Understanding Analytics

Data consumption, manipulation, and decision-making are making their way into the everyday lexicon of the wine industry. The forward movement into the world of data means a bright future ahead for those who adopt analytics as a driver for their business. 

Business analytics are generally discussed in three parts: descriptive, predictive, and prescriptive. Understanding each style is as important as the data we choose to focus on. This and how we choose to visualize it builds the internal analytics culture of any organization. This, in turn, impacts how the data is acted upon and what types of outcomes we focus on.

Three Key Types of Analytics to Know

Descriptive Analytics

Descriptive analytics are just that – descriptions, or summaries, of what has happened. These statistics give general overviews of points of interest, describing past events primarily through sums, counts, averages, or percent changes. Much of the wine industry lives in this space when analytics are discussed. 

Many of the data insight companies out there provide this type of analysis. The dashboards range from basic-to-elaborate, providing time-constrained reports and graphs. Understanding these statistics can inform business decisions and play an important part in any business. They can be used to see historical trends, such as a basic understanding of club tenure or database growth, as well as patterns in product purchasing. 

Monthly, quarterly, yearly… perhaps even weekly… reports containing descriptive analytics provide information on areas where a winery’s efforts are succeeding or struggling. However, using this information to impact change can be limited, and is often based on industry common knowledge and cliché (Chardonnay in May for dads and grads, anyone?) rather than decisions that are driven by the data itself. Additionally, when the focus is placed solely on these types of analytics, a generic sense of the data is gained with visualizations designed to be accessible, rather than usable. Meaning, data is presented in easily understood chunks, instead of data that drives hypothesis testing, which tends to be more visually complex. If your team is used to seeing bar charts representing sales, then the focus is on sales trends, whereas if you visualize parts of the customer journey, the focus becomes guiding that customer journey. 

Predictive Analytics

While descriptive analytics look toward the past, predictive analytics look into the future. They focus on patterns in historical data and use various statistical models to predict future behaviors. This second wave of business analytics provides a depth of insight that descriptive statistics cannot. 

Wineries can use these types of predictions to make various decisions and set goals for the coming year. For example, use database growth in purchasers to predict the potential lifetime value of new customers. Or examine trends in the sales of a certain varietal to determine the ideal production for next year, and when that varietal should launch. The focus on these types of analyses in the wine industry is growing, however, many wineries lack the resources to pursue this in a dedicated way. 

The 2020 State of the Wine Industry Survey reported that only 18% of wineries have a full-time employee focused on analyzing consumer data and only an additional 27% have a part-time employee who does so. This means that not only do few wineries have a dedicated member of their team focused on analytics, but they also don’t have someone working to improve the data analytics and data visualization literacy of the organization as a whole. Additionally, the lack of staffing in this area likely means the person is focused on building reports and reverting to descriptive analytics. Predictive analytics allows organizations to start building hypotheses around trends, and using these behavioral points to guide behavior is key to changing data culture in the wine industry. 

Prescriptive Analytics

Prescriptive analytics is the 3rd category of analytics that drive business insights. They use various algorithms and machine learning to help guide actions for the optimal solution in a given situation. This type of analytics can optimize the customer experience. A company can weigh different options and choose the best course of action. Basically, prescriptive analytics generates insight into various interventions to influence outcomes. These analytics are often very complex and the hardest to build, but they also provide the most value. The complexity is why many businesses don’t use this type of analytics, however, predictive analytics software is quickly making it more accessible.

Mapping Out the Customer Journey 

The customer journey encapsulates the idea that the whole is greater than the sum of its parts. The experiences a customer has throughout their tenure with a brand feed on one another to form their customer journey. Tapping into those experiences allows a brand to guide the journey. Testing different hypotheses and intervention strategies based on prescriptive analytics is the best way to affect positive change. 

Descriptive, predictive, and prescriptive analytics work in synergy. When applied together, and with careful foresight, they can help a business understand and guide its consumers through specific paths creating optimal customer experiences and catalyzing brand loyalty. To do this, you have to begin with understanding your customer base and segmentation.

At Emetry, we use a proprietary model to segment customers into 4 distinct categories. For each segment, we layer in descriptive statistics. These include customer tenure, lifetime value, average order value, and purchasing patterns. These descriptive metrics are the basis for understanding how to interact with that segment. The level of personalization and effort you invest in each segment should match your goals. Is your company focused on transforming 1st-time purchasers into repeat customers? Is the focus on extending the lifespan of your casual buyer? Or are you focused primarily on your consistent buyers and top performers and keeping these upper tiers engaged with your brand? Each of these goals speaks to a different customer journey, because as we know, while every customer is valuable, not every customer has the same value.

Layering individual customer information on to these segments metrics is the next step. For example, if you have a customer whose purchasing velocity mimics that of your top performers early in their customer journey, you likely want to interact with that customer in ways similar to how you interact with those top performers. Predictive analytics can inform you if this velocity is likely to continue or is likely to dwindle. But what is the best way to interact with that individual? Prescriptive analytics can help determine the optimal touchpoint – an individual email, a phone call, a written invitation of some sort? Analytics is constantly evolving, as should how they are applied in business. 

Using Analytics to Build Better Answers 

Whether you’re just beginning your journey with analytics or you’re a guru there is likely room to take your analytics to the next level. Sometimes simple, but targeted, interventions can be the most impactful. The Dødsing point is a great example of this.

The landscape of data and analytics is constantly improving. For the wine industry, there is still lots of green space to grow into. Providing better answers and actionable insights depends on better questions being asked. This means moving away from solely using descriptive analytics and emphasizing the synergistic nature of descriptive, predictive, and prescriptive analytics when setting goals, building hypotheses, and determining interventions.