Extending analytics to the web: moving from in-store to online

In a time when we all struggle to have more time to ourselves, having the option of doing your weekly shopping without the need to travel to a store, is priceless. However, many retailers are not using analytics effectively to enhance the shopper’s experience throughout their online channels; missing a great opportunity that could enable them to provide a more personalised service. In fact, through the use of their martech (marketing technology) solutions, traditional retail outlets can enhance their online store conversion rates by taking into account some useful insights:


Personalising the experience

When customers are visiting a shop, retailers can always count on their shopping assistants to help buyers through their journey: from finding a particular brand or product in an aisle to answering any questions they might have.

At home, this experience can be lost online, so what can retailers do to differentiate themselves from others and make the customer shopping experience enjoyable?

Personalising the experience as much as possible can be the key to achieve this. Identifying new customers vs existing ones can help online retailers to tailor their services in the most effective way. For example, in the case of a returning customer, data can be tracked in order to know what their preferences are when they buy and show particular recommendations when revisiting the site based on data from their last visit. A new customer would require a different experience, as initially there is not enough data from them to be tracked, so offering a quick guide through the website or asking a few questions could help the retailer discover what the customer is looking for.


Staying relevant throughout the entire customer journey

It’s important to ensure your customer doesn’t lose interest throughout their buying journey. Don’t just entice them into your site, make sure you keep them. Do you track analytics for where customers most often leave the site? Analysing this type of information can help you to rearrange parts of the website so that the customer journey is improved. For example, before they check out, what can you do to increase their average spend value by offering add-ons to match with their product selection.

Additionally, how do you currently handle abandoned cart customers? Through the use of analytics, you can check when a customer has decided to leave your site: after tracking the particular selection of products they last viewed, you may be able to contact customers to remind them that they haven’t completed their last purchase, or send them an offer to encourage them to return and buy.

Tracking all data

Retailers are often not gathering enough information about their customers during the buyer journey. Moreover, knowing how a customer came to your website in the first place is key when deciding where to invest your energy and marketing budget, and tracking data more closely will help you with this.

Are your customers coming to you directly? Are they visiting you because they were looking for a particular product or location, or are they coming from Google? Is your AdWords budget a good investment? Or are customers coming to you because their favourite blogger has recommended your product? Learning which sources have the highest conversion rates will help you to tailor your marketing strategy, attract more customers in the future and ultimately enhance your service and the buying journey.

Analysing all that data could enable retailers to pre-empt customer demand; for example, depending on your customer’s buying patterns (when they usually buy, their previous requested delivery times and dates), specific delivery slots could be directly suggested to them without them having to go through all the available slots. Or, your site could send out timely reminders about certain products they regularly buy to encourage a follow-up purchase.


In short, more sophisticated e-commerce data analytics collection is a way for retailers to increase sales through gaining a better awareness of your customer. The sooner you fully incorporate them into your martech strategy and use that data to make informed commercial decisions, the sooner you will be able to increase your sales whilst providing the best service to your customer; creating a win-win situation for both of you.





(1)    https://blog.kissmetrics.com/the-8-most-important-conversion-metrics-you-should-be-tracking/

(2)    http://www.cmo.com.au/article/612861/gartner-how-cmos-will-spend-more-technology-than-cios-2017/



Are shipping fees costing you customers?

Online retailers are becoming incredibly sophisticated in their ability to impact conversion rates based on hundreds of different variables.  New technologies allow purchase screens to be customised based on user data (i.e. where a customer is purchasing from, time on site, location, previously browsed items, etc.), meaning that e-retailers can be super-specific in their targeting to increase conversion rates.

There’s already been a lot of work done to get the customer to the stage where they are ready to click “add to basket”.  You’ve attracted customers to your site, you’ve encouraged them to commit to a product, and it’s in their virtual basket – ready to be purchased.

So what is stopping customers from moving forward to complete the purchase?

It’s all comes down to one decision – to buy or not to buy.  Psychology sits at the heart of these decisions, and understanding the reason for your abandoned cart rates can help you to break down the psychological barriers that are stopping customers moving forward.

