It has been a little over a year since IBM’sWatson Analytics was released. Watson Analytics, a smart data discovery tool, continues to deliver easy analytics for everyone. Some competitors are trying to catch and imitate what IBM has delivered but frankly, their latest news is more “snooze worthy” than newsworthy.
Watson Analytics is so powerful and can address so many different uses cases. I am frequently faced with the following query: So, I get it! I have a ton of data and I need a way to make sense of it. But what exactly do I DO with it??
This really depends on what your role is. In our work lives we each fit into some type of role:
supply chain manager
head of strategy
So, as you read this and think about what YOU can do with Watson Analytics, think about the data you need to better visualize. Think about what finding patterns within your data could do for you. Finally, think about how adding social media data to your analyses can make you analyses event more effective.
But most importantly let’s put this in context of some of the major roles and use cases that will matter to you:
Over the next several weeks, I will be sharing the details behind some of these specific use cases and providing a “how to do this yourself” approach. Check back to hear more about how #watsonanalytics can help address many of these use cases.
In my post last week, I shared that many people are intimidated by analytics. This week I will share a practical application of analytics to demonstrate that there is no need to “fear” analytics and in fact, with a few simple steps, they can have a tremendous impact on organizations.
The following scenario is based on a true story (names, industry have been changed):
Fly First airlines is a major international airline which has experienced tough times over the past several years. With an aging fleet, Fly First has experienced widespread mechanical problems leading to delays which have impacted customer satisfaction and caused a lot of complaining on Social Media sites. Customer satisfaction scores have dropped over the past year and Fly First has seen a decline in passengers and fewer repeat flyers which has directly impacted the airline’s revenue streams.
Jay, Fly First’s CMO, has decided that deepening the knowledge of it’s customers is the best way to get the airline back on track. Jay knows that traditional data sets from internal systems such as purchase history and customer service inquiries would only tell him part of the story. He made it his mission to deepen his understanding customers by expanding data collection, ensuring all analog and manual touch points become an opportunity to understand Fly First’s customers better. To accomplish this his team digitized menu selections by implementing electronic menu selections on seat back touch screens, allowed only credit cards for in-flight purchases, and using data capture capability conducted frequent digital surveys that covered topics like service, preferences, and how they pass time during flight delays.
They used interaction data like marketing responses, emails, call center notes, web click-stream; attitudinal and sentiment data bought from third parties or from social media like twitter, facebook and blogs, behavioral data including flight history, frequent flyer history etc and descriptive data such as seat preferences and demographic data from third parties and from their own data sources to build out a complete view of their customers
Jay believed that their enterprise data was only telling a piece of the story, so he asked his team to use the sentiment analysiscapability to analyze what was being said about Fly First on social media channels.
Through the social sentiment analysis they discovered that:
Sentiment for Fly First had gone from positive to neutral to negative in 6 short months.
Negative sentiment was around frequent flight delays due to mechanical issues, lack of care and attention by gate staff to the inconveniences of the delays, and the flights always running out of the food they were going to order before it reached them.
Sentiment was extremely positive about the Fly First lounge experience.
Jay’s team also looked at which social media participants were positive, neutral or negative about Fly First and integrated the Fly First website with leading social channels enabling them to extend the social media analysis to individuals by linking frequent flyer accounts with social media accounts.
Jay knew to get the best understanding of customer needs he needed to continue to expand data collection activities to capture more customer information. To accomplish this, the Fly First team put in place a series of incentives for customers to share information about their preferences and opinions of the airline.
Jay’s team continued to increase data collection about customers
by creating incentives, such as frequent flyer awards for survey completion, for customers to give more information about themselves.
Jay had long leveraged analyticsto identify profitable customers and increase customer retention. Now he is using it to predict behavior, such as seat upgrades or
in-flight duty free purchases, as well as leveraging social network analysis and entity analyticsto understand his customers’ social networks.
With access to the all customer data, the Fly First team applied data mining capabilities to get a better picture of what their customers are likely to do and want.
They were able to predict:
Which offers would likely be accepted by their customers based on past history and sentiment.
Which customers are likely to refer a friend to the airline
Which customers are likely to buy flight upgrades, buy in flight meals, pay extra for lounge access.
What would increase their likely of leaving the airline
What increases their loyalty
One of their discoveries was that free lounge passes were the offers most likely to result in positive social sentiment.
Given these new insights into customer behavior, Jay and team are now able to engineer a new set of innovative experiences for their customers. Meet Rick and Andrea, two unrelated Fly First passengers waiting at a gate to board their flight to Chicago.
He’s a sales manager who fly’s frequently but not always with their airline so has no status.
He recently took his family to Europe on FlyFirst.
He travels with an iPod, iPad and Bose headphones
He complains about delays frequently (as captured by the gate agents and on the surveys) He has a large twitter following (linked his twitter to his account when he responded to a an offer sent by twitter) and actively tweets about the airline.
He also writes about his experiences flying on Fac ebook.
