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Failing Ahead – What We Realized at Cisco from a “Failed” Digital Orchestration Pilot


The trendy buyer expertise is fraught with friction:

You communicate to a buyer consultant, and so they let you know one factor.

You log into your digital account and see one other.

You obtain an e mail from the identical firm that tells an totally completely different story.

At Cisco, now we have been working to determine these friction factors and evaluating how we will orchestrate a extra seamless expertise—remodeling the shopper, associate, and vendor expertise to be prescriptive, useful – and, most significantly, easy. This isn’t a straightforward job when working within the complexity of environments, applied sciences, and consumer areas that Cisco does enterprise in, however it isn’t insurmountable.

We simply closed out a year-long pilot of an industry-leading orchestration vendor, and by all measures – it failed. In The Lean Startup Eric Ries writes, “in the event you can’t fail, you can not study.” I absolutely subscribe to this angle. In case you are not keen to experiment, to attempt, to fail, and to judge your learnings, you solely repeat what you recognize. You don’t develop. You don’t innovate. You could be keen to dare to fail, and in the event you do, to attempt to fail ahead.

So, whereas we didn’t renew the contract, we did proceed down our orchestration journey geared up with a 12 months’s price of learnings and newly refined path on methods to sort out our initiatives.

Our Digital Orchestration Objectives

We began our pilot with 4 key orchestration use instances:

  1. Seamlessly join prescriptive actions throughout channels to our sellers, companions, and prospects.
  2. Pause and resume a digital e mail journey primarily based on triggers from different channels.
  3. Join analytics throughout the multichannel buyer journey.
  4. Simply combine knowledge science to department and personalize the shopper journey.

Let’s dive a bit deeper into every. We’ll take a look at the use case, the challenges we encountered, and the steps ahead we’re taking.

Use Case #1: Seamlessly join prescriptive actions throughout channels to our sellers, companions, and prospects.

At this time we course of and ship business-defined prescriptive actions to our buyer success representatives and companions when now we have digitally recognized adoption limitations in our buyer’s deployment and utilization of our SaaS merchandise.

In our legacy state, we had been executing a collection of complicated SQL queries in Salesforce Advertising Cloud’s Automation Studio to affix a number of knowledge units and output the precise actions a buyer wants. Then, utilizing Advertising Cloud Join, we wrote the output to the job object in Salesforce CRM to generate actions in a buyer success agent’s queue. After this motion is written to the duty object, we picked up the log in Snowflake, utilized extra filtering logic and wrote actions to our Cisco associate portal – Lifecycle Benefit, which is hosted on AWS.

There are a number of key points with this workflow:

  • Salesforce Advertising Cloud just isn’t meant for use as an ETL platform; we had been already encountering trip points.
  • The associate actions had been depending on the vendor processing, so it launched complexity if we ever needed to pause one workflow whereas sustaining the opposite.
  • The event course of was complicated, and it was troublesome to introduce new beneficial actions or to layer on extra channels.
  • There was no suggestions loop between channels, so it was not doable for a buyer success consultant to see if a associate had taken motion or not, and vice versa.

Thus, we introduced in an orchestration platform – a spot the place we will join a number of knowledge sources by APIs, centralize processing logic, and write the output to activation channels. Fairly shortly in our implementation, although, we encountered challenges with the orchestration platform.

The Challenges

  • The complexity of the joins in our queries couldn’t be supported by the orchestration platform, so we needed to preprocess the actions earlier than they entered the platform after which they may very well be routed to their respective activation channels. This was our first pivot. In our technical evaluation of the platform, the seller assured us that our queries may very well be supported within the platform, however in precise follow, that proved woefully inaccurate. So, we migrated probably the most complicated processing to Google Cloud Platform (GCP) and solely left easy logic within the orchestration platform to determine which motion a buyer required and write that to the proper activation channel.
  • The consumer interface abstracted elements of the code creating dependencies on an exterior vendor. We spent appreciable time attempting to decipher what went improper through trial and error with out entry to correct logs.
  • The connectors had been extremely particular and required vendor assist to setup, modify, and troubleshoot.

Our Subsequent Step Ahead

These three challenges compelled us to assume in a different way. Our objective was to centralize processing logic and connect with knowledge sources in addition to activation channels. We had been already leveraging GCP for preprocessing, so we migrated the rest of the queries to GCP. With a view to resolve for our must handle APIs to allow knowledge consumption and channel activation, we turned to Mulesoft. The mixture of GCP and Mulesoft helped us obtain our first orchestration objective whereas giving us full visibility to the end-to-end course of for implementation and assist.

