CoreMedia Demo Jam 4 – HCL B2B Commerce

This demo jam shows how you can use the same CoreMedia Studio functions you use on a B2C site but in a B2B context featuring HCL B2B Commerce and the Aurora B2B Store.

Make sure you come see HCL B2B Commerce at B2B.Next in Chicago next month. I will be there and I would love to show you how well CoreMedia does B2B. Make sure you come to Booth 604 and see the HCL Commerce with CoreMedia demo!

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Product Recommendations made easy with Apache Druid Part 1

I have been playing with Apache Druid for a bit now and I have to say I am very impressed with this package. Druid provides fast analytical queries, at high concurrency, on event-driven data. Druid can instantaneously ingest streaming data and provide sub-second queries to power interactive UIs.-link. Apache Druid essentially does all of the bulk lifting of segmenting the data and putting it into high performing indexes for super fast queries . You can stream the data directly into Druid using API’s or Apache Kafka, or you can simply upload massive amounts of data at intervals appending or replacing.

Because Druid does so much for you, you could actually run different campaigns using completely different data sources that are stored and indexed in Druid. Imagine running a campaign for “Hottest Items Last Fall” or “Seasons top sellers”. This would produce a product shelf similar to this on your eCommerce site:

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Those products could have been returned by Druid in real time, sorting the resulting SKU’s by order value, quantity sold and even filtered for things like shopper attributes (age, gender, location).

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Druid let’s you store as many data sources as you want, so you could actually build dynamic components in CoreMedia that can run the same campaigns on different data sources. This could be used for different brands and their SKU’s or even seasonal order data.

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For my use case, this means you could essentially push order line item data into Druid and get fast queries for product shelves like “Top Sellers“, “Top Weekend Sales“, or even “This weeks hits” – all based on the order line sales and the time and date stamp of the order.

Pushing this line item level order information should be trivial for most order management systems. I started to ask myself what data would I actually need to satisfy a few use cases. So I started writing some use cases down as one liners:

  • Most products sold
  • Total sales
  • Highest Total count sold on day of week
  • Highest Total count sold in month of year
  • Highest Total sales on day of week
  • Highest Total sales in week of year
  • Region top seller
  • Men top seller
  • Women top seller in region

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When should I use Apache Druid?

Read about how Neilsen, Monetate, eBay, AirBnB, and others use Apache Druid.
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I then had to figure out the minimum amount of data needed to be able to do those use cases and this is what I came up with:

“time”, “order_id”,”shopper_id”,”sku”,”price”,”quantity”,”cost”, “shipping_info”

That is all pretty standard information you can get from a PO. What is not part of that is the customer demographic information.  Because Druid performs best with flat data we will most likely have to write a routine that combines order line data with customer attribute data. We could include fields like these (if they are known):

“age”, “region”,“gender”:

This would allow us to ask Druid many different queries and get the proper response. In the CoreMedia extension model this should really be a returned list of SKU’s that we can map to the current product catalog. Some error handling or SKU replacement code might be needed; especially if you are running against year old data. Hopefully for more current campaigns like “Hottest Weekend Products” or “What’s hot this month” the data and SKUs very up to date. The resulting JSON sent in for each row would look like this:

{
"time":"2019-06-30 03:53:35",
"order_id":"id_055300006130",
"age":"40",
"region":"Midwest",
"gender":"M",
"shopper_id":"U_09080785",
"sku":"PC_CHEF_CORP_MP_KNIFE_SIGNAL_RED_SKU",
"price":79.0,
"quantity":3,
"cost":237.0
}

Sending in each order line item separately will allow Druid to actually dynamically build orders, return SKU’s based on any time and date combination, bloom filters, numeric expression, and of course grouping (total sales for a single SKU)- link.

I created a dataset with six months of order data, broken out by each line item as described above. It ended up being 431,148 line items created for 4,323 SKU’s in 300,000 orders

I went ahead and created queries for each of those use cases and I find Druid is extremely fast (more on that in Part 2), even when running on my local machine. Check out the slide show below for the various ways you can use SQL (or JSON) to query Druid. The real power comes with the way Druid can quickly return rows and run on functions like TIME_EXTRACT. Each query essentially returns a list of SKU’s ordered descending from either a total sales count or an items sold count.

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Stay tuned for part 2 where I show how easy these kinds of dynamic product shelves based on sales and shopper data can be integrated into CoreMedia Studio. I will also show a demonstration where Apache Druid is accessed in realtime from our Studio where the maketing person can easily preview this dynamic behavior. A little teaser showing how the authoring environment (Preview CAE) and the runtime environment could access the same Druid data, giving marketers the same products as the shoppers would see.

 

I am really interested in hearing your thoughts on this, send me an email or leave a comment!