He always orders a bud and a chicken quesadilla when flying over lunch or dinner
She’s a Marketing executive
Loyal frequent flyer who has Elite status and access to the Lounge.
Travel economy and upgrades when possible
Frequent Facebook user with low social media influence
Purchases food on most flight
Frequently orders from Duty free.
Rick and Andrea’s flight has been delayed by two hours. When the long delay gets recorded it triggers analytics to be run on all passengers determining which offers to text to which passengers. Due to the analytics initiative, Fly First is in a place to delight potentially disgruntled customers by offering customized offers to help make the wait a little bit easier.
Fly first knows Rick is likely to Tweet negative items to his large following and post negative comments on facebook. Fly Firsts systems analyze Rick’s data in real time and push an offer to him to go to the lounge for free.
Fly First also knows that Andrea is loyal customer who will likely head to the lounge and frequently purchases from duty free so they offer her a discount on in flight duty free.
Rick immediately takes First Flight up on their offer. While enjoying a beverage in the lounge, he publiclyshares his new found appreciation for the airline via Twitter.
Andrea, also relaxes in the lounge. Later she boards the flight and buys her favorite perfume applying her discount and updating her Facebook status using the on board WIFI to “Just bought my favorite perfume compliments of Fly First”
The previous scene played out tens of thousands of times over the past year with thousands of customers. Fly First has increased their use of information to anticipate what their customers will want and do and act upon it in a way that yields incredible results.
Social media sentiment for Fly First has measurably improved from negative to neutral, with top detractors transforming to neutral or advocates. Lounge revenue is up, and frequent flyer participation has increased because of referrals. Customer insights are being leveraged to target promotions. Overall, marketing costs are down and yield is up.
I don’t know about you, but I wish my airline knew … and served me as well as this.
When I was running marketing operations, I had hired a statistics expert to come into the organization to help me make sense of all of our marketing data. As I worked with this excellent “stats guy”, I realized that predictive analytics can be pretty darn complicated and required a more specialized skill set than I had originally thought.
This all changed on Tuesday June 11, 2013. IBM announced the availability of IBM SPSS Analytic Catalyst – a statistician in the software – which makes predictive analytics on big data available to mere mortals like myself. I was admittedly skeptical that this new offering could actual make predictive analytics accessible. There had to be some gotcha…. well, I was wrong (not something I admit often!).
So, what does this thing do exactly? Let’s say you are a marketer and want to understand which customers would likely “churn” or leave for your competitor. This is where Analytic Catalyst comes in. There is a three step process to get the answers you need.
Add your data (csv file)
Select the field you would like to predict
Review plain English results
Sounds simple? Well it is. Review a demo here to see how this can help marketers, customer service teams, sales and other organizations find the small data within the big data!
Big Data is a reality. Big data is also big hype. So where is the real value in the Big Data hype?
Like it or not, Big Data is a reality marketing and other organizations are facing today and will face well into the future. According to the IBM Global Technology outlook, it is estimated that data growth will explode exponentially from 3,000 exabytes today to 9,000 exabytes by 2015. (I had to look up the word exabyte!) So what do we marketers do about incorporating this new source of data? Where are the best use cases?
We are faced with a challenge – Big Data alone does not help us. We need ways to get value out of the data. The challenge is that traditional analytic tools are not exactly suited to take advantage of Big Data. Analytics vendors are developing offerings in an attempt to keep up with the ever changing needs. With that said, we need to be thinking about where it fits in.
For me, the best use case is squarely in the camp of customer analytics. I love my data from the marketing automation system. It tells me a great deal about the patterns and behaviors my customers and prospects display. But it only gives me a piece of the puzzle.
As I think about my customer marketing initiatives, I need to be able to prevent customer defection (commonly known as “churn”). By leveraging unstructured data(call center notes, customer interactions, survey responses…) with internal structured data to detect & proactively mitigate factors that lead to defection, I can better design marketing programs to avoid that “aha moment” when customers realize they no longer want to do business with us versus defecting to a competitor. Conversely, applying the same approach can help marketers identify the “aha moment” which leads to improved customer loyalty
So, is Big Data hype or is it really something we marketers can benefit from? If marketing organizations do several things, I believe it will improve marketing’s ability reach the target audience with more appropriate offers.
Big Data platform: Do not look at Big Data as this mysterious beast that can solve world hunger! It should be considered an additional data source which can augment existing data sources such as CRM, ERP or marketing automation data. If you are a marketer, you should be connecting with your IT team to determine how to incorporate this data into your existing data.
Analytics: Do not forget the analytics! Discover, visualize and explore big data alongside traditional information to drive action and share your insights with others. Employ predictive analytics to identify patterns within the data leading to more accurate answers.
Answers! Ultimately, it is what you do with the data versus the data itself! Use Big Data combined with traditional data sources to deliver more accurate answers by analyzing, predicting and automating decisions.
Connect with me to continue this discussion: @BrendanRGrady