In a report from UPS (1), the number one reason for abandoned carts globally was that shipping costs increased the overall purchase price more than expected.  If we think of the psychological process at play here: customers firstly decide on a product, are happy with its price and features, and then add the product to their basket.  The customer is mentally calculating the overall price as they add their different products to their basket.  They then move forward to complete their order and shipping costs get added.  The price is more than they were expecting.  Or maybe it is the price they expected but mentally it pushes the overall basket value above their desired purchase price.

And this is enough for customers to abandon their cart and leave their items.

One factor that could be causing the high rates of abandoned carts is that customers are testing the shipping costs and how they will be applied to their purchase.  The cart isn’t really abandoned as it was never a serious purchase – it was an elaborate calculator to help gauge the shipping costs.

Even removing this factor, the addition of shipping costs is still a blocker for customers moving ahead with their purchase.  So what can you do to step in and impact the psychology of your buyer so the sale has a higher chance of moving ahead successfully?

How could retailers address this issue earlier?

Is it an option to try and put this (even potentially unattractive) shipping time and cost information upfront so that customers are not surprised when they see shipping costs added?  Could shipping costs be added to the basket subtotal when any items are added so that the increased cost is not a late addition to the purchase journey?

The other option is offering free shipping and removing shipping costs altogether.  This could put a strain on profit margins, but it might be a worthwhile activity to calculate the cost of abandoned carts to the business – i.e. what revenues would you have achieved if even just 10% of those customers had completed their purchased, and compare that to the cost of offering free shipping costs to all customers.

If this is a psychological blocker that is preventing customers from buying then increasing product costs ever so slightly across the board to account for the cost of offering free shipping could mean the difference between high rates of abandoned carts and winning lots of new customers.

In Europe, 49% of consumers abandoned their cart because shipping costs were too high (which is actually lower than the averages in the US at 54% and Canada at 61%).  Sometimes this is because orders haven’t been large enough to result in free shipping qualification.

Is the problem actually worse than we expect?

However, some reports show even higher numbers of abandoned carts due to shipping costs.  A report highlighted in eMarketer by FuturePay (2) said that 86% of surveyed respondents said that the cost of shipping resulted in cart abandonment, so it’s clearly a problem for retailers to get right.

Amazon has tried to combat this barrier through its free shipping option for Prime members.  A report by cg42 (3) found that 91% of Amazon Prime members said they signed up for the service based on the lure of free 2-day shipping.

What appears to be a common theme amongst many of the reports and surveys is that better communication about delivery options and associated costs in order set more realistic expectations earlier on.

A report by Meta Pack (4) found that 66% of consumers would move to another brand if there were more attractive delivery options available – so shipping costs and delivery times are clearly at the top of the agenda for customers when deciding whether to complete a purchase.

An article from the Royal Mail highlights an interesting point from a Deloitte report (5), saying that retailers will need to move quickly to better respond to consumers’ expectations – with same day delivery becoming more standard in our online shopping experiences. Some reports are also suggesting many consumers are now expecting free same-day delivery which is obviously ahead of many retailers’ current offerings.

So with the huge impact that delivery costs have on customers’ decision making, combined with the availability of suitable delivery options, retailers will be looking at ways to make delivery option information more prominent on their sites to enable consumers to be updated earlier in the process.



(1)   https://www.ups.com/media/en/gb/ups_global_paper.pdf

(2)   https://www.emarketer.com/Article/Cart-Abandonment-Really-Come-Down-Cost/1015092

(3)   http://cg42.com/

(4)   http://www.metapack.com/report/delivering-consumer-choice-infographic/

(5)   http://www2.deloitte.com/au/en/pages/consumer-business/articles/global-powers-of-retailing.html

Virtual Reality: The Next Step for Business Intelligence

Everything is being virtual realit-ified – from product development through to buying clothes.  And it’s now being seen as the next step for Business Intelligence and Data Analytics.

Even simple data visualisation tools are now enabling businesses to make sense of their data – which often runs to thousands of lines of spreadsheet data.  It’s impossible to get a handle on trends and patterns when data is represented in rows, and too big to fit on one screen.

But when turned into graphical representations where trends can be understood in seconds and insights gained in moments, the value of data visualisation becomes clear.

So taking that to the next level and creating a more ‘immersive’ experience is the natural next step.

