Who cares about sports and data? Not just athletes, coaches and fans. It turns out that many companies outside of sporting organisations are also associated with the sports industry. For example, financial services organisations are actively involved in sports sponsorships. Retailers sell fan merchandise. Telcos build social engagement strategies around […]
While men still outnumber women in the analytics field, there are plenty of opportunities available for women. At a recent Chief Data and Analytics forum, I was encouraged to see a well-balanced number of senior executives presenting about the business of analytics. Speakers included 12 women and 14 men, which indicates a […]
Recently, I was talking to a director of analytics from a large telecommunications company, and I asked her, “Do you think we have a skills shortage?” She replied, “NO, I think we’re just looking in the wrong place.” I wanted to hear more as this analytics expert may have just […]
When I walk into my local chicken shop I always feel that Mary, the owner, has roasted chickens, prepared salads and put on extra rice pudding for my family’s Friday night meal. Mary welcomes me with open arms, greets me and my son by first name and always has an honest and empathetic conversation. She knows exactly what my order will be and always has new food items on offer that I may like to try and buy next time. It’s the ultimate customer experience I have on a weekly basis. I always compare other shopping experiences to my local chicken shop.
So why can’t all my online and offline experiences be like Mary’s chicken shop?
In a world where data storage is getting bigger and cheaper, technology is faster and wiser and the Internet of Things (IoT) promises to make my life convenient and happier, organisations still struggle to get my first name right! Is this because they may be over complicating the whole customer experience hype? Are they too focused on getting the product right rather than listening to the customer's needs? Many debates can be had but, I believe that if we stick to the basics in a big wide world of “things”, then the ultimate customer experience will grow naturally.
Count down! My top four ways to make customer experience like visiting your local Chicken Shop:
4. Greet your customers and get their name RIGHT!
My telco provider still can’t get my name my right on their billing system, even with numerous attempts of me telling them to update it. It often makes me wonder whether they have their data quality system switched on. Personalisation with every communication channel is crucial for customer relationships and knowing that someone really does care about getting your name “right” makes you as the consumer take them more seriously when doing business.
Unfortunately, I have seen organisations jump on the one-to-one marketing movement without proper planning. While implementation can be intricate, proper planning on how to get the basics right is critical to create that “local chicken shop” customer experience.
3. Make the store inviting and easy to buy
Before I walk into the chicken shop I am already salivating at the choices on offer that Mary’s created. She knows I will definitely buy because of how easy and inviting she has made her shop. Without even realising it, Mary has intelligently advertised the right and relevant products for her target market. That’s probably through years of experience and historical “conversations” in her head.
In a digitally spinning world, you have to make advertising intelligent. Extracting data insights from all touchpoints of digital and social can help drive your company’s marketing efforts. The benefits of applying even basic analytics to this data can provide the ability to forecast and segment, to ensure that advertising for sales are more targeted to make the customer experience richer.
2. Always listen empathetically to feedback, emotion and sentiment
Have you rung a call centre and found that the person on the other end is doing all the talking and not listening to your real needs? Why? Because – they have a script to follow and an outcome to achieve that is not at all empathetic to your needs. I recently reviewed a project where we used voice-to-text technology to further analyse the two-way conversations between agent and callers. We discovered that the agent script and sentiment led the customer down a path to churn from the company. Not the outcome expected! In my recent experience, it seems that call centres are so far behind in listening to the real voice of the customer – actual two-way conversations. Instead, they tend to extract “negative” words that the agent has transcribed from the call. See anything wrong with this picture? Yes – it’s not the “Voice of the Customer” but more like an interpretation on the transcript of the agent’s conversation. At SAS we "drink our own champagne" by listening to the true webchat voice of customers - find out more here.
1. Make it a memorable and seamless experience
If your customer enjoys their encounter with you, they will be more likely to return. So make it a memorable experience and live up to your customers’ expectations. Invest in synchronising your data and build a platform for long term relationship – not just transactional. Integrate your channels of communications so the conversation feels like one seamless conversation with your company. I get so annoyed when I am on a service call with a provider and I have to explain my story five times before I get to the right agent. Even when I have already “Tweeted”, “Instagramed” and “Facebooked” my issues.
Finally, don’t make excuses about accessing, data, legacy processes, siloed systems, limited skills or high costs. As in today’s world these excuses are longstanding and should have been resolved by now. So make it a mandate to modernise your customer experience before your customer has moved to your forward-thinking competitor.