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IBM WebSphere Commerce Made Easy with CoreMedia!

Yes, supporting WebSphere Commerce sites as a marketer or merchandiser can not only be easy but it can be fun! If you are at THINK2019 come by our booth #616 and see an amazing demonstration or ask us what we think is the best path for your WebSphere Commerce implementation. If you can’t make it to our booth or set up a meeting with me and the team then take a look at this short video series and see how easy eCommerce and Content can be managed in a single studio – the CoreMedia Content Cloud.

Using CoreMedia, clients have reduced their time-to-web by 75%

Yep, continues to be one of my favorite integrations with our Commerce engine. This is a first class CMS that is best in class and the integrations with IBM Commerce are like none other. They have extremely tight integrations with IBM Commerce, giving merchandisers and marketers a seamless administration of their entire eCommerce site from learning to purchasing, this single CMS does it all.

Using CoreMedia, marketing teams can now publish promotional pages, inspirational guides, enhanced product detail pages and thematic microsites – all with no IT involvement. – link

If you are not familiar with these integrations, you might want to check out the video series I did with Coremedia to show how cool it can be to run an eCommerce site.

You can watch the entire series here: link

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Competitive markets demand competitive pricing

Are you having gross and net margin pressures in your industry? Having a hard time keeping pace with the competition? Take a look at this great interview Natalie Lamb, Vice President, Europe – Watson Customer Engagement, has with Paul Rodford, Finance Director at the Southern Cooperative, and learn how Paul and his team uses IBM’s Price Optimization offering to help them compete in a very competitive market.

Frost & Sullivan declares IBM a leader in customer value within the Integrated Commerce Order Management Space

“IBM Watson Commerce order management brings together capabilities, such as inventory visibility, distributed order management, order promising, delivery service scheduling, and reverse logistics. It collects orders from online, call centers, and stores, and it provides transparency through a single view of inventory and demand across channels and throughout the supply chain,” said Frost & Sullivan Lead Consultant, Integrated Commerce Martin Hoff ter Heide. – link

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Webinars Tomorrow: 7am, 11am, and 9PM Eastern: Brick and Mortar + Online: How to Bring Customer Experience Together

Come hear yours truly pitch an end to end demonstration using the Watson Customer Engagement platform!

7 am Link: https://event.on24.com/wcc/r/1508947/A3D0C04153E3784004C099EB66C5EB18?partnerref=internalpromo

11 am Link: https://event.on24.com/wcc/r/1508974/6C30DA045534EEEFDD9CEBF2AD7EBC85?partnerref=internalpromo

9 pm Link: https://event.on24.com/wcc/r/1508981/63B24089615343E918C0C00CFE74B32E?partnerref=internalpromo

Watson Customer Engagement for Developers is here!

We have several offerings spanning Marketing and Commerce and many more coming this year. As a developer, you can now sign up to be notified when products and API’s become available on the IBM marketplace. Bookmark it and sign up today for notifications.

Check it out today.

IBM named as Leader in Gartner Magic Quadrant for Digital Commerce

This is good news for IBM and while Gartner mentions Watson Commerce Insights for predictive merchandising and predictive search, I am not sure why it doesn’t include products like Dynamic Pricing – which is clearly stated as a primary initiative for most B2B sites by 2018.  Analytic’s and augmented intelligence is a big area IBM is investing in and it ties closely with managing customers for both B2C and B2B sites. As stated in this report, by 2020, smart personalization engines used to recognize customer intent will enable digital businesses to increase their profits by up to 15%. I don’t think products like IBM Predictive Customer Intelligence or Watson Customer Experience Analytics are well represented in this report and by next year there will be even more augmented intelligence features in the IBM suite. At some point, the Digital Commerce quadrant has to recognize these add-ons as a base part of doing business in this report and this year they should have clearly been listed as strengths in the industry.

Another area I think the report falls short on is order optimization. With more and more companies moving to buy on-line pick up in store or more specifically buy on-line ship from store, you are going to see more demand for order fulfillment optimization, something Watson Order Optimizer covers very well.

Some key messages from the report IBM is focusing on:

  • IBM is a Leader, based on its product functionality, ability to support B2C and B2B business models, and its supporting ecosystem of applications that connect to its commerce platform across the globe.
  • IBM maintains an extensive set of partners in the digital commerce ecosystem, which was highly thought of by its reference customers — all scored it “very high” or “outstanding.”
  • By 2018, 40% of B2B digital commerce sites will use price optimization algorithms and configure, price and quote tools to dynamically calculate and deliver product pricing.
  • By 2020, 25% of leading online sellers will have enabled first-generation “commerce that comes to you” capabilities.
  • By 2020, smart personalization engines used to recognize customer intent will enable digital businesses to increase their profits by up to 15%.

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