Plans are underway within data analytics organisations to create applications that enable Data Scientists to literally step inside their data.  They will be able to see the data represented visually in 3D; they can walk around it, stand under it, move it and see it from lots of different angles to better understand what is happening.  They can layer over other data points – over and over to see correlations between information; which is usually limited in a 2D data visualisation tool where it soon gets messy and complex to understand once multiple different data points are overlaid.

Product development departments are already starting to look at how virtual reality can be integrated into their own data workflows, to reduce time spent developing and be able to interact with physical products without the cost or time of developing models first to spot errors.  This offers a great saving opportunity, but also the possibility to explore many ‘what-if’ scenarios that might be too costly to explore with physical products.


The world of data is changing

As many businesses now employ Data Scientists, it doesn’t make sense to use the same old tools to do different jobs.  Virtual Reality will provide a new way of visualising and engaging with data.  If we can uncover insights quickly from just seeing a pictorial representation of data, just think what’s possible when you take that into a 3D environment where you can ‘walk through’ information.

The Wall Street Journal has a good example of a Virtual Reality-esque visualisation of the stock market history: http://graphics.wsj.com/3d-nasdaq/.  This helps to give a bit of a flavour of what could be to come – imagine if you could stop on your journey and delve into the detail of a particular year, see a video of reporting at the time in the background while in front of you is a number of visual graphs showing different info that you can move around effortlessly, layering up to gain insights.

It’s a more intuitive and immersive form of data analytics – and I can see how stock markets would benefit from having all of their data in a virtual reality environment that traders use instead of desktop screens.  Or perhaps instead of wall mounted monitors showing tech support graphs, IT technicians will be wearing VR headsets – analysing server capacity and alerts in real-time with virtual reality versions of their remote customer datacentres in front of them to help resolve issues – even when they’re not there in person.

It also offers the opportunity to be more collaborative when analysing and reviewing data analytics – imagine being able to walk your entire Board of Directors through a virtual reality data visualisation of your financial performance to date.  Or perhaps you offer customers the opportunity to understand the data results from their recent Quarterly Business Review with your company via virtual reality headsets instead of emailing over a quick report they probably won’t even open.  Some companies are even developing virtual reality chat assistants to bring an in-person experience to online engagements.

Underneath it all, the aim is to get from complex lines of data through to actionable insights sooner.  And virtual reality certainly looks like it has the power to transform how we engage with data.








Why Rio 2016 is both a problem and an opportunity for your data project

The data visualisations that we all see online and on social media are making us more open to using this kind of information at work.

Our daily lives are now drenched in data, delivered to us from our televisions, our computers, and the smartphones in our hands. Charts and tables are commonplace, but the online world and social media have also made infographics a powerful tool for the presentation of this information, especially when coupled with images, animations, video clips and written commentary. This data can come from a huge number and variety of sources, brought in from places all around the world.

A perfect example of this is what we have seen in the coverage of the Rio Olympics. Online news providers have taken great leaps of the imagination in how data can be delivered to us, harnessing the tools at their disposal. The Guardian is a prime example, with their coverage of Great Britain’s cycling success over Australia that saw Sir Bradley Wiggins win his fifth Olympic gold medal (1). The newspaper created an animated version of the race that showed what happened every second of the way, with the reader being able to click through each stage at their own pace.

Not to be left trailing in second place, The New York Times has drawn up an extensive set of data visualisations that shows exactly how well each country has done at each Olympics since the games began (2). The graphics are a brilliant mixture of aesthetics and information, delivering a huge amount of complicated data at a glance.

These high-tech ways of accessing data are becoming everyday experiences for many people, but how does this affect businesses beyond the mass media outlets, and should companies strive to access and make use of these new tools?

There is a danger that if some data analytics projects are at a fairly embryonic stage they could seem outdated by the time they’re implemented. After all, with online trends changing day-by-day, what seemed like a great idea just a few months ago could be old-fashioned by now.

This poses problems for business who are then pushed to keep up with the latest innovations but don’t want to shake-up their operations. However, there is a good chance that your staff are using better, newer technology at home than they have access to when at work.

There are echoes of the BYOD (Bring Your Own Device) phenomenon, where the smartphones that people were buying with their own money were far more advanced than the ones they were being given by their employers. BYOD was a clever way of working around this without companies having to regularly shell out for new phones.

