Tag Archives: Social media

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Planning the perfect party for the big game!

Guest post by Steve Archut

Hosting a party for the Big game? Here’s what to feed your guests

The game this weekend has practically become holiday in America with many people hosting parties and epic amounts of junk food consumed. Roughly 28,000,000 pounds (13,000,000 kg) of chips, 1.25 billion chicken wings, and 8,000,000 pounds (3,600,000 kg) of guacamole are consumed during the game. The question remains for those hosting a party, what do I serve my guests?

Using Watson Analytics for Social Media, I wanted to see what people were saying about some of the most popular dishes out there. In under 10 minutes I was able to identify what to serve and maybe more importantly what NOT to serve.

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We can see that wings, hoagies(subs & heroes to those not from Philadelphia), and pizza are dominating the conversation.

The overwhelming majority of people prefer to order their food instead of opting for home cooking.  This is great since it will free up time to watch the pre-game festivities!

Watson Analytics for Social Media also gives me the ability to quickly view the sentiment around the different dishes:

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Party trays have a surprisingly amount of negative sentiment.  (What did party trays ever do?)  Maybe I will avoid that this year.

When it comes to the Super Bowl, everyone is in on the food action.  Though men and women tend to have different opinions on what to serve.  This will really help plan what I should serve based on who is coming to the party.

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In just a few minutes, Watson Analytics for Social Media gave me an in depth look at what people are saying about popular dishes for the big game this Sunday and I was able to glean insights previously unavailable to me. As for me, I’ll be serving meatball sandwiches this Sunday.

 

How to plan the BEST office party!

So, it is that time of year again….the holiday season from Thanksgiving (in the US), to Chanukah, Christmas, Festivus and New Years.  It is during this season when many companies decide to throw an office party.  If you are tasked with finding the “perfect” location to host your party, you know you have your hands full in pleasing everyone…

There is a way you can hedge your bets and find the “perfect” location which will please most people. How about using social media and your social network to understand peoples opinions and sentiment about certain locations.

Check out the steps below to get started!!!

Click here to register for a free trial of Watson Analytics for Social Media.

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Fear not! Analytics are here! A real world example

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. IBM CMOWith 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.

Jay and his team in partnership with IT built detailed, individual 360 degrdigital thumb printee views of individual customers by combining multiple data sources from behind and outside the firewall.
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 analysis capability 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.surveys  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 analytics to 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 analytics to 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.
Rick:
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
Andrea:
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 getsphone 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 publicly shares 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.

Do companies REALLY care what customers think?

If not, they should! And there is a means to truly understand customers…

Social media analytics seems to be all the rage and for some, it is the Holy Grail to understanding customers’ needs, wants, desires, and opinions.  While much has been done in the realm of customer analytics, this has largely been focused on historical analysis of data behind the firewall.  This only tells one piece of the story.

Social media analytics enables organizations to add that missing link in customer analytics – sentiment.  What do people really think or feel about a product or service.  Yes, you can infer this information from historical purchase history or marketing/sales interaction history – But isn’t is just downright more accurate when you read something like: “This is the most USELESS product ever!”  or “I cannot believe that anyone would pay money for this piece of crap!!!” or “Hey, airline X!  A $10 voucher is not going to cut it this time!!!”

For you marketers out there, social media analytics can be a powerful ally as you determine how to position your offering, to whom and whether or not your messaging is resonating.  Social media analytics enables you to determine:

  • Who is speaking about your product – where are they from and what is their demographic?
  • Is the overall sentiment negative or positive?
  • What other topics are people speaking about which are related to your offering?
  • Are the people key influencers?

Now, many organizations, including marketing, make the mistake of looking at social media analytics as a stand alone set of information.  Those who see social media as a complementary data source will get a better picture of their customers and prospects.  Bringing Social Media analytics into the mix is part of a solid customer analytics strategy.

Tactically speaking,  marketers need to act on the information gleaned from social media.  Navel gazing will get you nowhere fast…….

Here are a few examples of companies responding to a social media crisis:

Would you have married your spouse if you only knew his or her address?

Marketers claim to know their customers because they have captured demographic and historical transaction information about them but would you have married your spouse based on age, height, address and the fact that they bought shoes once?  I think not.  Why should marketers think this enough to truly address the needs of today’s customers?

Many marketers are doing a very good job an incorporating traditional data critical to being able to target marketing efforts.

Data such as:

  • Self-declared demographic information
  • Marketing inquiries
  • Sales leads
  • Orders, payment history

This information is a great place to start.  There is no doubt that applying advanced analytics help marketers find patterns and trends while also predicting what is likely to happen next.  

Think about being on your first date with you spouse.  The conversation starts with the basics:

First Date

  • Where are you from?
  • Where do you live now?
  • Where did you study?

But quickly moves to:

  • What is your opinion of the President?
  • What do you do for fun?
  • What do you really dislike?

Now think about applying this in a business context.  As a marketer, I would like nothing more than to put an offer in front of customers and prospects that would not only speak to their business need but would also speak to personal likes.  How can you do this?

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Every interaction is an opportunity to get to know your buyers better.

Traditional interaction data from a marketing automation system such as Unica, Eloqua, Marketo or Neolane is a valuable source of information about  buyers’ behavior.  By capturing all interactions, regardless of channel, allows marketing organizations to apply predictive models to predict which customers are likely to respond to marketing offers via which channel and how frequently.    Using predictive models as part of a broader  customer analytics initiative helps marketing organizations identify which buyers to target and personalize offers for cross and up-sell opportunities

Great, we now know which offers my buyers will likely respond to and how frequently!  Now, go back to my first example – the date.  Getting to know your buyers personal preferences allows you to gain a deeper understanding of customer attitudes, preferences and opinions to make them part of the decision making process. Think about collecting customer opinions, attitudes and interest via surveys or data collection.  Use the interaction opportunity to capture a hobby or other personal activity.  Then apply this in your marketing activities.  If your buyer likes golf, find a way to incorporate it into your outreach (e.g. my marketing team has used direct mail/dimensional mailers giving away a free driver!).  

Social Media is another data source which can provide tremendous insight into customer opinions, both positive and negative.  Apply social media analytics to get the real opinion of your products to ultimately engage brand advocates and detractors in a conversation. CI Social media analytics allows organizations to capture consumer data from social media to understand attitudes, opinions and trends.

They key here is not to look at each of the pieces of data as stand alone pieces of information.  It is about combining them to get a “thumb print” which identifies the uniqueness of an individual.

Please feel free to connect with me on Twitter to discuss further @BrendanRGrady