Get to know the IBM Commerce Business Partners

Next time you fee like bingeing on a video series you might want to check out the 68 IBM Commerce partners in this YouTube list. Break out the popcorn and open this list on your smart tv and just sit back and enjoy!

Get 2X conversion rates with Apple Pay and WebSphere Commerce

See how Apple Pay and Commerce on Cloud can help you add Apple Pay to your storefront, giving your customers the latest shopping experience.

12% Increased conversion with Instart Logic and WebSphere Commerce

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Instart Logic has an amazing solution that will increase your conversion rates and speed up your site greatly. Check out this video where I introduce the solution and then see a quick demonstration of the Instart Logic solution implemented on the WebSphere Commerce starter store.

Watson drives IBM Digital Experience

If you missed it, at the IBM Connect conference, Chris and Rob demonstrated how to build a page using Watson to assist in suggesting the content for the page with a single click.  Very impressive and as Chris said “that is awesome”.  Totally agree!

In the video below (which is much longer than the snippet I provide here), Rob shows the Cognitive Assist feature to help in constructing a new landing page for their campaign.

Cognitive Assistant

Take a look at the video to see how cool this new feature is.

The future of eCommerce is with blockchain

If you have not heard about blockchain then here is a primer, otherwise skip to What’s Next below.

Blockchain is a trust protocol and one of the first implementations is BitCoin, where each bitcoin is has its entire owner history reference stored with it. Think of it as an absolute proof of exactly where the money came from and you can only use it if the history is correct. And to make it more cool, its stored all over the place – kind of like a global spreadsheet or ledger (link). This means its virtually impossible to spoof or cheat the system.  Bitcoin Core checks each block of transactions it receives to ensure that everything in that block is fully valid—allowing it to trust the block without trusting the miner who created it (link).


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Best introduction video of IBM BlueMix I have seen so far

I think this video is an excellent primer for BlueMix. Platforms like BlueMix take care of a lot of the manual steps of setting up a server including infrastructure, devops, and runtime administration for scaling your applications. It really is a one stop shop for developing applications in the cloud.

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Measuring Social Sentiment: Trump versus Clinton

Ok, yes, that title probably has a little phishing characteristics to it but it was the only thing I could compare on Twitter without getting into trouble and also something I knew would yield some kind of results very quickly given what is going on in the news.

So what I am researching is how brand sentiment could potentially affect campaigns either negatively or positively. So, if you take for instance a campaign that is tagged “#trump” or “#clinton” you can compare over time the sentiment of that campaign. The term being searched for should be fairly unique, once again, that is why I chose “trump” and “clinton”, otherwise you will be bound to get completely irrelevant results. The biggest problem I noticed in the result set was that when Trump and Clinton were mentioned in the same tweet they both scored either negative or positive sentiment. To fix that there would need to be additional logic to understand the structure of the sentence and place the blame on the correct person – unfortunately I won’t be going into that in this post so it is what it is as they say.

I used IBM BlueMix to create a Node-RED application from the Node-RED boilerplate and was able to assemble this application in minutes. If you have not checked out BlueMix you can get a free trial here.

Screen Shot 2016-06-22 at 11.05.22 PM

From the Node flow below you can see I have two flows executing simultaneously. Passing each tweet through the Sentiment Analysis node, tagging the result with the name passed in and storing just the sentiment score in the Cloudant database.

Screen Shot 2016-06-22 at 5.47.24 PM.png

The sentiment function uses the AFINN word list to figure out sentiment, once again, not the greatest solution in the end. It then passes it to my custom function where I append the search term to the msg.sentiment object as the query property. I then assign the total sentiment to the msg.payload object because that is what is stored in the database. We might want to add date in the future.

msg.sentiment.query = "clinton";
msg.payload = msg.sentiment;
return msg;

The result stored in the database is a single record for each tweet analyzed, and as you can see this particular post is exactly what I explained above where the tweet is about Trump calling Clinton a world class liar, so in this case they both received a -3 score. We could minimize what is stored in the future, for instance we may not be interested in the tokens.

 "id": "019f579026accf466754d104efed5d83",
 "key": "clinton",
 "value": -3,
 "doc": {
  "_id": "019f579026accf466754d104efed5d83",
  "_rev": "1-22dff255bde0b4dcba91f6a392116f7c",
  "score": -3,
  "comparative": -0.1875,
  "tokens": [
  "words": [
  "positive": [],
  "negative": [
  "query": "clinton"
 "_id": "019f579026accf466754d104efed5d83"

However, in general, you might want to compare your brand to another brand and most likely they would not be mentioned in the same tweet like in politics. So, given all of its flaws, here is the average sentiment for “clinton” and “trump” for over 7000 tweets that spanned about 20 minutes.

Screen Shot 2016-06-22 at 6.02.04 PM

As you can see, they both are pretty negative sentiments but Clinton is almost twice as bad as Trump. Oh boy.

Now, storing these daily would actually give you average sentiment over time. Meaning you could then compare the campaign or brand sentiment with your other marketing factors, like social engagement, sales, inventory, promotions, campaigns, etc. to understand if social media in fact helps or hinders sales or campaigns.

Like I said, this is barely scientific but I do think overall it averages out over time. I would really appreciate your thoughts on this concept in general ignoring some of the fallbacks my quick little application has on its own.