Now your staff will be using the data analytics power of social media such as Twitter and LinkedIn in their personal lives, along with gathering data on themselves with mobile apps such as Run Keeper. It is commonplace to have data at our fingertips, and people will be happy to use equivalent tools at work.

It would be very easy for anyone in the position of running a company that is making use of data visualisations to look at the sort of tools that are being used elsewhere and become despondent at what they have at their disposal. Adopting new technologies can be expensive, and many employers could worry that the changes this can bring to a workplace could have a negative effect.

But what needs to be remembered is that any new data technology that is brought in will probably not be unfamiliar to your colleagues, and may be something they are already using in their everyday lives. With so much technology, and so much data, now available to each and every one of us, that new piece of software that you’re apprehensive about buying may not have the disruptive effect on your team’s way of working that you think, and could give a massive boost to your profit margins.

What’s also very important to remember is that data visualisations are only the endgame of a very long process, one that begins with gathering good quality data itself. While new tools designed to present this data are emerging all the time, the basic foundation that they build upon is information. And if you’re looking at new ways of visualising data then you probably have a good bedrock of this information at your disposal already.

There’s an opportunity here, a massive one, that could see your company pushing itself to the forefront of the way in which data is presented and getting everyone involved in its use, not just data scientists and your IT team.

What’s needed to make the most of this is the realisation that the apps, websites and social media that your staff are using on a daily basis are indicative of a wider acceptance among them of how data now works in our world, and how it touches every aspect of our lives. People are now comfortable with digesting huge amounts of information, and even expect it to be delivered to them. If they do this in their own time, they’ll have no trouble doing it at work.



Image provided courtesy of Ian Burt

Are You on the Data Offensive or Defence?

Understanding the different types of data positions – data offensive or data defence.


Companies are either on the data offensive or data defence – and organisations need to move to being on the offensive to actively take hold of data and make tangible use of it.

There is a huge amount of data that any company will gather over time. This can be deliberate, and be something that you have set out to obtain, or it can be something that simply gathers as a result of the IT systems that we all use.

There are two ways a business can approach this data, and it’s a choice between a position of defence or offensive. One could hold you back, but the other is much more positive, allowing you to push your company into new areas and target your approach so that you achieve exactly what you need to.


The defensive approach

Data defence is the traditional approach to managing the information that your company holds. It’s all the regular things that have to be done with large amounts of data, such as maintaining security to make sure none of it leaks or is compromised. It’s the governance of data, the everyday handling of it and the processes around it.

This also includes ensuring privacy and making sure that the quality of the data is up to scratch. These are certainly things that have to be done with data that is gained in a commercial context, and many of them are done to make sure that your business falls in line with whichever set of regulations you have to adhere to.  It’s a case of preventing data from becoming a problem – rather than seeing it as a valuable asset.

This is the approach that many companies take towards data, and the one that can seem to be sensible and correct. That is, until you look further than the data defence attitude and closer at what could otherwise be done. There are opportunities to take the data that you have and use it to push your business on to the next step.


Go on the offensive

Being on the data offensive is about taking the wealth of information that you have at your disposal and exploring the possibilities of what it can do for your business in a proactive sense. Whereas data defence is about making sure that everything is in order, data offensive sees you pushing the boundaries and creating new opportunities.

The data that you have at your disposal can open doors for your business that were closed before. This information can support marketing and help to target outbound campaigns, making sure you are reaching the right people in the right way. In turn, this helps to build new revenue, all of which can lead to further data being gathered as time goes on.

Data management can be at the forefront of your company’s strategy rather than being something that simply has to be done. In the modern, digital world the companies that are using data well are those that are harnessing its power and using it to change their behaviour and the way they work. Data is driving their behaviour and they are allowing it to take the lead rather than letting their existing behaviour govern the way data is collected and protected.


A light in the dark

There is another kind of data out there that might not seem so full of opportunity until it is put under the microscope and given a closer look.