The above described are the old school, traditional customer experience basics that your grandmother may talk to you about. My visits to the local Milk Bar (General Store) when growing up are memorable because of the experience created. We just need to take the basics and apply them to the wide world of Big Data and Analytics. I'd suggest you view this checklist to get started Analytics checklist for Customer Intelligence.
All opinions are my own based on conversations and feedback from the professional field and customers looking to create the ultimate customer experience.
In guest lectures I give at universities, I often refer to the Harvard Business Review report which states that being a Data Scientist is the sexiest job of the 21st century. Naturally, this always seems to capture the students’ attention, and drives their enthusiasm to sit up and listen carefully. As a result, one question I am consistently asked by inquisitive students is, “What skills do I need to become a Data Scientist?”
In my experience with solving analytical problems and conversations with customer looking to hire their next Data Scientist, I have drawn out the 5 most sought after skills you need to consider when applying for a Data Scientist role. Here are my top 5:
5. Know how to develop a predictive model using regressions and decision trees
This doesn’t sound too sophisticated to a pure statistician, I know, but businesses want to know the best outcome for a particular event. The most common business questions asked are, “Which customers are most likely to leave? Which customers will take up a product? Which customers should I approve for a loan?” In most cases a regression or decision tree results in an easy to explain predictive model to address these questions. More importantly, they are easy to productionise to meet the organisation’s demands.
This is a data science skill that contributes to companies creating a more targeted customer experience to increase profits while reducing marketing spend – a fantastic result for organisations!
4. Know how to develop a segmentation model
The first thing organisations want to do with their database is understand the characteristics displayed by their customers. And of course, from a marketing perspective, they want to know what groups of customers look like and what makes the groups different. Applying the skill of clustering (and there are many different kinds in this discipline), to obtain cohorts or segments that are distinct, is extremely valuable in driving successful business outcomes. It may be one of the first tasks you are asked to perform in a Data Scientist role.
3. Know how to use SAS with R
It is no secret that R is the common tool of choice for many students graduating from Information Management courses. But the reality is, when you need to apply analytics to commercial corporate data that is often exponential in growth, you must be able to incorporate R skills with SAS skills. Organisations have invested in analytical enterprise platforms that are often already embedded successfully into the organisations model lifecycle environment. So, those who bring a variety of both SAS and R skills will make valued Data Scientists.
2. Know how to access relevant data quickly
About 80% of a Data Scientists’ work is focussed on knowing where the appropriate data is housed and knowing how to access relevant data quickly. In my experience at one particular company I worked at, I developed a data dictionary for every data source I needed to access. The data dictionary was like the Holy Grail to fast and accurate data extraction, and let me get on with the science of deriving insights from the data. Everyone wanted a copy of the data dictionary… even IT!
1. Know how to articulate your analytical results to drive business outcomes
Communication is critical to your success. I often practice communicating the business outcomes of my analytical results with colleagues that aren’t analytically inclined. Once I know that they understand the value of the results from a business context, only then am I satisfied that my analytical results are actually useful to my organisation. You need to move away from statistical jargon and be creative with how you illustrate data and models in a business friendly manner. Do it in a fashion that is easy on the eyes and ears – you don’t want to scare people away, but rather welcome them into the journey of business analytics.
The tips described above aren’t rocket science. However, they’re not something you develop overnight either. If you can practice these skills continuously, and keep them at the front of your mind when applying for a Data Scientist role, then you will be a step ahead of your competition. Success awaits!
All opinions are my own, and based on my experience, conversations and feedback from the professional field and customers looking to hire quality Data Scientists.
Ever looked into what being “behind the curve” means? I’ll save you the time – it is being less advanced or getting somewhere slower than other people. Remember grade school? No-one likes being behind the curve. That’s where people tend to get crunched. So how do you know if you’re ahead or behind the curve? Measurement. Thanks to the poll we ran in our last newsletter, you’ll be interested to know that most of your peers in Australia and New Zealand stated that they were either “behind the curve or comparable”.
Coincidentally when we ran our poll question, MIT Sloan Management Review and SAS surveyed 2000 executives globally which resulted in a report titled “The Analytics Mandate”. Results showed that, “organisations have to do more to stay ahead of the curve,” as Pamela Prentice, SAS chief research officer, put it. “Nine in ten believe their organisations need to step up their use of analytics. This is true even among those who report having a competitive advantage.”
