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3 Predictive Strategies to Drive Revenue and Impact the Bottom Line
CloudCherry | Featured | August 31, 2018
Every customer experience team wants to improve their bottom line, but it’s usually one of the hardest things to make an impact on. While most customer experience platforms focus on increasing NPS because it’s a helpful indicator of customer intention, NPS is ultimately just a more easily measured, less accurate stand-in for true customer loyalty. When we increase NPS, we don’t necessarily impact our bottom line or increase our revenue. However, when you start to pull in customer transactional data from a CRM (like average revenue per user) and tie that to both survey responses and key drivers, predictive analytics become tied deeply to both the finances and the actions of customers —not just what customers say they will do.
Using deeper, more scientific insights can help your company make smart decisions when it comes to lowering and maintaining improvements to the bottom line of your business. Use your knowledge to move forward with choices in regards to how to use your money, delivering better proactive experiences for your customers, and pivoting quickly from decisions that may be draining or detrimental to your customer base.
With limited time and resources to dedicate to improving the customer experience, organizations often have to make difficult decisions about what to prioritize. While listening to the customer is a good place to start, the things they are most vocal about might not be the best for your business. They might say they really want more color options, but will be providing more customization increase sales? How do you decide which Voice of the Customer initiative gets the green light and which never sees the light of day?
Ideally, we’d want to know exactly what impact changes will have on the metrics that matter— top line revenue and profit. Without a crystal ball, seeing into the future can be incredibly difficult, leaving strategists guessing at what initiatives will truly move the needle. Often the only way to know if something will work is to try it out and see.
Fortunately, with the advances in machine learning and the ability to analyze large amounts of unstructured data, customer experience teams can now be more strategic with the initiatives they choose to pursue. Using structural equation modeling, a tool called path analysis can connect the dots between experiential aspects and financial metrics.
Instead of simply relying on correlation analysis and regression models, CloudCherry uses structural equation modeling to identify the impact of various aspects on the overall experience. For example, if you were to ask consumers what they valued in a car, the appearance would likely be rated highly. But if you were to provide them with two cars: one that drove really well, but looked horrible, and one that looked amazing but was barely driveable, people would still choose to drive the car that drove incredibly well. Structural equation modeling could accurately identify how these aspects overlap in the overall enjoyment of the driving experience to determine what truly matters. It also makes it possible to identify how specific key drivers of the customer experience overlap in impacting financial metrics.
Secondly, instead of measuring whether initiatives will drive NPS scores higher, we take it one step further and use real revenue data to determine the financial impact of the changes. Because of the unique single-question method of building surveys and storing data in CloudCherry, we can connect metrics like lifetime value (LTV) and average revenue per user (ARPU) to the Voice of the Customer. This means that our predictive engine is based on customer feedback, their actions, and their purchasing data.
With advanced predictive analytics, customer experience leaders can pinpoint exactly what they need to do to move the needle and drive ROI.
In addition to understanding the future impact of changes, we also need to identify where returns start decreasing. Fast service at a restaurant might improve the overall dining experience—until patrons start feeling like they are being rushed out of the restaurant. Quicker responses to customer service contacts might increase customer satisfaction—but at which point does the cost of providing instantaneous support surpass the benefits?
As the number of customers you serve starts to grow, so does the number of employees you need to hire. Customers calling or emailing in for help is expensive. You have to staff the phone lines and email inboxes, and hiring more contact center employees costs money. As a result, the cost per customer contact is usually somewhere between $12 to $25 per contact! By anticipating the evolving needs of your customers using predictive analytics, your company can begin to shift the customer experience in a direction that lessens the financial burden on your bottom-line.
For example, say you’ve noticed that a lot of the calls into your Credit Union’s contact center were about a confusing part of the monthly statement. Instead of shouldering the $15/call cost of answering each customer, you could improve the statement so it’s more clear, prevent customers from needing to call in and save a ton of money on staffing the phone line.
There are two types of proactive customer experiences: global and individual. Individual proactive customer service is like the waiter filling the water glass for a customer. That customer’s experience is made better by the waiter thinking about what they need ahead of time. This is typically what’s referred to as proactive customer service today – anticipate the needs of the customer in front you. Predictive analytics can help companies become better at spotting the opportunities to head off individual issues, but they generally have limited ROI. At this level, hiring smart engaged employees, empowering them and training them to anticipate what customers need is the most important strategy.
Global proactive customer service is more about spotting trends at a macro level and taking action. For example, an e-commerce company might identify a trend of customers who experienced a late delivery, not re-purchasing. They could hypothesize that more frequent updates might help set better expectations and prevent customer frustration. By proactively notifying customers of changes, the company would improve the experience of late deliveries and hopefully encourage more repeat customers. This isn’t just about one customer interaction, it’s about making a company-wide change to prevent every customer going forward from running into the same issue. These changes are frequently led by your CEM.
The insights from predictive analytics are only valuable if they are put into action. To do so, organizations need to connect the right metrics to every decision that impacts the end consumer and study them to see if they are viable in the short as well as the long run. This means organizations can close the loop at two levels – both with the individual customers through proactive support and at a global level by implementing customer experience improvements across the organization.
Typically, customer experience analysis has been farmed out to consulting firms and data analysts. A few weeks or months later a report is returned with a laundry list of action items to be addressed, along with some charts and graphs. There are a few issues with this cycle of insights.
First of all, businesses can’t wait months to act on emerging trends. By this time, customers will already have complained, gotten fed up and left. The issues could have already gone viral and spread across the internet through reviews and social media – tarnishing the brand for the foreseeable future and making a much bigger problem. Predictive insights need to be agile to be effective – reacting to the constantly changing ecosystem in real time provides a huge competitive advantage over organizations who are still waiting for their reports to be returned.
Secondly, taking the analysis out of the hands of the practitioners puts a level of abstraction into the data. When frontline managers are able to identify trends and insights, they can act much faster to make improvements. They can deep dive into key drivers to get a much more granular view of the customer’s experience. Multi-level classification hierarchies help CX leaders answer the why, not just the what. To get this level of information out of consultants would take months.
CloudCherry brings this level of analysis directly into the platform so that anyone can predict the impact of future CX initiatives. For example, Regional Managers can confirm that increasing the speed of checkout would drive 5% more foot traffic into their stores. Product Managers can preview the impact of a new customization feature on churn.
Real time insights mean no more missed opportunities, and no more time wasted on the initiatives that don’t move the needle.
Your bottom line does not need to bloat in order to better your customers’ experiences. Using predictive analytics, you can gain a deeper, better understanding of where changes need to be made, and what experiences you can provide that will keep your customers overjoyed by your experience while maintaining or even dropping your spend. Don’t work harder, use predictive analytics and the insights they provide to work smarter.