Dark data, as it is known, is the information that tends to be ignored by businesses and just builds up in the background over time. This could be server logs, data about old employees, and outdated login information, for example. In his book Dark Data: A Business Definition, Isaac Sacolik describes it as “data that is kept ‘just in case’ but hasn’t (so far) found a proper usage.” (1)

Much of this data will be seen as having little or no value to your firm, and simply something that is given the minimum amount of attention to make sure it is secure and stored correctly. But harnessing this data can be a big step in the process of moving towards data offensive and taking your company forward.

Any business that finds itself in possession of a significant amount of dark data needs to look at how to harness the opportunities that it can create, and how to capitalise on that information and turn it into something proactive rather than letting it impact your business’ resources.

While dark data can be turned to good use and create opportunities, the failure to do this could pose a risk to your company. Instead of letting it become a burden on your business, why not turn dark data into something positive?

Most companies are currently stuck in the data defence approach, but there are new solutions to this problem that can put you on the offensive. Dark data could be the key to where you go next, helping you to explore new avenues that you hadn’t thought of before. This approach will become even more effective as data analytics tools become standardised and the ability to pull information from the unlikeliest of sources increases through technology such as IoT sensors.

There is a wealth of information that any company builds up over time, and the choices are either to let it become a drain on what you do or harness the power that it can give you and allow it to take you forward.





Image provided courtesy of KamiPhuc


What’s next for the Internet of Things?

Some of our followers tell us from time to time that they’re too busy to read a full blog sometimes – and hey, we understand that!

Which is why we have created a simple slidedeck of the latest developments in the Internet of Things industry – from 5g to cloud and device integration. We bet it takes you 30 – 45 seconds to look through. All the info, a fraction of the time.

What do you think?

Big Data on the High-street

Improving retail customer experience through analytics


Retail has changed

The internet has had a massive effect on high-street retail. With customers able to order practically anything they desire online, often with next or even same day delivery, there is an increasing worry that physical retailers are starting to become a thing of the past. To counter this high-street retail needs to evolve. Gone are the days that retail can be seen as a simple exchange of money for goods or services. Customers expect a fuller “shopping experience”; one that offers something extra over simply using an online store. These experiences need to be developed in an effective and intelligent manner, which data analytics can provide.  Customer experiences can now be improved, altered and measured through big data.

Big data has been a hugely successful tool for online retailers and services, such as Amazon and Uber, and there’s no reason it can’t be implemented for high-street retailers as well, to improve their services and better align their resources.

What can Big Data do for retailers?

Big data can serve a variety of roles in retail, both in developing new strategies or even updating some classic marketing tools. Coupons and sales have long been a staple of a retail marketer’s arsenal, and have been shown to have a measureable effect in getting people into a store. But it can be difficult to measure the actual effectiveness of these discounts in generating additional revenue once the customer is in store. By analysing historical data, analysts can create models of what could have happened if the discount scheme was never introduced and run these models side by side with real-time analytics.  What about if a coupon had been released on a sunny day, in mid-June compared to what would happen if no coupon had been released on a week day in February when it was raining?  Would a coupon deliver better results depending on the weather, football fixtures, location or recent news stories?

Retailers can now weigh up the gains of the new purchases against the loss of revenue produced by the discount. Businesses can then adjust their discount strategies in an effective manner, ensuring that they are used to their maximum benefit.

Analytics can also be used as a form of crowd sourcing. Rather than relying on focus groups or anecdotal reports, retailers can identify in real time the marketing language, displays, and pricing that is most attractive to both current and potential customers, and adapt their offerings accordingly. Furthermore it can be used to uncover the key issues that might cause a loss in customer loyalty, which is essential to any successful business. It is well documented that the cost of acquiring a new customer is up to six or seven times more expensive than retaining a current customer, so anything that can aid in retaining customers will be a massive advantage to any retailer.
The Big Picture

On a larger scale, data acquired at the Point of Sale (POS) can be used to develop national or regional strategies for retailers. Identifying top selling stores and the demographics that purchase from there can allow retailers to effectively target the right customers. It can even go beyond this: the physical positioning of these stores can be analysed to best identify where a store should be located for maximum gains i.e. Are the stores freestanding or outlets in shopping centres? How close are they to competitors? How are the stores laid out to create an easy shopping experience?

