with trend prediction technology, warehouses will effectively be capable enough to almost run themselves  negating the risk of human error and wasted time. Automating the forecasting process decreases cycle time, while providing early warning signals for unexpected issues, leaving businesses better prepared for most eventualities that may not have been spotted by the human eye.  Big Data is continuing to transform the world of logistics, and utilising it in the best way possible is essential to meeting customer demands and exercising agile supply chain management.  If you’re interested in utilising Artificial Intelligence and Machine Learning to help improve processes, Harnham may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.  Author Bio: Alex Jones is a content creator for Kendon Packaging. Now one of Britain's leading packaging companies, Kendon Packaging has been supporting businesses nationwide since the 1930s.

Data Science Interview Questions: What The Experts Say

Our friends at Data Science Dojo have compiled a list of 101 actual Data Science interview questions that have been asked between 2016-2019 at some of the largest recruiters in the Data Science industry – Amazon, Microsoft, Facebook, Google, Netflix, Expedia, etc.  Data Science is an interdisciplinary field and sits at the intersection of computer science, statistics/mathematics, and domain knowledge. To be able to perform well, one needs to have a good foundation in not one but multiple fields, and it reflects in the interview. They've divided the questions into six categories:  Machine LearningData AnalysisStatistics, Probability, and MathematicsProgrammingSQLExperiential/Behavioural Questions Once you've gone through all the questions, you should have a good understanding of how well you're prepared for your next Data Science interview. Machine Learning As one will expect, Data Science interviews focus heavily on questions that help the company test your concepts, applications, and experience on machine learning. Each question included in this category has been recently asked in one or more actual Data Science interviews at companies such as Amazon, Google, Microsoft, etc. These questions will give you a good sense of what sub-topics appear more often than others. You should also pay close attention to the way these questions are phrased in an interview.  Explain Logistic Regression and its assumptions.Explain Linear Regression and its assumptions.How do you split your data between training and validation?Describe Binary Classification.Explain the working of decision trees.What are different metrics to classify a dataset?What's the role of a cost function?What's the difference between convex and non-convex cost function?Why is it important to know bias-variance trade off while modeling?Why is regularisation used in machine learning models? What are the differences between L1 and L2 regularisation?What's the problem of exploding gradients in machine learning?Is it necessary to use activation functions in neural networks?In what aspects is a box plot different from a histogram?What is cross validation? Why is it used?Can you explain the concept of false positive and false negative?Explain how SVM works.While working at Facebook, you're asked to implement some new features. What type of experiment would you run to implement these features?What techniques can be used to evaluate a Machine Learning model?Why is overfitting a problem in machine learning models? What steps can you take to avoid it?Describe a way to detect anomalies in a given dataset.What are the Naive Bayes fundamentals?What is AUC - ROC Curve?What is K-means?How does the Gradient Boosting algorithm work?Explain advantages and drawbacks of Support Vector Machines (SVM).What is the difference between bagging and boosting?Before building any model, why do we need the feature selection/engineering step?How to deal with unbalanced binary classification?What is the ROC curve and the meaning of sensitivity, specificity, confusion matrix?Why is dimensionality reduction important?What are hyperparameters, how to tune them, how to test and know if they worked for the particular problem?How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession, and gender? Define a machine learning algorithm for this.How will you inspect missing data and when are they important for your analysis?How will you design the heatmap for Uber drivers to provide recommendation on where to wait for passengers? How would you approach this?What are time series forecasting techniques?How does a logistic regression model know what the coefficients are?Explain Principle Component Analysis (PCA) and it's assumptions.Formulate Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) techniques.What are neural networks used for?40. Why is gradient checking important?Is random weight assignment better than assigning same weights to the units in the hidden layer?How to find the F1 score after a model is trained?How many topic modeling techniques do you know of? Explain them briefly.How does a neural network with one layer and one input and output compare to a logistic regression?Why Rectified Linear Unit/ReLU is a good activation function?When using the Gaussian mixture model, how do you know it's applicable?If a Product Manager says that they want to double the number of ads in Facebook's Newsfeed, how would you figure out if this is a good idea or not?What do you know about LSTM?Explain the difference between generative and discriminative algorithms.Can you explain what MapReduce is and how it works? If the model isn't perfect, how would you like to select the threshold so that the model outputs 1 or 0 for label?Are boosting algorithms better than decision trees? If yes, why?What do you think are the important factors in the algorithm Uber uses to assign rides to drivers?How does speech synthesis works? Data Analysis Machine Learning concepts are not the only area in which you'll be tested in the interview. Data pre-processing and data exploration are other areas where you can always expect a few questions. We're grouping all such questions under this category. Data Analysis is the process of evaluating data using analytical and statistical tools to discover useful insights. Once again, all these questions have been recently asked in one or more actual Data Science interviews at the companies listed above.   