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@qualtrics went public today in a very successful $1.5B IPO. But beyond “doesn’t their CEO own the Utah Jazz?” and “didn't @SAP buy them?” -- a lot of people don’t understand what they do.
A thread below on the evolution of #CX, trying to explain it in layperson’s terms (1/x)
A thread below on the evolution of #CX, trying to explain it in layperson’s terms (1/x)
While techies may understand what #CX companies like @Qualtrics and @Medallia do, others aren’t familiar with the shifts in customer experience that have happened over the years. #CX, or customer experience, has become a very hot buzzword in technology. Why? (2/x)
Listen to any front-office SaaS vendor today, and part of the pitch is that “companies must differentiate on customer experience to win.”
Sounds cool.
But what does "differentiating on customer experience" actually mean? (3/x)
Sounds cool.
But what does "differentiating on customer experience" actually mean? (3/x)
In the “old days” (15 years ago, haha), being “customer-centric” meant companies would do customer research. Marketing teams ran focus groups to get feedback. Product and customer research teams did “in person” observations to see how customers used the product. (4/x)
For example, @Intuit -- who was famous in the industry for being deeply connected to customers -- had a practice called “Follow Me Homes” to derive customer insight, started by their founder, Scott Cook.
Here’s a tutorial:
(5/x)
Here’s a tutorial:
(5/x)
Consultants used “follow me homes” and “mystery shopping.” While at BCG, I visited a business owner in SF who worked out of their house. Upon arriving, they asked me to take my shoes off - so I conducted a CX interview while trying to hide a hole in my socks!
(I digress) 6/x
(I digress) 6/x
This represented “wave one” of customer experience, oriented around “real life” observations.
Obviously, "wave one" was pretty manual and cumbersome to execute and analyze. So you took a few qualitative observations and projected them across the broader customer base. 7/x
Obviously, "wave one" was pretty manual and cumbersome to execute and analyze. So you took a few qualitative observations and projected them across the broader customer base. 7/x
As the world went digital, “wave two” came -- you could get feedback through the Internet. Qualtrics was early to this, using surveys on the website or via email to get feedback. This moved the #CX practice forward - giving companies the ability to collect feedback at scale. 8/x
As feedback was digital, it could be gathered easily and quickly -- and segmented to see if different types of customers had different feedback. #CX teams processed and analyzed the data. As technology matured, you could access it from more sources: Twitter, email, chat 9/x
It also became easier and cheaper to get feedback at scale. With platforms like @usertesting, you can reach panels of target customers over the Internet - without the complexity of sending researchers to meet people in real life. We use tech like this at @Invoca, it’s great! 10/x
But the output of this "second wave" of #CX was still mainly aggregate level insights to inform broad business strategy. You’d get nice charts and graphs in PDFs, and then managers would need to decide what to change in day to day operational practices. 11/x
There was a pretty significant gap from insight to action, and #CX teams needed to find ways to have more direct impact on the business and operational practices. 12/x
The third wave of #CX is where things have gotten interesting -- and where Qualtrics and Medallia have focused (and also where @invoca is relevant). First, technology has made it easier to observe consumer behavior and mine insight automatically from these interactions 13/x
Consumers no longer have to do "extra work."
Example 1 - in the ‘old days’, you asked the consumer to rate a website visit. Today, you use diagnostic tools like @fullstory @quantummetric @Decibelinsight to evaluate web sessions -- so you get a sense of quality “natively” 14/x
Example 1 - in the ‘old days’, you asked the consumer to rate a website visit. Today, you use diagnostic tools like @fullstory @quantummetric @Decibelinsight to evaluate web sessions -- so you get a sense of quality “natively” 14/x
Example 2 - you used to ask customers to complete surveys after a phone call to rate the agent. Today, you use AI to detect automatically how the conversation went, delineating between what the agent said versus the consumer. 15/x
That means you can analyze the quality of every conversation, not just the ones where consumers bothered to stick around and fill out a survey. You no longer ask them to do any extra work, so you widen your data set automatically. 16/x
There was an evolution in this "third wave" around data collection -- going from "heavy" opt-in feedback from a small percent of consumers, to "light" opt-in feedback from all consumers. 17/x
("Light" opt-in means the consumer consents to have the interaction analyzed -- "heavy" opt-in means the consumer has to do “extra work” - like complete a survey - to provide feedback) 18/x
There was also an evolution in this third wave around level of granularity and detail. Previously, everything was done at the aggregate level. A #CX team would analyze data at an aggregated level and make decisions to drive change across wide swaths of customers. 19/x
But now, #CX is getting operationalized at a consumer-by-consumer level, in real-time, by a combination of humans and digital technology. For example, if you as a consumer have a conversation with a contact center agent that goes poorly, a company can detect that and... 20/x
automatically tie that into your customer record. That means they might provide a special offer or tailor language in their next digital touch point to be extra sensitive. Or if things are really bad, have a brand representative proactively reach out and talk to you. 21/x
This makes for a few changes. One - it speeds up the process of improving #CX. You can make adjustments in near-real time, rather than debating about graphs in committee meetings before any changes happen. 22/x
Second -- it takes #CX from being an aggregate level change, and enables it at a consumer-by-consumer level. Companies can truly interact with consumers in a data-driven way at a 1:1 level. This is the transformation you see companies like @Qualtrics and @Medallia making. 23/x
Medallia, for example, bought Voci, a speech analytics company, to auto-classify contact center conversations, so companies don’t have to rely on consumers “doing the work” in a survey to indicate how things went. https://techcrunch.com/2020/04/22/medallia-acquires-voice-to-text-specialist-voci-technologies-for-59m/ 24/x
And they’ve build out employee training capabilities, so they can take that individual level feedback and incorporate it into employee-specific coaching plans. https://www.medallia.com/press-release/experience-management-leader-medallia-to-acquire-contact-center-coaching-automation-platform-stella-connect/ 25/x
This is also the promise of @Qualtrics + @SAP working together - combining “experience data” (Qualtrics) with “operational data” (SAP) to drive a constant, real-time feedback loop. Companies can improve #CX continuously and build operational flows to make ongoing changes 26/x
At @Invoca, we’re doing something similar -- combining data from digital interactions and human experiences, with a focus on the "buying" part of the journey, to help marketing, e-commerce, and sales teams deliver a better buying experience. 27/x
We help these teams at B2C companies use data to drive the right interactions with individual consumers through technologies like @google @adobe @salesforce and feed data into #CX platforms like Qualtrics + Medallia to provide a better view of the overall customer journey. 28/x
So in sum on #CX...
Wave 1 = customer research IRL
Wave 2 = use of opt-in digital inputs for more data
Wave 3 = shift to "in motion" data + AI and customer-level orchestration
It's a really interesting space to watch as big players and new entrants navigate these trends. 29/x
Wave 1 = customer research IRL
Wave 2 = use of opt-in digital inputs for more data
Wave 3 = shift to "in motion" data + AI and customer-level orchestration
It's a really interesting space to watch as big players and new entrants navigate these trends. 29/x
Congrats to our friends at Qualtrics on your big day!
That's it, I'm off to my 1pm meeting
30/30 (fin)
That's it, I'm off to my 1pm meeting
30/30 (fin)
