Last year with the pandemic we saw a dramatic change in the way that people work and how enterprise value is created. Digital Transformation went from buzzword to reality, Customer Success became a category and the subscription economy accelerated faster than anyone thought. Who would have thought that a company like Snowflake (SNOW) would have a higher market cap on the NYSE than IBM with 1/5th the revenue?
We saw some of this coming with predictions in our article last year titled Customer Intelligence Will Drive Customer Success in 2021. We hit some predictions big in 2021, but a few dropped like a bomb and made people nervous with an overload of technology like facial recognition of CSMs and AI cloning of personalities and skills.
Too much data has become a problem for everyone
Historically “too much data” was a problem reserved for only the largest Enterprise organizations, with hundreds of systems that look at millions of data points in similar ways.
With the digital transformation that was super imposed because of the pandemic, this is now an everyone problem. For example, every hour of a Zoom or Gong conversation takes over a gigabyte of data and this compounds hourly and daily to be quickly unmanageable with older strategies of relational data. For the people involved, there are so many meetings (4-8 per day!) now that most resort to re-watching their calls at 1.5X speed to simply remember what happened and add notes to their CRM!
The Customer conversations are happening at an exponential rate in Slack, emails, webchats and compounded with in-product apps like Intercom, Pendo, PX and others. The signals that your customers are sending to you have multiplied 100X fold!
Customers are being very clear on what they “intend” to do with each of their software subscriptions. It’s on companies to separate the signals from the noise and listen to their customers through these additional digital channels.
Get Intelligence from data instead of lagging indicators
What worked in 2021 was pretty clear; Customer Intelligence became the solution to a problem of too much data; specifically with Revenue operations teams trying to piece together a mix of interaction data, operationalization data and usage data.
On the lower end of the maturity curve, a team that has NPS surveys as the only way to measure sentiment is not only incomplete, but inaccurate because NPS can be gamified to the point that it has no value.
In higher maturity organizations there has been an uptake in their intelligence strategy to a more objective approach based on data and facts. This generally includes a data scientist added to their staff to curate and make sense of the data. Intelligence means that you can process your data with AI, use ML to learn from patterns in data, then recommend actions that make an impact on the customer – meaning actions that have worked before.
What’s the result? Customers will comment on how you’re reading their mind because you are able to take action on situations before they happen. You’ll be in the know because AI is listening to every conversation and interaction that is being had and recommending not only which customers to take action on, but how to work with the people in the accounts to create win-win situations.
This changes the way that teams work with customers. Knowing if your customer is going to expand, renew flat or churn is on everyone’s holiday wish list. Listening to conversations gives companies the ability to predict what their customers will do next, giving a line of sight to Net Revenue Retention (NRR).
Health Scorecards have become more objective, when based on data
Heard of a watermelon customer?
They are just like they sound, green on the outside, and RED on the inside.
How many watermelon customers do you have because your current health scorecard is too subjective?
CSM health, P1 support tickets, Project health, onboarding health, Survey health, Overall Experience are all based on how a person feels – both on the score giver and the score receiver. It’s also based on who you already have a rapport with, it’s so much easier to talk with users, but executive stakeholders are sometimes out of reach, and they are the ones that make the decisions!
Teams are human-first and when feelings get involved that’s what makes it subjective – it’s a person’s opinion of how a conversation went and based on who they are talking to.
Artificial Intelligence has no feelings, it’s looking at everything objectively and providing fact based observations and insights. With the right algorithms to separate the signals from the noise, the technology is out there to know what a customer intends to do using natural language processing and statistical time series models. Pairing those intentions with a mix of operational and adoption data is the formula to understand what a customer will do with your subscription in terms of expansion or churn.
Conclusion
The way that people work has changed. Customer Expectations have changed. Customer Experience has changed.
In 2021, the outcome was clear. Using Customer Intelligence gives you the extra edge with customers to personalize their experience for what matters to them . . and what matters to your business.
Reposted from our Blog at https://intelligage.io/2021/12/21/customer-intelligence-drove-nrr-in-2021-and-some-predictions-we-missed/
Special thanks to Keith Mattes for the image!
Learn more at CompleteCSM.AI and we’d love to chat with you!