Customer experience is one of those nebulous topics and functions that everyone talks about, all companies need but few know where to start. Perhaps because it doesn’t sit neatly in any one category. It bleeds across every function in a business, from sales to service, from marketing to operations, to finance, and IT. It’s hard partly because customer data and interactions live in multiple systems, which makes it difficult to get a full picture of the customer journey and the many ways the interact with a company. Salesforce just made this challenge a little easier with their new Analytics Cloud, Wave.
When I first heard that Appirio was one of the early partners for Salesforce Wave and saw a demo of what it could do, I nearly jumped out of my seat. Here finally was a tool built for regular people like me who know the ins and outs of the business, but don’t have a degree in data science. Someone who is more gifted at the art of relationships and asking the right questions than at the science of data correlation and predicting future behaviors.
I look after Customer Experience at Appirio, a fairly new function at the company with a pretty simple mission: to better understand what our customers need and thoughtfully design an experience that delights them, not just meets their expectations.
Like many customer experience functions in other companies, we’re rich in ideas and support, but aren’t always swimming in resources. However, I’m more fortunate than some of my peers. As one of Salesforce’s global partners, Appirio made the decision early on to use the Salesforce platform as our single source of customer truth. We use a lot of other systems — Marketo for marketing automation, InsideView and RingDNA for marketing intelligence, FinancialForce for PSA, GetFeedback for customer sat and NPS surveys, DocuSign for document management, etc. — but they all feed into Salesforce. This has made getting that consistent view of customer interactions and reporting much easier than in most companies. However, it hasn’t been as easy to correlate data, discover patterns and visualize those discoveries in a way that my colleagues can understand and take action on. Now it is.
Now I can answer questions like:
- Which AEs or project team members tend to have higher customer satisfaction scores? Who are our top performers? If these team members are together on an account, is the probability of project success higher?
- Do our strategic accounts have higher customer satisfaction (Net Promotor Scores) than our non-strategic accounts over time? How fast are they growing compared to other accounts?
- How do our customer satisfaction scores compare across key touchpoints in the customer journey? How are those scores trending? How do they look across projects in different practices? In different types of accounts?
- How do our customers with high NPS scores (9-10) compare in terms of bookings over time with customers that have passive NPS scores (7-8), or detractors (0-6)? What might be the revenue impact of increasing overall NPS by 1 point?
- Which customers are doing the most number of references for us? How active are these customers in our marketing campaigns? Which customer references are bringing in the most revenue? Which AEs are associated with our top referers?
But this is just the beginning. As I started looking into the questions above, it spawned another round of questions. And then another. What if I took out certain data sets? What if I compared these specific accounts with these specific accounts? What if I created a heat map of customers who had been with us for varying durations?
I could have requested these types of things from our very talented offshore team, or for more complex predictive analytics, tapped into our Topcoder community of data scientists. However, that would likely take days or weeks and an extensive amount of back and forth trying to explain the nuances of the data. To someone who doesn’t live in this data every day, it’s tough to understand the small nuances that can quickly render a false assumption (e.g. the difference between transactional customer surveys and relationship surveys, which data sets are less relevant or outdated, etc.). That’s why giving business users like myself the ability to dig in and explore the data directly is so powerful, and why competition from other BI tools like QlikSense will be fierce.
These self-service tools make it easy for us business leaders. And when something is easy, it gets used. When it’s not, we avoid it. Making it easy is something Salesforce does well. Not only can I go in and explore the data using Wave, but Salesforce has made it easy to share the data in my existing workstreams through Chatter, to create Dashboards through their data visualizer, and look at it on any device since it’s built on the Salesforce1 platform. That’s a tough value proposition with which to compete.
For all you customer experience professionals out there, especially those whose companies have already invested in Salesforce for Sales, Service or Marketing Cloud, I’d highly encourage you to check out this newest offering. Appirio’s new eBook “7 Ways to Prepare for the Salesforce Analytics Cloud” is a great place to start.