What are the core steps of the data analysis process?How do you detect if a new observation is an outlier?Facebook wants to analyse why the "likes per user and minutes spent on a platform are increasing, but total number of users are decreasing". How can they do that?If you have a chance to add something to Facebook then how would you measure its success?If you are working at Facebook and you want to detect bogus/fake accounts. How will you go about that?What are anomaly detection methods?How do you solve for multicollinearity?How to optimise marketing spend between various marketing channels?What metrics would you use to track whether Uber's strategy of using paid advertising to acquire customers works?What are the core steps for data preprocessing before applying machine learning algorithms?How do you inspect missing data?How does caching work and how do you use it in Data Science? Statistics, Probability and Mathematics As we've already mentioned, Data Science builds its foundation on statistics and probability concepts. Having a strong foundation in statistics and probability concepts is a requirement for Data Science, and these topics are always brought up in data science interviews. Here is a list of statistics and probability questions that have been asked in actual Data Science interviews. How would you select a representative sample of search queries from 5 million queries?Discuss how to randomly select a sample from a product user population.What is the importance of Markov Chains in Data Science?How do you prove that males are on average taller than females by knowing just gender or height.What is the difference between Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP)?What does P-Value mean?Define Central Limit Theorem (CLT) and it's application?There are six marbles in a bag, one is white. You reach in the bag 100 times. After drawing a marble, it is placed back in the bag. What is the probability of drawing the white marble at least once?Explain Euclidean distance.Define variance.How will you cut a circular cake into eight equal pieces?What is the law of large numbers?How do you weigh nine marbles three times on a balance scale to select the heaviest one?You call three random friends who live in Seattle and ask each independently if it's raining. Each of your friends has a 2/3 chance of telling you the truth and a 1/3 chance of lying. All three say "yes". What's the probability it's actually raining? Explain a probability distribution that is not normal and how to apply that?You have two dice. What is the probability of getting at least one four? Also find out the probability of getting at least one four if you have n dice.Draw the curve log(x+10) Programming When you appear for a data science interview your interviewers are not expecting you to come up with a highly efficient code that takes the lowest resources on computer hardware and executes it quickly. However, they do expect you to be able to use R, Python, or SQL programming languages so that you can access the data sources and at least build prototypes for solutions. You should expect a few programming/coding questions in your data science interviews. You interviewer might want you to write a short piece of code on a whiteboard to assess how comfortable you are with coding, as well as get a feel for how many lines of codes you typically write in a given week.  Here are some programming and coding questions that companies like Amazon, Google, and Microsoft have asked in their Data Science interviews.  Write a function to check whether a particular word is a palindrome or not.Write a program to generate Fibonacci sequence.Explain about string parsing in R languageWrite a sorting algorithm for a numerical dataset in Python.Coding test: moving average Input 10, 20, 30, 10, ... Output: 10, 15, 20, 17.5, ...Write a Python code to return the count of words in a stringHow do you find percentile? Write the code for itWhat is the difference between - (i) Stack and Queue and (ii) Linked list and Array? Structured Query Language (SQL) Real-world data is stored in databases and it ‘travels’ via queries. If there's one language a Data Science professional must know, it's SQL - or “Structured Query Language”. SQL is widely used across all job roles in Data Science and is often a ‘deal-breaker’. SQL questions are placed early on in the hiring process and used for screening. Here are some SQL questions that top companies have asked in their Data Science interviews.  How would you handle NULLs when querying a data set?How will you explain JOIN function in SQL in the simplest possible way?Select all customers who purchased at least two items on two separate days from Amazon.What is the difference between DDL, DML, and DCL?96. Why is Database Normalisation Important?What is the difference between clustered and non-clustered index? Situational/Behavioural Questions Capabilities don’t necessarily guarantee performance. It's for this reason employers ask you situational or behavioural questions in order to assess how you would perform in a given situation. In some cases, a situational or behavioural question would force you to reflect on how you behaved and performed in a past situation. A situational question can help interviewers in assessing your role in a project you might have included in your resume, can reveal whether or not you're a team player, or how you deal with pressure and failure. Situational questions are no less important than any of the technical questions, and it will always help to do some homework beforehand. Recall your experience and be prepared!  Here are some situational/behavioural questions that large tech companies typically ask:    What was the most challenging project you have worked on so far? Can you explain your learning outcomes?According to your judgement, does Data Science differ from Machine Learning?If you're faced with Selection Bias, how will you avoid it?How would you describe Data Science to a Business Executive? If you're looking for new Data Science role, you can find our latest opportunities here.  This article was written by Tooba Mukhtar and Rahim Rasool for Data Science Jojo. It has been republished with permission. You can view the original article, which includes answers to the above questions here. 

