February 27, 2020
Where does your business fall on the customer analytics maturity curve?
Every interaction you have with your customers provides you with valuable data that can inform and improve the way you do business. To take advantage of the opportunities locked in their data, many retailers are investing in customer analytics tools and teams. However, before you invest in a new solution, assess your readiness: Where do you stand on the customer analytics maturity curve, and what do you need to get to the next level?
Customer loyalty is as elusive as ever.
Today’s consumers are fickle, and they’re willing to switch brands at the drop of a hat for a better, more personalized experience. In an increasingly competitive retail and e-commerce landscape, brands must pay close attention to who their customers are, how they shop, and what they need to experience in order to develop an unshakeable sense of loyalty for their favorite brands.
Enter: Customer analytics. Drawing on the data that you already have, you can gain a thorough and all-encompassing view of your customers. Lexer provides the customer insight required to curb customer attrition, boost engagement, and increase order frequency, size and ultimately customer lifetime value.
According to a McKinsey study:
- Businesses that use customer analytics extensively are significantly more likely to outperform the market in profit, sales growth, and ROI.
- Businesses that strive for “excellence” in IT, analytics, and execution perform significantly better than those that have merely average analytics maturity.
- Establishing a culture that values analytics and data-driven decision-making is key to reaping the full value of customer analytics.
… and those statistics should confirm what you already know: The better you understand your customers, the more powerful and profitable the customer relationship will be.
However, trying to perform sophisticated customer analytics before you’re ready is like trying to run before you’ve learned to crawl. In this post, we’ll walk through the four stages of the customer analytics maturity curve and show you how to take your analytics maturity to the next level.
The 4 stages of customer analytics maturity
1. Descriptive Analytics: What happened?
At this stage of analytics maturity, you should be able to pull your data together to paint an accurate picture of the past. For example, are sales growing or declining? Which product categories are performing well, and which are underperforming? What percentage of your customers have churned over the past 12 months?
Although descriptive analytics is valuable in its own right, it doesn’t provide any insight into the reasons behind certain business events. For example, it may tell you that a particular product category is underperforming, but it doesn’t tell you why. It could be that the product itself is defective, that the price is too high for your typical customer, that it received a number of poor reviews on social media, or something else—but you wouldn’t know which combination of these factors, if any of these at all, are the culprit if you settle for descriptive analytics only.
That’s where diagnostic analytics comes in.
2. Diagnostic Analytics: Why did it happen?
Diagnostic analytics helps you combine and interrogate data to understand why something is happening. In other words, diagnostic analytics looks at the many variables or factors that could cause a particular problem to help you pinpoint which variables are the primary cause.
This stage of analytics maturity requires a much wider and more detailed dataset than the first stage does, as well as more sophisticated retail data analytics solutions. In particular, businesses with a diverse set of data sources need a unification tool to bring all of their data together in a format that is easily accessible and understood; otherwise, achieving diagnostic analytics can become a highly complex and time-consuming process.
3. Predictive Analytics: What’s likely to happen next?
Using machine learning and artificial intelligence, a predictive analytics tool draws on the insights gained from descriptive and diagnostic analytics to tease through potential outcomes. Then, it presents users with the most likely future events and trends which can guide both marketing and business-wide decisions. For example, predictive analytics could tell you which of your customers have the highest likelihood of churning within the next 12 months so you can build a retention strategy for them.
Predictive analytics is an incredibly powerful tool for any business; it gives businesses a glimpse into the future, allowing them to make smarter, more strategic decisions. The customer insights gleaned from a predictive analytics tool are a data-driven retail forecast, and like anything data-driven, the accuracy of these predictions depends on a number of different factors, including the quality of one’s data.
4. Prescriptive Analytics: What should we do about it?
Finally, prescriptive analytics tells you what action to take to avoid a potential problem or to capitalize on an emerging trend. For example, if your data tells you that the majority of your repeat customers make their second purchase within 90 days after their first purchase, a prescriptive analytics tool might tell you to deliver promotional messages during that time to drive the second sale.
Like predictive analytics, prescriptive analytics requires clean and comprehensive data, advanced technology, and sophisticated data management processes to ensure its accuracy. If your business is still using messy data collection processes or siloed tools, drawing reliable insights from prescriptive analytics will be next to impossible.
Additionally, prescriptive analytics can only provide value to your business if you have the ability to act on its insights. If you don’t have the necessary tools and processes in place to leverage your data effectively, then the bottom-line impact of your prescriptive analytics tool will fall flat.
The bar is higher than ever for customer analytics
In today’s hyper-competitive market, the businesses with the most advanced customer analytics capabilities are the ones with the highest engagement rates, the happiest customers, and the steepest growth. But if you don’t have an in-house IT resource already, increasing your analytics maturity and creating business value from your data can be a challenge.
The first step is finding the right customer analytics software for your business because not every solution will meet your unique needs and objectives. Wherever you are on your journey toward advanced customer analytics, you need to make sure you’re investing in the right tools, techniques, and services to get the best results. For help choosing the right cutomer data and analytics solution for your business, click here to read “The Marketers Guide to Evaluating Customer Analytics Software.