For us in the antipodes, there’s good news and bad news. The good news is that you are not alone! Don’t feel bad - if you think your organisation is behind the curve with your analytics mandate, so do your global peers. Now’s the right time to shine and open that window of opportunity to get ahead of the curve!
Unfortunately, there’s bad news too. Isn’t there always?! Being behind the curve might not seem like a big deal. Your organisation is still profitable, right? Assuming you are and that you’re not going anywhere, have you heard the saying, “if you snooze you lose”? That’s the real issue. It’s not about what you’re not doing. It’s about what your competitors are doing. How will your business survive if this is not part of your future strategic goal?
I refer back to the Analytics Mandate research that states – an analytics culture is the most significant driving factor in achieving a competitive advantage from data and analytics.
Changing the culture of an organisation does not happen overnight. In fact, it is probably the hardest transition to make as you try to juggle people, process, technology and the big O - Opinion! But once it is achieved, the business outcomes and competitive advantage are rewardingly accelerated. Here’s proof from Australian Natural Care, a medium-size online retailer of vitamins and supplements, “We have taken the results we’ve been getting to board level to report on the wins and everyone is pleased. We went from having no analytic capabilities to building analytical models within four weeks of implementation. This has had a direct impact on our entire business.”
How does that happen? To drive analytical aspiration and encourage an analytics culture, the Analytics Mandate study recommends you answer the following:
1. Is my organisation open to new ideas that challenge current practice?
2. Does my organisation view data as a core asset?
3. Is senior management driving the organisation to become more data driven and analytical?
4. Is my organisation using analytical insights to guide strategy?
5. Are we willing to let analytics help change the way we do business?
How did you go? Did you score five out of five? If so, then you’re on your way to analytical competitive genius!
If you need some guidance, then you need to read Evan Stubbs book, Delivering Business Analytics: Practical Guidelines for Best Practice to get some ideas on mandating the Analytics Mandate.
And if you were analytically enthusiastic enough to read this complete blog and are the first 25 readers to complete this Business Analytics Assessment, I will send you a signed copy of his book. Natalie.Mendes@sas.com
See you on the other side of the curve!
I recently attended an event where a speaker from LinkedIn presented. He mentioned an interesting trend – the demand for analytical professionals has increased in the last five the years. We all know analytics is the hottest field around. Better than mining even - as of last year, the median analyst in Australia was earning over twice the median Australian salary. Although, some evidence suggests that salary growth may be cooling somewhat overseas.
Big data’s hot, then it’s not. Machine learning’s cool, then it’s old school. LASR, Spark, in-memory, and good old data mining; what’s a person to focus on?! Given the rate that everything’s changing out there, have you ever wondered how the skills you choose affect your salary?
Don’t guess, find out. The Institute of Analytics Professionals of Australia is running their annual skills and salary survey for the second time. As with last year, it covers:
• The industries that people work in
• The tools people have used and are using
• The challenges people are facing
New to this year, it also covers:
• The degree to which analytics is being centralised or federated in organisations
• Whether people are interested in formal or informal education on business analytics
With the recent launch of a Masters of Data Science at the University of South Australia, the Masters of Business Analytics at Deakin University, the Masters of Analytics at RMIT and the Masters of Analytics by Research at UTS, there’s clearly a lot of pent-up demand for learning.
Respondents have the option of receiving the report once it’s completed. And, given what was in last year’s report, it’s sure to be interesting reading.
Regardless of what you use or where your focus lies, as long as you’re an analyst and you’re working in Australia make sure to complete it before it closes. Tell your friends, tell your enemies, tell anyone and everyone you know who works in the field. Tweet it, blog it, talk about it over the water cooler - as the most comprehensive survey of its kind in Australia, it represents a unique opportunity to really understand what’s hot, what’s not, what’s trending.
SAS is an active sponsor of IAPA and supports the broad development of skills across the industry.
I recently spent the weekend in the beachside town of Byron Bay to escape the madness of the BIG cities around the world that I had been visiting over the last ten weeks. Cape Byron, the most easterly point of mainland Australia and home of the iconic BIG lighthouse, is the first place where the sun rises in Australia. Why is this so relevant to a big data discussion? Because I thought I had escaped the BIG world of BIG DATA … at least for a weekend. How wrong I was. Everything I experienced during the weekend had some association with big data and the three Vs that are often used to characterise it. Let me explain.