Historical data can be used to develop forecasts for order quantities at every level, from national forecasts right down to at an individual level. For example, in the past a clothing retailer might have seen an increase in swimsuit purchases in the summer months on a national level. Rather than simply increasing the quantity of swimsuits available in every store across the country, appropriately analysed data allows them to see which individual stores are reporting the highest sales increases and adjust their orders appropriately, allowing for more intelligent and efficient management of stock.
How to get the data

An issue facing physical retailers is simply acquiring the data needed in the first place. Perhaps the simplest way is just to follow online retail’s example. Most online retailers require creating an account to shop with them, which instantly provides them with customer data. Similarly, an in-store loyalty scheme (with incentives to attract customer engagement) can allow retailers to acquire data on an essential core of loyal customers. Several retailers have even began offering digital receipts, sent to a customer’s email at the physical checkout, allowing for easy acquisition of customer data.

Perhaps the most important strategy is offering an “omnichannel” retail experience. Customers in the 21st century like to do their research online now before purchase, even if they eventually end up going into a physical store. This can take a variety of forms, from providing instore Wi-Fi that requires customers to log-in with email and details, to a fully furnished app developed to enhance the in-store experience, but also acts as a way to acquire customer data for the retailer.

Finding the right tools

However this is all reliant on retailers being adaptable and intelligent over how they use data. Recent studies by eCommera found that 23% of UK retailers can’t make sense of data in order to make appropriate business decisions. 50% of retailers believe that they don’t have the correct intelligence and analytics tools to suit their needs, and only 16% have confidence in their data analytics to provide organizational insights. This just shows that retailers need to do their research to find the right tools for their needs.

Adapt and thrive

As customers become more and more tech savvy, retailers need to evolve too. With such a vast amount of competition out there, both from physical and online rivals, the high-street retailers that are going to not only survive, but also thrive, will be the ones that acquire, analyse, and utilize Big Data in intelligent and innovative ways to further the customer experience.



Business Solutions Monthly Big Data Analytics: Real World Benefits for Retailers

Computer Weekly Are retailers using data analytics to their advantage?

WWD POS Data: Retail’s Biggest, ‘Untapped’ Opportunity

10 Weird and Wonderful ways IBM Watson is entering our everyday lives

We produce data all the time, both in our personal and private lives. IBM’s analytics engine, IBM Watson, is set to transform how we process that data in our everyday lives – from how we shop, to how we take care of our pets.


What is IBM Watson?

IBM Watson is a data analysis tech platform that uses advanced algorithms, natural language processing, and machine learning to analyse unstructured data incredibly quickly, allowing the platform to learn. With 80% of all data being unstructured, any program that can quickly make sense of this can have interesting applications for many industries. In fact, it’s being used for some very “out there” ideas that you might not expect.


What can IBM Watson do?

  1. It can fight crime.

Eight universities across the US are running a year long research study to develop “Watson for Cybersecurity”. By feeding Watson computer security reports and data, the cloud based system will be able to learn how to identify threats and how best to counter them, with the hope of developing a truly responsive counter to cybercrime. TechRepublic


  1. It can help you keep you healthy

Alder Hey Children’s Hospital, UK, has partnered with IBM to develop the “cognitive hospital”. Along with helping doctors make medical decisions, Watson processes parent and patient feedback to identify problems that might cause patients undue discomfort, allowing for both pre-emptive and on-demand responses, tailored specifically to patients’ needs. CNET

There is also a range of lifestyle apps, such as Welltok’s employee app: CaféWell Concierge, which provides users with tailored healthcare advice. Welltok

On a bigger scale, Watson is being put to use countering viral outbreaks, such as the Zika Virus, in using data analysis to help develop vaccines, by monitoring clinical trials, identifying suitable test candidates, and even actively measuring the effectiveness of vaccines. techradar


  1. It can also help keep your pets healthy

Watson is also the basis for LIfeLearn’s Sofie app, a diagnosis and query tool for veterinarians, allowing them to quickly diagnose and identify treatments for their animal patients, who obviously can’t verbalise their ailments. Simply asking a question to the app allows it to analyse huge amounts of relevant data to craft the best estimate of what’s wrong with the animal. LifeLearn