MeasureCamp Berlin

MeasureCamp Berlin: A Preview

In preparation for this year's MeasureCamp Berlin, we sat down with Benjamin Bock, communications lead, to discuss what to expect, as well as his thoughts on the industry in general. Here's what he had to say: Can you explain MeasureCamp for people who haven’t been yet? MeasureCamp is an open, free-to-attend analytics 'un-conference' made by analytics professionals for analytics professionals (and everyone who wants to get there) around the globe. In that sense, it’s different to any conference you know of. Our schedule is created on the day of the event, and our speakers are fellow attendees. Listen to talks, give a talk, and discuss topics that really tickle your fancy. What can we expect at MeasureCamp Berlin this year? Let’s begin with what you can’t and never will expect at MeasureCamp Berlin: Sales pitch presentations. We’ve all been there… you are visiting a fancy, expensive conference and all you get is Heads of 'This n’ That' talking about what their team did, what they spent money on and that you should buy Product X to be as Data-driven as them (mind the cynicism). At MeasureCamp you can expect talks and discussion rounds by around 150 fellow experts, who all know the daily adventures of cleaning Data, setting up analytics or debugging tracking code or running mind-bending analysis first hand.  What is your best tip for someone that has never been at MeasureCamp before? Don’t rush it! MeasureCamp is about mingling with the analytics community as much as it is about the talks and discussion rounds. Pick a few talks that really interest you and use the rest of the day to get to know other attendees. Our awesome sponsors are also more than happy to talk to you. What is the best advice you got last year at MeasureCamp? On a personal level, I was able to get some really good advice when it came to data privacy topics. GDPR was still fairly fresh and nobody really knew if what they had done was actually enough to not get into trouble. That’s the kind of advice you only get if you have the chance to talk to other professionals face to face. On another note, what are the most sought-after skills and technologies currently used? I can only speak of my experience here. On a hard skill level and depending on the individual role, you need a solid understanding of web technologies (JavaScript, HTML, CSS) and tag managing systems to be able to implement tracking (plus some knowledge in mobile development when your focus lies on apps). When it comes to analysing and visualising Data, you should understand the tool you are working with and its underlying Data-structures. Being able to retrieve tool-agnostic Data with SQL and running more sophisticated calculations (e.g. with Python) has become more and more important over the last few years. But there are some softer skills, that should not be overlooked as well. As an analytics professional, you should never assume that your knowledge and language are common ground. You need to be a strong communicator, who is able to explain complicated concepts broken down to the absolute basics. In your opinion, what will be the biggest challenge in digital analytics in the next year? Two weeks ago, I would have answered “bringing web and app Data together”. Now that we know Google is working on that topic, it’s still a challenge, but one I am happy to tackle in the coming year. Digital Analytics is constantly changing. What do you expect to be the most talked about topic at MeasureCamp this year? As a Tracking Specialist with a focus on Google products, I’d love to hear some talks about Google Tag Manager Custom Templates. But my top guess is, that the newly released Apps and Web properties beta for Google Analytics will be the talk of the hour. MeasureCamp Berlin is an open and free-to-attend 'un-conference', taking place this year on the 28th of September. The final batch of tickets will be released on the 21st of August at 03:00 PM (CEST). Click here for more information and to get hold of your place. 