My first experience was with BIG airline DATA. Given I have been on and off planes (average four flights a week) in the last six months, I had collected many loyalty points along the way, but was too busy to review my loyalty status. So when I checked in at the desk to get my flight to Byron Bay, the customer service agent provided me with great news. I had moved up in the world to another level in the BIG loyalty program. I felt special as if I was the only one. Millions of people fly each day and leave a valuable volume of transactional and behavioral data. For airlines to turn this BIG DATA asset around in minutes makes the difference between making each customer feel special or losing them to the competitor. There is simply no excuse to lose a customer this way?
The BIG DATA experience continued when using the airline’s loyalty points and hiring a car. My loyalty program has been busy collecting information from a variety of sources, in particular affiliate rental car agencies where I had claimed loyalty points in the past. What was relevant was the “Rental Cars” offers. This to me was the right information at the right time as I needed to hire a car for the Byron escape. So of course I did with my airline loyalty program. Naturally, being a marketing analyst, I recognised this as a great example of BIG loyalty DATA being used in a ‘cross-sell’ activity. The rental company managed to squeeze some extra dollars out of me, but I didn’t mind because I received another loyalty ‘reward’ and I felt special. There was now a variety of data being collected about me. Do all companies take advantage of their BIG DATA to create strategic assets? If not – why not? There seems to be big benefits in real dollar terms.
Let’s look at my next BIG DATA in little Byron experience. Given I had travelled to many countries and many Australian states recently, there was significant irregular activity happening on my credit card, well so my bank thought. There were many different transactions in different places worlds apart. So of course when I went to pay for the BIG breakfast I had just happily consumed, my transaction was declined several times, only to discover after I called the bank that their fraud system had stopped activity instantly – that’s BIG banking DATA in action! My credit card details had been hacked and yes – there was fraudulent activity happening. I appreciated the velocity in which the data was collected and the speed to react to this critical issue. How much more money could I have lost if this was not detected in time?
So what is the big hype about BIG DATA? It seems like we’ve been trying to work with this for a long time. A company has BIG DATA when the volume, velocity and variety of data exceeds the organization’s storage or computing capacity for accurate and timely decision making. Is this where organisations need to think about high performance analytics? How will your business survive if this is not one of your strategic goals?
Let us start with a brief text analytics history lesson. Australia’s first postal services began with the early settlers in 1809 - communication was hard, and they would wait for mail for months on end. Moving forward in time (approximately four decades), recognising the communication needs of people became the focus.
This is when the post office took control of what was the most modern means of communicating - the telegraph. There seem to be no public records available on the number of messages created and delivered at the time, however I am sure I can count on one hand the telegraphs delivered in a day in the 1850s. I am also guessing that the content of the communication expressed sentiment, historical events, current happenings and future wishes in approximately eight hand-written pages or a thousand plus words. The level of detail allowed the reader to interpret the meaning and context.
Fast forward 200 years to 2012! BOOM!
My neighbour, George, is also a postmaster in some ways - in that he delivers communications from his home. For George is a 'serial tweeter' and he is not alone. In March 2012, Twitter announced that it had 140 million active users, sending 340 million tweets per day. That is a lot of ‘letters’ – 140 characters at a time – being sent worldwide to anyone and everyone every second of the day. The ‘Noughties’ version of the pen pal. There is even a new language that has its roots in Tweets and text messages: 'Tweetish' … LOL, OMG, think I cre8ted a nu word – SMS speak is now so pervasive (used in chat, on Twitter, in SMS messages) that we even have the SMS Dictionary.
If a picture tells a thousand words, do a thousand words give us a picture?
With the millions of words communicated in text conversation today, we can analyse these words and phrases to provide a good understanding of the hot topics of discussion, as well as society’s sentiment, from all around the world.
This project investigates how social media and online user-generated content can be used to enrich the understanding of the changing job conditions in the US and Ireland by analyzing the moods and topics present in unemployment-related conversations from the open social web and relating them to official unemployment statistics.
It is fascinating research and I recommend you take a look – we have had a lot of interest across Asia in this project. People today are talking much more than they ever did and to everyone in the world about everything in the world. The next steps are to make sense of the data and turn it into information.
Why is this so important? Marketing, fraud specialists, risk advisors, journalists, and advertising agencies could all use text analytics to gain competitive advantage and understand the consumer voice. If my health insurance company analysed my last conversation I had with them a week ago, they would be worried. My last words to them were “It’s taking you three days to issue me a new policy quote. I am not happy with your pricing on the policy package, so I will look into other insurers. Goodbye!”
Question: Think about the online conversations you have had recently. What would sentiment analysis reveal?