  1. It can pick your outfit

Fashion Brand, Marchesa, used Watson-based analysis tools to design a dress for the 2016 Met Gala, in a process they described as “cognitive creation”, to create a “cognitive dress”. Using a colour palette and fabric selection based on Watson’s suggestions, they even incorporated cognitive processing into the weaving, as LED lights were threaded through the dress which responded to social media traffic about the dress, all analysed and processed by Watson tech. IBM


Watson tech is also being used in fashion lifestyle apps, such as The North Face’s personal shopper app, which customizes clothing suggestions purchases based on customers’ needs and preferences, even informing users what clothing might be appropriate for certain occasions. Economic Times


  1. It can run a hotel

In the Hilton hotel in Mclean, Virginia, USA, there is a new member of staff: a robot concierge named Connie. It’s able to answer a variety of questions on hotel services, offer travel advice, and even give tips on local attractions and dining, all thanks to a variety of IBM Watson based platforms. Information Week



  1. It can teach kids

The Sesame Workshop, the non-profit organization that underpins the kid’s TV show, Sesame Street, has partnered with IBM to develop personalized learning applications for pre-schoolers. Constructed around a “cognitive tutor” system, the applications will be able to develop the best and most effective means to create interactive learning experiences, based not only on an individual child’s responses, but based on data analysed from thousands of others. The aim is to develop software that actively monitors a child’s progress and development, and craft teaching tools that are tailored to their specific needs. Forbes


  1. It can also teach adults

Professor Ashok Goel of the Georgia Institute of Technology recently introduced a new Teaching Assistant to his students. Jill Watson did all the normal TA work: updating students of work deadlines, answering queries through email correspondence, etc. But they never saw Jill Watson in the flesh, and for good reason, because Jill Watson was an AI developed by Georgia Tech researchers, based on IBM Watson technology. Apparently the project was good enough to fool all of Professor Goel’s students, which was the intention, as the project was introduced as a prank. However, since it was so successful, there could be a serious implementation of similar services very soon. The Verge


  1. It can pick presidents

Recently debuted at the TechCrunch Disrupt Hackathon, Watson Elections analyses speech content of the current US presidential candidates for mood and content, and then matches the candidate to the user. It’s still in very early stages of development, but the hope is to implement more tools, such as instant fact checking on speeches, or even projections of eventual victors. TechCrunch


  1. It can make you breakfast

IBM has partnered with Kellogg’s Bear Naked subsidiary to create custom granola. Users select a base ingredient for their granola, and Watson based analysis tools select ingredient suggestions that might go with it best. engadget


  1. It can win quiz shows

Jeopardy is one of the longest running and hardest quiz shows in America. In 2011, two of the greatest contestant ever were defeated, by IBM Watson technology. This doesn’t sound like too much of an achievement, as of course anyone, be they man or machine, with access to the internet would be expected to do better. But the structure and nuances of Jeopardy style questions (contestants are given the answers and must supply the questions), means that until now, it appeared to be beyond the means of computer intelligence systems. Watson won by a landslide. TechRepublic

This is just scratching the surface of what Watson, and data analysis as a whole can do currently. The potential is huge, and is only set to increase in the future.


Check out our mini infographic on IBM Watson uses.

IBM Watson Infographic C24

More Info



Image courtesy of IBM Espana

What does David Cameron have to do with Big Data?

The recent Panama Papers news stories wouldn’t have been possible without big data.  The time that it would have taken humans to sort, process and analyse a reported 11.5 million files, some of which were paper based, would have meant we would still be here next year waiting for juicy data about David Cameron’s family tax affairs.

Or maybe we would never have heard about these revelations, because it is one thing to read millions of documents, but it is an entirely other activity to review terabytes of data to uncover links, trends and relationships between many thousands of pieces of information.

Big data has revolutionised information discovery in situations like this – enabling the dissection of data in a matter of seconds, and the pulling of insights based on keyword searches – within a system that instantly links related information to start uncovering interesting relationships between individuals mentioned in the data.

The Panama Papers leak contained a reported 5 million emails, 3 million database files, 2 million PDFs, 1 million images and over 320,000 text files.  Some of these files are searchable, but many are PDFs or images that cannot be searched against.  Big data technology and complimentary tools have meant that documents can be processed with Optical Character Recognition where images containing written info can be ‘read’ and converted into text – making images searchable and open to analysis.  Additionally, many document management systems collect metadata associated with files and store it as searchable data – such as information around who saved the file, when it was saved, what the file location was (i.e. was it in a folder marked “The Camerons” 😉 ).