Where Tech Meets Tradition

Where Tech Meets Tradition

If you’re lamenting the decline of handmade traditional products, cast your cares aside. There’s a new Sheriff in town and its name is, Tech. Just a generation ago, children would leave the farm or the family business, go to school, and then move on to make their place in the world doing their own thing. Away from family.  Today, the landscape has changed and those who have left are coming home. But this time, they’re bringing technology with them to help make things more efficient and more productive. Is Tech-Assisted Still Handmade? In a word, yes. Artists still make things “from scratch”, except now technologies allow them to not only see their vision in real-time, but their customers, too. Have you ever wondered what the image in your head might look like on paper or in metal? What about the design of prosthetic arms and healthcare devices by 3D printers? You’re still designing, creating.  But just like any new technology, there’s still a learning curve. Even for cutting-edge craftspeople who find that sometimes, the line between craftsmanship and high-tech creativity may be a bit of a blur. Not to mention the expense for either the equipment required or being able to offer art using traditional tools at technology-assisted prices. Somewhere between the two, there is a trade-off. It’s up to the individual to determine where and what that trade-off is. Life in the Creative Economy One of Banksy’s paintings shredded itself upon purchase at an auction recently. AI is making music and writing books. Augmented Reality, Virtual Reality, and Blockchain all have their place in the creative economy from immersive entertainment to efficient manufacturing processes. Each of these touches the way we live now. In a joint study between McKinsey and the World Economic Forum, 'Creative Disruption: The impact of emerging technologies on the creative economy', the organisations broke down the various technologies used in the creative economy and how they’re driving change. For example: AI is being used to distill user preferences when it comes to curating movies and music. The Associated Press has used AI to free up reporters’ time and the Washington Post has created a tool to help it generate up to 70 articles a month, many stories of which they wouldn’t have otherwise dedicated staff.Machine Learning has begun to create original content. Virtual Reality and Augmented Reality have come together as a new medium to help move people to get up, get active, and go play whether it’s a stroll through a virtual art gallery or watching your children play at the playground.  Where else might immersive media play out? Content today could help tell humanitarian stories or offer work-place diversity training. But back to the artisan handicrafts.  Artistry with technology Whilst publishing firms may be looking to use AI to redefine the creative economy, they are not alone. Other artists utilising these technologies include:  SculptorsDigital artistsPaintersJewellery makersBourbon distillers America’s oldest distiller has gotten on the technology bandwagon and while there is no rushing good Bourbon, but you can manage the process more efficiently. They’ve even taken things a step further and have created an app for aficionados to follow along in the process. Talk about crafted and curated for individual tastes and transparency. It may seem almost self-explanatory to note how other artisans are using technology. But what about distilleries? What are they doing? They’re creating efficiency by: Adding IoT sensors for Data Analytics collection Adding RFID tags to their barrels Creating experimental ageing warehouses (AR, anyone?) to refine their craft. Don’t worry, though. These changes won’t affect the spirit itself. After all, according to Mr. Wheatley, Master Distiller, “There’s no way to cheat mother nature or father time.” Ultimately, the idea is to not only understand the history behind the process, but to make it more efficient and repeatable. A way to preserve the processes of the past while using the advances of the present with an eye to the future. If you’re interested in using Data & Analytics to drive creativity, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expect consultants to find out more. 