Orchestration Architecture
Orchestration Structure

Use Case #2:  Pause and resume a digital e mail journey primarily based on triggers from different channels.

We targeted on making an attempt to pause an e mail journey in a Advertising Automation Platform (Salesforce Advertising Cloud or Eloqua) if a buyer had a mid-to-high severity Technical Help Heart (TAC) Case open for that product.

Once more, we set out to do that utilizing the orchestration platform. On this state of affairs, we would have liked to pause a number of digital journeys from a single set of processing logic within the platform.

The Problem

We did decide that we may ship the pause/resume set off from the orchestration platform, nevertheless it required establishing a one-to-one match of journey canvases within the orchestration platform to journeys that we would wish to pause within the advertising and marketing automation platform. Using the orchestration platform truly launched extra complexity to the workflow than managing ourselves.

Our Subsequent Step Ahead

Once more, we regarded on the recognized problem and the instruments in our toolbox. We decided that if we arrange the processing logic in GCP, we may consider all journeys from a single question and ship the pause set off to all related canvases within the advertising and marketing automation platform – a way more scalable construction to assist.


One other strike in opposition to the platform, however one other victory in forcing a brand new mind-set about an issue and discovering an answer we may assist with our present tech stack. We additionally count on the methodology we established to be leveraged for different varieties of decisioning similar to journey prioritization, journey acceleration, or pausing a journey when an adoption barrier is recognized and a beneficial motion intervention is initiated.

Use Case #3: Join analytics throughout the multichannel buyer journey.

We execute journeys throughout a number of channels. As an illustration, we could ship a renewal notification e mail collection, present a customized renewal banner on for customers of that firm with an upcoming renewal, and allow a self-service renewal course of on We acquire and analyze metrics for every channel, however it’s troublesome to point out how a buyer or account interacted with every digital entity throughout their complete expertise.

Orchestration platforms provide analytics views that show Sankey diagrams so journey strategists can visually evaluation how prospects interact throughout channels to judge drop off factors or significantly vital engagements for optimization alternatives.

Sankey Diagram Sample
Pattern of a Sankey Diagram

The Problem

  • As we set out to do that, we discovered the biggest blocker to unifying this knowledge just isn’t actually a problem an orchestration platform innately solves simply by executing the campaigns by their platform. The most important blocker is that every channel makes use of completely different identifiers for the shopper. Electronic mail journeys use e mail handle, internet personalization makes use of cookies related at an account stage, and the e-commerce expertise makes use of consumer ID login. The basis of this subject is the shortage of a novel identifier that may be threaded throughout channels.
  • Moreover, we found that our analytics and metrics crew had present gaps in attribution reporting for websites behind SSO login, similar to
  • Lastly, since many groups at Cisco are driving internet site visitors to, we noticed a big inconsistency with how completely different groups had been tagging (and never tagging) their respective internet campaigns. To have the ability to obtain a real view of the shopper journey finish to finish, we would want to undertake a typical language for tagging and monitoring our campaigns throughout enterprise models at Cisco.

Our Subsequent Step Ahead

Our crew started the method to undertake the identical tagging and monitoring hierarchy and system that our advertising and marketing group makes use of for his or her campaigns. It will enable our groups to bridge the hole between a buyer’s pre-purchase and post-purchase journeys at Cisco—enabling a extra cohesive buyer expertise.

Subsequent, we would have liked to sort out the info threading. Right here we recognized what mapping tables existed (and the place) to have the ability to map completely different marketing campaign knowledge to a single knowledge hierarchy. For this specific instance for renewals, we would have liked to sort out three completely different knowledge hierarchies:

  1. Celebration ID related to a novel bodily location for a buyer who has bought from Cisco
  2. Internet cookie ID
  3. Cisco login ID
Data Mapping Example
Knowledge mapping train for Buyer Journey Analytics

With the introduction of constant, cross Cisco-BU monitoring IDs in our internet knowledge, we’ll map a Cisco login ID again to an online cookie ID to fill in a few of the internet attribution gaps we see on websites like after a consumer logs in with SSO.