The data collected in the leak amounted to 2.6tb – with a date range of between 1977 to 2015.  It showed that Mossack Fonseca worked with over 14,000 banks, law firms and middlemen to deliver their services – now how would even a department of 100 analysts start to piece these 11 million files to 14,000 clients within a reasonable timeframe?

And how would that data be stored in a way that trends and relationships could be easily spotted?

It’s no wonder that in large scale investigations, (such as the notable Enron case), big data tools are the go-to solution to make sense of the data at hand.

And that is exactly what big data is all about – it’s about making sense of huge volumes of information; turning seamlessly unimportant reams of emails into a story, when linked together and layered against other contextual information.

The process employed by the International Consortium of Investigative Journalists (ICIJ) who handled the Panama Papers meant that all of the documents were inputted into the big data tool, extracting text information and file metadata from all documents.  The process took roughly two weeks from starting the information inputting activity to getting a database that was able to be queried and searched against.

Once the information was centralised and in a sensible format, it was then possible to perform keyword searches against the database, which is probably how key public figures have been uncovered as being involved in ‘unusual’ tax activities.

Without that ability of big data to look across millions of files, and pick out instances where a particular name was highlighted (whether that’s many times within a clearly marked document, or just a name buried deep within a 30 page report) – key insights that we have read about in the past few days charting the Cameron family’s money flow through these offshore tax havens would not have been possible, as data would not have been linked together so quickly.

Coming back to my earlier point about Enron, there is also now an Enron Corpus where over 600,000 emails generated by over 150 Enron employees are held within a publicly accessible database – which researchers and anyone with a keen interest in data analytics can get their hands on.

Maybe this is where the Panama Papers will end up once the news stories die down?









Image provided courtesy of Medill DC.

Does Big Data Suit Your Law Firm?

This is the third in a series of posts about our whitepaper titled Building a data driven professional services firm which is all about how to be data driven, not data wary.

In this post, we are going to look at whether big data is right for all types of law firms, but for the full detail – you’ll need to visit our website to access the whitepaper in full.

Data analytics is useful for all organisations, but it depends how you use it.

For high street firms, we see big data being an incredibly useful tool for managing a large list of transient customers, who might contract a service from you for a relatively short period of time (I.e. a property purchase) – then never engage with your company again.  How do you keep track of those customers?  How do you ensure you are the go-to firm when they come to sell that same house?  What about when they need a will writing?

Being able to capture this information within a CRM tool and analyse the data to see where you should target and which marketing methods work best means that

For B2B companies with large clients, analytics can be helpful in preparing individual reporting back to the clients – as the billing information will be vastly more complex for multi-year, multi-service contracts where fees are generated across a range of different departments.  Reporting helps these clients to stay on top of their legal partner’s fees, understand how their chosen firm is performing against KPIs and ensure that firms are explaining where their fees are being generated from.

For larger providers who operate at scale such as Alternative Business Structures (i.e. multinational insurance companies offering legal services), big data gives these organisations the ability to monitor their entire operations and make efficiency changes that could mean the difference between a 1% profit increase or decrease.

In our whitepaper, Martyn Wells, IT Director at UK leading law firm Wright Hassall, offers his views on how data analytics is changing service delivery in the legal sector.

Martyn highlights how smart interfacing will become more of a requirement as customers want to natively instruct their legal partners from inside their own ERP systems, or handle invoices through their own procurement tools without having to separately pick up the phone to instruct a lawyer.

This means that lawyers have to be able to integrate with these systems and be up to date with the technology being used by their clients.  And it means that data has to be able to flow through these different systems in a safe and secure manner.

Wells also cites that data will be useful for firms operating a fixed price model, as access to data tools will enable legal organisations to closely monitor fees and associated activity being carried out by lawyers to ensure profit margins remain at an appropriate level.

For the full whitepaper, and to read about how Wright Hassall use business analytics tools to create client dashboards for their customers, visit our website to download the report.