How Will New Financial Risk Regulations Affect European Banks?

How Will New Financial Risk Regulations Affect European Banks?

The financial crisis of 2007-2008 changed banking. The world moved from taking mortgage loans in our dogs’ names to introducing strict regulations for banks prohibiting them from giving out loans to “anyone” without assessing Risk properly. In 2010 the Basel Committee on Banking Supervision (BCBS) introduced BASEL III, a regulatory framework that builds on BASEL I, and BASEL II. This framework changed how banks and financial institutions asses risk. It introduced an Advanced Internal Rate Based Approach (Commonly known as the AIRB approach).  Now, the committee has introduced new changes and, by 2022, all banks and institutions will have to implement the revised IRB Framework, as well as new revised regulations for the standardised approach, CVA Framework and new frameworks for Operational Risk and Market Risk. So, what does this mean for those working Risk? Change Is Coming Change is inevitable, no matter what you do. If you work in Risk Management and Compliance, change is something you can expect to happen, often. As mentioned above, by 2022 there will be lots of changes. The Basel Committee calls this initiative the “finalised reforms”, or BASEL IV which builds on the current regulatory framework BASEL III. Quickly summarised, the changes limit the reduction in capital that effect banks IRB models.  This change is predicted to impact banks in Sweden and Denmark the most, with estimations that capital ratio will fall by 2.5-3%, far higher than the 0.9% expected for the average European bank.  So what does all this mean for Swedish and Danish banks?  What’s Happening Now? One of the main things that Swedish and Danish banks need to revise for these new regulations, are their internal models. The new regulations introduced a new definition of Probability of Default, measured through a model commonly known as a PD model. Effectively this means that every bank must “re-develop” their internal PD Models in the IRB approach. Consequently, we are already seeing a clear response from the banks in their strategies moving forward. It has already become quite apparent that many banks are looking to make IRB model development their focus for 2019-2020 and 2021. This has resulted in a boom in the hiring space for developers with experience in IRB Modelling and Credit Risk Modelling in general, which in turn has led to high demand in the face of the low supply of these types of candidates. Understandably aware of this, modellers are now looking to negotiate higher salaries.  What You Can Do  For candidates that hold the right experience, there are good opportunities at hand. If so inclined, they can utilise this chance to finally see if the grass actually is greener on the other side, or not. However, there are a couple of things worth considering before making a move.   Firstly, are you actually keen on switching jobs? Your skills are probably equally in demand at your current employer and, if you are having doubts about moving from the get-go, you may well be able to negotiate a rise without pursuing a new opportunity. However, if you are serious about finding something new, this is a great time to do so. The majority of banks have found that these new regulations are creating an unsustainable workload,  and are now looking for talent externally to expand their teams. This means that the experienced modeller can pretty much have their pick of the litter.  Furthermore, if you are a junior modeller, there are now plenty of opportunities for you to enter a niche area known for being exciting and innovative. So, wherever you are in your career, these regulatory changes  are likely to have a large impact and open up new avenues for you to explore.   We all know that regulations in banking and finance are now essential, we all agree, even if they can be a little frustrating. However, what people often fail to think of are the opportunities new regulatory requirements create. In the case of BASEL IV, we’re already seeing an increase in demand for strong talent, and a demand for people who are passionate about Risk Management and model development.  For businesses, new regulations also provide the chance to not only improve their teams, but to  create new models that can be utilised to optimise and automate. A lot of financial institutions are already aware of this and are using these models to gain competitive advantage over their competitors, as well as to stay one hundred percent compliant.  If you’re looking to build out you Risk Management team or take on a new Risk opportunity for yourself, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

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