As soon as we had established that stage of information threading, we may develop our personal Sankey diagrams utilizing our present Tableau platform for Buyer Journey Analytics. Moreover, leveraging our present tech stack helps restrict the variety of reporting platforms used to make sure higher metrics consistency and simpler upkeep.

Use Case #4: Simply combine knowledge science to department and personalize the shopper journey.

We needed to discover how we will take the output of a knowledge science mannequin and pivot a journey to offer a extra personalised, guided expertise for that buyer. As an illustration, let’s take a look at our buyer’s renewal journey. At this time, they obtain a four-touchpoint journey reminding them to resume. Prospects can even open a chat or have a consultant name or e mail them for added assist. In the end, the journey is similar for a buyer no matter their probability to resume. We’ve got, nevertheless, a churn danger mannequin that may very well be leveraged to switch the expertise primarily based on excessive, medium, or low danger of churn.

So, if a buyer with an upcoming renewal had a excessive danger of churn, we may set off a prescriptive motion to escalate to a human for engagement, and we may additionally personalize the e-mail with a extra pressing message for that consumer. Whereas a buyer with a low danger for churn may have an upsell alternative weaved into their notification or we may route the low-risk prospects into advocacy campaigns.

The objectives of this use case had been primarily:

  1. Leverage the output of a knowledge science mannequin to personalize the shopper’s expertise
  2. Pivot experiences from digital to human escalation primarily based on knowledge triggers.
  3. Present context to assist buyer brokers perceive the chance and higher interact the shopper to drive the renewal.

The Problem

This was truly a reasonably pure match for an orchestration platform. The problem we entered right here was the info refresh timing. We wanted to refresh the renewals knowledge to be processed by the churn danger mannequin and align that with the timing of the triggered e mail journeys. Our renewals knowledge was refreshed in the beginning of each month, however we maintain our sends till the top of the month to permit our companions a while to evaluation and modify their prospects’ knowledge previous to sending. Our orchestration platform would solely course of new, incremental knowledge and overwrite primarily based on a pre-identified main key (this allowed for higher system processing to not simply overwrite all knowledge with each refresh).

To get round this subject, our vendor would create a model new view of the desk previous to our triggered ship so that every one knowledge was newly processed (not simply any new or up to date data). Not solely did this create a vendor dependency for our journeys, nevertheless it additionally launched potential high quality assurance points by requiring a pre-launch replace of our knowledge desk sources for our manufacturing journeys.

Our Subsequent Step Ahead

One query we stored asking ourselves as we struggled to make this use case work with the orchestration platform—had been we overcomplicating issues? The 2 orchestration platform outputs of our attrition mannequin use case had been to:

  1. Customise the journey content material for a consumer relying on their danger of attrition.
  2. Create a human touchpoint in our digital renewal journey for these with a excessive attrition danger.

For primary, we may truly obtain that utilizing dynamic content material modules inside SalesForce Advertising Cloud if we merely added a “danger of attrition” discipline to our renewals knowledge extension and created dynamic content material modules for low, medium, and excessive danger of attrition values. Accomplished!

For quantity two, doesn’t that sound form of acquainted? It ought to! It’s the identical downside we needed to resolve in our first use case for prescriptive calls to motion. As a result of we already labored to create a brand new structure for scaling our beneficial actions throughout a number of channels and audiences, we may work so as to add a department for an “attrition danger” alert to be despatched to our Cisco Renewals Managers and companions primarily based on our knowledge science mannequin. A suggestions loop may even be added to gather knowledge on why a buyer could not select to resume after this human connection is made.

Failing Forward

Discovering Success

On the finish of our one-year pilot, we had been compelled to consider the techniques to realize our objectives very in a different way. Sure, we had deemed the pilot a failure – however how can we fail ahead? As we encountered every problem, we took a step again and evaluated what we discovered and the way we may use that to realize our objectives.

In the end, we found out new methods to leverage our present programs to not solely obtain our core objectives but additionally allow us to have end-to -end visibility of our code so we will arrange the processing, refreshes, and connections precisely how our enterprise requires.

Now – we’re making use of every of those learnings.  We’re rolling out our core use instances as capabilities in our present structure, constructing an orchestration stock that may be leveraged throughout the corporate – an enormous step in the direction of success for us and for our prospects’ expertise.  The end result was not what we anticipated, however every step of the method helped propel us towards the best options.






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