Today I’m pleased to share a guest post by Annette Franz, CCXP who is consistently named one of the most active influencers in the world of Customer Experience through her blog, CX Journey.
(Image courtesy of Pixabay)
I’m often asked about the future of customer experience: What does it look like? What will companies focus on this year? What advancements have we seen or should we expect to see? What are the latest tools to help companies improve the experience?
For this post, it’s the latter I’ll address.
Data is available in abundance these days. There are a ton of statistics out there about the volume of data we see today vs. just a few years ago, but I think we can all agree that there’s a ton of it! And I think we can all agree that most companies don’t even use – or know how to use – even a tenth of it.
I’ve written previously about the six steps you should take to use data to transform the customer experience. Those steps – centralize, analyze, synthesize/contextualize, socialize, strategize, and operationalize – are handy, but most companies get hung up on the first step. And if they know where their data is and have pulled it all into a data lake, or if they’ve gotten as much data together as they believe they can for now, they then get stuck on the next step, analyze. This is where I think there is a real opportunity for customer experience professionals. This is where we’ve got some new tools.
Companies are sitting on a goldmine of data. It’s time to do something with all of that data. It’s time to forecast the future and remedy the present. What am I talking about? Predictive analytics and, more importantly, prescriptive analytics. These are two important tools that customer experience professionals must have in their toolboxes in 2016. Unfortunately, many of these professionals are still either unaware of the tools or aren’t sure what they (can) do.
Traditionally, companies were able to identify and prioritize improvement opportunities from their customer surveys via correlation or regression analysis and quadrant charts, which identified priority improvements, fundamental essentials, and things to continue doing. But I don’t think a lot of folks put faith into those quad charts, which don’t offer up the ability to identify the impact those improvements (or the “continue doings”) would have on the desired business outcomes.
In recent years, there’s been an evolution from purely descriptive analytics (basic, summary statistics) to predictive analytics (predicting some future outcome based on what you know about the customer or on historical data). And now, we have prescriptive analytics, which takes that prediction, tells you why and then what to do, outlining the next best action to take in order to achieve a desired outcome.
According to Wikipedia, prescriptive analytics: not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.
You can already see how this is a windfall for customer experience professionals and, more importantly, for customers.
Prescriptive analytics isn’t just for survey data, though. If you’ve got customer demographic, transaction, interaction, or other behavioral data, you can analyze it to predict not only some outcome but also which customers (down to the individual customer) will most likely be aligned with that outcome, and then use prescriptive analytics to prescribe the next course of action to take with each customer to ensure the outcome is achieved. In plain English, here’s an example: first you predict who is most likely to buy a certain type of car, and then you can identify which messaging, discounts, offers, sales approach, etc. to use to take the customer over the line.
Customers want personalized experiences; this is one tool, one method to use to ensure that happens. Use prescriptive analytics to take your customer data to the next level, improving the experience and adding value for your customers.
I’ve often said that data are just data until you do something with them. You need a tool to identify the what: what is it that you’re supposed to do with the data? Ultimately, you need those prescriptive analytics to identify why, how, and where – from which action – you’ll get the biggest bang for your buck.
Having prescriptive analytics in your customer experience toolbox gives you a huge, first-mover advantage. There’s still a large awareness and education effort required to help companies realize the beauty and the benefits of using prescriptive analytics to transform the customer experience, especially the part about personalizing the experience down to the individual level.
If you’ve been stuck in a rut and haven’t been able to make any progress toward improving the customer experience, it’s time to rethink how you’ve been analyzing your data. Making data-driven decisions will only lead to better outcomes – for the customer and for the business.
If you torture the data long enough, it will confess. – Ronald Coase
Annette Franz is Vice President of Client Experience at Compellon, a prescriptive analytics software firm. She’s also author of the popular customer experience blog, CX Journey, where she shares her passion for all things employee experience and customer experience. And she’s actively involved in the Customer Experience Professionals Association (CXPA), as a mentor, expert, local networking event lead, and executive officer on the Board of Directors.
This post first appeared on the ICMI blog in February, 2022. I started my career in my early 20’s working
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